Fault Lines Research Articles

Marital Contracts on the Fault Lines: A Liberal Inquiry

Abstract The revolution in Western family law over the past 50 years—often described as ‘liberalization’—involves a decrease in the importance of fault-based factors alongside an increase in the significance of marital contracts. While these two trends generally complement each other, they may conflict when a couple seeks to assign economic consequences to sexual fault through a contract. Should such an agreement be legally enforceable? Which aligns more with a ‘true liberal’ perspective: advocating against fault or for the use of contracts? This paper suggests a new approach that goes beyond simply determining which trend should prevail. We illustrate how the perceived conflict between proponents of sexual liberalization and proponents of contractual liberalization could be resolved by identifying the underlying reasons that motivate each ‘camp’, proposing potential legal mechanisms and specific legal contexts in which broad agreement might be reached, and explicating the multidimensionality of family law liberalization.

Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

AbstractConventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi‐location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi‐location information convolution transformer adopts a new model combining multi‐location information convolution and multi‐scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi‐location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

Onsite Seismic Monitoring Behavior of Undamaged Dams During the 2023 Kahramanmaraş Earthquakes (M7.7 and M7.6)

On 6 February 2023, two major earthquakes struck Türkiye, with their epicenters in the Pazarcık (M7.7; focal depth: 8.6 km) and Elbistan (M7.6; focal depth: 7 km) districts of Kahramanmaraş city. Most of the dams in the earthquake region remained structurally safe and stable. However, 17 dams in Türkiye and 1 dam in Syria were damaged during the 2023 Kahramanmaraş earthquakes. The main objective of this study was to better understand the real seismic behaviors of the dams during the two mainshocks and significant aftershocks. An earthfill dam, a concrete-faced rockfill dam (CFRD), and a roller-compacted concrete (RCC) dam constructed in the disaster area were selected to identify the real seismic behaviors of different types of dams during strong earthquakes. Acceleration records measured at the crest, right and left abutments, and foundations of the selected dams during the 2023 Kahramanmaraş earthquakes were taken into account to determine the real seismic behavior of the dams before, during, and after the earthquakes. The results of this investigation provide valuable insights into the real seismic behaviors of different types of dams in the vicinity of fault lines during strong earthquakes.

A Multicriteria Approach for Landslide Hazard Zonation in the Lesser Kumaun Himalaya

ABSTRACTThe socioeconomic prosperity of a region is intricately tied to its infrastructure development, particularly the connectivity provided by highways. Preserving the economic well‐being of an area demands meticulous planning and the execution of construction projects with minimal risk. Landslide hazard zonation (LHZ) is a crucial tool for assessing the likelihood of landslide occurrences in specific regions. This study delves into the LHZ of a 171.2 km2 area, focusing on critical causative factors such as slope inclination and aspect, slope mass material, Land Use/Land Cover (LULC), distance from fault lines, roads, and drainage systems. The classification resulted in four distinct zones along the National Highway (NH‐109) stretch from Bhowali to Almora. Notably, the moderate‐hazard zone encompasses 60.4% of the area, followed by the high‐hazard zone at 24.4%, the low‐hazard zone at 11.9%, and the very high‐hazard zone at 3.3%. To enhance the reliability of the study, 63 previously identified landslide locations were marked based on diverse sources, including field studies and landslide inventory data from the Geological Survey of India (GSI). In the validation process, 12 past landslide occurrences were situated within the very high‐hazard zone, 32 in the high‐hazard zone, 18 in the moderate‐hazard zone, and only one in the low‐hazard zone. The receiver operating characteristic (ROC) curve yielded a commendable quality with a % area under the curve (AUC) of 71.2%. In summary, this research underscores the importance of LHZ in assessing and mitigating landslide risks along a stretch of the NH‐109 corridor. The findings provide valuable insights for informed decision‐making in infrastructure development and risk management, contributing to the sustainable growth and protection of the region's livelihood.

Characteristics and Typical Influential Factors of Wildfire Caused by High-Voltage Transmission Line Breakage Faults

In order to investigate the characteristics and typical influential factors of wildfires caused by accidental faults in high-voltage transmission lines, a bespoke platform was constructed for the purpose of conducting simulation experiments. Discharge and ignition experiments were conducted on a variety of substrates, including kidney fern fragments, cedar needle fragments, poplar sawdust, and eucalyptus leaf fragments, to investigate the effects of different gaps on the initiation and propagation of wildfires. The results demonstrate that the discharge-inducing ignition stages can be succinctly summarized as “two phases and two points” (the discharge induction period, the gap breakdown point, the arc induction period and the fault removal point) when a suitable gap is maintained between the simulated falling lines and the vegetation surface. In the event of direct contact, the removal of the fault is not possible. The potential for ignition of the aforementioned vegetation types by the discharge is as follows: cedar needles > eucalyptus leaf fragments ≈ poplar sawdust > kidney fern fragments. As the water content increases, eucalyptus leaf fragments can still be ignited, and the breakdown voltages required for discharge-inducing ignitions gradually decrease. In the case of different forest ground vegetation types, when ignited by the discharge between the falling lines and the vegetation under conditions of proper gap and moisture content, the resulting ignition and sustained flames will promote the formation of streamer channels and further aggravate the discharges and burning processes, potentially leading to the ignition of a wildfire.

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

Making green cocoa: Deforestation, the legacy of war, and agrarian capitalism in Côte d'Ivoire

AbstractThis article examines the legacy of war on environmental policy, contributing to recent literature on the linkages between armed violence, conservation, rural livelihoods and global value chains. It argues that environmental norms reshape agricultural practices, but also the means by which people claim control over land and labour. Using the case of cocoa in Côte d'Ivoire, this paper examines the impact of ‘zero‐deforestation’ policies on the country's last agricultural frontier: its western forestlands, where migration and deforestation have driven the development of the cocoa economy for years. The region is now feeling the effects of global trade policies such as the European Deforestation‐Free Regulation (EUDR), competition for the last remaining forests and social fault lines inherited from the war. This article traces the origins of the zero‐deforestation policy, its national and local impact and its implications for social struggles over the control of land and labour.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.

Us or them: How COVID‐19 vaccine priority influences perceptions of local‐migrant worker relations

AbstractThe COVID‐19 pandemic laid bare pre‐existing societal fault lines, placing immense strain on intergroup relations and giving rise to xenophobic sentiments. Drawing on Realistic Conflict Theory (RCT) and Social Identity Approach (SIA), our study examined how the public allocation of COVID‐19 variant vaccines could influence the dynamics between local citizens and migrant workers, and under what conditions. One key idea is that valued resource distribution that favour migrant workers may produce increased perceptions of realistic threats among local citizens which could lead to exacerbated negative attitudes and behaviours of high‐status local citizens towards low‐status migrants, whereas higher fairness perceptions of such resource distributions could potentially mitigate these negative attitudes and behaviours. To investigate these predictions, we employed a cross‐cultural approach and conducted an experiment in both an Asian country (Malaysia; N = 197) and a Western country (Australia; N = 210). We manipulated vaccine prioritisation, assessed local citizens' perceptions of the realistic threat and fairness of vaccine prioritisation, and measured their attitudinal responses towards migrant workers. Our results showed that the perceived unfairness in migrant‐favouring vaccine prioritisation, rather than realistic threat, negatively affected local citizens' perceptions of the migrant workers. We discuss the implications of these findings in the context of intergroup relations.

Social epidemiology of urban COVID-19 inequalities in Latin America and Canada

BackgroundThe COVID-19 pandemic has spread through pre-existing fault lines in societies, deepening structural barriers faced by precarious workers, low-income populations, and racialized communities in lower income sub-city units. Many studies have quantified the magnitude of inequalities in COVID-19 distribution within cities, but few have taken an international comparative approach to draw inferences on the ways urban epidemics are shaped by social determinants of health.MethodsGuided by critical epidemiology, this study quantifies sub-city unit-level COVID-19 inequalities across eight of the largest metropolitan areas of Latin America and Canada. Leveraging new open-data sources, we use concentration indices to quantify income- and vulnerability-related inequalities in incidence, test positivity, and deaths over the first 125 weeks of the pandemic between January 2020 and May 2022.ResultsOur findings demonstrate that incidence, deaths, and test positivity are all less concentrated in low-income sub-city units than would be expected, with incidence ranging concentration in lower income neighbourhoods in Toronto (CI = -0.07) to concentration in higher income neighbourhoods in Mexico City (CI = 0.33). Drawing on relevant studies and evaluations of data reliability, we conclude that the best available public surveillance data for the largest cities in Latin America are likely not reliable measures of the true COVID-19 disease burden. We also identify recurring trends in the evolution of inequalities across most cities, concluding that higher income sub-city units were frequent early epicentres of COVID-19 transmission across the Latin America and Canada.ConclusionsJust as critical epidemiology points to individuals biologically embodying the material and social conditions in which we live, it may be just as useful to think of cities reifying their material and social inequities in the form of sub-city unit-level infectious disease inequities. By shifting away from a typical vulnerability-based social determinants of health frame, policymakers could act to redress and reduce externalities stemming from sub-city unit-level income inequality through redistributive and equity-promoting policies to shift the centre of gravity of urban health inequalities before the next infectious disease epidemic occurs.

Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Сейсмотектоническая активность Ляховичского разлома в Припятском прогибе

The main result of the work was the identification of the present-day seismotectonic regime of an active zone of the Lyakhovichi regional fault. The areal and space-time distribution of the earthquake epicenters along the fault line was reconstructed. The coordinates of the seismically active fault zone located within an area of 52.738—53.037° N and 26.830—28.034° E were determined. It was found that the distribution of earthquakes along the fault line occurred non-uniformly in space and time. The period from 2012 till 2021 was the most active one by the quantity of occurred earth quakes (169) and released total seismic energy (4.5929•109 J). The strongest earthquake with a magnitude M = 2.9 was recorded in the eastern part of the fault on September 03, 2021. Our reconstructions determined the value of seismic dislocation along the fault, which corresponded to the kinematic slip fault. To characterize the modern seismogeodynamic activity in the Lyakhovichi fault zone, a comprehensive technique based on long-term monitoring of seismic events was used. Based on the performed studies, the seismotectonic potential of the seismoactive zone of the Lyakhovichi fault was determined.

Historical Foundations of Green Developmental Policies: Divergent Trajectories in United States and France

ABSTRACTIn recent years, many countries have adopted biodiversity offset policies to internalize the ecological impacts of land developments. Although national policies share the general principle of equalizing ecological harm with gain, there is substantial variation across programs regarding the institutional forms governing offsetting. In this paper, we compare biodiversity governance in the United States and France to reflect more broadly on the factors shaping divergent trajectories of green developmental policies. Both countries have some form of biodiversity offsetting in place, but the major fault line of difference is the more extensive use of market‐based instruments (MBI) in the United States. Using a historical lens, we argue that one important reason for this variation lies in the different legal‐institutional definitions of biodiversity. A narrower definition in the United States focused on individual species, versus a broader definition in France focused on ecosystems, has facilitated a more standardized biodiversity governance arrangement in the United States. Leveraging this standardization, biodiversity markets have expanded in the United States while similar efforts to institutionalize market mechanisms have struggled in France. The comparison allows us to draw insights into the challenges in greening economic development, particularly in showing how historical scientific, legal, and institutional structures condition policy outcomes.

Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping

AbstractAccurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting.

Making of the marginal: gender in a new education city

ABSTRACT Global urban studies are undergoing a paradigm shift recognizing the link between urban and rural studies. In this sense, ‘agrarian urbanism’ has become the research focus, especially in the Global South. This paper analyses the changing social relations in Sonipat (Haryana, India) after it was developed as an ‘Education city’. The focus is gender relations with analysis under two categories: ‘locals’ and ‘outsiders’. While urbanization was thought of as a linear process where surplus production in the rural areas would naturally lead to cities, in the global south capitalist real estate-led development has meant that urbanization is often superimposed on existing rural societies, pushing them towards marginalization in favor of gated enclaved urban infrastructure. Employing qualitative interviews, I hypothesize that the private capital-driven urbanization in Sonipat not only geographically marginalizes the existing villages but also engenders dissonant experiences of local and outsider women. The fault lines between the two mean that interaction between them is defined by exploitation and mutual suspicion. Further, I argue that within this dichotomy, women bear the brunt of social control, restricted movement and potential violence.

Zimbabwe: Relational Peacebuilding Amidst Protracted Social Conflict. Strategies and Significance of Endogenous Approaches

Independent Zimbabwe has experienced continual episodes of mostly violent intra-state conflict that have left the country polarised and divided along mostly ethnic, racial, and class lines. In the aftermath of each conflictual episode, peacemaking attempts have been implemented to se cede hostilities, however, these attempts have only built peace at the superstructure while neglect ing the grassroots, which has been the microcosm of conflict wherein most of the actors are, be they perpetrators, victims and survivors. State-led attempts at reconciliation have failed to address the after-effects of conflict, and it is the argument of this paper that endogenous approaches to relational peacebuilding cannot continue to be ignored as they can potentially address some of the fault lines. Respondents to the study were purposively selected from three provinces in the coun try. Findings indicate the relevance and efficacy of these approaches in building peace at the family and community levels. Keywords: Relational peace, conflict, endoge nous, tradition, Zimbabwe.

Use of UAVs in Earthquakes and UAV Base Location Selection for a Possible Marmara Earthquake

UAV’s are widely used in many fields today, and one of the most important of these fields is disaster management. The most significant disaster that comes to mind when mentioning disasters is undoubtedly earthquakes. UAV’s are used to support search and rescue operations before and especially after an earthquake. As Türkiye is located in an earthquake zone, earthquakes have affected and will continue to affect us from past to present. The Marmara earthquake, which is predicted to occur in the coming years, will cause great destruction considering the population density of the region. In this study, the use of UAV’s in earthquakes and the selection of the most suitable UAV base location for quick intervention without being affected by a potential Marmara earthquake was conducted using the TOPSIS method, which is one of the multi-criteria decision-making methods with criteria weights. As for the UAV class, MALE class UAV’s, which are produced by Türkiye and classified by NATO, were preferred due to their mission duration and payload capacity. While determining the alternatives, seven airports close to the Marmara region but not on the fault line were selected. The criteria and their weights were determined based on the opinions of five UAV pilots, and a total of six criteria were chosen. As a result of applying the TOPSIS method, Sivrihisar Aviation Center was determined to be the most suitable UAV base for intervention in a potential Marmara earthquake, being the closest to the ideal solution among the selected alternatives.

Fault Lines: The Social Justice Movement and Evangelicalism’s Looming Catastrophe, by Voddie T. Baucham

Fault Lines: The Social Justice Movement and Evangelicalism’s Looming Catastrophe, by Voddie T. Baucham

2D-convolutional neural network based fault detection and classification of transmission lines using scalogram images

The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.