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Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV's owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth. [ABSTRACT FROM AUTHOR]

This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations. [ABSTRACT FROM AUTHOR]

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND). On this basis, a Strategy of Integration-Coupling (SIC) is proposed to address multiple error function problems, improving model stability and application scenarios. This strategy is experimentally proven to be effective and has potential expansion capability. After that, the convergence and robustness of our AREND is theoretically analyzed. Furthermore, the proposed AREND is verified by numerical experiments of low-to-high dimensional factorization in comparison with existing solutions. Finally, the real-time 3-D Angle of Arrival (AoA) localization in multiple high-noise conditions, is validated to the accuracy of the proposed model. Code is available at https://github.com/Alana2a3/AREND-Code-Implementation. [ABSTRACT FROM AUTHOR]

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare. [ABSTRACT FROM AUTHOR]

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control. [ABSTRACT FROM AUTHOR]

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research. [ABSTRACT FROM AUTHOR]

The generation of a substantial amount of construction waste (CW) poses significant challenges to the green transition of highway construction projects. However, the construction industry lacks integration of construction waste management (CWM) practices into the construction process. This paper aims to develop a calculation for assessing the performance of CWM to monitor its effectiveness in highway construction. Initially, this study utilizes the Site waste management plan (SWMP) as a foundation and develops a Construction Waste Management Performed Assessment Method (CWMPAM). The extension of CWM is incorporated into earned value management (EVM) to manage costs, schedules, and the performance of CWM within a unified framework. The functionality and sensitivity of CWMPAM and CWM-embedded EVM are validated through pilot testing and scenario analysis. The research results are computerized into a calculator to facilitate the practical application of CWM, and its validity is tested using random cases. CWMPAM and CWM-embedded EVM prove satisfactory in assessing and monitoring the execution and performance of CWM. The developed calculator simplifies the practical application of the outcomes. Essentially, the developed calculator represents a groundbreaking integration of CWM and construction management, enabling project managers to monitor and compare the CWM performance across different segments of highway construction. Not only does this contribute to the green transformation of highway projects, it also promotes high quality and sustainable development in the construction industry. [ABSTRACT FROM AUTHOR]

The quantification of centipede populations is one of the key measures in achieving intelligent management of edible centipedes and promoting the upgrade of the rural centipede industry chain. However, current centipede counting techniques still face several challenges, including low detection accuracy, large model size, and difficulty in deployment on mobile devices. These challenges have limited existing network models to the experimental stage, preventing their practical application. To tackle the identified challenges, this study introduces a lightweight centipede detection model (FCM-YOLO), which enhances detection performance while ensuring fast processing and broad applicability. Based on the YOLOv5s framework, this model incorporates the C3FS module, resulting in fewer parameters and increased detection speed. Additionally, it integrates an attention module (CBAM) to suppress irrelevant information and improve target focus, thus enhancing detection accuracy. Furthermore, to enhance the precision of bounding box positioning, this study proposes a new loss function, CMPDIOU, for bounding box loss. Experimental results show that FCM-YOLO, while reducing parameter size, achieves an improved detection accuracy of 97.4% (2.7% higher than YOLOv5s) and reduces floating-point operations (FLOPs) to 11.5G (4.3G lower than YOLOv5s). In summary, this paper provides novel insights into the detection and enumeration of centipedes, contributing to the advancement of intelligent agricultural practices. [ABSTRACT FROM AUTHOR]

Scratches and cracks in steel severely affect its service life and performance. However, owing to the irregular shapes and sizes of steel surface defects, defects within the same class may be different, whereas defects between classes may be similar. Existing methods focus only on spatial information, resulting in low detection accuracy. To alleviate these problems, this paper proposes the ECDY (EIFEM CARAFE DyHead) network to enhance the detection capability of steel defects. We first design a feature extraction module that focuses on the edge information of feature contours. This module uses the Sobel operator to extract the edge information of a feature and fuses it with the overall spatial information so that richer semantic information can be obtained. The module has improved accuracy in the YOLOv5, YOLOv8, and YOLOv10 versions, and uses fewer parameters and calculations. In particular, in YOLOv8x, mAP@0.5 increased by 2.5%, and the number of parameters was reduced by 12.4 M. Second, to retain the detailed information in the feature pyramid, and to better reconstruct features, we choose the content-aware reassembly feature method (CARAFE) as the upsampling method. Finally, the detection head was replaced with a dynamic unified detection head (DyHead) to adapt to different defect sizes and different task requirements. Compared with YOLOv8s, the proposed method improves precision by 1.6%, recall by 4%, and mAP@0.5 by 4%. This value is 4.2% higher than the mAP@0.5 of the current SOTA model RT-DETR-L in the field of object detection and has 23.2 M fewer parameters. [ABSTRACT FROM AUTHOR]

Lightweight porous structural materials have excellent energy absorption performance, and their application are gradually appeared. This paper takes the shock-absorbing material of nuclear equipment transportation casks as the starting point, and manufactures an orthogonal trapezoidal aluminum honeycomb by welding (WOTAH). The mechanical properties with different cell thicknesses and cell size ratios under different impact loads are studied through experimental and simulation methods, and the deformation process and energy absorption performance of the materials are obtained. The energy absorption performance of materials under quasi-static loading was investigated using plastic deformation theory and compared with experimental results. Based on the experimental results, the least squares method was used for data fitting to obtain the C-S dynamic constitutive relationship of the material. The equivalent structure was used to replace the honeycomb structure for simulation, and the influence of strain rate effect and structural size on material properties was further studied. Through the above mechanical performance analysis and comparison with wood, it is shown that WOTHA has feasible for the application of nuclear equipment impact limiter. [ABSTRACT FROM AUTHOR]

With the chemical formula CaCl2, calcium chloride is a salt as well as an inorganic material. At room temperature, it has the consistency of a white, crystalline solid and is very water-soluble. It can be created by neutralizing calcium hydroxide with hydrochloric acid. Calcium chloride is a solution with a large enthalpy change. It is extensively utilized in research facilities, manufacturing facilities, and pharmaceuticals, including all types of food-graded applications, the treatment of acute illnesses, packaging for drying tubes, dust controllers, and de-icing, among other uses. In this paper, firstly we compute the topological indices, coindices, and reverse indices of CaCl2. Further, we employ machine learning strategies to capture the best suitable set of indices for the proximity of the prediction of distinct physio-chemical properties of CaCl2. To strengthen the results, different regression techniques are implemented to predict HOF of CaCl2 based on our features, and the most influential features were detected to verify our results. [ABSTRACT FROM AUTHOR]

The genus Ilex (holly) in the monotypic family Aquifoliaceae contains more than 600 species distributed worldwide. Pollen fossils and macrofossils of fruits of Ilex are known from the late Cretaceous (90 Ma) and verified leaves from the upper Miocene (10 Ma), but until now only a few more or less incomplete flower fossils have been suggested as referable to this genus. This paper reviews the fossil flowers earlier attributed to Ilex and presents a complete and indisputable staminate Ilex flower from Baltic amber (34–38 Ma) examined in detail with synchrotron X-ray tomography. This finding may challenge the hypothesis of late Neogene dispersal of the genus from a center of origin in subtropical East Asia. [ABSTRACT FROM AUTHOR]

Real-time detection and accurate segmentation of chip pads are important tasks to ensure chip alignment and position correction. To address the challenges of small target chip pad detection, segmentation accuracy and model lightweight, this paper proposes a lightweight chip pad instance segmentation algorithm based on an improved YOLOv8-seg, named YOLOv8-seg-CP (chip pad). Firstly, we integrate the next-generation lightweight StarNet into the original backbone network to enhance fine feature capture capabilities while reducing the number of parameters. Then, we construct the C2f-Star module in the neck network, which enhances the feature extraction performance for small chip pad targets. This maintains accuracy, reduces computational load, and improves detection and segmentation speed. On this basis, we introduce a lightweight shared convolution segmentation head (LSCSH), significantly reducing both parameter count and computational load while enhancing segmentation performance. Additionally, we propose a CGCAFusion convolutional attention fusion module. This module uses a content-guided convolutional attention fusion mechanism to dynamically adjust attention weights based on the content of input features, capturing both global and local feature information and enhancing multimodal feature fusion. Experiments on the chip pad dataset demonstrate that our algorithm achieves a detection and segmentation accuracy of 89.8%. The model size, parameters, and FLOPs are 3.7 M, 1.7 M, and 8.2 G respectively, representing reductions of 45.6%, 50%, and 31.7% compared to the baseline model. The FPS is 1399.3, an improvement of 25.8% over the baseline model. The inference time is 0.72ms, which is 0.18ms less than the baseline model. Extensive experimental results on COCO, carparts-seg, and crack-seg datasets further show that the improved YOLOv8n-seg model outperforms many existing advanced methods in terms of performance. This approach holds significant industrial application value for fully automated chip testing and sorting integrated production. [ABSTRACT FROM AUTHOR]

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy's potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system. [ABSTRACT FROM AUTHOR]

Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions. [ABSTRACT FROM AUTHOR]

The induction motor is known to be the most reliable motor in the industry and is also the most energy-consuming load worldwide. It is noticeable in some production areas that the use of a high-capacity induction motor is not required at some operating times, and therefore it is important to control the speed of the induction motor to suit the operating requirements and obtain highly efficient performance. This paper presents a case study of a drinking water purification plant that uses a variable speed drive (VSD) as a speed controller for one of its induction motors to drive the pump. The VSD is connected to the motor in question to regulate its speed and maintain the external pressure of the station at an appropriate value that ensures water delivery to all branches of the network. 24-hour power consumption measurements of the respective engine with and without the use of a VSD are studied and analyzed to determine the potential for energy savings. Finally, a case study simulation is presented to compare the simulation results with the actual results. [ABSTRACT FROM AUTHOR]

Recent studies revealed the importance of tracking continuous blood pressure (BP) changes in monitoring and controlling hypertension and diagnosing cardiovascular diseases. However, current evaluation protocols utilize distance measures as primary metrics, which cannot properly evaluate the ability of the estimation model to track BP changes. This paper proposes a comprehensive evaluation framework which evaluates the distance and trend similarity metrics, and the composite metric of both between the reference and estimated BPs. The results of applying both widely used conventional metrics and the new proposed metrics for BP estimations are demonstrated in an example of comparing the reference with a set of different BP estimations. Then, the metrics are applied to BP estimations obtained using state-of-the-art (SOTA) algorithms. It is shown that even though SOTA algorithms have a low mean and standard deviation of absolute difference, they are not capable of tracking short-term blood pressure changes. Additionally, the proposed metrics are normalized metrics and range from -1 to 1, making them intuitively interpretable, similar to well-known correlation coefficients. Therefore, we suggest that the proposed evaluation framework should be used regularly in evaluating continuous BP monitoring systems. [ABSTRACT FROM AUTHOR]

While there is a considerable body of data regarding the sources of minerals employed in Bronze Age Egypt, the supply chains to Sudanese Lower Nubia are virtually unknown. This paper presents results of lead isotope analysis of 11 samples of kohl from C-group, Pan-grave and New Kingdom funerary contexts across the Debeira and Ashkeit areas in Sudanese Lower Nubia during the Bronze Age. The samples could be divided in two groups based on their lead isotope ratios. A comparison between the kohl samples from Sudanese Lower Nubia and galena ores from various mining sites on the Red Sea coast of Egypt indicated that some of the galena used in kohl mixtures in Sudanese Lower Nubia came from the Pharaonic mining site of Gebel el-Zeit. The second group of Nubian samples seems to have originated from an alternative galena source yet to be determined. This is the first time that kohl samples from Sudanese Lower Nubia are integrated into known northeast African networks of supply by using Lead isotope analysis. [ABSTRACT FROM AUTHOR]

This paper presents a new method of eccentric magnetic-abrasive finishing as applied to 316 stainless steel. The proposed solution is based on solid ferromagnetic abrasive grains (Fe-Si) held by an external magnetic field. A workpiece fixed in a holder with a certain eccentricity is inserted between the grains and the holder is rotated. The use of an additional eccentricity has a significant impact on the nature of the process, in particular, it increases the speed of the sample relative to the abrasive grains, makes it possible to use more abrasive grains, induces mixing abrasive grains, which can prolong their active life time. In this study, a three-factor, five-level design of experiment was conducted. The variable parameters were: machining time, eccentric value, and rotational speed. Samples in the form of rollers with a diameter of 3 mm were mounted in a special holder that allowed to obtain the assumed eccentricity. Based on the results obtained, regression equations were developed for selected roughness parameters Ra, Rz, Rq, Rt. The value of Ra roughness decreased from the initial value of 0.575 μm to a value below 0.1 μm (an 80% decrease) in 9.1 min. The results obtained provided the basis for further research in conducting eccentricity in magnetic-abrasive machining as an intensifying element of the process. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networks (CNNs) offer state-of-the-art models of the ventral visual stream. Furthermore, no proposed model estimates the distance between objects as a function of the dorsal stream. In this paper, we present a quantitatively accurate model for the visual system. Specifically, we propose a VeDo-Net model that comprises both ventral and dorsal branches. As in the ventral visual stream, our model recognizes objects. The model also locates and estimates the distance between objects as a spatial relationship task performed by the dorsal stream. One application of the proposed model is in the simulation of visual impairments. In this study, however, we show how the proposed model can simulate the occurrence of dorsal stream impairments such as Autism Spectrum Disorder (ASD) and cerebral visual impairment (CVI). In the end, we explore the impacts of learning on the recovery of the synaptic disruptions of the dorsal visual stream. Results indicated a direct relationship between the positive and negative changes in the weights of the dorsal stream's last layers and the output of the dorsal stream under an allocentric situation. Our results also demonstrate that visual–spatial perception impairments in ASD may be caused by a disturbance in the last layers of the dorsal stream. [ABSTRACT FROM AUTHOR]

Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm's efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm. [ABSTRACT FROM AUTHOR]

Object detection in drone aerial images is challenging due to the difficulty of small object detection and complex backgrounds. To address these issues, this paper proposes an improved object detection model, perception and target capture detector (PTCDet), to increase detection accuracy and robustness in complex scenes. Specifically, the proposed multiple feature extraction attention (MFEA) module significantly enhances the ability of the model to detect small objects through multidimensional feature map augmentation. The weighted perceptive field augmentation (WPFA) module is designed to improve the contextual awareness and feature representation of the model, optimizing detection accuracy for small objects. Based on the multiscale feature fusion structure, an enhanced scale fusion detection (ESFD) module is used to improve small object detection by generating larger scale feature maps. Ultimately, the inner focaler IoU loss (INFL) function effectively accelerates the regression of detection bounding boxes, enhancing the generalization ability and overall detection performance of the model. The experimental results on three public datasets demonstrate that PTCDet outperforms other detection algorithms. For example, on the VisDrone dataset, compared with the baseline model YOLOv8, map@0.5 and map@0.5:0.95 are improved by 6.21% and 4.21%, respectively. PTCDet exhibits excellent performance in addressing complex backgrounds and small object detection, providing an effective and robust solution for object detection tasks in drone aerial images. [ABSTRACT FROM AUTHOR]

The ecological restoration of historical abandoned mines has emerged as a prominent issue that has been of great concern to the Chinese government and academia in recent years. In order to enhance the systematic and comprehensive evaluation of ecological restoration efforts in abandoned mines, this study explores and researches a new perspective, i.e. the subjective perception perspective based on residents' satisfaction, and applies it to the ecological restoration performance evaluation of abandoned mines in Ganzhou, which is the most representative city of mineral resources in the southern hilly and mountainous area. An ecological restoration performance evaluation index system has been developed, encompassing three criteria layers: ecology, economy, and society, as well as twenty indicator layers. In this paper, a combination of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and importance-performance analysis (IPA) method is proposed to empirically analyze the ecological restoration performance of abandoned mines in the study area. The study revealed that the three counties predominantly prioritized ecological benefits, whereas the satisfaction of residents with social and economic benefits was lacking. Based on the findings, corresponding countermeasures were proposed, offering a fresh perspective and theoretical framework for assessing ecological restoration in abandoned mines. [ABSTRACT FROM AUTHOR]

This paper shows that the level and timing of fertility are converging strongly over different measures of educational attainment using 65 years of data from 146 countries. Global convergence patterns are primarily driven by high-income societies, while sub-Saharan Africa is the world region that is converging most slowly, if not converging at all. Most importantly, levels of education matter heavily for explaining strength and variation in global fertility convergence, with two intersecting educational gradients suggesting: (i) stronger convergence over tertiary education followed, in turn, by secondary and primary; (ii) stronger convergence over education completed relative to education attended. Our findings provide important insights for addressing key challenges in global development and demography, and for informing policymakers as they evaluate the suitability of specific educational policies aimed at further narrowing inequalities between societies—such as supporting higher education as well as the successful completion of targeted educational cycles. [ABSTRACT FROM AUTHOR]

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the 'FF-SPC-RS-MARCOS' framework. The developed model firstly determines the decision experts' weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts' opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment. [ABSTRACT FROM AUTHOR]

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions. [ABSTRACT FROM AUTHOR]

Rural solid waste (RSW) exhibits distinct characteristics compared to municipal solid waste (MSW), such as dispersed distribution, long governance chains, and low recycling value, making it unsuitable to apply the same management measures as MSW. Government-led RSW management results in excessive administrative costs, and the inherent characteristics of RSW reduce market enthusiasm for its management. To address these issues, this paper integrates the " multi-subject governance " model with RSW management and establishes a systematic evaluation index system for RSW governance. Utilizing the Best-Worst Method (BWM), key factors were identified. Utilizing the VIKOR method, representative provinces in China were used as case studies to validate the scientific nature of the evaluation indices. The research findings indicate that the multi-subject governance model is an effective approach for RSW management. Key factors influencing RSW management effectiveness include various governmental measures, waste sorting, and a long-term multi-subject governance mechanism. Case analysis reveals a strong correlation between the level of economic development and the effectiveness of RSW management. However, constructing a government-led, multi-subject collaborative urban-rural interconnected RSW management model can effectively address RSW management issues in economically underdeveloped areas. This research provides innovative solutions for RSW management, contributing to high-quality and sustainable development in rural areas. [ABSTRACT FROM AUTHOR]

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges. [ABSTRACT FROM AUTHOR]

This paper investigates the effects on the physical and mechanical properties and microstructure of lime-hemp concrete (LHC) materials at different binder mass ratios. A mixture of three cementitious materials, slaked lime, cement, and coal gangue powder, was used as binder, and hemp shives were used as concrete aggregate, and five test groups were designed, i.e., binder/hemp shives (B/H) mass ratios of 1.2, 1.4, 1.6, 1.8, and 2.0. The physical properties of LHC, including density, drying rate, water absorption, thermal conductivity coefficients, and mechanical properties (compressive strength and flexural strength) with different binder ratios were investigated. In addition, the microstructural properties of 56d LHC were investigated by SEM, XRD, and TG-DTG micro-scale analysis methods. The results showed that with the increase in binder content, the drying speed of LHC became faster, the thermal conductivity coefficients, compressive strength, and flexural strength increased, and the water absorption was just the opposite. With a fit ratio of 2.0, the strength is the greatest and the toughness is the best; with a fit ratio of 1.2, the thermal conductivity coefficient is the smallest and the insulation effect is the best. In addition, the hydration of the binder produced different forms of hydration products (CaCO3 and C-S-H), and the hydration products increased in quantity with the increase of the binder, resulting in stronger CaCO3 and C-S-H diffraction peaks. The hydration products produce a better bond with the rough cannabis particle surface, filling the internal voids of the LHC, improving the density of the material, and enhancing the mechanical properties of the material. [ABSTRACT FROM AUTHOR]

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS). [ABSTRACT FROM AUTHOR]

In this paper, we show the interaction phenomena about the first-order lump wave and multiple solitons by four theorems. The interaction phenomena are divided into two types, one type is absorbing collision phenomenon and the other type is elastic collision phenomenon. Six sets of dynamic plots over time are showed the phenomenon of absorbing interactions with energy transfer process, the phenomenon of elastic interactions with restoration of the initial state after collision. We apply the four theorems to an equation which can characterize the Rossby waves' amplitude, this equation can be used to describe the resonance phenomenon of Rossby waves. Different test functions with the same parameter values can present different wave-wave interaction properties. This study contributes to in-depth investigation of the mechanism of lump wave generation, evolution and interaction. [ABSTRACT FROM AUTHOR]

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 μm × 1.4 μm) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below − 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment. [ABSTRACT FROM AUTHOR]

The occurrence characteristics of arsenic in matte phase are unclear, which leads to the current treatment technology not being able to remove arsenic from matte phase significantly, thus causing a large amount of arsenic to affect smelting links such as copper converting and electrolytic refining. This paper uses instrumental analysis such as XRD, SEM-EDS, MLA and chemical analysis methods such as chemical phase extraction to comprehensively analyze the occurrence characteristics of arsenic in matte phase. The results show that the occurrence states of arsenic in matte are mainly arsenic sulfide, arsenic oxide, arsenate and residual arsenic. Most of the arsenic is in residual state in matte; arsenic in matte is extremely stable and difficult to exchange with the outside world; in high-grade matte, the arsenic content is relatively high. The main occurrence state of arsenic is copper-based complex alloy compounds, in which the arsenic content is about 1-10%. Since the metal bond between Cu-As is extremely stable, it is difficult to separate arsenic from it, which is also the key to the difficulty in completely removing arsenic from matte. [ABSTRACT FROM AUTHOR]

SAR-to-optical (S2O) translation is able to covert SAR into optical images, which help the interpreter to extract information efficiently. In the absence of strictly matched datasets, it is difficult for existing methods to complete training on unpaired data with a minimum amount of data. By employing the recent Schrödinger bridge-based transformation framework, a multiscale axial residual module (MARM) based on the concept of multi-scale feature fusion has been proposed in this paper. To enable efficient translation of SAR to optical images, the generator and discriminator of the model have been designed. Extensive experiments on the SEN1-2 dataset conducted, and the results show the superiority of the proposed method in terms of the generation quality. Compared with the classical CycleGAN, the proposed method can improve the FID metrics by 42.05%. [ABSTRACT FROM AUTHOR]

With buildings representing a substantial portion of global energy consumption, exploring alternatives to traditional fossil fuel-based heating and cooling systems is critical. Thermoelectricity offers a promising solution by converting temperature differentials into electrical voltage or vice versa, enabling efficient indoor thermal regulation. This paper presents a comprehensive investigation into the integration of thermoelectric wall systems for sustainable building climate control through numerical simulations and experimental analyses. Numerical simulations using computational fluid dynamics (CFD) techniques were conducted to model fluid flow and heat transfer within the thermoelectric wall systems under various operating conditions. These simulations provided insights into the system's thermal behavior, which were validated through experimental setups designed to measure temperature differentials, airflow rates, and power consumption. The results showed that power consumption is directly correlated with electrical current, ranging from 0.19 W to 77.4 W as the current increased from 0.1 A to 2 A. Additionally, the heat absorbed by the system increased significantly with electrical current, by 706–1044%, depending on the air velocity. The thermal energy released from the hot side of the thermoelectric modules also rose substantially, ranging from 9850 to 5285% with increasing electrical current, and from 275 to 51% with higher air velocities. Moreover, increasing air velocity led to a 6.78–9.37% reduction in power consumption for currents between 0.1 A and 2 A. The coefficient of performance (COP) analysis revealed that optimizing both electrical current and air velocity is essential for maximizing system efficiency. While fan power consumption reduces COP at higher air velocities, neglecting fan power consumption results in COP improvements ranging from 6.5 × 10⁻⁴ to 49.0%. These findings highlight the potential of thermoelectric wall systems to enhance indoor comfort and energy efficiency in buildings. [ABSTRACT FROM AUTHOR]

In this paper, an electro-hydraulic exciter controlled by an alternating current distribution valve is proposed, and a systematic analysis of the influence of pipe effect on the vibration characteristics of the electro-hydraulic exciter is investigated by simulation and experiments. The vibration characteristics curves for different pipe parameters are obtained, and the relative errors between simulation and experiment are evaluated. Moreover, a significant regression model of pipe parameters and vibration characteristics of the electro-hydraulic exciter is established by using the quadratic regression analysis method and the Box-Behnken experiment design method. Significant differences in the influence of pipe parameters on vibration characteristics are obtained by ANOVA (Analysis of Variance), and the influence of pipe parameter interaction on vibration characteristics is discussed. The results show that the relative importance of pipe parameters on the vibration characteristics of electro-hydraulic exciter, from high to low, is as follows: pipe diameter, elastic modulus, and pipe length. Notably, there is also an interaction between the pipe parameters, and the significance of these interactions is ranked from high to low as the interaction between the layers of steel wire and diameter, the interaction between diameter and length, and the interaction between the layers of steel wire and length. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) of several user evaluations on e-commerce platforms can be used to increase customer happiness. This method automatically extracts and identifies subjective data from product evaluations using natural language processing (NLP) and machine learning (ML) methods. These statistics may eventually reveal information on the favourable, neutral, or negative attitudes of the consumer base. Due to its capacity to grasp the complex links between words and phrases in reviews as well as the emotions they imply, deep learning (DL) is very useful for SA tasks. A unique approach termed Weighted Parallel Hybrid Deep Learning-based Sentiment Analysis on E-Commerce Product Reviews (WPHDL-SAEPR) is introduced by the proposed system. Accurately distinguishing between distinct sentiments found in online store reviews is the aim of the WPHDL-SAEPR technique. Additional data pre-processing processes are implemented within the WPHDL-SAEPR architecture to guarantee compatibility. Words are embedded into the paper using the word2vec model, while sentiment is classified using the WPHDL model. The Restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD) models are combined in this model. The results of the WPHDL-SAEPR approach's simulation were assessed using a consumer review database, with the results being emphasized at each stage. [ABSTRACT FROM AUTHOR]

Quaternary mudstone biogas reservoirs in the Qaidam Basin have shown great potential. However, complex pore structures with high clay contents and high heterogeneity limit the understanding of the storage and migration principles of these reservoirs. In this paper, HPMI and nitrogen adsorption experiments, in combination with NMR experiments under water saturation, centrifugation, various drying temperatures and other conditions, were adopted to determine the pore structure characteristics. Specifically, the reservoir space types and pore radius distribution characteristics were clarified. The cutoff values for different types of pores were identified based on the water-saturated mudstone NMR T2 spectra for full aperture distribution scales jointly characterized by mercury injection and nitrogen adsorption experiments. Furthermore, the three pore components and the saturation of different fluids were obtained. The research results indicate that the mudstone biogas reservoir has developed various reservoir spaces, and the pore size is primarily in the micronanometer range. The average total porosity reaches 27.28%, but the proportion of movable water pores is only 9.23% with poor fluid mobility, and the fluids in the pores are mostly capillary-bound water and clay-bound water. Among the different lithologies, argillaceous sand is more likely to become a good production layer. [ABSTRACT FROM AUTHOR]

Compounds that can induce oxidative stress in cancer cells while remaining nontoxic to healthy cells are extremely promising for potential anticancer drugs. 2,2′:6′,2′′-terpyridine-metal complexes possess these properties. The high level of activity (IC50 = 0.605 µM) of 2,2′:6′,2′′-terpyridine-metal complexes on lung, breast, pancreatic, and glioblastoma multiforme cancer lines and their selectivity (SI > 41.32) on human normal fibroblasts were confirmed and presented in this paper. The mechanism of action of these compounds is associated with the generation of reactive oxygen species, which affects several cellular pathways and signals. The results demonstrate that 2,2′:6′,2′′-terpyridine-metal complexes affect cell cycle inhibition in the G0/G1 phase as well as the activation of apoptosis and autophagy cell death. These results were confirmed in several independent studies, including experiments measuring the fluorescence levels of reactive oxygen species, flow cytometry, and gene and protein analysis. [ABSTRACT FROM AUTHOR]

For secure network coding to prevent eavesdropping attacks, a general design is used that puts restrictions on the eavesdropping channel set or encryption technology is embedded, which can cause problems in practical applications. Based on random linear network coding technology, in this paper, a transmission strategy to prevent eavesdropping attacks is proposed for single-source multicast networks, without either precondition of restricting the eavesdropping channel set or embedding encryption technology. The data transmission process is divided into three phases, and the global encoding vectors are separated from the encoded data on the channel. The proposed strategy can enable the sink to decode successfully, making it very difficult for eavesdroppers to meet the conditions for successful decoding by randomly selecting the eavesdropping channel set without obtaining network topology knowledge. The feasibility of the proposed method is strictly demonstrated using layered network technology, and its security is analyzed. The simulation results confirm the validity of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R2 scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube () and the inner width of the inner steel tube (). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML. [ABSTRACT FROM AUTHOR]

It is demonstrated that the eXtended Finite Element Method (XFEM) is of remarkable efficiency in simulating crack evolution by eliminating the need for remeshing and refinement. In this paper, it is shown how to enhance the solution efficiency through a comprehensive mathematical investigation of the solution process using XFEM. A typical example is presented to illustrate the disparities in nodal displacements along the two symmetric faces of the crack resulting from the approximation of XFEM. By analysing the structure and components of the global stiffness matrix, the underlying causes of these discrepancies are identified. Building upon these findings, two improvements of the solution are proposed to gain an acceptable accuracy in computing the nodal displacements. The first improvement consists of the subdivision of the enriched elements depending on the characteristic of the distribution of Gauss points. The second improvement is set by determining the optimal number of Gauss points in each sub-element near the crack tip. To calculate the stress intensity factor of the crack under surface pressure, such improvements are applied in conjunction with the interaction integral method, which significantly reduces computational time and eliminates the influence of surface tractions. The numerical solution is validated by comparing it with the analytical solution and the standard XFEM solution. The proposed improvements can enhance both the accuracy of the solution and the computational efficiency of XFEM. [ABSTRACT FROM AUTHOR]

Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a challenge due to the scarcity of available data. This study introduces the use of Transfer Learning (TL) to train models on limited data for high accuracy in detecting surface deformations, marking the first attempt to apply a model trained on one defect type to another. Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. This results in faster training and evaluation, ideal for real-time systems with limited computing capabilities. We further employed Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the model's decision-making, highlighting the areas influencing defect detection. This step is vital for developing a trustworthy model, showcasing the effectiveness of our approach in broadening the model's applicability while ensuring reliability and efficiency. [ABSTRACT FROM AUTHOR]

Extended reality (XR) technologies are emerging as promising platforms for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a high-fidelity methodology to perform real-time 3D catheter tracking and visualization during simulated cardiac interventions. A custom 3D-printed setup with mounted cameras enables biplane video capture of a catheter. A computer vision algorithm processes the biplane images in real-time to reconstruct the 3D catheter trajectory represented by any designated number of points along its length. This method accurately localizes the catheter tip within 1 mm and can reconstruct any arbitrary catheter configuration. The tracked catheter data is integrated into an interactive Unity-based scene rendered on the Meta Quest 3 headset. The visualization seamlessly combines a reconstructed 3D patient-specific heart model with the dynamically tracked catheter, creating an immersive extended reality training environment. Our experimental study, involving six participants, demonstrated that the 3D visualization provided by the proposed XR system significantly outperformed 2D visualization in terms of speed and user experience. This suggests that the XR system has the potential to enhance catheterization training by improving spatial comprehension and procedural skills. The proposed system demonstrates the potential of XR technologies to transform percutaneous cardiac interventions through improved visualization and interactivity. [ABSTRACT FROM AUTHOR]

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol's potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs. [ABSTRACT FROM AUTHOR]

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions. [ABSTRACT FROM AUTHOR]

Due to the use of multi winding high-frequency isolation transformers in the three port isolated bidirectional DC–DC converter to achieve port isolation and power transmission, there is a power coupling problem between each port. This article proposes a Model Prediction Control (MPC) strategy to address this issue. Considering the control objectives for each port of the Triple Active Bridge (TAB) DC–DC converter, a discrete predictive model of the TAB converter is established based on phase-shifting modulation and average model. The MPC problem is solved optimally and a predictive controller is designed with control accuracy to achieve decoupling control effect between each port. And the traditional single voltage closed-loop control, diagonal matrix decoupling control, and model predictive control proposed in this paper are compared through simulation. Finally, a TAB converter experimental platform is built based on the DSP control chip TMS320F28335. The experimental results are verified the effectiveness and superiority of the proposed method, as well as its faster dynamic characteristics and power decoupling ability between each port. [ABSTRACT FROM AUTHOR]

Electrocatalytic water splitting for green hydrogen production necessitates effective electrocatalysts. Currently, commercial catalysts are primarily platinum-based. Therefore, finding catalysts with comparable catalytic activity but lower cost is essential. This paper describes spinel-structured catalysts containing nickel cobaltite NiCo2O4, graphene, and additionally doped with heteroatoms. The structure and elemental composition of the obtained materials were analyzed by research methods such as TEM, SEM-EDX, XRD, XPS, and Raman spectroscopy. The electrochemical measurements showed that hybrid materials containing nickel cobaltite NiCo2O4 doped with graphene are highly active catalysts in the hydrogen evolution reaction (Tafel slopes = 91 mV dec−1, overpotential = 468 mV and onset potential = -339 mV), while in the oxygen evolution reaction (Tafel slopes = 51 mV dec−1, overpotential = 1752 mV and onset potential = 370 mV), bare NiCo2O4 without the addition of carbon has a worse activity (for HER: Tafel slopes = 120 mV dec−1, overpotential - does not achieve and onset potential = -404 mV, for OER: Tafel slopes = 54 mV dec−1, overpotential = 1796 mV and onset potential = 410 mV). In terms of stability, comparable results were obtained for each synthesized compound for both the HER and OER reactions. [ABSTRACT FROM AUTHOR]

Prolgolimab is a recombinant IgG1-based anti-PD-1 antibody, whose properties were improved by the introduction of the LALA mutation, and which has demonstrated high efficacy in patients with metastatic melanoma. This paper presents the results of comparative preclinical studies of antigen-binding and effector functions involving prolgolimab and conventional IgG4 antibodies, nivolumab and pembrolizumab. None of the studied antibodies had undesirable antibody-dependent cellular cytotoxicity activity. Prolgolimab has shown higher PD-1 receptor occupancy and T-cell activation, but lower propensity to activate antibody-dependent cellular phagocytosis as compared to nivolumab and pembrolizumab. An in vivo study in mice inoculated with CT26.wt cancer cells showed that tumor growth inhibition was 16% for pembrolizumab and 56% for prolgolimab. This study warrants clinical comparison of IgG1- and IgG4-based anti-PD-1 antibodies. [ABSTRACT FROM AUTHOR]