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The development of in-memory computing hardware components based on different types of resistive materials is an active research area. These materials usually exhibit analog memory states originating from a wide range of physical mechanisms and offer rich prospects for their integration in artificial neural networks. The resistive states are classified as either non-volatile or volatile, and switching occurs when the material properties are triggered by an external stimulus such as temperature, current, voltage, or electric field. The non-volatile resistance state change is typically achieved by the switching layer’s local redox reaction that involves both electronic and ionic movement. In contrast, a volatile change in the resistance state arises due to the transition of the switching layer from an insulator to a metal. Here, we demonstrate volatile resistive switching in twinned LaAlO3 onto which strained thin films of La0.67Sr0.33MnO3 (LSMO) are deposited. An electric current induces phase transition that triggers resistive switching, close to the competing phase transition temperature in LSMO, enabled by the strong correlation between the electronic and magnetic ground states, intrinsic to such materials. This phase transition, characterized by an abrupt resistance change, is typical of a metallic to insulating behavior, due to Joule heating, and manifested as a sharp increase in the voltage with accompanying hysteresis. Our results show that such Joule heating-induced hysteretic resistive switching exhibits different profiles that depend on the substrate texture along the current path, providing an interesting direction toward new multifunctional in-memory computing devices.

Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inundation. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inundation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.

Cementing is an essential downhole operation during the drilling of oil, gas, and geothermal wells, with the primary objective of providing structural support and a seal in the wellbore. It is important to attain adequate cement strength to preserve well integrity, inhibit fluid movement, enhance zonal isolation, and hold the wellbore casing in place. However, a significant challenge arises from the potential contamination of the cement by drilling mud (DM) during the placement process. This study aims to assess the impact of both water-based drilling mud (WBDM) and oil-based drilling mud (OBDM) contamination on the strength of API Class G wellbore cement by comprehensively investigating the underlying causes and the mechanism of strength degradation. Following a systematic methodological approach and the guidelines provided by API and ASTM, cement slurries were prepared and contaminated with DM at 10 %, 20 %, and 30 % concentrations by volume. Subsequently, both neat and contaminated samples were cured for 1, 3, 7, and 14 days, after which a series of uniaxial compressive strength (UCS) tests were conducted, and the results were further reinforced by microstructural and petrophysical property analysis. The results demonstrate that both WBDM and OBDM contamination significantly reduce cement strength, irrespective of curing time. Though the effect of OBDM contamination appears to be more pronounced, the effect of WBDM contamination is also remarkable. After final curing of 14 days, WBDM-contaminated cement samples exhibited an average strength reduction of 45.06 %, while OBDM contamination led to a 66.32 % reduction in strength compared to the neat cement with 30 % contamination. Additionally, for the same curing time and contamination percentage, porosity was found to be increased by 71.18 % and 91.33 %, respectively. The mechanism of neat and contaminated cement hydration is explained with the support of SEM-EDS tests, which confirmed the presence of contamination and revealed how two different drilling muds generate dissimilar pore structures within the cement sheath, affecting the hydration and ultimately contributing to the observed reduction in strength. The findings quantify the negative impact of WBDM and OBDM contamination on wellbore cement, emphasizing the importance of mitigating mud contamination during cementing operations.

The Rohingyas are an ethnic minority community of Myanmar. Despite their long history in Rakhine State, Myanmar, they were made stateless by the government of Myanmar in 1982 enacting the Burma Citizenship Act of 1982 . They have endured communal violence, resulting in death, forced displacement and migration, experiencing ongoing traumatic events, particularly among women and children. They have become victims of torture and persecution, with females enduring rape and many witnessing their loved ones brutally killed before their eyes. These life events have a profound impact on their physical and mentalwell-being, even now while they are living as refugees in camps in neighbouring Bangladesh. Due to numerous associated issues, recent history has witnessed a complex emergency involving internal and external displacements of individuals from this ethnic minority group, rendering them refugees in various countries, including Bangladesh. This study is focusing on the Rohingya children's physical and mental well-being, who are almost 52% of the total Rohingya refugee population living in the camps in Bangladesh. This study, drawing on secondary data, presents evidence of the well-being of the Rohingya children in Bangladeshi refugee camps and investigates the challenges they face and their coping mechanisms in these situations. This article sheds light on current conditions and outlines future directions for support and research.

Being homeless is broadly understood to be traumagenic. Thus, support services for individuals experiencing homelessness (such as youth refuge) are encouraged to use trauma-informed models of care. However, there is a dearth of research that (1) focuses on youth refuge specifically, despite refuges being the most common response for youth homelessness worldwide, and (2) explains trauma-informed care models in detail so that they may be evaluated in practice. This paper outlines a trauma-aware framework used for nearly a decade within a youth refuge located in Melbourne, Australia: a psychologically informed environment (PIE). The paper provides: (1) an overview of trauma-informed care before describing what a PIE entails; (2) the how of a PIE, including core principles, their theoretical underpinnings, and how these principles are practically applied; and (3) the why of a PIE, focusing on implications for practice. A PIE is underpinned by key theoretical approaches such as attachment theory, the core emotional needs model, psychodynamic theory and formulation, social cognitive theory, and the transtheoretical model of change. A PIE encompasses five core principles of (1) relationships, rules, responsiveness, and roles; (2) physical and social spaces; (3) learning and enquiry; (4) staff support and training; and (5) psychological awareness. Overall, PIEs have been found to increase consumer engagement and decrease evictions, instill confidence and improve empathy within the staff, and decrease the risk for organizations, as seen by low incident rates. It is hoped that by providing this detailed outline of a PIE, more research can be undertaken into youth refuge care models, and more psychologically informed frameworks that address the multi-directional relationship between trauma and homelessness can be employed.