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Robust Cu2S‐Cu3P@C Nanomatrix electrode material has been synthesized via a two‐step process involving carbonization of Cu‐MOF, HKUST‐1, followed by simultaneous phosphorization and sulphurization of the carbonized product. This seamless approach leads to the efficient incorporation of highly active Cu2S‐Cu3P hybrid nanostructures within a conductive carbon matrix, exhibiting exceptional electrochemical glucose sensing performance. Synergistic interactions between Cu2S‐Cu3P and the carbon matrix boost glucose electro‐oxidation kinetics. The resulting Cu2S‐Cu3P@C displays a broad linear response (0.005–5 mM), low detection limit (2.3 μM), and high sensitivity (10,790 μA mM−1 cm−2), with excellent selectivity towards glucose even in the presence of interferents. This biomimetic Cu2S‐Cu3P@C nanomatrix catalyst holds promise for biosensor development in bioanalysis and clinical diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concepts of cubic soft (CS) algebra, CS o-subalgebra, and CS ideals within the framework of B-algebra. We provide comprehensive characterizations of these new structures, elucidating their unique properties and interrelationships. Specifically, we present detailed conditions under which a CS subalgebra can be classified as a closed CS ideal. Our analysis explores the intricate relationships among closed cubic soft ideals, cubic soft subalgebras, and cubic soft o-subalgebras. By doing so, we aim to provide a deeper understanding of how these structures interact and coexist within the broader context of B-algebra The findings offer significant insights into the application and theoretical underpinnings of cubic soft sets in algebraic systems, contributing to the ongoing evolution of fuzzy set theory and its applications in various mathematical domains. Our work not only broadens the scope of B-algebra but also enhances its utility in solving complex problems where traditional algebraic approaches may fall short. Through this exploration, we seek to advance the field and open new avenues for research and practical applications in mathematical sciences. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention ( O 2 SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O 2 SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method's effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach. [ABSTRACT FROM AUTHOR]

The world is progressing towards sustainable, eco-friendly, recyclable materials to enhance the circular economy and mitigate the issues of carbon footprint, overburdened landfills, and waste of natural resources. As increasing greenhouse gas (GHG) emissions are a major contributor towards climate change and given that the construction industry is one of the major producers of GHG emissions, it is crucial to meticulously quantify and lower its emissions, especially in the context of developing countries. This research presents a novel framework by combining advanced tools i.e., building information modeling (BIM), life-cycle assessment (LCA), geographic information systems (GISs), and quantification of embodied emissions to optimize construction's design, material-selection, operations, maintenance, and waste-management processes. The effectiveness of the proposed approach has been demonstrated with the help of a real-world case study in Islamabad, Pakistan. A building model has been generated using BIM, and a comprehensive LCA has been conducted. Additionally, GIS tools have been utilized to identify the locations and accessibility of available-waste-management facilities. Based on this data, embodied emissions related to handling and transportation of waste material to disposal facilities have been computed using mathematical analyses. Furthermore, targeted mitigation strategies have been proposed and an optimized route has been designed using GIS-based route-optimization tools along the suggested facility centers in the Islamabad region. The case study has been reassessed with alleviation strategies, and the results show that 29.35% of the materialization stage, 16.04% of the operational stage, and 21.14% of the end-of-life-phase GHG emissions can be effectively reduced. Hence, pre-evaluating the environmental degradation caused by construction projects throughout their life cycle might offer an opportunity to comprehend and reduce prospective environmental impacts. [ABSTRACT FROM AUTHOR]

Following the success of soft biometrics over traditional biometrics, anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video. Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics. However, one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video. The level of accuracy is usually affected by several contextual factors such as overlapping body components, an angle fromthe camera, and ambient conditions. Exploring and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or retrieval. For this purpose, anthropometric soft biometrics, which are originally geometric measurements of the human body, can be computed with ease and higher accuracy using landmarks information from the human body. To this end, several key contributions are made in this paper; i) summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body, ii) a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field, iii) an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system, and iv) finally, a novel bag of anthropometric soft biometrics containing a list of anthropometrics is presented those are practically possible to measure from an image or video. To the best of our knowledge, anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained and unconstrained scenarios and they also minimize the approximation level of feature value estimation than traditional biometrics. In our opinion, anthropometric soft biometrics constitutes a practical approach for recognition using closed-circuit television (CCTV) or retrieval from the image dataset, while the bag of anthropometric soft biometrics presented contains a potential collection of biometric features which are less sensitive to contextual factors. [ABSTRACT FROM AUTHOR]

Developing cost-efficient, stable, and environmentally benign electrocatalysts is a strong enticement towards the realization of commercial clean energy devices. Ni–Co-based systems have been particularly interesting owing to the well-recognized catalytically active redox pairs of Co3+/Co2+ and Ni3+/Ni2+. The sulphides of the latter have been frequently investigated catalyst systems with a particular focus on the sulphide-based heterostructured unions that offer better interfacial contacts. In this study, we report an electrochemically efficient hetero-catalyst (Co9S8–NiCo2S4/N-rGO) consisting of a binary sulphide–sulphide system, Co9S8/NiCo2S4, anchored on N-doped rGO) through a simple sulphurization strategy. The hetero-catalyst with ideally integrated interfaces ensures facile interfacial electron movement within the sulphide components and the rGO backbone, rendering excellent electrocatalytic properties. The highly exposed surface and N-doping in rGO further offer to expedite the mass transfer rate, and the suitable integration with the latter reduces the electrolyte corrosion to improve the activity and stability. Co9S8–NiCo2S4/N-rGO exhibits impressive bifunctional activity with an overpotential requirement of 149 mV for a benchmark hydrogen evolution reaction (HER) current of 10 mA cm−2 and an overpotential requirement of 230 mV for a benchmark oxygen evolution reaction (OER) current of 20 mA cm−2. The enhanced HER and OER kinetics are further supported by low Tafel slopes of 89 mV dec−1 and 93 mV dec−1, respectively. The full cell constructed from Co9S8–NiCo2S4/N-rGO exhibits water-splitting to the tune of 10 mA cm−2 and 100 mA cm−2 at cell voltages of 1.59 V and 2.1 V, respectively. Moreover, excellent catalytic stability was demonstrated with negligible loss in the catalytic performance for 15 continuous hours of electrocatalysis. [ABSTRACT FROM AUTHOR]