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To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Deep-buried tunnels with weak surrounding rock are frequently encountered issues in traffic engineering. It plays an important role in the excavation process and the project operation. This paper applies the theoretical analysis and laboratory test related to four different conditions in terms of their thickness to determine the mechanical response of deep-buried tunnel lining. Then, the energy dissipative structure theory is employed to explain the experimental results. This paper has made the following achievements: firstly, it is found that the toughness of the secondary lining was found to be often the most important indicator of tunnel safety, with better-toughness linings having higher tensile strength and crack resistance. Secondly, it suggests that the inclusion of steel reinforcement in the concrete lining can effectively improve the secondary lining toughness. Finally, it proves that the more ductile liner had more energy, higher load-carrying capacity, and was better able to maintain the overall stability of the structure. [ABSTRACT FROM AUTHOR]

The structural plane characteristic was the most critical factor for determining the self-stability ability of deep foundation pit vertical-rock-wall in layered rock stratum. Multiple methods such as model testing, numerical calculation, and theoretical calculation were utilized comprehensively in this paper. The self-stabilizing control effect on the deep foundation pits vertical-rock-wall that under the different structural plane inclination angle (α) and under the different structural plane strength was systematically studied. The results indicated that the overall variation trend of "Sharp decrease ~ Slow decrease ~ Slow increase ~ Sharp increase" in the symmetrical distribution for the self-stability critical height (Hcr) varied with the gradually increasing of α was presented. Meanwhile, the variation trend of "continuously decreasing and rapidly decreasing first, and then slowly decreasing and tending to stabilize" with the structural plane strength reduction coefficient (k). The key factor to control the self-stability of the deep foundation pit vertical-rock-walls lied in fully grasping and utilizing the basic characteristics of rock structural planes. The research results of this paper provided the theoretical basis for scientifically determining the safety level and designing reasonable support structures of the deep foundation pit vertical-rock-walls in layered rock stratum. [ABSTRACT FROM AUTHOR]

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have gained much attention in recent years because of their improved energy conversion efficiency, simple fabrication process, low processing temperature, flexibility, light weight, and low cost of constituent materials when compared with their counterpart silicon based solar cells. Besides, stability and toxicity of PSCs and low power conversion efficiency have been an obstacle towards commercialization of PSCs which has attracted intense research attention. In this research paper, a Glass/Cu2O/CH3NH3SnI3/ZnO/Al inverted device structure which is made of cheap inorganic materials, n-type transparent conducting oxide (TCO)-free, stable, photoexcited toxic-free perovskite have been carefully designed, simulated and optimized using a one-dimensional solar cell capacitance simulator (SCAPS-1D) software. The effects of layers' thickness, perovskite's doping concentration and back contact electrodes have been investigated, and the optimized structure produced an open circuit voltage (Voc) of 1.0867 V, short circuit current density (JSC) of 33.4942 mA/cm2, fill factor (FF) of 82.88% and power conversion efficiency (PCE) of 30.17%. This paper presents a model that is first of its kind where the highest PCE performance and eco-friendly n-type TCO-free inverted CH3NH3SnI3 based perovskite solar cell is achieved using all-inorganic transport materials. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput. [ABSTRACT FROM AUTHOR]

Green finance (GF) is recognized as a key driver of sustainable development. While existing studies have extensively discussed the relationship between GF and the Sustainable Development Goals (SDGs), few have explored the coupling coordination relationship between GF and SDGs. In this paper, we use data from thirty Chinese provinces (municipalities and autonomous regions) from 2008–2021 to examine the degree of coupling coordination development (CCD) between GF and the SDGs systems using the CCD model. We find that most SDGs and their sub-goals exhibit a significant upward trend, except for SDG8, 14–16. GF presents a fluctuating upward trend, with a significant decline in 2010 and 2019. The CCDs between GF and SDGs and their sub-goals generally show an M-shaped upward trend in most regions, with most of them experiencing a synchronous decline in 2011–2012 and 2019. In the analysis of regional heterogeneity, the eastern region performs better in SDG8–9, the central region performs better in SDG3, 14–15, while the western region performs better in SDG7. This paper provides empirical evidence for a further in-depth understanding of the relationship between GF and SDGs, which can contribute to advancing GF development and the SDG process. [ABSTRACT FROM AUTHOR]

Buildings are energy- and resource-hungry: their construction and use account for around 39% of global carbon dioxide emissions; they consume around 40% of all the energy produced; they are responsible for over 35% of the EU's total waste generation; and account for about 50% of all extracted (fossil) materials. Therefore, they present a significant challenge to meeting national and international Net Zero targets of reducing greenhouse emissions and fossil resource use. The CircularB Project, is at the heart of this issue, which will underpin synergies of multi-scale circular perspectives (from materials, to components, to assets and built environments), digital transformation solutions, data-driven and complexity science, stakeholder behavioral science, and interdisciplinary capabilities towards achievable, affordable and marketable circular solutions for both new and existing buildings, for sustainable urban design, and for circular built environments across Europe. This paper contributes to the project by deriving new insights into the stakeholders' influences, inter-relationships, and obstacles in the implementation of circular economy concepts on existing building stocks in Europe, which represent over 90% of whole building assets. In order to identify and derive the insights, our study is rigorously based on (i) a robust critical literature review of key documentations such as articles, standards, policy reports, strategic roadmaps and white papers; and (ii) interviews with relevant stakeholders and decision makers. Uniquely, our work spans across all scales of CE implementation from materials, to products and components, to existing building stocks, and to living built environments. The findings point out the current challenges and obstacles required to be tackled. Inadequacies of financial incentives and governmental enforcement (via policy, legislation, or directive) are commonly found to be the most critical obstacles found throughout Europe. Circular economy is the global challenge and not just a single country can resolve the climate issue without the cooperation of other countries. The insights thus highlight the essential need for harmonized actions and tactical/pragmatic policies promoted and regulated by the European Commission, national and local governments who can dominate the influence, promote inter-relationship, and overcome the barriers towards circular economy much more effectively. [ABSTRACT FROM AUTHOR]

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters. [ABSTRACT FROM AUTHOR]

In a coal mine, air leakage exists in some roadways through doors and other ventilation structures inevitably. Based on this opinion, there are different views on whether these roadways must be assigned airflow in coal mine ventilation design. This paper analyses some relevant regulations and criteria on the designed air quantity of coal mines. Then, based on the ventilation design of the Guizhou Yizhong Coal Mine, through the study of the calculation of needed air quantity of every working place and its distribution method in coal mine ventilation design, this paper puts forward that explosion-proof door, safety exit, and other short distance roadways with ventilation structures need not assign airflow in coal mine ventilation design, while some long-distance roadways need. Additionally, it presents the main reason to support this opinion, gives the distribution method of inner air leakage quantity, which comes with the calculation of the designed mine total air quantity, puts forward the remedy method for the air leakage through ventilation structures in a coal mine ventilation system, then offers the mine operator with the basic opinions for the day-to-day planning and effective operation of a coal mine ventilation system. [ABSTRACT FROM AUTHOR]

In the late stage of underground brine mining in salt lakes, the method of injecting fresh water is often used to extract the salt from the brine storage medium. This method of freshwater displacement breaks the original water–rock equilibrium and changes the evolution process of the original underground brine. To explore the mechanism of salt release in saline water-bearing media under conditions of relatively fresh lake water dissolution, this paper analyzes the changes in the chemical parameters of brine from 168 sampling points in the Mahai salt lake in the Qaidam Basin at three stages (before exploitation, during exploitation, and late exploitation) by correlation analysis, ion ratio analysis, and other methods and investigate the variations in porosity and the evolution laws of brine. The results show that the changes in the main ion content and brine mineralization during the exploitation process are small. The changes in Ca2+ content are significant due to the low solubility of calcium minerals, the precipitation of gypsum during the mixing process, and the adsorption of cations by alternating with Ca2+. Primary intergranular pore skeletons are easily corroded to form secondary pores, which increase the geological porosity. Na+ and Cl- are the dominant ions in the brine in the study area, but the concentration of Ca2 + decreased significantly under the influence of mining, by 41.7% in the middle period and 24.5% in the late period. The correlation between Ca2+ and salinity changes significantly in different mining stages, and the reason for the decrease of Ca2+ may be due to the influence of mineral dissolution, mixing, and anion-cation exchange. The porosity of the layer in the study area showed the opposite trend of Ca2+, and the porosity increased first and then decreased. The innovation of this paper lies in analyzing the reasons and mechanisms of the disturbance of artificial dissolution mining on stratum structure by comparing the hydrochemical characteristics and porosity of underground brine storage media in three different mining stages. The research in this paper provides a theoretical basis for the calculation of brine resource reserves and the sustainable development of underground brine in salt lake areas. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative. [ABSTRACT FROM AUTHOR]

The deep integration of digital technology and the real economy not only affects the production and operation mode of enterprises, but also becomes the promoter of service-oriented transformation and the driving force of sustainable development. Based on the text analysis method, this paper uses the data of Chinese listed manufacturing enterprises from 2011 to 2020 to study the impact of digital technology application on the service-oriented transformation and sustainable development of enterprises. It is found that digital technology application significantly improves the environmental performance and economic performance of enterprises by driving their service-oriented transformation and technological innovation, and then enhances their sustainable development. The improvement effect of digital technology application on the sustainable development of resource-based enterprises and capital-intensive enterprises is more significant. The conclusion in this paper provides micro-evidence for understanding the role of digital technology in addressing environmental issues and sustainable development. [ABSTRACT FROM AUTHOR]

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper. [ABSTRACT FROM AUTHOR]

The ever-growing threats in cybersecurity growing with the rapid development of quantum computing, necessitates the development of robust and quantum-resistant cryptographic systems. This paper introduces a novel cryptosystem, Public Key Cryptosystem based on Systematic Polar Encoding (PKC-SPE), based on the combination of systematic polar encoding and public-key cryptographic principles. The Systematic Polar Encoding (SPE), derived from the well-established field of polar codes, serves as the foundation for this proposed cryptographic scheme. Here, we have used MATLAB Software to introduce and implement the PKC-SPE Cryptosystem. The paper examines key generation, encryption, and decryption algorithms, providing insights into the adaptability and efficiency of systematic polar encoding in public-key cryptography. We assess the efficiency of the PKC-SPE Cryptosystem in three aspects: key size, computational complexity, and system implementation timings. In addition, we compare the PKC-SPE Cryptosystem with PKC-PC cryptosystem and find that it has reduced key sizes ( P r = 0.8436 kbytes). The results obtained through simulations validate the effectiveness of the proposed cryptosystem and highlighting its potential for integration into real-world communication systems. Thus, in the paradigm shift to quantum computing, the PKC-SPE cryptosystem emerges as a promising candidate to secure digital communication in the quantum computing era. [ABSTRACT FROM AUTHOR]

Optical cross-correlation is a technique that can achieve both high specificity and high sensitivity when deployed as the basis for a sensing technology. Offering significant gains in cost, size and complexity, it can also deliver significantly higher signal-to-noise ratios than traditional approaches such as absorption methodologies. In this paper, we present an optical cross-correlation technology constructed around a bespoke customised Fiber Bragg Grating (FBG). Exploiting the remarkable flexibility in design enabled by multiple aperiodic Bragg gratings, optical filters are devised that exactly mimic the absorption features of a target gas species (for this paper, acetylene C 2 H 2 ) over some waveband of interest. This grating forms the heart of the sensor architecture described here that employs modulated optical cross-correlation for gas detection. An experimental demonstration of this approach is presented, and shown to be capable of differentiating between different concentrations of the C 2 H 2 target gas. Furthermore these measurements are shown to be robust against interloper species, with minimal impact on the detection signal-to-noise arising from the introduction of contaminant gases. This represents is a significant step toward the use of customised FBGs as low-cost, compact, and highly customisable photonic devices for deployment in gas detection. [ABSTRACT FROM AUTHOR]

Previous studies reported microplastics (MPs), antibiotics, and antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs). There is still a lack of research progress on the origin, fate, impact and hazards of MPs and ARGs in WWTPs. This paper fills a gap in this regard. In our search, we used “microplastics”, “antibiotic resistance genes”, and “wastewater treatment plant” as topic terms in Web of Science, checking the returned results for relevance by examining paper titles and abstracts. This study mainly explores the following points: (1) the origins and fate of MPs, antibiotics and ARGs in WWTPs; (2) the mechanisms of action of MPs, antibiotics and ARGs in sludge biochemical pools; (3) the impacts of MPs in WWTPs and the spread of ARGs; (4) and the harm inflicted by MPs and ARGs on the environment and human body. Contaminants in sewage sludge such as MPs, ARGs, and antibiotic-resistant bacteria enter the soil and water. Contaminants can travel through the food chain and thus reach humans, leading to increased illness, hospitalization, and even mortality. This study will enhance our understanding of the mechanisms of action among MPs, antibiotics, ARGs, and the harm they inflict on the human body. [ABSTRACT FROM AUTHOR]

In Geophysics, topographic factors are observations that can be directly measured, but they are often ignored to simplify the model. Studying the coseismic deformation caused by earthquakes helps accurately determine the epicenter's parameterization. It provides a reference for the reasonable layout of coseismic observation stations and GNSS observation stations. After the Mw7.8 earthquake in Nepal in 2015, GCMT, USGS, GFZ, CPPT, and other institutions released their epicenter parameter. However, according to their parameters, the coseismic displacements simulated by the spectral-element method are quite different from the GNSS observations. Firstly, this paper inverts the geometric parameters of the seismogenic fault with Nepal's coseismic GNSS displacement. The spectral-element method determines the source's location and depth under the heterogeneous terrain and outputs the source parameters. Among the results of many studies, the surface source is more consistent with the generation mechanism of large earthquakes. Secondly, this paper calculates the fault slip distribution of this earthquake using SDM (Steepest Descent Method) based on GNSS and InSAR data, which is divided into 1500 subfaults, and the moment tensor of each subfault is calculated. This paper investigates the distribution characteristics of the coseismic deformation field of the 2015 Mw 7.8 earthquake in Nepal under three different models. The results show that the influence of topographic factors is ~ 20%, and the influence of heterogeneous factors is ~ 10%. This paper concludes that the influence of topographic factors is much more significant than that of heterogeneous factors, and the influence of both should be addressed in coseismic deformation calculations. [ABSTRACT FROM AUTHOR]

Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO's superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness. [ABSTRACT FROM AUTHOR]

Durability is one of the technical bottlenecks restricting fuel cell electric vehicle development. As a result, significant time and resources have been invested in research related to this area worldwide. Current durability research mainly focuses on the single cell and stack levels, which is quite different from the usage scenarios of actual vehicles. There is almost no research on developing durability test cycles on the fuel cell system level. This paper proposes a universal model for developing a durability test cycle for fuel cell system based on the China automotive test cycle. Large-scale comparison tests of the fuel cell systems are conducted. After 1000 h test, the output performance degradation of three mass-produced fuel cell system is 14.49%, 9.59%, and 4.21%, respectively. The test results show that the durability test cycle proposed in this paper can effectively accelerate the durability test of the fuel cell system and evaluate the durability performance of the fuel cell system. Moreover, the methodology proposed in this paper could be used in any other test cycles such as NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Procedure), etc. And it has comprehensive application value and are significant for reducing the cost of durability testing of fuel cell systems and promoting the industrialization of fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3–10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2–3-day forecast for the Amur and Yangtze rivers in East Asia, 5–10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6–8-day forecasts for the Danube River in Europe, 2–5-day forecasts for the Parana River in South America, and 2–7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide. [ABSTRACT FROM AUTHOR]

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

City reputation is a valuable asset for the local economy and firms in the contemporary society. However, the impact of city reputation on micro-level firms has been largely overlooked by the literature. This paper uses the National Civilized City (NCC) policy in China as a quasi-natural experiment to enhance city reputation. We employ the DID approach to investigate the relationship between city reputation and corporate risk-taking. The result shows that corporate risk-taking significantly increases following the NCC policy adoption. Moreover, information asymmetry can strengthen the positive impact of city reputation on corporate risk-taking. Channel tests show that city reputation improves financial condition and decreases default risk, leading to improved risk-taking tolerance. Overall, our paper indicates that city reputation is an important mechanism to improve corporate financial performance, providing empirical evidence for local governments to pursue the NCC title. [ABSTRACT FROM AUTHOR]

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor. [ABSTRACT FROM AUTHOR]

Time-domain solutions of Maxwell's equations in homogeneous and isotropic media are paramount to studying transient or broadband phenomena. However, analytical solutions are generally unavailable for practical applications, while numerical solutions are computationally intensive and require significant memory. Semi-analytical solutions (e.g., series expansion), such as those provided by the current theoretical framework of the multipole expansion, can be discouraging for practical case studies. This paper shows how sophisticated mathematical tools standard in modern physics can be leveraged to find semi-analytical solutions for arbitrary localized time-varying current distributions thanks to the novel time-domain Cartesian multipole expansion. We present the theory, apply it to a concrete application involving the imaging of an intricate current distribution, verify our results with an existing analytical approach, and compare the proposed method to a finite-difference time-domain numerical simulation. Thanks to the concept of current "pixels" introduced in this paper, we derive time-domain semi-analytical solutions of Maxwell's equations for arbitrary planar geometries. [ABSTRACT FROM AUTHOR]

With a significant number of mechanical products approaching the retirement phase, the batch recycling of discarded mechanical parts necessitates a preliminary assessment of their surface condition. However, the presence of surface rust poses a challenge to defect identification. Therefore, this paper proposes a method for detecting heavily rusted surface defects based on an improved YOLOv8n network. In the Backbone, the C2f-DBB module of re-parameterized deep feature extraction was introduced, and the attention module was designed to improve the accuracy of information extraction. In the Neck part, a Bi-Afpn multiscale feature fusion strategy is designed to facilitate information exchange between features at different scales. Finally, Focal-CIoU is employed as the bounding box loss function to enhance the network's localization performance and accuracy for defects. Experimentally, it is proved that the improved network in this paper improves the Recall, Precision, and mAP0.5 by 1.2%, 2.1%, and 1.9%, respectively, on the original basis, which is better than other network models. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability. [ABSTRACT FROM AUTHOR]

Large earthquakes (EQs) occur at surprising loci and timing, and their descriptions remain a long-standing enigma. Finding answers by traditional approaches or recently emerging machine learning (ML)-driven approaches is formidably difficult due to data scarcity, interwoven multiple physics, and absent first principles. This paper develops a novel artificial intelligence (AI) framework that can transform raw observational EQ data into ML-friendly new features via basic physics and mathematics and that can self-evolve in a direction to better reproduce short-term large EQs. An advanced reinforcement learning (RL) architecture is placed at the highest level to achieve self-evolution. It incorporates transparent ML models to reproduce magnitude and spatial location of large EQs ( 6.5) weeks before of the failure. Verifications with 40-year EQs in the western U.S. and comparisons against a popular EQ forecasting method are promising. This work will add a new dimension of AI technologies to large EQ research. The developed AI framework will help establish a new database of all EQs in terms of ML-friendly new features and continue to self-evolve in a direction of better reproducing large EQs. [ABSTRACT FROM AUTHOR]

The captivating realm of the nonlinear coupled Davey–Stewartson Fokas system is explored in this research paper. As a powerful tool, the proposed system is utilized for the realistic representation of various non-linear dynamical mechanisms in different fields of sciences and engineering including non-linear optical fibers, plasma physics and water waves theory. Two distinct exact methods, namely the modified auxiliary equation method and the extended (G ′ / G 2) -expansion method, are utilized to acquire the exact soliton solutions of the non-linear coupled Davey–Stewartson Fokas system. A plethora of novel soliton solutions containing anti-kink, kink, bright, dark, dark-bright, bright-dark and some other singular soliton solutions, have been obtained using the employed exact methods. The significance of proposed manuscript lies in the novelty of obtained solutions. Kink, dark and bright solitons have wide applications in optical fiber communications, plasma physics and water waves dynamics. The acquired nontrivial exact solutions contain exponential, trigonometric, rational and hyperbolic functions. Some obtained solutions are visually represented through graphical simulations of 3D, 2D-contour and 2D-line plots, providing a comprehensive visualization of the soliton dynamics.The modulation instability of the proposed nonlinear system has been investigated, which ensures the stability of the system. [ABSTRACT FROM AUTHOR]

This paper proposes the design of a metasurface for polarization and incident angle-insensitive RCS reduction applications. An ellipse-shaped unit cell is utilized as a polarization converter, which is then arranged to form a checkerboard surface. While a single layer checkerboard structure gives a wideband RCS reduction, a double layer structure yields polarization and incident angle independent operation. The two layers have unit cells rotated 450 to each other. Experimental results demonstrate an RCS reduction bandwidth of around 90%. Further the RCS reduction remains stable with polarization and incident angle variation. [ABSTRACT FROM AUTHOR]

To let water jets land at the fire, pitch angle of the fire monitor has to be adjusted manually with successive rounds, which seriously affects the efficiency of fire extinguishing. To improve the efficiency, this paper proposes a technology for water jet trajectory modeling and landing point prediction to help with extinguishing automatically. Considering fragmentation and atomization of water jets, trajectories are analyzed and the predicted trajectory is closer to the real situation. Secondly, a compensation method for the prediction is proposed to further reduce the deviation between the predicted and the actual landing point, taking into account the combined effects of high altitude, initial jet velocity, and wind. On this basis, considering the difficulty of directly solving the analytical solution to the target initial pitch angle of the jets, a searching method is also proposed, which greatly improves the solving efficiency. Finally, through practical experiments and verification, the proposed model takes an average time of 0.00292 s, which is far less compared with other methods. The prediction error is improved by at least 45.3%, and the average deviation is less than 2 m. [ABSTRACT FROM AUTHOR]

Aiming at the oscillation caused by the hydraulic cylinder of independent metering system (IMS) running between multiple working quadrants, a novel multiple quadrants switching displacement tracking control strategy is proposed in this paper. In this control strategy, both the cylinder chamber pressure under resistance and overrunning conditions are controlled by the inlet valve, and the outlet valve is utilized to control the displacement of cylinder. This method allows the hydraulic cylinder to always run in energy-efficient. When the working quadrant changes, the valve’s working modes are unchanged, which avoids oscillation caused by changes of valves working modes in principle. And a reduced-order active disturbance rejective displacement controller and an active disturbance rejective pressure controller are designed to get high-precision tracking of the displacement and pressure. Numerous experimental results show that the proposed method is reasonable and effective, hydraulic cylinder operates smoothly between multiple quadrants, the pressure and displacement controllers have good anti-interference ability and robustness, and the energy consumption can be reduced by more than 23% compared to the traditional constant pressure valve control system. [ABSTRACT FROM AUTHOR]

Aiming at the oscillation caused by the hydraulic cylinder of independent metering system (IMS) running between multiple working quadrants, a novel multiple quadrants switching displacement tracking control strategy is proposed in this paper. In this control strategy, both the cylinder chamber pressure under resistance and overrunning conditions are controlled by the inlet valve, and the outlet valve is utilized to control the displacement of cylinder. This method allows the hydraulic cylinder to always run in energy-efficient. When the working quadrant changes, the valve’s working modes are unchanged, which avoids oscillation caused by changes of valves working modes in principle. And a reduced-order active disturbance rejective displacement controller and an active disturbance rejective pressure controller are designed to get high-precision tracking of the displacement and pressure. Numerous experimental results show that the proposed method is reasonable and effective, hydraulic cylinder operates smoothly between multiple quadrants, the pressure and displacement controllers have good anti-interference ability and robustness, and the energy consumption can be reduced by more than 23% compared to the traditional constant pressure valve control system. [ABSTRACT FROM AUTHOR]

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application. [ABSTRACT FROM AUTHOR]

For the electric hydrostatic pump unit (EPU), which is a crucial component in the electro hydraulic servo pump control system (EHSPCS), nonlinear flow modelling is a key challenge to achieve high performance operation, especially in position control. For the nonlinear flow of EPUs, simple linearization combined with compensation has become a popular control method recently. However, the control performance is constrained by the reliance on deviation correction. This paper proposes utilizing the Gauss Newton iterative method to investigate the nonlinear flow modeling in the EPU, aiming to obtain the mapping relationship between the load flow and operating parameters in the EPU, thereby directly improving the control performance. A nonlinear flow model is built by utilizing the Gauss Newton iterative method based on test data obtained from various operating conditions, and the mapping relationship is presented accordingly. Experimental studies show that compared to well-established methods, this model exhibits high accuracy and practicality in improving the control performance, particularly in position control of the EPU. [ABSTRACT FROM AUTHOR]

Getting complementary physical information from a single image acquisition is particularly valuable for materials analysis. Grating based X-ray Phase Contrast Imaging (XPCI) methods allow decoupling attenuation, phase and scattering information. However, the phase and scattering extraction processes can easily suffer from artefacts, which is detrimental to implement this imaging technique in societal applications. In this paper, we demonstrate that grating based XPCI can provide a robust phase measurement in complex materials such as damaged composites. The technique allows the phase to be analysed using a self-assessment method that first identifies the artefacts from the imaging setup, and then can be used as an indicator to interpret the signal from a material. We focus on carbon fibre reinforced polymers which we subjected to laboratory-controlled lightning strikes. We evidence that the combined information from phase and attenuation allow identifying the type of defect induced by the lightning current. The phase information is converted into relative mass density variation within the sample and depicts areas with a loss in density up to 40%. We ensure that these results are valid by comparing them with an X-ray attenuation contrast tomographic reconstruction. [ABSTRACT FROM AUTHOR]

Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents’ movements correspond to the components of the value functions. We test this framework by simulating foraging agents and using Bayesian statistical modeling to correctly identify the factors that best predict the agents’ behavior. As further validation, we use this framework to analyze an open-source locust foraging dataset. Finally, we collect new multi-agent real-world bird foraging data, and apply this method to analyze the preferences of different species. Together, this work provides an initial roadmap to integrate cognitive, neuroscience, and statistical approaches for reasoning about animal foraging in complex multi-agent environments. [ABSTRACT FROM AUTHOR]

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation. [ABSTRACT FROM AUTHOR]

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance. [ABSTRACT FROM AUTHOR]

The Rey Auditory Verbal Learning Test (RAVLT) is a commonly used tool for evaluating verbal learning and memory in neuropsychological assessments. In recent years, we developed a Virtual Reality (VR) adaptation of the RAVLT (VR-RVLT), aiming for increased ecological validity compared to the traditional pen and paper gold standard (GS-RAVLT). Following validation in healthy cohorts, the VR-RAVLT was validated with thirty individuals with Parkinson's Disease (PD) that completed both the GS-RAVLT and the VR- RAVLT. Validity of the VR-RAVLT was evaluated by assessing its construct and discriminant validity, and test–retest reliability, in comparison to the GS-RAVLT. Results of the PD participants were compared to those of 46 previously recruited healthy participants with comparable age and level of education. Main outcome measures derived from the remembered items on the test lists, exhibited significant and comparable correlations between VR-RAVLT and GS-RAVLT, both among healthy participants and PD participants. Likewise, serial position effects were similar for both formats amog the PD participants. Additionally, both formats showed similar discriminatory ability between healthy controls and PD participants, as well as comparable test–retest reliability measures. Taken together, the results suggest that the VR-based RAVLT is equally effective in measuring verbal memory capabilities in individuals with PD as compared to the GS-RAVLT. Certain results indicate that the virtual reality version has the capability to encompass additional factors that might impact memory performance, thereby suggesting an enhanced ecological validity. [ABSTRACT FROM AUTHOR]

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289. [ABSTRACT FROM AUTHOR]

University learning and teaching involves contrasting and interacting emotional experiences. Even in disciplines considered as objective as Science and Engineering, emotion plays a significant role in catalysing and sustaining learning. Although emotions are individually felt, they are socially constructed between people. This is especially relevant in group-based learning evident in much contemporary higher education. This paper applies the concept of emotion socialization to illustrate how groups of students and teachers cope and succeed in cognitively and emotionally challenging learning settings. The study is based on qualitative data collected across six STEMM university departments, from 280 students via in-situ questionnaires and from 20 teachers via group and follow-up interviews. Two key findings offer insight into processes of reciprocal influence on cognition and emotion. The first relates to ways in which students’ and teachers’ differing knowledge-related goals and relationships with knowledge influenced cognition and emotion, uncovering previously unacknowledged connections. The second relates to how students’ comparisons of progress towards academic goals with peers contributed considerably to their emotional experiences in cognitively and emotionally helpful and unhelpful ways. Practical implications are presented, including possibilities for capitalising on existing emotion socialization processes and enhancing how they influence cognition and emotional well-being. [ABSTRACT FROM AUTHOR]

This paper presents the design and analysis of a high voltage gain converter utilizing a coupled inductor with reduced voltage stress, specifically for photovoltaic energy-based systems. The proposed converter employs a two-winding coupled inductor and voltage multiplier cells to achieve an increase in output voltage while mitigating voltage stress across semiconductor components. Additionally, the voltage multiplier cells function as voltage clamps for the power switch, further enhancing the converter's performance. The converter features a single switch design, which simplifies control, reduces cost, and improves reliability. Key advantages of the converter include a low component count, a common ground between input and output ports, and high efficiency. The converter's performance is thoroughly investigated through mode analysis and steady-state analysis. Comparative evaluations with similar converters are conducted to highlight the benefits and performance of the proposed design. To validate the theoretical analysis, a 125 W prototype with 26 V input and 200 V output voltages operating at a 50 kHz switching frequency is developed, and experimental results are presented, demonstrating the effectiveness and practicality of the proposed high voltage gain converter. [ABSTRACT FROM AUTHOR]