Добірка наукової літератури з теми "Operation diagnosing"

The object of the research is the use of thermal imagers for quick diagnostics of assemblies and parts heated during normal operation of the vehicle. It is shown that the use of a thermal imager makes it possible to obtain the temperature field on the object under study and, based on a comparison of actual temperatures and temperatures corresponding to normal operation, to assess the technical condition of the object.

Problem. This article highlights the current state of diagnostics of gears on the level of accumulated fatigue damage in operation. The generalization of the known information on determination of a technical condition of separate gear wheels, especially with big modules (m> 20 mm), allows to outline prospects of development of such diagnostics of a technical condition of gear wheels directly in the course of operation. Goal. The goal of this study is to develop an algorithm for diagnosing the technical condition and forecasting a fault-free operation of the gears of heavy-duty machines. Methodology. The least squares method and the confidence interval method are used to predict accident-free guaranteed gear operation. Results. A procedure for diagnosing the technical condition of gears by measuring the hardness of the metal has been developed, which includes five main stages: selection of the device; choice of measurement scheme; selection of the number of measurements, number and relative position of measuring points; development of a design of a template for measurements; development of a device for fastening templates. When determining the accident-free guaranteed operating time on the basis of several criteria of the limit state, the final decision is made on the criterion that determines the minimum term. Originality. The method of forecasting accident-free guaranteed operating time of gears is chosen taking into account the stages of running-in and stable in linear function accumulation of damage in operation. Practical value. The use of the developed procedure for diagnosing the technical condition of the gears of gearboxes of heavy-duty machines will significantly increase the efficiency of their operation due to the transition from scheduled preventive maintenance to maintenance according to the actual technical condition.

Abstract This study presents the use of a machine learning method from the artificial intelligence area, such as the support vector machines, applied to the construction of data-based classification models for diagnosing undesired scenarios in the hydrogen production process by photo-fermentation, which was carried out by an immobilized photo-bacteria consortium. The diagnosis models were constructed with data obtained from simulations run with a mechanistic model of the process and assessed on both modelled and experimental batches. The results revealed a 100% diagnosis performance in those batches where light intensity was below and above an optimum operation range. Nevertheless, 55% diagnosis performance was obtained in modelled batches where pH was away from its optimum operation range, showing that diagnosis model predictions during the first observations of those batches were classified as normal operation and revealing diagnosis delay in pH oscillations. In general, results demonstrate the reliability of classification models to be used in future applications such as the on-line process monitoring to detect and diagnose undesired operating conditions and take corrective actions on time to maintain high hydrogen productivities.

The operation of various vehicles in the field of urban electric transport is accompanied by high costs to maintain their working condition throughout the service life. Preservation of serviceability of vehicles is provided by performance of planned and preventive works on maintenance (MOT) and repair, and also the unscheduled repairs which are carried out for elimination of failures which arise in the interprophylactic periods, and malfunctions. As already noted, in the planned preventive maintenance and repair system, the vehicle after a certain mileage (time) is forcibly subjected to preventive actions in the prescribed amount. At the same time, despite the adjustment of maintenance and repair modes depending on a number of factors, there is no individual approach to each rolling stock. However, there is a need for such an approach, because even when rolling stock under the same conditions, the technical condition of each of them at the same time due to a number of reasons (individual characteristics of rolling stock, driving quality, maintenance, etc.) can differ significantly. Not every rolling stock requires all the operations provided by the "hard" volume of a particular type of maintenance. Execution of these "unnecessary" operations leads, on the one hand, to incomplete realization of individual properties of a rolling stock, increase in expenses for MOT, on the other, at all does not promote improvement of its technical condition. On the contrary, more frequent interventions in the work of joints of units and mechanisms contribute to increased wear of bonded surfaces, the appearance of damage to joints, violation of the tightness of joints. Significant losses of labor and material resources are also associated with a large amount of repair work due to late detection of failures. The fullest use of individual capabilities of rolling stock and ensuring on this basis the high efficiency of rolling stock during operation can be done through the widespread introduction into the technological process of maintenance and repair of diagnosing the technical condition of rolling stock. To increase the efficiency of the vehicle, use, methods and diagnostic tools have been developed, which are used both during maintenance and repairs, and as an independent technological process. Diagnosis allows to increase the coefficient of readiness and probability of trouble-free operation of vehicles, to reduce the complexity and cost of operation, to increase the maintainability and controllability of vehicles.

An increasing number of bearings in the components and units of the mechanical systems for motor vehicles is one of the many effects of the rapid growth of their technical and design advancement. Due to the nature of their responsibility, it's necessary to diagnose. The article describes the currently used methods of diagnosis workshop practice of bearings and presents own proposals based on vibroacoustic diagnostic methods.

Heat exchangers are used to stabilize and maintain the temperature regime of various units and systems of cars. The technical condition of heat exchangers in operation is different and unstable. At a certain operating time interval, the technical condition of any of the heat exchangers can become limiting due to the influence of various kinds of operational factors. The article analyzes the possibility of improving the means and methods of diagnostic support of automobile heat exchangers in operation. In the design and manufacture of automotive heat exchangers, they are tested in specialized laboratories equipped with test stands characterized by high capital and operating costs. The use of these stands for diagnosing heat exchangers in operation is not applicable. Due to the limitations and imperfections of the known methods and means of diagnosing automobile heat exchangers in operation, an objective assessment of their current technical condition is difficult and requires the search for new, more advanced diagnostic options. The relevance of the topic of the article lies in the search for ways to improve the diagnostic support, adapted for a quantitative assessment of the technical condition of heat exchangers in operation. The aim of the work is to improve the method and means of diagnosing heat exchangers in operation on the basis of modernizing the design of the test bench for radiator modules. Potentially a possible upgrade option was chosen previously developed by the authors stand for testing modules of automobile radiators, which is distinguished by the effect of resource saving when obtaining the test result. Achievement of the goal requires updating the architecture of the structural elements of the stand, and the search for design and technological solutions that contribute to the achievement of the goal. The methodological research toolkit is based on dialectically interrelated methods: analysis of a problem situation and subsequent design and technological synthesis. Scientific novelty lies in the development of conditions for modernization, allowing to expand the functionality of the basic structure of the stand. The practical significance of the results lies in the choice of the direction of research, with a new set of technical proposals and conditions for achieving the goal of the work.

The complexity of the tasks of diagnosing and modeling the main oil pipelines operating modes is a large number of possible operating modes that depend on many parameters, such as: the physical and chemical properties of oil, the scheme of operation, the status and quantity of running pumping and power equipment. The authors developed program complex that allow solving the problem of determining the optimal operating duties for main oil pipelines.

This paper introduces a probabilistic approach to the classification and diagnosis of erroneous rules of operations that result from misconceptions (“bugs”) in a procedural domain of arithmetic. The model is different from the usual deterministic strategies common in the field of artificial intelligence because variability of response errors is explicitly treated through item response theory. As a concrete example, we analyze a dataset that reflects the use of erroneous rules of operation in problems of signed-number subtraction. The same approach, however, is applicable to the classification of several different groups of response patterns caused by a variety of different underlying misconceptions, different backgrounds of knowledge, or treatment.

The paper proposes procedures for diagnosing the electrostatic precipitator's technical state in terms of its key function as a facility in a complex waste gas dedusting system. The presented state diagnosing procedures concern the assessment, genesis and forecast of the electrostatic precipitator's state during its rational operation.

Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.02.08 – технологія машинобудування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. Дисертація присвячена рішенню важливої науково-технічної проблеми підвищення продуктивності бездефектного профільного зубошліфування на верстатах з ЧПК на основі розробки відповідних технологічних передумов та підсистем проектування, моніторингу і технологічної діагностики операції, які дозволяють виконувати адаптацію елементів технологічної системи до більш високої продуктивності. Для цього розроблено методологію дослідження технологічної системи зубошліфування з використанням наукових методів моделювання, оптимізації і керування, а також відповідні технологічні передумови у вигляді комплексу цілеспрямованих методів і засобів інноваційної технології профільного зубошліфування: математичні моделі припуску для перетворення невизначеності припуску у величину відводу шліфувального круга, метод вирівнювання припуску по периферії зубчастого колеса без внесення корекції в його кутове положення, метод адаптивної правки профільного шліфувального круга тощо. Теоретично показано і практично підтверджено технологічну перевагу високопоруватих шліфувальних кругів у порівнянні зі переривчастими кругами. Виконано комплекс експериментальних досліджень і заводських випробувань, що підтвердили ефективність розроблених методів і засобів.
Thesis for the degree of doctor of technical sciences on specialty 05.02.08 – manufacturing engineering. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2018. The thesis is devoted to solving an important scientific and technical problem of increasing the productivity of defect-free profile gear grinding on CNC machines on the basis of the development of appropriate technological preconditions and subsystems for the designing, monitoring and diagnosing of the operation, which allow adapting the elements of the grinding system to higher productivity. For this purpose a methodology is developed for researching the profile grinding system using scientific methods of modeling, optimization and control, as well as corresponding technology preconditions in the form of a set of purposeful methods and means of innovative profile grinding technology, to wit: grinding stock mathematical models for the transformation of the grinding stock uncertainty into the taking grinding wheel away from a gear to be grinded, method of the grinding stock aligning on the gear periphery without making corrections in its angular position, method of a profile grinding wheel adaptive dressing, etc. The software for these subsystems is created on the basis of the mathematical models of the temperature field with and without taking into account the effect of forced cooling. The technological superiority of high-porosity grinding wheel has been theoretically demonstrated and practically confirmed in comparison with special discontinuous wheel. Complex of experimental research and factory tests is performed for confirming the effectiveness of the methods and means developed.

Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.02.08 – технологія машинобудування. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2018. Дисертація присвячена рішенню важливої науково-технічної проблеми підвищення продуктивності бездефектного профільного зубошліфування на верстатах з ЧПК на основі розробки відповідних технологічних передумов та підсистем проектування, моніторингу і технологічної діагностики операції, які дозволяють виконувати адаптацію елементів технологічної системи до більш високої продуктивності. Для цього розроблено методологію дослідження технологічної системи зубошліфування з використанням наукових методів моделювання, оптимізації і керування, а також відповідні технологічні передумови у вигляді комплексу цілеспрямованих методів і засобів інноваційної технології профільного зубошліфування: математичні моделі припуску для перетворення невизначеності припуску у величину відводу шліфувального круга, метод вирівнювання припуску по периферії зубчастого колеса без внесення корекції в його кутове положення, метод адаптивної правки профільного шліфувального круга тощо. Теоретично показано і практично підтверджено технологічну перевагу високопоруватих шліфувальних кругів у порівнянні зі переривчастими кругами. Виконано комплекс експериментальних досліджень і заводських випробувань, що підтвердили ефективність розроблених методів і засобів.
Thesis for the degree of doctor of technical sciences on specialty 05.02.08 – manufacturing engineering. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2018. The thesis is devoted to solving an important scientific and technical problem of increasing the productivity of defect-free profile gear grinding on CNC machines on the basis of the development of appropriate technological preconditions and subsystems for the designing, monitoring and diagnosing of the operation, which allow adapting the elements of the grinding system to higher productivity. For this purpose a methodology is developed for researching the profile grinding system using scientific methods of modeling, optimization and control, as well as corresponding technology preconditions in the form of a set of purposeful methods and means of innovative profile grinding technology, to wit: grinding stock mathematical models for the transformation of the grinding stock uncertainty into the taking grinding wheel away from a gear to be grinded, method of the grinding stock aligning on the gear periphery without making corrections in its angular position, method of a profile grinding wheel adaptive dressing, etc. The software for these subsystems is created on the basis of the mathematical models of the temperature field with and without taking into account the effect of forced cooling. The technological superiority of high-porosity grinding wheel has been theoretically demonstrated and practically confirmed in comparison with special discontinuous wheel. Complex of experimental research and factory tests is performed for confirming the effectiveness of the methods and means developed.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 97-103).
This thesis presents new methods for exploring data using visualization techniques. The first part of the thesis develops a procedure for visualizing the sampling variability of a plot. The motivation behind this development is that reporting a single plot of a sample of data without a description of its sampling variability can be uninformative and misleading in the same way that reporting a sample mean without a confidence interval can be. Next, the thesis develops a method for simplifying large scatter plot matrices, using similar techniques as the above procedure. The second part of the thesis introduces a new diagnostic method for regression called backward selection search. Backward selection search identifies a relevant feature set and a set of influential observations with good accuracy, given the difficulty of the problem, and additionally provides a description, in the form of a set of plots, of how the regression inferences would be affected with other model choices, which are close to optimal. This description is useful, because an observation, that one analyst identifies as an outlier, could be identified as the most important observation in the data set by another analyst. The key idea behind backward selection search has implications for methodology improvements beyond the realm of visualization. This is described following the presentation of backward selection search. Real and simulated examples, provided throughout the thesis, demonstrate that the methods developed in the first part of the thesis will improve the effectiveness and validity of data visualization, while the methods developed in the second half of the thesis will improve analysts' abilities to select robust models.
by Rajiv Menjoge.
Ph.D.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
O estado do Rio de Janeiro, a segunda maior economia do Brasil, exerce forte influência na Região Sudeste nas atividades de exportação e importação, principalmente por sua estrutura portuária privilegiada. Bilhões de dólares em mercadorias são exportadas e importadas anualmente através de terminais portuários no estado. Para auxiliar o planejamento dos sistemas de transporte do estado, torna-se necessário um diagnóstico das operações de comércio exterior, assim como o uso de um sistema de informação para apoio a decisões, devido ao grande volume de dados e da complexidade do problema. Essa dissertação aborda a proposta, os testes e a implementação de um modelo de sistema de informações para o armazenamento e análise de dados referentes a operações de exportação que envolvem o estado do Rio de Janeiro e seu entorno, juntamente com um modelo de interface de consultas que possibilite o diagnóstico dessas operações. A dissertação também aborda o uso de um Sistema de Informações Geográficas para a visualização dos resultados das consultas e auxílio ao diagnóstico. O principal resultado deste trabalho é tornar disponível um sistema de informações para diagnósticos logísticos. Entre diversas constatações feitas com o uso desta ferramenta, extraídas de dados entre os anos de 1999 e 2002, estão a confirmação da necessidade de um anel rodoviário circundando a cidade do Rio de Janeiro, que o importador principal são os Estados Unidos com 18% do total exportado e que a maior parte das exportações está ligada aos produtos da indústria de petróleo.
Rio de Janeiro has the second strongest state economic movement in Brazil, as a consequence it has great influence over the Brazilian southeastern region in international trade operations, based on a privileged harbor infrastructure. Over the years, goods and products worth billions of dollars have been imported and exported through harbors located in Rio. A diagnostics of these operations is a necessary tool for the transportation systems planning in the state, as well as the use of information systems to support decision-making, since there`s a large amount of data to be handled and the ensuing complexity of the problem. This dissertation proposes, develops, tests and implements an information system model for storage and analysis of data concerning the international trade operations involving the state of Rio de Janeiro and its outskirts together with a query interface model for computer aided human diagnostics of these operations. This work also includes the use of Geographic Information Systems for visualization of query results and diagnostic assistance. The main result of this work is to make available a useful information systems tool for diagnostics in Logistics. Among various sample conclusions of a prototypical use of the tool with data in the range 1999- 2002 were: the confirmation of the need for a surrounding expressway, that the main foreign client is the US with 18% of the total and that the majority of the products belong to the Oil industry.

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS.

The use of systems that span many knowledge domains is becoming more common as technology advances, requiring expert-performance in a domain from users who are usually not experts in that domain. This study examined a means of communicating expertise (in system operation and fault diagnosis) to non-experts and furthering the understanding of expert mental models. It has been suggested that conceptions of abstract models of system-functions distinguish expert performance from non-expert performance (Hanisch, Kramer, and Hulin, 1991). This study examined the effects on performance of augmenting a simple control panel device with a model of the functions of the device, interacting with the model, and augmenting the device with graphically superimposed procedural indicators (directions). The five augmented display conditions studied were: Device Only, Device + Model, Device + Procedural Indicators, Interactive Model, and Interactive Model + Procedural Indicators. The device and displays were presented on a PC workstation. Performance measures (speed and accuracy) and subjective measures (questionnaires, NASA TLX, and structured interviews) were collected. It was expected that participants who interact with the device + procedural indicators would exhibit the shortest performance time and least errors; however, those who interacted with the simplest display (device only) were fastest and exhibited the least errors. Results of this study are discussed in terms of building a mental model and identifying situations that require a mental model.
Ph. D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.
Includes bibliographical references (p. 107-111).
Variations in training and individual doctor's listening skills make diagnosing a patient via stethoscope based auscultation problematic. Doctors have now turned to more advanced devices such as x-rays and computed tomography (CT) scans to make diagnoses. However, recent advances in lung sound analysis techniques allow for the auscultation to be performed with an array of microphones, which send the lung sounds to a computer for processing. The computer automatically identifies adventitious sounds using time expanded waveform analysis and allows for a more precise auscultation. We investigate three data mining techniques in order to diagnose a patient based solely on the sounds heard within the chest by a "smart" stethoscope. We achieve excellent recognition performance by using k nearest neighbors, neural networks, and support vector machines to make classifications in pair-wise comparisons. We also extend the research to a multi-class scenario and are able to separate patients with interstitial pulmonary fibrosis with 80% accuracy. Adding clinical data also improves recognition performance. Our results show that performing computerized lung auscultation offers a low-cost, non-invasive diagnostic procedure that gives doctors better clinical utility especially in situations when x-rays and CT scans are not available.
by Bryan C. Flietstra.
S.M.

This thesis is based on diagnosing, assessing and optimising the design and operation of membrane bioreactors (MBR) used for treating municipal wastewater. Specifically, this thesis has been carried out within the framework of seven municipal MBR wastewater treatment plants (WWTP) located in Catalonia with the collaboration of the Catalan Water Agency. Firstly, the design and operational issues of MBRs have been diagnosed, together with determining the main operational problems related to this technology. Secondly, the optimisation strategies applied in each full-scale MBR and the resulting costs were assessed. Finally, two of the operational problems identified were exhaustively evaluated: The ragging phenomenon and the biological nitrogen removal and operational costs optimisations. The research carried out in this thesis has enabled the design and operation of the municipal MBRs to be assessed while, at the same time, presenting several optimisation strategies which will improve the operation and costs of this technology
Aquesta tesi es centra en la diagnosi, avaluació i optimització del disseny i l’operació dels bioreactors de membranes (BRM) pel tractament d’aigües residuals. Concretament, l’estudi s’ha realitzat dins del marc de treball de set estacions depuradores d’aigües residuals (EDARs) municipals amb tecnologia BRM presents a Catalunya en col·laboració amb l’Agència Catalana de l’Aigua. Primerament s’ha dut a terme la diagnosi de l’estat del disseny i operació dels BRMs i s’han determinat les problemàtiques associades a aquesta tecnologia. Així mateix, s’han avaluat les estratègies d’optimització realitzades i els corresponents costos d’operació. A partir de la diagnosis realitzada, dos dels principals problemes operacionals observats s’han caracteritzat i optimitzat: El ragging i l’optimització de l’eliminació biològica de nitrogen i dels costos d’operació. La recerca presentada en aquesta tesi ha permès diagnosticar l’estat dels BRMs, alhora que ha mostrat possibles vies d’optimització que permetran millorar l’operació i els costos associats a aquesta tecnologia

For" smart " electric networks, intelligent software tools that perform new functions and increase the level of computer support for dispatching solutions are considered. Given that one of the goals of building "smart" networks is to ensure recovery after accidents, the main focus of the textbook is on the problems of diagnosing emergency situations, intelligent monitoring of the state of electrical networks, and planning for the post-accident restoration of power supply. A new type of software simulator for dispatchers of electrical networks — a simulator for analyzing emergency situations-is considered in detail. The theoretical material is accompanied by many examples in the form of protocols for the operation of real intelligent systems. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of electric power specialties, managers and specialists of operational services of enterprises of power systems, electric and distribution networks and power stations, branches of PJSC ROSSETI, PJSC FGC UES, as well as students of advanced training courses.

The textbook contains the main provisions of the theory of automobile diagnostics, methods and characteristic technological processes for diagnosing automobile engines, structural elements of the transmission, chassis, bodywork, and active safety systems. The theoretical material is supported by the description of practical technologies for conducting diagnostics of modern car systems. Meets the requirements of the federal state educational standards of higher education of the latest generation. Recommended for undergraduates and bachelors of all forms of education in the areas of training 23.04.03 and 23.03.03 "Operation of transport and technological machines and complexes".

In the textbook fundamentals of the theory of diagnostics of electrical equipment, organization of technical maintenance, service and repair. The methods of organization of maintenance of electrical machines, transformers, transmission lines and cables. Designed for undergraduate students enrolled in the specialty "power and electrical engineering".

AbstractThis chapter focuses on the data science-based management for another three key parts during battery operations including the battery ageing/lifetime prognostics, battery fault diagnosis, and battery charging. For these three key parts, their fundamentals are first given, followed by the case studies of deriving various data science-based solutions to benefit their related operation management.

Abstract Spiral bevel gears are important part of many mechanical transmission systems and are known for their smooth operation and strong load carrying capacity. This type of gear has a high contact ratio, which makes it very difficult to diagnose even serious defects. Therefore, spiral bevel gears have rarely been used as a reference for defect diagnosis techniques. To overcome these challenges, artificial intelligence (AI) techniques are used in this research to diagnose defects in spiral bevel gears. Although Al techniques in the field of fault diagnosis have been very successful, however, these methods largely use the assumption that the training and test data come from the same operating conditions. However, when the operating conditions in which the trained model is deployed for predictions, differ from the operating conditions in which the model was trained, the performance of these approaches might be significantly reduced. Outside the laboratory, in real-world applications, operating conditions significantly vary, and it is difficult to obtain data for all potential operating conditions. To overcome this limitation and to make AI techniques suitable for diagnosing spiral bevel gear faults under different operating conditions, an effort is made to find fault distinguishing features, which are lesser sensitive to operating conditions. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers for fault detection. Performance comparison between both classifiers is made to determine their individual capability and suitability for diagnosing defects of spiral bevel gears under different operating conditions.

The first law of thermodynamics is universally used in steady-state modeling and performance analysis of power plant cycles. Mass and energy balances may be effectively utilized in analyzing and evaluating turbine cycle performance issues and associated heat rate and capacity losses. Utilizing a case study for a fleet of coal-fired power plants and different examples, this paper examines applications of heat balance techniques in evaluating and quantifying performance losses. The focus of the paper will be in analyzing, diagnosing and quantifying performance, operation and reliability issues for the turbine cycle and major equipment.

Rotordynamic instability, commonly observed as subsynchronous vibration, is a serious problem that can cause heavy damage to a turbomachine or make it incapable of operation due to high vibration levels. However, all subsynchronous vibrations are not necessarily unstable. If the amplitude of the subsynchronous vibration is large, it can cause damage to seals, bearings, or process wheels. If it is small, the question arises as to whether it has the potential to grow larger (“instability”) or whether it is benign and harmless. A way to know would be helpful. The objective of this study is to signal-analyze subsynchronous vibration in turbomachinery and distinguish benign subsynchronous vibration from true rotordynamic instability. Effort is also made to identify unique signatures to a cause, thereby aiding in faster diagnosis. A computer simulation study is conducted on four rotors, including two gas-reinjection compressors that went unstable, to examine the possibility of using the change in synchronous phase angle as a possible indicator of impending instability. Two other rotors in laboratory test rigs were also studied, both experimentally and with computer simulations of the phase angle response. The computer simulations and experimental results agree very closely on the test rigs. Measured signals from another rotor, with bearings having a dead-band clearance are studied as an example of a benign cause that can result in subsynchronous vibration. The effect is studied with the rotor in both horizontal and vertical positions and clear indicators are discovered that confirm the subsynchronous vibration to be benign in nature. Signatures from another rotor with internal friction are also presented. The experimental data clearly shows indicators that distinguish it as a genuine instability. Dry friction whip is also experimentally produced on a test rig. The measurements show that dry friction whip defies all general rules of thumb for diagnosing a true instability and at the same time is a very violent one as well.

The paper presents an application in the field of a method for the analysis of the gas turbine operating state, which uses the Cycle-Deck developed by the gas turbine manufacturer and the measurements taken by means of the standard machine instrumentation. The method is applied for diagnosing the operating state of a number of gas turbines in operation at the test-bench in order to check in the field the validity and applicability of the methodology.

Rotordynamic instability, commonly observed as subsynchronous vibration, is a serious problem that can cause heavy damage to a turbomachine or make it incapable of operation due to high vibration levels. However, all subsynchronous vibrations are not necessarily unstable. A way to quickly diagnose them would be helpful. In an earlier paper, the authors presented data from experiments that simulated various causes of sub-synchronous vibrations, some causes being genuine rotordynamic instabilities and some others being benign (stable), and identified ways to diagnose and classify the subsynchronous motions. In a continuation of the same study, subsynchronous vibrations due to coupled lateral-torsional effects are experimentally simulated, the objective being to signal-analyze these vibrations to find unique signatures that identify this cause and also be able to recognize if they are a true rotordynamic instability or not. To this end, a test rig was built with parallel shafts coupled by gears, driven by a DC motor at one end and loaded at the other end, to closely simulate a real-world machine. A torsional mathematical model for the test rig is also presented to predict its torsional natural frequencies. Experiments were conducted wherein the first torsional natural frequency was externally excited, with the shaft spinning at a higher speed. The result was a false sub-synchronous “instability” signal in the lateral measurements. A method to distinguish these vibrations from a genuine lateral non-synchronous instability is presented. Also, a new diagnostic method to classify the subsynchronous vibration as benign is elucidated.

Lower hybrid current drive (LHCD) is an efficient method for noninductive current drive in fusion devices. The LHCD system has been constructed on the Experimental Advanced Superconduct Tokamak (EAST). It is a complex system due to lots of devices involved. Each device has possibility of faults, which causes great difficulties in fault diagnosis. Consequently, a fault diagnosis expert system is essential for a safe and steady operation of the LHCD system. This paper proposes an expert system called LFDES (lower hybrid current drive fault diagnosis expert system) to aid operators in diagnosing and analyzing abnormal situations of the LHCD system. After a brief description of the structure of LHCD system, the LFDES architecture, the knowledge base, the inference engine and the database are presented in detail. Based on an empirical knowledge, the diagnostic tree of LHCD system is built. A fuzzy group multiple attribute decision making method is used to determine the priorities of nodes in the diagnostic tree. KDevelop tool, QT Designer tool and Linux operation system have been used in developing the proposed system. In the study, satisfactory results were obtained. The analyses of the results indicated that LFDES can provide reliable, efficient and economical service.

Review question / Objective: This meta-analysis aimed to determine the accuracy of ultrasound in distinguishing pathology of malignant thyroid diseases. Eligibility criteria: Type of study. This study will only include high quality clinical cohort or case control studies. Type of patients. The patients should be those who had undergone breast diseases. Intervention and comparison. This study compares AI with pathology for diagnosing breast diseases. Type of outcomes. The primary outcomes include sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and the area under the curve of the summary receiver operating characteristic.

Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.

Reproductive tract infections (RTIs) are common in Zimbabwe. Many RTIs increase the risk of human immunodeficiency virus (HIV) infection. In 1998, the Zimbabwe National Family Planning Council (ZNFPC) conducted an operations research study to assess the feasibility of adding RTI diagnosis and treatment to its menu of services. The study population consisted of 1,634 clients at three ZNFPC clinics. Each client was asked about lower abdominal pain, vaginal discharge, and other RTI symptoms; examined for clinical signs of RTIs; and given laboratory tests to confirm the accuracy of diagnosis based upon symptoms and signs. Findings detailed in this brief were that existing methods for screening RTIs among family planning clients are not cost-effective, laboratory tests are too costly, and syndromic case management often leads to missed infections and unnecessary treatment. Health programs should continue to emphasize preventive measures—changing individual behavior and promoting condom use.

The Population Council and the Employees’ State Insurance Company are collaborating in a Men in Maternity (MiM) study in India to test a model of antenatal and postnatal services designed to help thousands of couples, especially men, redefine their roles in reproductive health and improve birth outcomes and maternal health. This is part of a global study that will assess the impact of male partnership in improving pregnancy outcomes and reproductive health, primarily by reducing the prevalence of STIs and increasing postpartum family planning use. Studies suggest that the lack of men’s participation in reproductive health actually undermines women’s health. The MiM study is the first of its kind in India. It includes more than a focus on contraception or safe motherhood and covers topics ranging from child survival and postpartum services to STI diagnosis and treatment and prevention of HIV/AIDS. This research update provides an overview of the MiM experimental intervention and some lessons learned in establishing the new services and maintaining operations during the first 14 months of the study’s intervention period.

The HIV/AIDS epidemic has led program managers to seek approaches to family planning (FP) that will also help combat HIV/AIDS and other sexually transmitted infections (STIs). These approaches include use of simplified STI diagnosis and treatment protocols and promotion of male and female condoms. One aspect of condom promotion that is receiving increased attention is the idea of adding men to community-based distribution (CBD) programs. The male latex condom is the only contraceptive method that, when used correctly and consistently, provides protection against STIs, including HIV/AIDS. Currently, an estimated 6 to 9 billion male condoms are used worldwide annually. However, experts estimate that 24 billion male condoms should be used annually to provide adequate protection against STIs and HIV/AIDS. CBD programs are important sources of temporary FP methods in many countries, but most programs distribute far fewer condoms than oral contraceptives. One reason may be that most CBD agents are women, and gender differences between distributors and potential clients may limit the sale of condoms. This program brief reviews evidence from operations research and other studies in 13 countries about the effectiveness of men as CBD workers.

Banco de la República provides a comprehensive overview of Colombia’s finan¬cial infrastructure in its Payment Systems Report, which is an important product of the work it does to oversee that infrastructure. The figures published in this edition of the report are for the year 2020, a pandemic period in which the con¬tainment measures designed and adopted to alleviate the strain on the health system led to a sharp reduction in economic activity and consumption in Colom¬bia, as was the case in most countries. At the start of the pandemic, the Board of Directors of Banco de la República adopted decisions that were necessary to supply the market with ample liquid¬ity in pesos and US dollars to guarantee market stability, protect the payment system and preserve the supply of credit. The pronounced growth in mone¬tary aggregates reflected an increased preference for liquidity, which Banco de la República addressed at the right time. These decisions were implemented through operations that were cleared and settled via the financial infrastructure. The second section of this report, following the introduction, offers an analysis of how the various financial infrastructures in Colombia have evolved and per¬formed. One of the highlights is the large-value payment system (CUD), which registered more momentum in 2020 than during the previous year, mainly be¬cause of an increase in average daily remunerated deposits made with Banco de la República by the General Directorate of Public Credit and the National Treasury (DGCPTN), as well as more activity in the sell/buy-back market with sovereign debt. Consequently, with more activity in the CUD, the Central Securi¬ties Depository (DCV) experienced an added impetus sparked by an increase in the money market for bonds and securities placed on the primary market by the national government. The value of operations cleared and settled through the Colombian Central Counterparty (CRCC) continues to grow, propelled largely by peso/dollar non-deliverable forward (NDF) contracts. With respect to the CRCC, it is important to note this clearing house has been in charge of managing risks and clearing and settling operations in the peso/dollar spot market since the end of last year, following its merger with the Foreign Exchange Clearing House of Colombia (CCDC). Since the final quarter of 2020, the CRCC has also been re¬sponsible for clearing and settlement in the equities market, which was former¬ly done by the Colombian Stock Exchange (BVC). The third section of this report provides an all-inclusive view of payments in the market for goods and services; namely, transactions carried out by members of the public and non-financial institutions. During the pandemic, inter- and intra-bank electronic funds transfers, which originate mostly with companies, increased in both the number and value of transactions with respect to 2019. However, debit and credit card payments, which are made largely by private citizens, declined compared to 2019. The incidence of payment by check contin¬ue to drop, exhibiting quite a pronounced downward trend during the past last year. To supplement to the information on electronic funds transfers, section three includes a segment (Box 4) characterizing the population with savings and checking accounts, based on data from a survey by Banco de la República con-cerning the perception of the use of payment instruments in 2019. There also is segment (Box 2) on the growth in transactions with a mobile wallet provided by a company specialized in electronic deposits and payments (Sedpe). It shows the number of users and the value of their transactions have increased since the wallet was introduced in late 2017, particularly during the pandemic. In addition, there is a diagnosis of the effects of the pandemic on the payment patterns of the population, based on data related to the use of cash in circu¬lation, payments with electronic instruments, and consumption and consumer confidence. The conclusion is that the collapse in the consumer confidence in¬dex and the drop in private consumption led to changes in the public’s pay¬ment patterns. Credit and debit card purchases were down, while payments for goods and services through electronic funds transfers increased. These findings, coupled with the considerable increase in cash in circulation, might indicate a possible precautionary cash hoarding by individuals and more use of cash as a payment instrument. There is also a segment (in Focus 3) on the major changes introduced in regulations on the retail-value payment system in Colombia, as provided for in Decree 1692 of December 2020. The fourth section of this report refers to the important innovations and tech¬nological changes that have occurred in the retail-value payment system. Four themes are highlighted in this respect. The first is a key point in building the financial infrastructure for instant payments. It involves of the design and im¬plementation of overlay schemes, a technological development that allows the various participants in the payment chain to communicate openly. The result is a high degree of interoperability among the different payment service providers. The second topic explores developments in the international debate on central bank digital currency (CBDC). The purpose is to understand how it could impact the retail-value payment system and the use of cash if it were to be issued. The third topic is related to new forms of payment initiation, such as QR codes, bio¬metrics or near field communication (NFC) technology. These seemingly small changes can have a major impact on the user’s experience with the retail-value payment system. The fourth theme is the growth in payments via mobile tele¬phone and the internet. The report ends in section five with a review of two papers on applied research done at Banco de la República in 2020. The first analyzes the extent of the CRCC’s capital, acknowledging the relevant role this infrastructure has acquired in pro¬viding clearing and settlement services for various financial markets in Colom¬bia. The capital requirements defined for central counterparties in some jurisdic¬tions are explored, and the risks to be hedged are identified from the standpoint of the service these type of institutions offer to the market and those associated with their corporate activity. The CRCC’s capital levels are analyzed in light of what has been observed in the European Union’s regulations, and the conclusion is that the CRCC has a scheme of security rings very similar to those applied internationally and the extent of its capital exceeds what is stipulated in Colombian regulations, being sufficient to hedge other risks. The second study presents an algorithm used to identify and quantify the liquidity sources that CUD’s participants use under normal conditions to meet their daily obligations in the local financial market. This algorithm can be used as a tool to monitor intraday liquidity. Leonardo Villar Gómez Governor