Добірка наукової літератури з теми "Accumulated Local Effect"

The exponential growth of nanoparticle-containing goods and waste of the recent years will lead unavoidably to nanomaterials entering environment, generating in local biota and abiotic environment, and potentially transmitting to man. Aerial and water transmission of nanoparticles eventually will cause its generation in soil and further transmission to plants and animals via food chains.

Purpose The purpose of this paper is to evaluate the impact of the Law on Budgetary Stability of 2012 over the level of accumulated debt in Spanish municipalities. The paper also analyses the influence of the socioeconomic environment, political factors and budgetary indicators on the level of accumulated debt for the 2008–2014 period, which coincides with the economic crisis. Design/methodology/approach The paper uses panel data methodology. First, the t-test of difference of means is used to analyse which political variables are significant. Then, the analysis is carried out using the generalised method of moments in order to obtain the explanatory variables of the level of debt. Findings The results show that in 2013–2014, the Law on Budgetary Stability did not have a significant effect on reducing the accumulated debt. However, the law has led to a change of the trend in debt levels, as the debt decreased from 2013 to 2014. Moreover, population, unemployment, immigration, personnel expenditure, direct fiscal pressure and level of investment have an influence over the level of accumulated debt. Originality/value This paper contributes to analyse to what extent the Law on Budgetary Stability has affected accumulated debt. The study reveals a slight impact on reducing debt, although it is not significant. An original aspect of this paper is that it uses dynamic models to study the accumulated debt of Spanish municipalities. The study shows the impact of socioeconomic, environmental and political factors as well as of budgetary indicators on the level of debt in the context of economic crisis.

: Several previous studies have shown that there are differences in the effect of corruption prevention on private investment. Corruption eradication by the KPK is expected to have a positive and significant effect on investors' decisions when investing in Indonesia. This research studied the long-term effects of corruption prevention on accumulated private investment at the regional level, using cross-sectional data analysis from 507 districts and cities between 2018-2020. The test was conducted using multiple linear regression with the independent variable being the Monitoring Center for Prevention (MCP) as the corruption prevention index. Using White Robust estimator, the study results prove that corruption prevention has long-term positive and significant effects on overall accumulated investment at the district and city levels. The MCP score of a region was greater than other regions for three years, therefore the accumulated investment was greater. These results encourage an increase of urgency when mainstreaming corruption prevention, proving it to be the right choice for investment in Indonesia. To enable local governments to be involved in corruption eradication, the government is expected to provide sustainable local incentive funds (DID).

Microalgal biomass is characterized by high protein, carbohydrates, and lipids concentrations. However, their qualitative and quantitative compositions depend not only on the cultivated species but also on the cultivation conditions. Focusing on the microalgae’s ability to accumulate significant fatty acids (FAs) amounts, they can be valorized either as dietary supplements or for biofuel production, depending on the accumulated biomolecules. In this study, a local isolate (Nephroselmis sp.) was precultured under autotrophic conditions, while the Box–Behnken experimental design followed using the parameters of nitrogen (0–250 mg/L), salinity (30–70 ppt) and illuminance (40–260 μmol m−2 s−1) to evaluate the accumulated biomolecules, with an emphasis on the amount of FAs and its profile. Regardless of the cultivation conditions, the FAs of C14:0, C16:0, and C18:0 were found in all samples (up to 8% w/w in total), while the unsaturated C16:1 and C18:1 were also characterized by their high accumulations. Additionally, the polyunsaturated FAs, including the valuable C20:5n3 (EPA), had accumulated when the nitrogen concentration was sufficient, and the salinity levels remained low (30 ppt). Specifically, EPA approached 30% of the total FAs. Therefore, Nephroselmis sp. could be considered as an alternative EPA source compared to the already-known species used in food supplementation.

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d), we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. In this study, a method to forecast monthly extreme precipitation using artificial neural networks (ANNs) is assessed using past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia. The use of combined inputs (MX5d with SOI, MJO, and DMI) is proposed to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four cases of data are sampled representing two major seasonal variations in Malaysia. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. ANNs are widely applied in the hydrology field because of their nonlinear ability in predicting nonstationary and seasonal data. In this paper, we have compared ANNs with seasonal autoregressive integrated moving average (ARIMA) and regression analysis using out-of-sample test data. The results for Kuantan indicate that seasonal ARIMA is the best method to forecast extreme precipitation when MX5d lags are used as input. For Kota Bharu, ANN exhibits better generalization ability than ARIMA and regression analysis when dual inputs (lagged MX5d and lagged global climate indices) are utilized in the model.

The fatigue behavior of a high-strength bearing steel tempered under three different temperatures was investigated with ultrasonic frequency and conventional frequency loading. Three kinds of specimens with various yield strengths exhibited obvious higher fatigue strengths under ultrasonic frequency loading. Then, a 2D crystal plasticity finite element method was adopted to simulate the local stress distribution under different applied loads and loading frequencies. Simulations showed that the maximum residual local stress was much smaller under ultrasonic frequency loading in contrast to that under conventional frequency at the same applied load. It was also revealed that the maximum local stress increases with the applied load under both loading frequencies. The accumulated plastic strain was adopted as a fatigue indicator parameter to characterize the frequency effect, which was several orders smaller than that obtained under conventional loading frequencies when the applied load was fixed. The increment of accumulated plastic strain and the load stress amplitude exhibited a linear relationship in the double logarithmic coordinate system, and an improved fatigue life prediction model was established.

One drill pipe’ tool joint fractured rarely after just servicing ten days in drilling process of one natural gas well. Failure reason of tool-joint pin was researched through visual inspection, mechanical properties testing and working condition analysis. Results show that fracture failure reason is mainly due to the dramatic friction between the tool joint and the accumulated debris during the drill-string’s rotating process. Accumulated debris blocked mud’s circulation channel and leaded to poor circulation. The dramatic friction between the tool joint and accumulated debris leaded tool joint’s local temperature to risen significantly, and reduced its carrying capacity. In the end, tool joint fractured under the effect of tensile and torsional loads.

Backfill mining methods are widely used in metal mines. The boundary part of the backfill has a direct effect on the local stability in mining engineering. The distribution of stress on the boundary part of the backfill and surrounding rock had their own features. To study the characteristics of stress distribution of backfill and surrounding rock on the boundary part, we conducted a field investigation, field monitoring, and numerical simulation. According to the underground monitoring, the overall characteristics of the boundary part of the backfill were that the accumulated horizontal stress was larger than the accumulated vertical stress on the deep sublevel and the accumulated horizontal stress was smaller on the shallow sublevel. On the contact zone (i.e., the boundary part), the stress of the surrounding rock was larger than the stress of the backfill. Combined with the numerical model analysis, we determined that the geometric features of the backfill boundary had an influence on the stress distribution of stress. The multistep boundary helped the integrity of the contact zone and local stability in deep mining.

The Badain Jaran Desert (BJD) is characterized by the coexistence of over 110 perennial lakes and thousands of megadunes in its southeast part. Unlike the cold island effect, we found a special phenomenon of the warm island effect in the lake group region of the BJD. However, the concept and formation mechanism remains unclear. In this study, based on observations of land surface processes in the area, we first used the daily mean temperature from 23 automated meteorological stations from 2010 to 2017 to calculate the mean daily temperature (T) ≥ 0 °C, T ≥ 10 °C accumulated temperature and negative accumulated temperature. Furthermore, using the net radiation from two eddy covariance measurement systems, characteristics of the net radiation between the lake and megadunes were analyzed. When comparing observed data in the lake group region to surrounding areas, accumulated temperature from all three meteorological stations in the lake group region were higher; the duration days of T ≥ 0 °C and T ≥ 10 °C were longer, whereas duration days of negative accumulated temperature were shorter. In addition, the initial dates for T ≥ 0 °C and T ≥ 10 °C accumulated temperature were earlier, whereas the end dates were delayed. Variations in heat were observed between stations in the lake group region that may be reflective of microclimate environments between lakes. The authors relate warm island formation in the BJD lake group region to (1) the heat carried by groundwater recharge to the desert lake groups has a great impact on the local temperature. (2) Net heat radiation to the atmosphere through sensible heat flux owing to sparse vegetation in the desert areas. Hence, heat resources are richer in the lake group region. This study aims to improve our understanding of the warm island effect from a comprehensive analysis of its intensity and distribution pattern around the lake group region as compared to its surroundings. In addition, the results from this study will provide a scientific basis for determining the source of lake water in the BJD.

AbstractThe article “Violence reduction or relocation? Effects of United Nations troops presence on local levels of violence” by Laura Peitz and Gregor Reisch is one of several recent articles that explore the local effects of peacekeeping deployments. We provide an overview of accumulated knowledge and conflicting findings, and identify a few remaining gaps in the literature. The finding that more peacekeepers are better at reducing violence has been replicated by several studies, although a few studies have identified conditional effects. Taken together, studies find that peacekeepers can reduce both violence between armed actors and violence against civilians. While Peitz and Reisch do not make a distinction between different perpetrators, previous work suggest that peacekeepers are better at reducing violence against civilians by non-state actors. Peitz and Reisch are thus far one of the few studies that explores the impact of the type of peacekeepers – although the findings are ambiguous. Lastly, there is a tension in the literature between Peitz and Reisch, who claim that peacekeepers diffuse violence to nearby location, and other studies that find no such relocation effect, or even the opposite. Future work should continue to explore the local effects of peacekeeping, directing attention to questions about types of peacekeepers, local conditions as enabling factors, the role of military capabilities (as opposed to capacity), and actions taken on the ground.

La conception de processus robustes revêt une importance capitale dans divers secteurs, tels que le génie chimique et le génie des procédés. La nature de la robustesse consiste à s'assurer qu'un processus peut constamment produire les résultats souhaités pour les décideurs, même lorsqu'ils sont confrontés à une variabilité et à une incertitude intrinsèques. Un processus conçu de manière robuste améliore non seulement la qualité et la fiabilité des produits, mais réduit également de manière significative le risque de défaillances coûteuses, de temps d'arrêt et de rappels de produits. Il améliore l'efficacité et la durabilité en minimisant les déviations et les défaillances du processus. Il existe différentes méthodes pour améliorer la robustesse du système, telles que la conception d'expériences, l'optimisation robuste et la méthodologie de la surface de réponse. Parmi les méthodes de conception robuste, l'analyse de sensibilité pourrait être appliquée comme technique de soutien pour mieux comprendre comment les modifications des paramètres d'entrée affectent les performances et la robustesse. En raison du développement rapide en science de l’ingénieure, les modèles mécanistiques ne captant pas certaines parties des systèmes complexe, peuvent ne pas être l'option la plus appropriée pour d'analyse de sensibilité. Ceci nous amène à envisager l'application de modèles d'apprentissage automatique et la combiner avec l’analyse de sensibilité. Par ailleurs, la question de l'interprétabilité des modèles d'apprentissage automatique a gagné en importance, il est de plus en plus nécessaire de comprendre comment ces modèles parviennent à leurs prédictions ou à leurs décisions et comment les différents paramètres sont liés. Étant donné que leurs performances dépassent constamment celles des modèles mécanistiques, fournir des explications, des justifications et des informations sur les prédictions des modèles de ML permettent non seulement de renforcer leur fiabilité et leur équité, mais aussi de donner aux ingénieurs les moyens de prendre des décisions en connaissance de cause, d'identifier les biais, de détecter les erreurs et d'améliorer les performances globales et la fiabilité des systèmes. Diverses méthodes sont disponibles pour traiter les différents aspects de l'interprétabilité, ces dernières reposent sur des approches spécifiques à un modèle et sur des méthodes agnostiques aux modèles.Dans cette thèse, notre objectif est d'améliorer l'interprétabilité de diverses méthodes de ML tout en maintenant un équilibre entre la précision dans la prédiction et l'interprétabilité afin de garantir aux décideurs que les modèles peuvent être considérés comme robustes. Simultanément, nous voulons démontrer que les décideurs peuvent faire confiance aux prédictions fournies par les modèles ML. Les outils d’interprétabilité ont été testés pour différents scénarios d'application, y compris les modèles basés sur des équations, les modèles hybrides et les modèles basés sur des données. Pour atteindre cet objectif, nous avons appliqué à diverses applications plusieurs méthodes agnostiques aux modèles, telles que partial dependence plots, individual conditional expectations, accumulated local effects, etc
Robust process design holds paramount importance in various industries, such as process and chemical engineering. The nature of robustness lies in ensuring that a process can consistently deliver desired outcomes for decision-makers and/or stakeholders, even when faced with intrinsic variability and uncertainty. A robustly designed process not only enhances product quality and reliability but also significantly reduces the risk of costly failures, downtime, and product recalls. It enhances efficiency and sustainability by minimizing process deviations and failures. There are different methods to approach the robustness of a complex system, such as the design of experiments, robust optimization, and response surface methodology. Among the robust design methods, sensitivity analysis could be applied as a supportive technique to gain insights into how changes in input parameters affect performance and robustness. Due to the rapid development and advancement of engineering science, the use of physical models for sensitivity analysis presents several challenges, such as unsatisfied assumptions and computation time. These problems lead us to consider applying machine learning (ML) models to complex processes. Although, the issue of interpretability in ML has gained increasing importance, there is a growing need to understand how these models arrive at their predictions or decisions and how different parameters are related. As their performance consistently surpasses that of other models, such as knowledge-based models, the provision of explanations, justifications, and insights into the workings of ML models not only enhances their trustworthiness and fairness but also empowers stakeholders to make informed decisions, identify biases, detect errors, and improve the overall performance and reliability of the process. Various methods are available to address interpretability, including model-specific and model-agnostic methods. In this thesis, our objective is to enhance the interpretability of various ML methods while maintaining a balance between accuracy and interpretability to ensure decision-makers or stakeholders that our model or process could be considered robust. Simultaneously, we aim to demonstrate that users can trust ML model predictions guaranteed by model-agnostic techniques, which work across various scenarios, including equation-based, hybrid, and data-driven models. To achieve this goal, we applied several model-agnostic methods, such as partial dependence plots, individual conditional expectations, accumulated local effects, etc., to diverse applications

Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated features well. However, it has two limitations. First, it does not quantify the deviation of instance-level (local) effects from the average (global) effect, known as heterogeneity. Second, for estimating the average effect, it partitions the feature domain into user-defined, fixed-sized bins, where different bin sizes may lead to inconsistent ALE estimations. To address these limitations, we propose Robust and Heterogeneity-aware ALE (RHALE). RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting. In this paper, we prove that to achieve an unbiased approximation of the standard deviation of local effects within each bin, bin splitting must follow a set of sufficient conditions. Based on these conditions, we propose an algorithm that automatically determines the optimal partitioning, balancing the estimation bias and variance. Through evaluations on synthetic and real datasets, we demonstrate the superiority of RHALE compared to other methods, including the advantages of automatic bin splitting, especially in cases with correlated features.

In Norway, the reindeer exists as both a wild and semi-domesticated species. The latter forms the basis of a livelihood and industry that is the clearest characteristic of Sámi culture, and it is protected by international law and the Norwegian Constitution. Nevertheless, reindeer herding is threatened by loss of land used for infrastructure and recreation facilities development, as well as human activities in the outfields (utmark). Reindeer are physically vulnerable, and society’s institutions do not provide sufficient protection of land for grazing and herding of reindeer. Politicians and the general public are largely unaware of this. The chapter documents this by analyzing land-use challenges in two reindeer-herding districts. Besides loss of pastureland, the accumulated effect of former losses is a loss of flexibility, which makes adaptation to new challenges increasingly difficult. The authors suggest several practical measures (e.g. plans, maps, etc.), but also point to the need for general education in Sámi culture and affairs, for politicians as well as the public. This should be a part of necessary reconciliation processes in the context of historical assimilation policies. Furthermore, the Norwegian government should strengthen its efforts to realize the intentions of plan and building laws by strengthening local and regional government obligations.

Abstract More so than today, poetry in this period was a matter of the voice spoken and heard. This fact aids in the close reading of various types of verse, such as debate poems, conversation pieces, flytings, songs, and prayers. Audiences hearing early poetry read aloud or recited had less local mobility within the text than the present-day readers do when reading by eye, so early poetry often rewards an approach sensitive to accumulated effects. Understanding a context of reading aloudhelps us see the function of asides, speaker indicators, and restatements of topic in poetry. Religious drama and secular interludes are explored for their crafty uses of dialogue and sound.

<em>Abstract.</em>—Advances in understanding wood dynamics in rivers of western North America have led to several important management trends. First, there is a trend away from using “hard” engineering approaches to anchoring wood in streams toward using “soft” placement techniques that allow some wood movement. Second, wood is being placed in locations where channel form and hydraulics favor stability and where wood is likely to accumulate. Third, there is an increased emphasis on passive recruitment of wood from natural source areas (instead of active placement) where the likelihood that it will enter streams through channel migration, windthrow, and landslides is high. Fourth, restoration targets for wood loads are incorporating landscape-scale objectives; thus, managing wood to emulate the spatial and temporal variability produced by natural disturbances is replacing fixed prescriptions for wood in individual reaches. Predicting the effects of wood restoration on individual fish populations in western North America is problematic because local biophysical conditions generate so much experimental noise that it is rarely possible to partition the effects of wood restoration from other sources of variation. Development of appropriate monitoring techniques, combined with a regional network of experimental catchments that include restored and unrestored streams, would help track changes in population status and gauge the effectiveness of wood restoration efforts.

Local populations are in most cases open and connected with other populations through dispersal. Dispersal, aside from its multiplicative nature, has a demographic additive effect for the spatiotemporal dynamics and extinction–colonization turnover of the donor and the receiver populations. Population dynamics are more sensitive to dispersal under perturbations, because dispersing is a resilience mechanism to avoid or reduce novel mortality risk. Furthermore, dispersing individuals carry information, a process that may create dynamic landscape information networks. In social species, the decision to stay or to disperse is made based on decisions made by others. When perturbations accumulate and jeopardize survival or fecundity, leading individuals may decide to disperse, and this decision is copied by others, generating a runaway dispersal to other patches. The decision trade-off between staying and dispersing depends on the dynamic spatiotemporal heterogeneity in patch quality. What matters for making a decision is not the difference in patch quality, but the ratio between the patch currently occupied and the rest of the patches. Decision-making in social animals for dispersing is explored under the frameworks of the prospect theory, the neoclassical economic theory, and the hypercycle theory. It is also shown how runaway dispersal may occur from a theoretical point of view due to a very simple mechanism of copying others in a density-dependent manner. This simple mechanism overruns a rational scenario when making decisions in social animals. This chapter ends by assessing the potential consequences of runaway dispersal for nonlinear responses in communities and entire ecosystems.

Nonlinear equations describing dynamics of 2D vortices are very important in the physics of the ocean and the atmosphere and in plasma physics and Astrophysics. Here linear and nonlinear 2D vortex perturbations of gravitating and light gaseous disks are examined in the geostrophic and post-geostrophic approximations. In the frame of geostrophic approximation, it is shown that the vortex with positive velocity circulation is characterized by low pressure with negative excess mass density of substance. Vortex with negative circulation has higher pressure and is a relatively tight formation with the positive excess mass density. In the post-geostrophic approximation, structures of the isolated monopole and dipole vortex (modons) solutions of these equations are studied. Two types of mass distributions in dipole vortices are found. The first type of modon is characterized by an asymmetrically positioned single circular densification and one rarefaction. The second type is characterized by two asymmetrically positioned densifications and two rarefactions, where the second densification-rarefaction pair is crescent shaped. The constant density contours of a dipole vortex in a light gas disk coincide with the streamlines of the vortex; in a self-gravitating disk, the constant density contours in the vortex do not coincide with streamlines. Possible manifestations of monopole and dipole vortices in astrophysical objects are discussed. Vortices play decisive role in the process of planet formation. Gas in a protoplanetary disk practically moves on sub-Keplerian speeds. Rigid particles, under the action of a head wind drags, lose the angular momentum and energy. As a result, the ~10 cm to meter-sized particles drift to the central star for hundreds of years. Long-lived vortical structures in gas disk are a possible way to concentrate the ~10 cm to meter sized particles and to grow up them in planetesimal. Here the effect of anticyclonic Burgers vortex on formation of planetesimals in a protoplanetary dusty disc in local approach is also considered. It is shown that the Burgers vortex with homogeneously rotating kernel and a converging radial stream of substance can effectively accumulate in its nuclear area the meter-sized rigid particles of total mass ∼1028 g for characteristic time ∼106 year.

Globalization is expanding the scope of employment forms in the labour market. The digital transformation of production effects both the Economy and Employment. The production processes automation requires constant updating of knowledge and increasing the competence of employees, high readiness to adapt to new conditions and mechanisms for the formation of Social and Labour relations. Such changes affect the functioning of society, contribute to the establishment of a new level of quality of life, when people’s priorities are changed, the needs for self-realization and intellectual development become a priority by increasing the educational level, qualifications, and improving their own skills and competencies. The purpose of the paper is to analyze, define and characterize the impact of digital technologies on the labor market. Digital technologies as well as digital services change the rules of employment and the requirements of competences, knowledge, skills and attitudes of employees. Beginning to promote online work platforms, changing local and global labor markets. Methodology of the study is based on Data Mining tools as a methodology and process for identifying large data sets that accumulate on various information resources. Results of the survey showed that technological changes, on the one hand, can lead to job displacement and technological unemployment, and on the other hand, increase worker efficiency and increase their wages. These two trends are not necessarily mutually exclusive. With the advent of the digital platforms, some professions are being automated and others are being reorganized. This has led to the emergence of alternative operating mechanisms, in particular the "Gig economy" or Concert economy. Gig economy is a labour relations model based on the short-term contracts or informal agreements (Uber, Lyft, TaskRabbit, GrubHub, Postmates). Practical implications. The formation of a global digital segment of the labour market is accompanied by economic and social contradictions, which are especially acute in institutionally underdeveloped countries. This situation requires strengthening the state socio-economic development policy in the direction of balancing the processes of transformation of the national labour market and the formation of the digital economy Value/originality. The shift to a digital economy is a natural process and requires modernization of the employment sector. The new COVID-19 pandemic has accelerated the processes of transformation of the labour market and acted as a catalyst. Due to the fact that most enterprises and government organizations switched to a remote format, employees had to master computer and digital technologies.

The effects of load history on component fracture behaviour have been studied at length in terms of the generation and resulting influence of residual stresses. Despite this, the effect of plastic strain history, separate from the generation of residual stresses, is still not clearly defined. This work presents an investigation into the effect of accumulated strain on subsequent fracture behaviour. The effects of load history on low temperature cleavage fracture are modelled by means of a number of local approach methods, accounting for variations in stress and strain throughout the component’s load history. Prior strain was found to reduce the mean fracture load of 20mm thick CT specimens, irrespective of the level of room temperature strain applied. Local approach methods, calibrated to low and high constraint fracture data, were able to correctly predict a reduction in fracture load, although the exact magnitude of the reduced loads were not always correctly resolved. Further experimental data and further work on model formulation is needed to confirm the conclusions drawn here.

Incremental forming is a flexible dieless forming process. In incremental forming, the metal sheet is clamped around its periphery. One or multiple generic stylus-type tools move along a predefined toolpath, incrementally deforming the sheet metal into a final, freeform shape. Compared with the traditional sheet metal forming process, the incremental forming process is more flexible, energy efficient and cost effective due to lower capital investment related to tooling. However, maintaining tight geometric tolerances in incremental formed parts can be a challenge. Specifically, undesired global bending is usually induced near the region between the tools and fixture resulting in a compromise in geometric accuracy. To address this issue, Accumulated Double-Sided Incremental Forming (ADSIF) is proposed, which utilizes two tools on both sides of the metal to better achieve localized deformation while simultaneously constraining global bending outside the forming area. Moreover, in ADSIF, the two tools are moving from inward to outward, and thus the tools are always forming virgin material and so as to limit forces on the already-formed part. Thus, ADSIF has a higher potential to achieve the desired geometry. Nevertheless, tool deflection due to machine compliance is still an issue that can have a considerable effect on geometric accuracy. In this work, the effect of tool deflection related to part geometry is studied for the ADSIF process. The nature of using two tools, rather than one, in ADSIF inherently implies that relative tool position is a critical process parameter. It is the region near these two tools where local squeezing and bending of the sheet occurs, the primary modes of deformation found in ADSIF. The change of relative tool positions (i.e., tool gap and relative position angle) are studied in detail by first developing an analytical model. It is concluded that the tool gap will be enlarged under the influence of tool compliance while the relative position angle is less affected. Additionally, a finite element simulation capable of modeling tool deflection is established. The comparison between the simulation results using rigid tools and deformable ones clearly demonstrated the significant influence of tool compliance on part geometry. Lastly, an axisymmetric part with varying wall angles was formed, and it was confirmed that ADSIF demonstrates improved geometry accuracy compared with conventional Double-Sided Incremental Forming.

A shutdown cooling (SDC) system was observed to have pressure spikes that lifted relief valves upon alignment with the Reactor Coolant System (RCS) hot leg. Investigations showed that gas could accumulate at several local high points of the system. An evaluation was conducted with the primary objective of determining if the accumulated gas could lift the relief valve and the secondary objective of increased understanding of how the gas affected the system. Modeling was performed to evaluate the effect of various gas pocket sizes on the over pressurization. Pressure data from operation of the LPSI pumps was used to test the model. This investigation determined that pressure magnitudes produced during the alignment of the SDC system would be limited by the acceleration of the fluid mass for small bubbles and by compressibility of the gas pocket for large bubbles. The study results showed that pressure was maximized for small to medium sized gas bubbles at locations outside of containment.

The development of trenches in the vicinity of mooring anchors can have a significant impact on the anchor performance. These trenches can develop due to the action of the anchor chain in the vicinity of and within the seabed, the properties of the soil and other processes that influence the interaction. This paper investigates the effect of strain softening of the soil on the seabed trenching due to cyclic chain movements through large deformation finite element (LDFE) analyses. The global chain system response during cyclic loading and the local behaviour of selected chain segments are compared between cases with various soil sensitivities. Soil remoulding (i.e., strength degradation with accumulated shear strain) is found as a driving factor that exacerbates the extent of a developing trench (including the tunnel portion) and its growth rate (per load cycle). The soil resistance profiles derived from these analyses provide good reflections of the chain kinematics during cycling and the chain-soil interaction. The resistance-transition depth for uplifting resistance profiles can be used as part of an interpretation of the potential extents that trenches could develop to.

Abstract. This work presents a damage imaging method exploiting full-field guided ultrasonic waves excited by a thermoelastic-effect laser. From the high spatial resolution data produced, a spatial gradient–based image processing technique was developed using gradient vectors to extract features sensitive to defects. Localized mechanical impedance changes in the damaged area induce a local distortion of the waveform, which was quantified by the variation of the gradient vectors in the scanning area as time evolves. Such variation was accumulated over time with an analytically derived optimal statistical threshold filter to generate a gradient-orientation map for damage imaging. The proposed algorithm is shown to detect distinctive damage patterns when tested experimentally on a 3 mm aluminum plate with multiple simultaneous simulated defects. Compared to conventional techniques like local wavenumber estimation, the generation of the accumulated orientation map involves no filtering process in the frequency or wavenumber domain, but it comes at the expense of less accurate shaping of the defect. A spatial covariance analysis was adopted to locate damage from the results as well as to evaluate the correlation among different kinds of defects.

Experiences show that mooring line failures have occurred due to crack propagation in chain links. Moreover, the crack propagation is accelerated by corrosion. To maintain the safety of floating structures in a long-term period of, say 20–30 years, it is therefore crucial that these chain links are properly designed as well as inspected and repaired during operation. The aim of this paper is to establish a reliability-based formulation for assessing mooring chains under the deterioration due to combined fatigue cracking and corrosion wastage. The crack growth in chain links has been modelled using the 2-dimensional Paris-Erdogan equation, which is validated by comparison with the SN data on a reliability basis. The corrosion is treated by considering the diameter reduction and the fatigue crack growth rate, using three zones of corrosion, namely the splash, catenary and bottom zones. A fatigue reliability case study of studless chain links is carried out considering various system models and proper correlation between links. The material correlation is modelled by splitting variables for the global and local effect. Accumulated failure probabilities of one year up to twenty years have been calculated to illustrate the effect of corrosion on fatigue reliability. A sensitivity study has been carried out on the initial crack size, the crack aspect ratio and the material correlation between links, when applying the fracture mechanics approach. Based on the case study, it has been found that the splash zone has an important effect on the failure probability of the entire mooring line, especially under corrosion of long time.

Abstract Sloshing is an important topic for the integrity of LNG tanks and the overall stability of the vessel. In the past, the interaction between the free surface and its substructure have been studied, especially where high-speed fluid jets impinge on the interior surface of the tank, and sometimes cause damage. In this paper, a boundary element method (BEM) with a fourth-order Runge-Kutta scheme is used to study the local phenomenon of nonlinear free-surface motion in a two-dimensional tank subject to roll motions. As the external excitations are nearly resonant with the fluid inside the tank, large free-surface deformations usually take place. The dynamic responses including the fluid velocity and pressure will grow drastically as the fluid is slamming on the walls. If no adequate conditions are applied, it is difficult to capture the peak physical quantities associated with strong nonlinear waves by most conventional Eulerian-Lagrangian methods. The numerical error will be accumulated and enlarged in just a few computational steps, which will eventually lead to unstable solutions. This paper will study the local phenomenon of a fast-moving jet forming on the wall of the tank, and its effect on the numerical stability and accuracy of the method overall. Some special numerical treatments are carried out for the local phenomenon approximation. The conservation of fluid mass is employed to obtain a reasonable geometry of the jet and its velocity along the wall. This provides a new set of rational boundary conditions applied on the walls, rather than using the artificial damping effect of other researchers. Results from the present method are compared with those from the volume of fluid (VOF) method implemented in ANSYS Fluent. The local free-surface shape in the vicinity of the jet and some local and global flow field patterns, including velocity and pressure, will be compared with and verified with experimental observations and measurements.

Manufacturing errors typically cause real (measured) spiral bevel and hypoid gear surfaces to deviate from the theoretical ones globally. Tooth surface wear patterns accumulated through the life span of the gear set are typically local deviations that are aggravated especially in case of edge contact conditions. An accurate and practical methodology based on ease-off topography is proposed in this study to perform loaded tooth contact analysis of spiral bevel and hypoid gears having both types of local and global deviations. It starts with definition of the theoretical pinion and gear tooth surfaces from the machine settings and cutter parameters, and constructs the theoretical ease-off and roll angle surfaces to compute unloaded contact analysis. Manufacturing errors and localized surface wear deviations are considered to update the theoretical ease-off to form a new ease-off surface that is used to perform a loaded tooth contact analysis according to the semi-analytical method proposed earlier. At the end, a numerical example with locally deviated surfaces is analyzed to demonstrate the effectiveness of the proposed methodology as well as quantifying the effect of such deviations on load distribution and the loaded motion transmission error.

Cyclic loads applied to a structure can develop local cyclic plasticity deformation, lead to fatigue damage and fracture at the high-stress regions, which can be assessed through a local strain approach. Each cycle of start-operation-stop steam turbine, making the low cycle fatigue (LCF) load of long blade, results in damage to the long blade, and the fatigue fracture occurs when the damage accumulated to its critical value. To evaluate the fatigue life, the experimental data illustrating the cyclic behavior of a material under simple loading condition must be gathered, and also a suitable local stress-strain range calculation approach needs to be chosen to represent the accurate material behavior under loadings. With the consideration of the difference between the specimen and actual blade, the influential factors, such as mean stress, geometry effect, blade surface quality, and water erosion, on the fatigue life should be investigated when using the cyclic fatigue data of specimen to predict fatigue life of actual blade. In this study, a new local stress-strain range approach is introduced based on elastoplastic finite element analysis and Neuber rule. And also a modified strain-life fatigue model is used by considering leading causes of fatigue and also the cumulative damage rule is set up to predict the LCF life of the steam turbine long blade. It is found that the assessment method proposed in this study is capable of predicting the LCF life of steam turbine long blade.

Abstract Ni-based superalloys used for thermal power plants have been subjected to random creep-fatigue load in high-temperature environment to counter global warming and compensate the fluctuation of the output of renewable energies. However, under the creep-fatigue condition, intergranular cracking was found to be accelerated, and the fracture life of the alloy was significantly decreased. Therefore, it is essential to elucidate the mechanism of this acceleration of crack propagation rate and to develop a quantitative evaluation method of the creep-fatigue damage. In this study, intermittent creep-fatigue tests were applied to Alloy 617, one of the Ni-based superalloys, and the degradation process of its crystallinity was monitored by EBSD (Electron Back-Scatter Diffraction) method. In particular, the effect of strain rate during loading and unloading processes were evaluated in detail. IQ (Image Quality) value which indicates the quality of crystallinity, in other words, the concentration of various defects and Schmid factor were applied to the crystallinity analysis, and the degradation process of the microstructure was continuously monitored. The results showed that under creep-fatigue loading, the faster the strain rate during unloading, the earlier the IQ value decreased and the shorter the time for the initiation of intergranular cracking. It was also found that intergranular cracking appeared when the IQ value reached a critical value regardless of the strain rate. The creep-fatigue damage was mainly accumulated grain boundaries with large difference in Schmid factor between nearby grains, indicating large local strain due to the large lattice mismatch. The unloading rate was one of the dominant factors of the degradation of the crystallinity around grain boundaries.

This paper provides a comprehensive early warning system (EWS) that balances the classical signaling approach with the best-realized machine learning (ML) model for predicting fiscal stress episodes. Using accumulated local effects (ALE), we compute a set of thresholds for the most informative variables that drive the correlation between predictors. In addition, to evaluate the main country risks, we propose a leading fiscal risk indicator, highlighting macro, fiscal and institutional attributes. Estimates from different models suggest significant heterogeneity among the most critical variables in determining fiscal risk across countries. While macro variables have higher relevance for advanced countries, fiscal variables were more significant for Latin American and Caribbean (LAC) and emerging economies. These results are consistent under different liquidity-solvency metrics and have deepened since the global financial crisis.

In July 2021, the Rhine-Meuse region straddling Belgium, Germany and the Netherlands was affected by devastating floods that have led to the loss of more than 240 lives and damage worth billions of Euros. The event was closely watched by regional agencies that had to organize response and recovery, and also received noticeable global attention. Diverse sets of responses and reflections accumulated among researchers, local and regional governments, local and international media, development organizations, public offices and citizen groups, wherein links to climate change and gaps in our preparedness for unexpected, extreme events were a common element of the discourse. In response to the floods, and in recognition of the cross-border effects of climate change, the United Nations University institutes in Belgium (UNU-CRIS), Germany (UNU-EHS) and the Netherlands (UNU-MERIT) have launched the “UNU Climate Resilience Initiative” with the aim to share knowledge, shape policy and drive action – and ultimately shift the focus from risk to proactive adaptation, innovation and transformation. Within the context of this initiative, researchers from the three institutes have conducted research in the flood affected areas and organized the two-day “Flood Knowledge Summit 2022: From Risks to Resilience”, which took place from 7 to 8 July 2022 in Maastricht, the Netherlands. Complementing existing national initiatives and efforts in the three countries, the event aimed to connect different actors – including affected citizens, first responders, authorities, researchers and civil society – from the region, the European Union (EU) and the Global South to share experiences, engage in dialogue and facilitate learning regarding how to strengthen climate resilience for all. This summit served to map various efforts to understand the data, information, governance and knowledge gaps at national, subnational and regional levels in order to address growing risks of climate change, including how to adapt to not only climate-induced extreme events like floods but also other hazard events, and created a regional momentum to support multidimensional efforts towards building resilience. Drawing on our research and outcomes of the Flood Knowledge Summit 2022, the UNU Climate Resilience Initiative has identified five key areas in which further research and action is needed to tackle climate risks and facilitate pathways towards climate resilience.

The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,

1. Macroeconomic Summary In December, headline inflation (13.1%) and the average of the core inflation measures (10.3%) continued to trend upward, posting higher rates than those estimated by the Central Bank's technical staff and surpassing the market average. Inflation expectations for all terms exceeded the 3.0% target. In that month, every major group in the Consumer Price Index (CPI) registered higher-than-estimated increases, and the diffusion indicators continued to show generalized price hikes. Accumulated exchange rate pressures on prices, indexation to high inflation rates, and several food supply shocks would explain, in part, the acceleration in inflation. All of this is in a context of significant surplus demand, a tight labor market, and inflation expectations at different terms that exceed the 3.0% target. Compared to the October edition of the Monetary Policy Report, the forecast path for headline and core inflation (excluding food and regulated items: EFR) increased (Graphs 1.1 and 1.2), reflecting heightened accumulated exchange rate pressures, price indexation to a higher inflation rate (CPI and the producer price index: PPI), and the rise in labor costs attributed to a larger-than-estimated adjustment in the minimum wage. Nevertheless, headline inflation is expected to begin to ease by early 2023, although from a higher level than had been estimated in October. This would be supported initially by the slowdown forecast for the food CPI due to a high base of comparison, the end anticipated for the shocks that have affected the prices of these products, and the estimated improvement in external and domestic supply in this sector. In turn, the deterioration in real household income because of high inflation and the end of the effects of pent-up demand, plus tighter external and domestic financial conditions would contribute to diluting surplus demand in 2023 and reducing inflation. By the end of 2023, both headline and core (EFR) inflation would reach 8.7% and would be 3.5% and 3.8%, respectively, by December 2024. These forecasts are subject to a great deal of uncertainty, especially concerning the future behavior of international financial conditions, the evolution of the exchange rate, the pace of adjustment in domestic demand, the extent of indexation of nominal contracts, and the decisions taken regarding the domestic price of fuel and electricity. In the third quarter, economic activity surprised again on the upside and the growth projection for 2022 rose to 8.0% (previously 7.9%). However, it declined to 0.2% for 2023 (previously 0.5%). With this, surplus demand continues to be significant and is still expected to weaken during the current year. Annual economic growth in the third quarter (7.1 % SCA)1 was higher than estimated in October (6.4 % SCA), given stronger domestic demand specifically because of higher-than-expected investment. Private consumption fell from the high level witnessed a quarter earlier and net exports registered a more negative contribution than anticipated. For the fourth quarter, economic activity indicators suggest that gross domestic product (GDP) would have remained high and at a level similar to that observed in the third quarter, with an annual variation of 4.1%. Domestic demand would have slowed in annual terms, although at levels that would have remained above those for output, mainly because of considerable private consumption. Investment would have declined slightly to a value like the average observed in 2019. The real trade deficit would have decreased due to a drop in imports that was more pronounced than the estimated decline in exports. On the forecast horizon, consumption is expected to decline from current elevated levels, partly because of tighter domestic financial conditions and a deterioration in real income due to high inflation. Investment would also weaken and return to levels below those seen before the pandemic. In real terms, the trade deficit would narrow due to a lower momentum projection for domestic demand and higher cumulative real depreciation. In sum, economic growth for all of 2022, 2023, and 2024 would stand at 8.0%, 0.2% and 1.0%, respectively (Graph 1.3). Surplus demand remains high (as measured by the output gap) and is expected to decline in 2023 and could turn negative in 2024 (Graph 1.4). Although the macroeconomic forecast includes a marked slowdown in the economy, an even greater adjustment in domestic absorption cannot be ruled out due to the cumulative effects of tighter external and domestic financial conditions, among other reasons. These estimates continue to be subject to a high degree of uncertainty, which is associated with factors such as global political tensions, changes in international interest rates and their effects on external demand, global risk aversion, the effects of the approved tax reform, the possible impact of reforms announced for this year (pension, health, and labor reforms, among others), and future measures regarding hydrocarbon production. In 2022, the current account deficit would have been high (6.3 % of GDP), but it would be corrected significantly in 2023 (to 3.9 % of GDP) given the expected slowdown in domestic demand. Despite favorable terms of trade, the high external imbalance that would occur during 2022 would be largely due to domestic demand growth, cost pressures associated with high freight rates, higher external debt service payments, and good performance in terms of the profits of foreign companies.2 By 2023, the adjustment in domestic demand would be reflected in a smaller current account deficit especially due to fewer imports, a global moderation in prices and cost pressures, and a reduction in profits remitted abroad by companies with foreign direct investment (FDI) focused on the local market. Despite this anticipated correction in the external imbalance, its level as a percentage of GDP would remain high in the context of tight financial conditions. In the world's main economies, inflation forecasts and expectations point to a reduction by 2023, but at levels that still exceed their central banks' targets. The path anticipated for the Federal Reserve (Fed) interest rate increased and the forecast for global growth continues to be moderate. In the fourth quarter of 2022, logistics costs and international prices for some foods, oil and energy declined from elevated levels, bringing downward pressure to bear on global inflation. Meanwhile, the higher cost of financing, the loss of real income due to high levels of global inflation, and the persistence of the war in Ukraine, among other factors, have contributed to the reduction in global economic growth forecasts. In the United States, inflation turned out to be lower than estimated and the members of the Federal Open Market Committee (FOMC) reduced the growth forecast for 2023. Nevertheless, the actual level of inflation in that country, its forecasts, and expectations exceed the target. Also, the labor market remains tight, and fiscal policy is still expansionary. In this environment, the Fed raised the expected path for policy interest rates and, with this, the market average estimates higher levels for 2023 than those forecast in October. In the region's emerging economies, country risk premia declined during the quarter and the currencies of those countries appreciated against the US dollar. Considering all the above, for the current year, the Central Bank's technical staff increased the path estimated for the Fed's interest rate, reduced the forecast for growth in the country's external demand, lowered the expected path of oil prices, and kept the country’s risk premium assumption high, but at somewhat lower levels than those anticipated in the previous Monetary Policy Report. Moreover, accumulated inflationary pressures originating from the behavior of the exchange rate would continue to be important. External financial conditions facing the economy have improved recently and could be associated with a more favorable international context for the Colombian economy. So far this year, there has been a reduction in long-term bond interest rates in the markets of developed countries and an increase in the prices of risky assets, such as stocks. This would be associated with a faster-than-expected reduction in inflation in the United States and Europe, which would allow for a less restrictive course for monetary policy in those regions. In this context, the risks of a global recession have been reduced and the global appetite for risk has increased. Consequently, the risk premium continues to decline, the Colombian peso has appreciated significantly, and TES interest rates have decreased. Should this trend consolidate, exchange rate inflationary pressures could be less than what was incorporated into the macroeconomic forecast. Uncertainty about external forecasts and their impact on the country remains high, given the unpredictable course of the war in Ukraine, geopolitical tensions, local uncertainty, and the extensive financing needs of the Colombian government and the economy. High inflation with forecasts and expectations above 3.0%, coupled with surplus demand and a tight labor market are compatible with a contractionary stance on monetary policy that is conducive to the macroeconomic adjustment needed to mitigate the risk of de-anchoring inflation expectations and to ensure that inflation converges to the target. Compared to the forecasts in the October edition of the Monetary Policy Report, domestic demand has been more dynamic, with a higher observed level of output exceeding the productive capacity of the economy. In this context of surplus demand, headline and core inflation continued to trend upward and posted surprising increases. Observed and expected international interest rates increased, the country’s risk premia lessened (but remains at high levels), and accumulated exchange rate pressures are still significant. The technical staff's inflation forecast for 2023 increased and inflation expectations remain well above 3.0%. All in all, the risk of inflation expectations becoming unanchored persists, which would accentuate the generalized indexation process and push inflation even further away from the target. This macroeconomic context requires consolidating a contractionary monetary policy stance that aims to meet the inflation target within the forecast horizon and bring the economy's output to levels closer to its potential. 1.2 Monetary Policy Decision At its meetings in December 2022 and January 2023, Banco de la República’s Board of Directors (BDBR) agreed to continue the process of normalizing monetary policy. In December, the BDBR decided by a majority vote to increase the monetary policy interest rate by 100 basis points (bps) and in its January meeting by 75 bps, bringing it to 12.75% (Graph 1.5). 1/ Seasonally and calendar adjusted. 2/ In the current account aggregate, the pressures for a higher external deficit come from those companies with FDI that are focused on the domestic market. In contrast, profits in the mining and energy sectors are more than offset by the external revenue they generate through exports. Box 1 - Electricity Rates: Recent Developments and Indexation. Author: Édgar Caicedo García, Pablo Montealegre Moreno and Álex Fernando Pérez Libreros Box 2 - Indicators of Household Indebtedness. Author: Camilo Gómez y Juan Sebastián Mariño

1.1 Macroeconomic summary In September, headline inflation (11.4% annually) and the average of core inflation indicators (8.6% annually) continued on a rising trend, and higher increases than expected were recorded. Forecasts increased again, and inflation expectations remained above 3%. Inflationary surprises in the third quarter were significant and widespread, and they are the result of several shocks. On the one hand, international cost and price shocks, which have mainly affected goods and foods, continue to exert upwards pressure on national inflation. In addition to these external supply shocks, domestic supply shocks have also affected foods. On the other hand, the strong recovery of aggregate demand, especially for private consumption and for machinery and equipment, as well as a higher accumulated depreciation of the Colombian peso and its pass-through to domestic prices also explain the rise in inflation. Indexation also contributes, both through the Consumer Price Index (CPI) and through the Producer Price Index (PPI), which continues to have a significant impact on electricity prices and, to a lesser degree, on other public utilities and rent. In comparison with July’s report, the new forecast trajectory for headline and core inflation (excluding food and regulated items) is higher in the forecast horizon, mainly due to exchange rate pressures, higher excess demand, and indexation at higher inflation rates, but it maintains a trend of convergence towards the target. In the case of food, a good domestic supply of perishable foods and some moderation in international processed food prices are still expected. However, the technical staff estimates higher pressures on this group’s prices from labor costs, raw material prices, and exchange rates. In terms of the CPI for regulated items, the new forecast supposes reductions in electricity prices at the end of the year, but the effects of indexation at higher inflation rates and the expected rises in fuel prices would continue to push this CPI group. Therefore, the new projection suggests that, in December, inflation would reach 11.3% and would decrease throughout 2023 and 2024, closing the year at 7.1% and 3.5%, respectively. These forecasts have a high level of uncertainty, due especially to the future behavior of international financial conditions, external price and cost shocks, the persistence of depreciation of the Colombian peso, the pace of adjustment of domestic demand, the indexation degree of nominal contracts, and the decisions that would be made regarding domestic fuel and electricity prices. Economic activity continues to surprise on the upside, and the projection of growth for 2022 rose from 6.9% to 7.9% but lowered for 2023 from 1.1% to 0.5%. Thus, excess demand is higher than estimated in the previous report, and it would diminish in 2023. Economic growth in the second quarterwas higher than estimated in July due to stronger domestic demand, mainly because of private consumption. Economic activity indicators for the third quarter suggest that the GDP would stay at a high level, above its potential, with an annual change of 6.4%, and 0.6% higher than observed in the second quarter. Nevertheless, these numbers reflect deceleration in its quarterly and annual growth. Domestic demand would show similar behavior, with a high value, higher than that of output. This can be explained partly by the strong behavior of private consumption and investment in machinery and equipment. In the third quarter, investment in construction would have continued with mediocre performance, which would still place it at levels lower than those observed before the pandemic. The trade deficit would have widened due to high imports with a stronger trend than that for exports. It is expected that, in the forecast horizon, consumption would decrease from its current high levels, partly as a consequence of tighter domestic financial conditions, lower repressed demand, higher exchange rate pressures on imported goods prices, and the deterioration of actual income due to the rise in inflation. Investment would continue to lag behind, without reaching the levels observed before the pandemic, in a context of high financing costs and high uncertainty. A lower projected behavior in domestic demand and the high levels of prices for oil and other basic goods that the country exports would be reflected in a reduction in the trade deficit. Due to all of this, economic growth for all of 2022, 2023, and 2024 would be 7.9%, 0.5%, and 1.3%, respectively. Expected excess demand (measured via the output gap) is estimated to be higher than contemplated in the previous report; it would diminish in 2023 and could turn negative in 2024. These estimates remain subject to a high degree of uncertainty related to global political tension, a rise in international interest rates, and the effects of this rise on demand and financial conditions abroad. In the domestic context, the evolution of fiscal policy as well as future measures regarding economic policy and their possible effects on macroeconomic imbalances in the country, among others, are factors that generate uncertainty and affect risk premia, the exchange rate, investment, and the country’s economic activity. Interest rates at several of the world’s main central banks continue to rise, some at a pace higher than expected by the market. This is in response to the high levels of inflation and their inflation expectations, which continue to exceed the targets. Thus, global growth projections are still being moderated, risk premia have risen, and the dollar continues to gain strength against other main currencies. International pressures on global inflation have heightened. In the United States, core inflation has not receded, pressured by the behavior of the CPI for services and a tight labor market. Consequently, the U.S. Federal Reserve continued to increase the policy interest rate at a strong pace. This rate is expected to now reach higher levels than projected in the previous quarter. Other developed and emerging economies have also increased their policy interest rates. Thus, international financial conditions have tightened significantly, which reflects in a widespread strengthening of the dollar, increases in worldwide risk premia, and the devaluation of risky assets. Recently, these effects have been stronger in Colombia than in the majority of its peers in the region. Considering all of the aforementioned, the technical staff of the bank increased its assumption regarding the U.S. Federal Reserve’s interest rate, reduced the country’s external demand growth forecast, and raised the projected trajectory for the risk premium. The latter remains elevated at higher levels than its historical average, within a context of high local uncertainty and of extensive financing needs from the foreign sector and the public sector. All of this results in higher inflationary pressures associated to the depreciation of the Colombian peso. The uncertainty regarding external forecasts and its impact on the country remain elevated, given the unforeseeable evolution of the conflict between Russia and Ukraine, of geopolitical tensions, and of the tightening of external financial conditions, among others. A macroeconomic context of high inflation, inflation expectations and forecasts above 3%, and a positive output gap suggests the need for contractionary monetary policy, compatible with the macroeconomic adjustment necessary to eliminate excess demand, mitigate the risk of unanchoring in inflation expectations, and guarantee convergence of inflation at the target. In comparison with the July report forecasts, domestic demand has been more dynamic, with a higher observed output level that surpasses the economy’s productive capacity. Headline and core inflation have registered surprising rises, associated with the effects of domestic and external price shocks that were more persistent than anticipated, with excess demand and indexation processes in some CPI groups. The country’s risk premium and the observed and expected international interest rates increased. As a consequence of this, inflationary pressures from the exchange rate rose, and in this report, the probability of the neutral real interest rate being higher than estimated increased. In general, inflation expectations for all terms and the bank’s technical staff inflation forecast for 2023 increased again and continue to stray from 3%. All of the aforementioned elevated the risk of unanchoring inflation expectations and could heighten widespread indexation processes that push inflation away from the target for a longer time. In this context, it is necessary to consolidate a contractionary monetary policy that tends towards convergence of inflation at the target in the forecast horizon and towards the reduction of excess demand in order to guarantee a sustainable output level trajectory. 1.2 Monetary policy decision In its September and October of 2022 meetings, Banco de la República’s Board of Directors (BDBR) decided to continue adjusting its monetary policy. In September, the BDBR decided by a majority vote to raise the monetary policy interest rate by 100 basis points (bps), and in its October meeting, unanimously, by 100bps. Therefore, the rate is at 11.0%. Boxes 1 Food inflation: a comparison with other countries