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Journal articles on the topic 'Soft-DTW'
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Venkata Ramudu, Dr Balasani, Mr Chiranjeevi Kondabathini, and Mr Udaya Kiran Mandhugula. "Enhancing Handwritten Signature Identification and Palm Biometric Objectives." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 30, 2023): 1–13. http://dx.doi.org/10.55041/ijsrem27802.
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Soft biometrics are already widely used as a support tool for user identification. However, it is not the only use for biometric information that is conceivable because such information can be sufficient to obtain minimal details from the user that are unrelated to his identity. Examples of what might be referred to as soft biometrics include gender, hand orientation, and emotional state. Utilizing physiologic modalities for soft-biometric work is extremely prevalent, prediction, but behavioral data is frequently disregarded. Keystroke dynamics and handwriting signature are two potential behavioral modalities that could be used to predict a user's gender, but they are rarely discussed in the literature together. This study seeks to fill this gap by examining the influence of combining these two distinct biometric modalities on the accuracy of gender prediction and the best way. Key Words: Item key-strokes, Bio-metric signatures, digital signs, dynamic temporal wrapping (DTW)
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Kang, Yi, Dong Yi Chen, Michael Lawo, and Shi Ji Xia Hou. "A Wearable Swallowing Detecting Method Based on Nanometer Materials Sensor." Advances in Science and Technology 100 (October 2016): 120–29. http://dx.doi.org/10.4028/www.scientific.net/ast.100.120.
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Obesity and dysphagia are of potential and direct serious harm to the human body health. A commonly used method is controlling food intake to avoid obesity or determining if dysphagia exists by monitoring the swallow . This paper proposes a swallow detecting principle based on nanometer materials sensor, and implements a wearable detecting system with advantage of improved DTW algorithm. The system efficiently detects and faithfully identifies swallowing. In addition, it reduces the demand for hardware computing power. The system meets the features of a wearable system, such as soft and comfortable, lightweight, portable, and noninvasive.
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Sun, Xiaojun, Yingbo Gao, Qiao Zhang, and Shunliang Ding. "Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications." Sustainability 16, no. 11 (June 6, 2024): 4840. http://dx.doi.org/10.3390/su16114840.
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The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies.
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Wang, Feng, Hongbo Lin, and Ziming Ma. "Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters." Energies 17, no. 4 (February 18, 2024): 945. http://dx.doi.org/10.3390/en17040945.
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Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional neural network and bidirectional gated recurrent unit were used to construct a transmission line icing thickness prediction model The model incorporated a weighted fusion of soft−dynamic time warping (Soft−DTW) and the icing change rule as the loss function. Optimal weights were determined through the utilization of the grid search algorithm and cross−validation, contributing to an enhancement of the model’s generalization capabilities and a reduction in prediction errors. The results indicate that the proposed prediction model can consider the impact of line operating parameters, avoiding the shortcomings of prediction results conflicting with actual physical laws. Compared with traditional non−mechanical models, the proposed model showed reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 0.26–0.51%, 0.24–0.44%, and 5.77–13.33%, respectively, while the coefficient of determination (R2) increased by 0.07–0.13.
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Li, Qing, Xinyan Zhang, Tianjiao Ma, Dagui Liu, Heng Wang, and Wei Hu. "A Multi-step ahead photovoltaic power forecasting model based on TimeGAN, Soft DTW-based K-medoids clustering, and a CNN-GRU hybrid neural network." Energy Reports 8 (November 2022): 10346–62. http://dx.doi.org/10.1016/j.egyr.2022.08.180.
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Wu, Xuning, Qian Li, Hu Yin, Zaoyuan Li, Jianhua Jiang, Menghan Si, and Yangyang Zhang. "Real-Time Intelligent Recognition Method for Horizontal Well Marker Bed." Mathematical Problems in Engineering 2020 (June 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/8583943.
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The accurate identification of the horizontal well marker bed is to guarantee the soft landing of the well trajectory. With the intelligent development of the petroleum industry, it is feasible to apply computers to identify the marker bed automatically. In case-based reasoning technology, the data of well logging while drilling (LWD) as characteristic parameters are compared with those of adjacent well. By taking the depth sequence of LWD data as time series and using Dynamic Time Warping (DTW) similarity measure algorithm, the similarity index of each drilling depth is calculated corresponding to the marker bed in the adjacent well. The total similarity curve is obtained by giving different weights of different feature parameters. Selecting natural gamma, deep resistivity, and shallow resistivity LWD curves as characteristic parameters, two horizontal wells in JL block of Junggar basin are analysed by this method. The result of similarity curve indicates the location of the marker bed and the total similarity value reaches 78%. The research shows that the method based on case-based reasoning can identify the marker bed of the horizontal well accurately and effectively, assist the geologist to carry out formation correlation of multiple wells at the same time, reduce the cost of human labour force, and improve work efficiency.
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Du, Yanling, Jiahao Huang, Jiasheng Chen, Ke Chen, Jian Wang, and Qi He. "Enhanced Transformer Framework for Multivariate Mesoscale Eddy Trajectory Prediction." Journal of Marine Science and Engineering 12, no. 10 (October 4, 2024): 1759. http://dx.doi.org/10.3390/jmse12101759.
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Accurately predicting the trajectories of mesoscale eddies is essential for comprehending the distribution of marine resources and the multiscale energy cascade in the ocean. Nevertheless, current approaches for predicting mesoscale eddy trajectories frequently exhibit inadequate examination of the intrinsic multiscale temporal data, resulting in diminished predictive precision. To address this challenge, our research introduces an enhanced transformer-based framework for predicting mesoscale eddy trajectories. Initially, a multivariate dataset of mesoscale eddy trajectories is constructed and expanded, encompassing eddy properties and pertinent ocean environmental information. Additionally, novel feature factors are delineated based on the physical attributes of eddies. Subsequently, a multi-head attention mechanism is introduced to bolster the modeling of the multiscale time-varying connections within eddy trajectories. Furthermore, the original positional encoding is substituted with Time-Absolute Position Encoding, which considers the dimensions and durations of the sequence mapping, thereby improving the distinguishability of embedded vectors. Ultimately, the Soft-DTW loss function is integrated to more accurately assess the overall discrepancies among mesoscale eddy trajectories, thereby improving the model’s resilience to erratic and diverse trajectory sequences. The effectiveness of the proposed framework is assessed using the eddy-abundant South China Sea. Our framework exhibits exceptional predictive accuracy, achieving a minimum central error of 8.507 km over a seven-day period, surpassing existing state-of-the-art models.
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Vuckovic, C., A. Cremer, C. Minsart, L. Amininejad, J. Bottieau, D. Franchimont, and C. Liefferinckx. "P0367 A Clustering approach to discriminate slow and rapid biologics switchers in difficult-to-treat Crohn’s Disease patients." Journal of Crohn's and Colitis 19, Supplement_1 (January 2025): i842—i844. https://doi.org/10.1093/ecco-jcc/jjae190.0541.
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Abstract Background CD patients exhibit highly variable responses to biologics. While some patients achieve sustained remission with only one biologic over the course of their disease, other will require the sequencing of multiple biologics (“difficult-to-treat” patients) for optimal disease control. The aim of this study was to delineate the profile of treatment regimen of the biological exposed CD patients with luminal disease. Methods Among CD patients diagnosed between 1999 and 2019 with B1 phenotype at diagnosis, 203 patients who maintained this phenotype at maximum follow-up (FU, median 11y [8–15]) were selected, based on inclusion criteria. All events were recorded from date of diagnosis to maximum FU, from disease characteristics to treatment adaptations. A temporal clustering approach using k-means was applied to determine biologics treatment regimen profiles. Patient profiles were compared using the Dynamic Time Warping (DTW) similarity measure, considering 3 successive cumulative biologic exposures (for primary/secondary non response only). Clusters number was chosen to balance maximizing silhouette score while ensuring sufficient individuals per cluster. Cumulative biologic exposures were summarized as cluster profiles, represented by their corresponding barycenters computed with soft-DTW distance. Clustering approach was performed using Python (tslearn). Results The 203 patients were clustered in 5 distinct profiles, based on their cumulative biologics exposure in the first 10 years after CD diagnosis (Figure 1). Patients in Cluster 1 were not exposed to biologics (n=55). Patients in Cluster 2 were treated late with one biologic (n=51) while patients in Cluster 3 were treated early with one biologic (n=53). Patients from Cluster 4 (n=23) and Cluster 5 (n=21) were both treated early with multiple biologics. Patients from Cluster 2 were exposed to biologics later after CD diagnosis than patients from Cluster 3 to 5 (Median 5.7y [3.6–6.9] vs 0.7y [0.2-1.8], p<0.0001), as also shown by their lower biological exposure compared to those of Cluster 3 to 5 (median 48% [37-58] vs 80% [60-89], p<0.0001). Most interestingly, patients from Cluster 5 required faster biologics changes compared to patients from Cluster 4 (Figure 2). In Cluster 5 (rapid switchers), patients were exposed earlier compared to patients in Cluster 4 (Slow Switchers) to the first (p<0.005), second (P<0.005) and third biologic (p=0.01), introducing the concept of rapid and slow biologic switchers among CD patients. Conclusion This clustering approach highlights the highly variable pattern of response to biologics in luminal CD patients. Furthermore, this study discriminatessingle and multiple biologics-exposed patients, in whom we identified slow and rapid biologic switchers.
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Chen, Yuyao, Christian Obrecht, and Frédéric Kuznik. "Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions." Integrated Computer-Aided Engineering, January 25, 2024, 1–14. http://dx.doi.org/10.3233/ica-230731.
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Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting.
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Ma, Yan, Yiou Tang, Yang Zeng, Tao Ding, and Yifu Liu. "An N400 identification method based on the combination of Soft-DTW and transformer." Frontiers in Computational Neuroscience 17 (February 16, 2023). http://dx.doi.org/10.3389/fncom.2023.1120566.
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As a time-domain EEG feature reflecting the semantic processing of the human brain, the N400 event-related potentials still lack a mature classification and recognition scheme. To address the problems of low signal-to-noise ratio and difficult feature extraction of N400 data, we propose a Soft-DTW-based single-subject short-distance event-related potential averaging method by using the advantages of differentiable and efficient Soft-DTW loss function, and perform partial Soft-DTW averaging based on DTW distance within a single-subject range, and propose a Transformer-based ERP recognition classification model, which captures contextual information by introducing location coding and a self-attentive mechanism, combined with a Softmax classifier to classify N400 data. The experimental results show that the highest recognition accuracy of 0.8992 is achieved on the ERP-CORE N400 public dataset, verifying the effectiveness of the model and the averaging method.
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Liu, Yingchang, Jie Tang, Zhengwei Tang, and Chengyu Sun. "Robust full-waveform inversion based on automatic differentiation and differentiable dynamic time warping." Journal of Geophysics and Engineering, April 18, 2023. http://dx.doi.org/10.1093/jge/gxad029.
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Abstract Full waveform inversion is a methodology that determines high-resolution parameters. The widely used L2-norm misfit function has local minima if the low wavenumber components are not accurate. Suffering from cycle skipping problem, the solution of waveform inversion will be trapped in the local minima. Dynamic time warping aims to find an optimal alignment between two signals, which is a more robust measure to avoid cycle-skipping challenges. However, the discontinuity makes the conventional dynamic time warping distance unsuitable for waveform inversion. We introduce a soft dynamic time warping distance as the misfit function, which is differentiable that inverted solution can converge to the accurate global minimum. We compare the convexity of the L2-norm and soft dynamic time warping distance and show that the soft dynamic time warping distance has a wider convexity range with different time shift and amplitudes. It can alleviate the half-wavelength limitation of the conventional L2-norm. We calculate the gradient using the automatic differentiation technique and the minibatch strategy and then analyze the alignment paths of different smooth parameters. A significant smooth parameter γ makes the Soft-DTW distance tending to the L2-norm, which generates new local minima. We recommend a small smooth parameter to ensure the convexity of the Soft-DTW distance. Numerical examples show that the soft dynamic time warping can effectively reconstruct the deep velocity parameters of the BG Compass and Marmousi models with noise robustness and lower dependence on the initial model.
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Tan, Jiayan, Weitao Wang, and Charles Langston. "Full Waveform Inversion Based on Dynamic Time Warping and Application to Reveal the Crustal Structure of Western Yunnan, Southwest China." Journal of Geophysical Research: Solid Earth 129, no. 9 (September 2024). http://dx.doi.org/10.1029/2024jb029303.
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AbstractWe develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle‐skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft‐shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe‐Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft‐shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle‐skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well‐aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft‐shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.
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Jiang, Jiajia, Songxuan Lai, Lianwen Jin, and Yecheng Zhu. "DsDTW: Local Representation Learning with Deep soft-DTW for Dynamic Signature Verification." IEEE Transactions on Information Forensics and Security, 2022, 1. http://dx.doi.org/10.1109/tifs.2022.3180219.
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Trelinski, Jacek, and Bogdan Kwolek. "CNN-based and DTW features for human activity recognition on depth maps." Neural Computing and Applications, May 12, 2021. http://dx.doi.org/10.1007/s00521-021-06097-1.
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AbstractIn this work, we present a new algorithm for human action recognition on raw depth maps. At the beginning, for each class we train a separate one-against-all convolutional neural network (CNN) to extract class-specific features representing person shape. Each class-specific, multivariate time-series is processed by a Siamese multichannel 1D CNN or a multichannel 1D CNN to determine features representing actions. Afterwards, for the nonzero pixels representing the person shape in each depth map we calculate statistical features. On multivariate time-series of such features we determine Dynamic Time Warping (DTW) features. They are determined on the basis of DTW distances between all training time-series. Finally, each class-specific feature vector is concatenated with the DTW feature vector. For each action category we train a multiclass classifier, which predicts probability distribution of class labels. From pool of such classifiers we select a number of classifiers such that an ensemble built on them achieves the best classification accuracy. Action recognition is performed by a soft voting ensemble that averages distributions calculated by such classifiers with the largest discriminative power. We demonstrate experimentally that on MSR-Action3D and UTD-MHAD datasets the proposed algorithm attains promising results and outperforms several state-of-the-art depth-based algorithms.
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Meattini, Roberto, Alessandra Bernardini, Gianluca Palli, and Claudio Melchiorri. "sEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control." IEEE Robotics and Automation Letters, 2022, 1–8. http://dx.doi.org/10.1109/lra.2022.3193247.
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Munoz-Montoro, Antonio Jesús, Julio José Carabias-Orti, Pedro Vera-Candeas, Francisco Jesús Canadas-Quesada, and Nicolás Ruiz-Reyes. "Online/offline score informed music signal decomposition: application to minus one." EURASIP Journal on Audio, Speech, and Music Processing 2019, no. 1 (December 2019). http://dx.doi.org/10.1186/s13636-019-0168-6.
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AbstractIn this paper, we propose a score-informed source separation framework based on non-negative matrix factorization (NMF) and dynamic time warping (DTW) that suits for both offline and online systems. The proposed framework is composed of three stages: training, alignment, and separation. In the training stage, the score is encoded as a sequence of individual occurrences and unique combinations of notes denoted as score units. Then, we proposed a NMF-based signal model where the basis functions for each score unit are represented as a weighted combination of spectral patterns for each note and instrument in the score obtained from a trained a priori over-completed dictionary. In the alignment stage, the time-varying gains are estimated at frame level by computing the projection of each score unit basis function over the captured audio signal. Then, under the assumption that only a score unit is active at a time, we propose an online DTW scheme to synchronize the score information with the performance. Finally, in the separation stage, the obtained gains are refined using local low-rank NMF and the separated sources are obtained using a soft-filter strategy. The framework has been evaluated and compared with other state-of-the-art methods for single channel source separation of small ensembles and large orchestra ensembles obtaining reliable results in terms of SDR and SIR. Finally, our method has been evaluated in the specific task of acoustic minus one, and some demos are presented.
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Zhao, Mengyi, Hao Tang, Pan Xie, Shuling Dai, Nicu Sebe, and Wei Wang. "Bidirectional Transformer GAN for Long-Term Human Motion Prediction." ACM Transactions on Multimedia Computing, Communications, and Applications, January 10, 2023. http://dx.doi.org/10.1145/3579359.
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The mainstream motion prediction methods usually focus on short-term prediction, and their predicted long-term motions often fall into an average pose, i.e. the freezing forecasting problem [27]. To mitigate this problem, we propose a novel Bidirectional Transformer-based Generative Adversarial Network (BiTGAN) for long-term human motion prediction. The bidirectional setup leads to consistent and smooth generation in both forward and backward directions. Besides, to make full use of the history motions, we split them into two parts. The first part is fed to the Transformer encoder in our BiTGAN while the second part is used as the decoder input. This strategy can alleviate the exposure problem [37]. Additionally, to better maintain both the local ( i.e. , frame-level pose) and global ( i.e. , video-level semantic) similarities between the predicted motion sequence and the real one, the soft dynamic time warping (Soft-DTW) loss is introduced into the generator. Finally, we utilize a dual-discriminator to distinguish the predicted sequence at both frame and sequence levels. Extensive experiments on the public Human3.6M dataset demonstrate that our proposed BiTGAN achieves state-of-the-art performance on long-term (4 s ) human motion prediction, and reduces the average error of all actions by \(4\% \) .
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Li, Weide, Zihan Hao, and Zhihe Zhang. "An Interpretable Time Series Clustering Neural Network Based on Shape Feature Extraction." International Journal of Pattern Recognition and Artificial Intelligence, October 27, 2022. http://dx.doi.org/10.1142/s0218001422540222.
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Time series is a very common but important data type. A large number of time series data are generated in various professional research fields and daily life. Although there are many models being developed to deal with time series, the cluster methods for time series are insufficient and need to improve. This paper is focused on time series clustering, which uses deep learning approach to discover the shape characteristics of time series. We establish a new neural network model of time series clustering to jointly optimize the representation learning and clustering tasks of time series. Focusing on shape features with time series, we built the Soft-DTW layer into the neural network to learn the interpretable time series representation. Maximized regularization mutual information is used to jointly optimize representation learning and clustering tasks. Experiments show that this model can help obtain an excellent representation of time series. In comparison with the benchmark model, the best clustering effect is achieved in the proposed model on multiple data sets. This model has broad applicability in time series data.
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li, qing, and zhang xinyan. "Multi-Step Ahead Photovoltaic Power Forecasting Model Based on Timegan, Soft Dtw-Based K-Medoids Clustering, And a Cnn-Gru Hybrid Neural Network." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4017353.
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Bünger, Dominik, Miriam Gondos, Lucile Peroche, and Martin Stoll. "An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification." Frontiers in Applied Mathematics and Statistics 7 (January 20, 2022). http://dx.doi.org/10.3389/fams.2021.784855.
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Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects are studied in this paper. Namely, suitable distance measures to evaluate the similarities between different time series, and the choice of learning method to make predictions based on a given number of pre-labeled data points. However, the relationship between the two aspects has never been studied systematically in the context of graph-based learning. We describe four different distance measures, including (Soft) DTW and MPDist, a distance measure based on the Matrix Profile, as well as four successful semi-supervised learning methods, including the recently introduced graph Allen–Cahn method and Graph Convolutional Neural Network method. We provide results for the novel combination of these distance measures with both the Allen-Cahn method and the GCN algorithm for binary semi-supervised learning tasks for various time-series data sets. In our findings we compare the chosen graph-based methods using all distance measures and observe that the results vary strongly with respect to the accuracy. We then observe that no clear best combination to employ in all cases is found. Our study provides a reproducible framework for future work in the direction of semi-supervised learning for time series with a focus on graph representations.
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Arfanuzzaman, Md, S. M. Tanvir Hassan, and Md Abu Syed. "Cost-benefit of promising adaptations for resilient development in climate hotspots: evidence from lower Teesta basin in Bangladesh." Journal of Water and Climate Change, January 27, 2020. http://dx.doi.org/10.2166/wcc.2020.130.
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Abstract It is very likely that climate change will increase the frequency and intensity of extreme events such as floods, flash floods, storms, heat and cold waves, riverbank erosion, and drought in the river basin of Hindu Kush Himalayan (HKH) region. This could mean detrimental impacts to the poor and marginal people in the lower Teesta basin (LTB) in Bangladesh. Though adaptation involves financial costs, the farmers' practicing adaptation in LTB experience diminished crop loss and damage. This study was aimed at assessing the promising adaptation practices, their economic return, and social welfare in the LTB through an extended cost-benefit analysis. The study suggests that among the adaptations, shallow tube-well (STW) based irrigation practice in both sandy and loamy soil has the highest marginal adaptation cost (MAC) but the lowest benefit-cost ratio (BCR). The deep tube-well (DTW) based irrigation practice generates superior benefits to the farmers compared to the STW based farming due to initial establishment by the government which is very expensive. Maize farming as an alternate and less resource consumptive cropping produces nearly five times higher economic benefits than the costs which can be acknowledged as the most profitable and resilient adaptation option in the LTB. Though MAC is relatively low for the short-duration variety (SDV) rice among the promising adaptations, its economic profitability is 62% lower than that of the maize cultivation. However, having higher BCR the maize cultivation generates US$86 higher welfare to the farmers than the SDV rice which may strengthen the farmer's preference of maize cultivation over the SDV rice. It can be stated with high confidence that strategic adaptation planning, soft credit, technological advancement, and subsidized agricultural inputs will encourage the farmers to carry out adaptation options which may reduce climate-induced loss and damages for the farmers and build socio-economic resilience in the LTB and other similar areas of South Asia.
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Kaufmann, Dan, Ariel Tikotsky, Tanhum Yoreh, and Anat Tchetchik. "Engaging faith-based communities in pro-environmental behavior using soft regulations: The case of single-use plastics." Frontiers in Environmental Science 10 (January 4, 2023). http://dx.doi.org/10.3389/fenvs.2022.1019904.
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The consumption of single-use plastics, such as disposable tableware (DTW), conveys a high benefit-cost ratio for consumers while having large environmental externalities. To encourage consumers to reduce their use of DTWs, governments could use small and non-coercive changes in people’s decision-making environments (nudges). This study focuses on the Israeli ultra-Orthodox communities a secluded population group that grows much faster- and consumes much more DTW than the rest of the Israeli population. Employing a quasi-representative sample (N = 450) of this population, this study conducted a discrete-choice experiment that presents the respondent with alternative options to reduce DTW. Two kinds of Nudges–framing and social norms–were utilized. The effectiveness of these Nudges in promoting PEB among faith-based communities has received little attention in previous studies. As another contribution to the literature, this paper also integrates latent constructs such as the respondents’ environmental attitudes and level of conservativeness. 46% of the respondents chose to opt-out whereas 29%, 14%, and 11% chose ‘1-day’, ‘2-days’, and ‘3-days’ per week avoiding DTW, respectively. Social norms, framings, and environmental attitudes had a significant mediating effect, with framing being associated with the highest effect on intentions to reduce DTW, i.e., a willingness to give up 0.31 USD per family member per month, compared to 0.07 USD for an increase in the description of the social norm. The results suggest that Nudges can enhance policies aimed at encouraging pro-environmental behavior among faith-based communities.