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Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD. [ABSTRACT FROM AUTHOR]

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [ABSTRACT FROM AUTHOR]

The concrete-faced rockfill dam (CFRD) has been widely constructed worldwide, and the reliability of the concrete panels, the most important containment structure, is critical to the safety of the CFRDs and downstream communities. Several practical projects show that concrete slab cracking usually occurs during the water storage period, which can be attributed to the rapid increase of hydrostatic pressure and uneven settlement of the dam body. In this paper, a time-dependent reliability analysis method considering the fuzziness of the failure criterion is presented to assess the slab cracking risk of the Houziyan CFRD during the water storage period. Based on the observed deformation, the material parameters are calibrated to ensure the fidelity of the numerical simulation. Based on the Drucker–Prager yield criterion and tensile strength criterion, the response surface method is utilized to construct the performance functions at different moments, and then the time-dependent reliability analysis considering the fuzzy failure criteria is implemented for the concrete slab. The case study of the Houziyan CFRD shows that, during the initial period of water storage, the reliability index of the concrete slab decreases with the rise of the water level. With a stable water level, the probability of cracking of the concrete slab slowly decreases as the deformation of the dam body and slab tend to be coordinated. Especially, considering the fuzziness of the failure criteria, the reliability index decreased by about 2%, indicating that the proposed evaluation method is biased toward safety. The method proposed in this paper can reflect the evolution law of concrete slab reliability with the operating environment and provides a new approach to evaluate the performance of the concrete slab during the water storage period. [ABSTRACT FROM AUTHOR]

Wideband digital phased array radar offers the advantages of high range resolution, which improves the recognition ability for multiple targets, group targets, and high-speed targets. Traditional wideband phased arrays use true time delay to compensate for aperture fill time; however, the cost increases significantly. In this paper, a wideband elemental digital array architecture based on the stretch processing method is proposed. By utilizing the time-domain and frequency-domain translation equivalence of the LFM (Linear Frequency Modulation) signal waveform, the equivalent aperture fill time is compensated for through frequency shift and phase shift after stretch processing. Compared to traditional wideband digital arrays, this method can dramatically reduce the required sampling rate and lower the requirements on antenna hardware, thereby reducing the manufacturing cost of system. A comprehensive analysis of the signal processing process and stretch processing method is provided. And an antenna array prototype is developed to verify the T/R channel compensation and wideband beamforming. Measured results show that the antenna is capable of ±60° scanning in azimuth plane and ±40° scanning in elevation plane, with a bandwidth of 500 MHz in S-band. The results demonstrate excellent wideband beam performance and accurate lobe scanning, which confirms the validity of the proposed wideband architecture for stretch processing, frequency shift, and phase shift. This method can be widely applied to the low-cost design and wideband performance improvement of wideband digital array radar. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R 2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a ground-based experimental system for solar array active vibration suppression has been established. Firstly, in order to establish an accurate model of the solar array, the solar array is regarded as a flexible cantilevered thin plate, and the corresponding dynamical equations are derived using the absolute nodal coordinate method. In addition, in this paper, the more advanced MFC piezoelectric patch is used instead of the traditional PZT piezoelectric ceramic patch. The electromechanical coupling finite element model of the P1-type MFC patch is established and substituted into the kinetic equation of the solar array. Finally, the accuracy of the electromechanical coupling modeling and the control effect of active vibration suppression were verified using the PID control. A set of experimental frameworks for evaluating the active vibration suppression effect, including the free vibration test, sinusoidal perturbation test, and white noise perturbation test, as well as the analysis strategy of the test data, are established. [ABSTRACT FROM AUTHOR]

In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system. [ABSTRACT FROM AUTHOR]

Unlike conventional single-phase seepage monitoring methods, monitoring multiphase flow in porous media is more complex. This paper addresses this complexity by analyzing the heat transfer in porous media models under multiphase seepage conditions. It introduces a set of theories, methods, and devices to effectively monitor the flow velocity in multiphase seepage processes. Utilizing a self-developed single-point self-heating temperature-sensing device combined with saturation testing at monitoring points, we establish a method to determine the relationship between different saturation and resistivity, as well as the saturation and thermal conductivity of the reservoir model, which provides essential parameter support for the calculation of results during flow velocity monitoring. The effectiveness of the flow velocity monitoring method was confirmed through a one-dimensional constant velocity multiphase seepage experiment. Furthermore, oil-water two-phase seepage simulation experiments were conducted based on the sandpack model. By comparing the real oil-water flow velocity with the monitored velocity, the accuracy can reach over 95%, validating the accuracy and reliability of the method proposed in this paper. The seepage flow velocity monitoring theory and technology established herein offer corresponding theories and methods for obtaining fluid seepage velocity in porous media with multiphase fluids. [ABSTRACT FROM AUTHOR]

Two problems exist in the study of the trajectory optimization problem of powered hypersonic gliding vehicles (HGVs) due to insufficient consideration of the overall design constraints as well as the strong couplings among relevant disciplines: (1) the engine and thrust models are not compatible with the existing HGV; (2) configuration parameters of the HGV are not included as design variables during trajectory optimization (i.e., propulsion discipline is decoupled in the process of the HGV configuration design), thus failing to fully explore the effect of power to improve the performance of the HGV. Therefore, the application of multidisciplinary design optimization (MDO) in the overall design of powered HGVs should be investigated. First, a MDO task analysis and a multidisciplinary model analysis are carried out for the powered HGV. Second, the multidisciplinary optimization problem is defined, and the couplings between disciplines of the powered HGV are analyzed so that a six-discipline model is established that is suitable for the overall design process, including the parameterized configuration geometry, aerodynamics, propulsion, mass properties, trajectory, and aerodynamic heat/thermal protection system (TPS). Finally, a surrogate model is used to replace the time-consuming accurate model, and numerical optimization examples verify the effectiveness of the method. The optimization results show that the method has a good convergence speed, which increases the gliding range of the optimized vehicle by 8.37%. In addition, by decoupling the propulsion discipline, the validation shows that the coupled propulsion discipline during the overall design can increase the range of the powered HGV by 3.87% compared to the powered HGV optimized with the decoupled propulsion discipline. The work done in this paper provides a new design idea for the overall design of a powered HGV. [ABSTRACT FROM AUTHOR]

This paper is aimed at performing a comprehensive review of the approaches employed in nanosatellite platforms, specifically 1 U and 3 U, for electrical power systems (EPS). Moreover, it seeks to develop a robust maximum power point tracking (MPPT) controller using the sliding mode control (SMC) method. The proposed control strategy is intended to monitor the solar panel's maximum power point (MPP) and adapt to changes in temperatures and solar irradiance in low Earth orbit (LEO). The EPS model, designed with the proposed sizing method, is built in MATLAB-Simulink and integrates a solar panel, battery storage, and power converters controlled by diverse MPPT methods. Then, simulation results demonstrate the effectiveness of the proposed SMC approach compared to other conventional control methods for the designed EPS under varying LEO conditions. To achieve a robust validation of the most appropriate MPPT control method under steady-state conditions, this paper presents an experimental investigation into the proposed EPS hardware design. The proposed SMC method achieved an increase in power generation from 10% to 12% for buck and boost power converters, respectively, compared to traditional control methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel concept of a TRISO-fueled salt-cooled reactor (TFSCR). The core contains circulating pipes filled with molten salt carrying TRISO particles. The reactor achieves online refueling by slowly circulating the molten salt through the pipeline. The reactor utilizes the same molten salt as the coolant and graphite as the moderator. The reactor design has the characteristics of safety, economy, and nonproliferation. TRISO particles exhibit greater resistance to neutron irradiation, corrosion, oxidation, and high temperatures compared to conventional fuels. A molten salt-cooled reactor can also operate at higher temperatures, consequently enhancing power generation efficiency. Furthermore, lower operating pressures can mitigate the risk of significant damages and loss of coolant caused by accidents, thereby enhancing reactor safety. This paper presents the basic nuclear design of TFSCR under the assumptions concerning the average fuel power density, the volumetric core power density, and the core temperature. At the same time, the feasibility of online refueling and long-life operation was evaluated by fuel burnup calculation. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China's attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R 2 , and VIF. The R 2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO. [ABSTRACT FROM AUTHOR]

Contemporary healthcare for those experiencing mental illness requires healthcare practitioners (HCPs) to effectively incorporate the prescription of exercise in their treatment, in accordance with clinical guidelines. However, there has been a lack of effective implementation of such recommendations. The purpose of this review was to identify barriers to exercise prescription in the treatment of people diagnosed with mental illness as perceived by HCPs. APA PsycINFO, CINAHL, MEDLINE, and PubMed electronic databases were searched for relevant articles published in the period from January 2005 to September 2023. A total of 18 papers were included for thematic synthesis. Four key themes were identified across the qualitative (8), quantitative (6), and mixed method (4) papers, including a lack of knowledge and confidence of HCPs in prescribing exercise; role and responsibility; HCPs' misconceptions of client barriers; and systemic issues impacting exercise prescription practices. A lack of knowledge or confidence was the most common barrier. Some HCPs indicated a desire to develop their skills in exercise prescription, while others indicated a preference for an exercise professional to take responsibility for this aspect of treatment. Systemic barriers were spread across a range of issues, with lack of time, excessive workload, and difficulties accessing qualified staff most commonly cited. This review provides further insight into the barriers to exercise prescription faced by HCPs and makes recommendations regarding how to address these barriers in order to better implement clinical guidelines and thus improve the quality of treatment provided to people diagnosed with a mental illness. [ABSTRACT FROM AUTHOR]

Wireless ad hoc/sensor networks (WASN) have seen increased application in three-dimensional (3D) environments, such as underwater and aerial scenarios. However, WASN may be fragmented or unable to connect continuously due to the harsh surrounding environment or high mobility. Therefore, restoring the network connectivity and transmit data in real time is very important. This paper focuses on the critical task of restoring network connectivity in 3D WASN, a complex issue given that existing connectivity restoration algorithms for two-dimensional environments are not directly applicable or become overly complicated in 3D contexts. We present a comprehensive analysis of the current research landscape, summarizing key findings related to various aspects of 3D WASN connectivity restoration. These aspects include the application environment (underwater, in the sky, and recovery disaster), opportunities for restoration (active, passive, and active/passive), implementation strategies (clustering and sleep scheduling), and resource constraints (node deployment and movement control). Our study also proposes a classification of connectivity restoration solutions for 3D WASN, identifying existing gaps and suggesting potential future research directions. By providing specific insights and a structured overview of the field, we aim to contribute to the ongoing development of robust and resilient 3D WASN. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%. [ABSTRACT FROM AUTHOR]

The low-bit quantization can effectively reduce the deep neural network storage as well as the computation costs. Existing quantization methods have yielded unsatisfactory results when being applied to lightweight networks. Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To address these problems, this paper proposes a custom network quantization approach. Firstly, an improved PArameterized Clipping Activation (PACT) method is employed during the quantization aware training to restrict the value range of neural network parameters and reduce the loss of precision arising from quantization. Secondly, the Consecutive Execution Of Convolution Operators (CEOCO) strategy is utilized to mitigate the resource consumption caused by the frequent environment switching. The proposed approach is validated on Xilinx Zynq Ultrascale+MPSoC 3EG and Virtex UltraScale+XCVU13P platforms. The MobileNetv1, MobileNetv3, PPLCNet, and PPLCNetv2 networks were utilized as testbeds for the validation. Moreover, experimental results are on the miniImageNet, CIFAR-10, and OxFord 102 Flowers public datasets. In comparison to the original model, the proposed optimization methods result in an average decrease of 1.2% in accuracy. Compared to conventional quantization method, the accuracy remains almost unchanged, while the frames per second (FPS) on FPGAs improves by an average of 2.1 times. [ABSTRACT FROM AUTHOR]

This paper presents a performance analysis and optimization of heat exchanger fin shapes in a thermoelectric power generation system for ship waste heat recovery. It proposes a new curved fin design aimed at enhancing heat transfer efficiency and maximizing thermoelectric power generation capacity. Numerical analysis and response surface methodology (RSM) focused on 225 parameters to identify the optimal fin shape. Computational analysis using CFX was conducted on the thermoelectric module system with curved fins. The methodology was verified by comparing and analyzing CFD and PIV experimental results for a curved fin. The paper comprehensively compares the fluid dynamics of curved fins and straight fins, highlighting how curved fins improve heat exchange by creating a tip leak vortex. The results demonstrate the superiority of the curved fin design in terms of heat transfer efficiency and net power generation over the conventional straight fin design. [ABSTRACT FROM AUTHOR]

Receiving inspection plays a crucial role in ensuring construction quality after the completion of engineering projects. Traditional inspection measurement methods, such as manual observation means and optical equipment measurement methods, have limitations in terms of the number of measurement sites and the range of measurements. These traditional methods fail to provide accurate curve parameters and continuous spatial morphology information for large-span curved bridge structures. This paper proposes a reverse model measurement method to address this issue. The reverse model is built based on point cloud data acquired by 3D laser scanning technology. Finally, take the Taizicheng No. 1 Bridge as an example, the validity of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Migration to South Africa is motivated by the pursuit of employment, safety, and improved living conditions. However, immigrants encounter significant challenges, such as restricted access to essential services, which were exacerbated by the COVID-19 pandemic. This paper investigates the impact of the COVID-19 pandemic on Malawian immigrants in South Africa, addressing the lack of attention given to this immigrant population by highlighting their vulnerabilities. Using a qualitative exploratory and descriptive approach, in-depth interviews were conducted with 24 Malawi immigrants who were over 18 years old and had established residency in South Africa before the onset of the COVID-19 pandemic. Five key stakeholders were also interviewed for additional perspectives and to ensure triangulation and improve data reliability. The interviews were transcribed verbatim and analyzed using thematic analysis strategies and coding with Nvivo12 software. The study highlighted the exacerbated struggles of Malawian immigrants in South Africa amid the COVID-19 pandemic, uncovering systemic discrimination in healthcare, marked by longer wait times and reluctance from health workers to treat undocumented immigrants. The study also revealed a dire security situation, with immigrants living in high-crime areas and feeling particularly targeted due to their foreign status, a situation worsened by the pandemic's economic effects. Additionally, the economic downturn induced by COVID-19 significantly impacted employment opportunities, with many immigrants facing prolonged unemployment and job losses, especially in sectors where they traditionally found work. The detailed accounts of participants highlight not only the multifaceted challenges imposed by the pandemic but also the critical need for inclusive policies and support systems that ensure healthcare access, safety, and economic resilience for immigrants, particularly during global health emergencies. Future research should focus on effective interventions for socioeconomic integration and well-being, particularly for immigrants from other African countries. [ABSTRACT FROM AUTHOR]

As an on-demand mobility service, parking reservations can greatly alleviate the issue of parking challenges. There are currently three primary strategies for parking reservation: first-come-first-served, permit reservation, and auction. In contrast to the first-come-first-served and permit-reserved strategies, the auction strategy uses dynamic pricing to allocate parking supplies efficiently based on the auction, which attracts more scholars for the research. However, parking reservations based on the auction process may have an inequity issue because drivers' age, gender, income level, and location of residence fluctuate. This inequity may limit the growth of reserved parking by influencing parking drivers' acceptance of reserved parking. But currently, very few scholars focus on the issue of reserved parking equity, and even fewer measure this nebulous and personal issue. In consideration of this, the Lorenz curve of parking reservation and the vertical equity index of parking reservation are proposed in this paper along with the calculation method for the index, which enables the problem of reserved parking vertical equity to be visualized and made concrete. The numerical experimental method is used to analyze the vertical equity of drivers with varying income levels, utilizing the Vickrey–Clarke–Groves (VCG) auction process as an example. According to the research, loss-averse drivers are more than gain-neutral and gain-seeking drivers when the income levels of the drivers using reserved parking are the same. With the increasing number of high-income drivers involved in the parking reservation, medium to low-income drivers would lose their chances of successful reservations because of their uncompetitive bid price which leads to inequity issues when the number is less than the number of parking spaces. In contrast, the vertical equity index changes more for gain-seeking drivers while being generally steady when loss-averse and gain-neutral drivers participate. For instance, when the profit and loss coefficient is 3 and 15% of drivers with high-income levels use the parking reservation platform, the vertical equity index rises from 0.09128 to 0.45434. The reference price has a moderating influence on the vertical equity index when the number of driver participants at high-income levels remains constant. In general, within a reasonable range, the higher the reference price, the more equitable the parking reservation procedure and the lower the vertical equity index. [ABSTRACT FROM AUTHOR]

The electromagnetic radiation sensitivity of the electric explosive device (EED) and its installed use state is closely related to the size of the equipment and radiation field strength constraints. The use of the traditional all-level electromagnetic radiation method for the effect of the actual installed EED test in the electromagnetic environment simulation encountered a technical bottleneck. The microwave band is difficult to effectively assess through the current standing wave distribution and skin effect. The temperature rise of the EED bridge wire has no relationship with the frequency of the electromagnetic wave. In this paper, through the analysis of the electromagnetic effect mechanism of the EED, the coupled power model of electromagnetic irradiation of the EED is obtained, and the relationship between the temperature rise of the bridge wire of the EED and the electric field strength model is established. Under the action of high-frequency continuous waves, the electromagnetic effect of the device is tested to verify the correctness of the mechanism analysis of the electromagnetic effect of the device under the action of continuous waves. The results provide crucial technical support for the electromagnetic protection of the device under the harsh electromagnetic environment of the battlefield. [ABSTRACT FROM AUTHOR]

The heat kernel associated with a discrete graph Laplacian is the basic solution to the heat diffusion equation of a strict graph or network. In addition, this kernel represents the heat transfer that occurs over time across the network edges. Its computation involves exponentiating the Laplacian eigensystem with respect to time. In this paper, we expand upon this concept by considering a novel network-theoretic approach developed in recent years, which involves defining the k-path Laplacian operator for networks. Prior studies have adopted the notion of integrating long-range interactions (LRI) in the transmission of "information" across the nodes and edges of the network. Various methods have been employed to consider long-range interactions. We explore here the incorporation of long-range interactions in network analysis through the use of Mellin and Laplace transforms applied to the k-path Laplacian matrix. The contribution of this paper is the computation of the heat kernel associated with the k -path Laplacian, called the generalized heat kernel (GHK), and its employment as the basis for extracting stable and useful novel versions of invariants for graph characterization. The results presented in this paper demonstrate that the use of LRI improves the results obtained with classical diffusion methods for networks characterization. [ABSTRACT FROM AUTHOR]

In the context of the rapid advancements in space technology and the increasing complexity of space missions, there is a growing need for efficient and effective approaches to tackle the multifaceted challenges faced by space systems. Traditional methods often fall short in providing comprehensive support throughout the entire life cycle of space systems. To address these challenges, this paper presents a novel parallel space system architecture based on ACP (artificial systems, computational experiments, and parallel execution) and explores its applications in the design, development, and operation of space systems. The proposed architecture integrates artificial systems with actual space systems and employs computational experiments to generate extensive sample data. This approach enhances the accuracy of the artificial systems' model and optimizes the performance of the real systems, facilitating parallel advancements between the two. The design, development, and operation processes of Q-Sat, implemented using the ACP framework, serve as a case study to illustrate the advantages of parallel space systems. Following adjustments made to the discrepancies between parallel systems under the ACP-based space system framework, the accuracy of missing orbit compensation improved by 86.5%, and the 24-hour forecast positional error was reduced by approximately 65 m. Furthermore, this paper discusses future trends, emphasizing the increasing efficiency and reliability of digitized, integrated, and adaptive space systems. The findings contribute to the understanding of parallel space systems and provide valuable insights for further advancements in the field. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

In contemporary wireless communication systems, multicarrier modulation schemes have become widely adopted over single-carrier techniques due to their improved capacity to address challenges posed by multipath fading channels, leading to enhanced spectral efficiency. Orthogonal frequency division multiplexing (OFDM), a prevalent multicarrier scheme in 4G, is favored for its ease of implementation, interference resilience, and high data rate provision. But it falls short of meeting the requirements for 5G and beyond due to limitations such as out-of-band (OOB) emissions and cyclic prefixes. This paper introduces the filter bank multicarrier (FBMC) and universal filtered multicarrier (UFMC) with quadrature amplitude modulation (QAM) and phase shift keying (PSK) waveforms through Additive White Gaussian Noise channel (AWGN), Rayleigh fading channel and Rician channel. The objective of this paper is to enhance the performance of UFMC with reduced complexity through the new filtering approach for achieving optimal outcomes. The proposed scheme, incorporating Tukey filtering technique, demonstrates superior performance in reducing peak-to-average power ratio (PAPR) and improving bit error ratio (BER) compared to the original UFMC signal without necessitating additional power increments. Specifically, the UFMC system with Tukey filtering achieves a notable net gain of 5 dB. Simulation results demonstrate that utilizing various filter types in FBMC and UFMC systems, combined with QAM modulation, significantly reduces OOB emissions compared to conventional systems. In aspect to BER, Tukey window showed almost 10−6 at 15 dB SNR in UFMC which is better than FBMC. [ABSTRACT FROM AUTHOR]

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way. [ABSTRACT FROM AUTHOR]

The axial deployment force is an indispensable parameter of a lanyard-deployed coilable mast, which reflects its load capacity in practical applications. However, research on the axial deployment force in the literature is very limited, and there are no mature numerical methods to determine this parameter in the design stage of coilable masts. In this paper, a numerical method for determining the axial deployment force of a lanyard-deployed coilable mast in the local coil mode is presented. Through this method, the designer can quickly obtain the estimated value of the axial deployment force in the design stage, which is convenient for the quantitative design of parameters. To verify the correctness of the proposed method, a dynamic simulation of the coilable mast is carried out, and a microgravity test is performed. The comparison results show that the error between the numerical method and the simulation and experimental results is less than 5%, which proves the correctness of the proposed method. In addition, the coilable mast studied in this paper has been verified by an actual microsatellite deployment in orbit. [ABSTRACT FROM AUTHOR]

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators. [ABSTRACT FROM AUTHOR]

A structured dynamic overlapping grid and a user-defined function are used to study the projectile launching process, and the hybrid Roe scheme is used to solve the flow field with strong shock wave. The launching process of a projectile with a muzzle decompression device is numerically simulated by using a three-dimensional transient model, and the flow field inside the muzzle decompression device and the development process of the muzzle flow field in the projectile launching process are discussed in detail. Compared with no device, the muzzle decompression device can effectively reduce the peak pressure around the muzzle; the numerical results are in agreement with the corresponding experimental values. The numerical investigation in this paper is helpful to understand the mechanism of pressure reduction of the device. It also provides a new way to reduce the muzzle pressure of aircraft gun. [ABSTRACT FROM AUTHOR]

Identifying objects in surveillance and reconnaissance systems with the human eye can be challenging, underscoring the growing importance of employing deep learning models for the recognition of enemy weapon systems. These systems, leveraging deep neural networks known for their strong performance in image recognition and classification, are currently under extensive research. However, it is crucial to acknowledge that surveillance and reconnaissance systems utilizing deep neural networks are susceptible to vulnerabilities posed by adversarial examples. While prior adversarial example research has mainly utilized publicly available internet data, there has been a significant absence of studies concerning adversarial attacks on data and models specific to real military scenarios. In this paper, we introduce an adversarial example designed for a binary classifier tasked with recognizing helicopters. Our approach generates an adversarial example that is misclassified by the model, despite appearing unproblematic to the human eye. To conduct our experiments, we gathered real attack and transport helicopters and employed TensorFlow as the machine learning library of choice. Our experimental findings demonstrate that the average attack success rate of the proposed method is 81.9%. Additionally, when epsilon is 0.4, the attack success rate is 90.1%. Before epsilon reaches 0.4, the attack success rate increases rapidly, and then we can see that epsilon increases little by little thereafter. [ABSTRACT FROM AUTHOR]

The rapid development of network freight platforms has directly increased the level of social logistics resource collaboration and improved both the efficiency and quality of logistics industry services. As a bilateral platform that connects freight shippers and freight carriers, the organizational structure and operational mode of network freight platforms differ substantially from those of traditional logistics service providers. In this paper, a tripartite evolutionary game model is constructed in which a network freight platform, freight shipper, and freight carrier are considered, and the evolutionary stability strategies of the parties and the tripartite system are dynamically analyzed. The reliability of the model is verified through a numerical case, and several countermeasures have been proposed to improve the stability of the system based on the sensitivity analysis of important parameters. This paper helps standardize the principal behavior of all parties under the network freight mode, reduce the default risk of all parties, and improve the overall cooperation stability of the logistics service supply chain. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model is formulated, suitable to explain the evolution of income distribution over a population in the presence of production. The model is conceived from the perspective of complexity. Indeed, the income distribution emerges as the result of a myriad of economic exchanges taking place between individuals. In fact, the aim of the paper is to provide a framework and mathematical tools for the construction and the investigation of models having an exploratory character. The framework is expressed in the form of a system of nonlinear ordinary differential equations, as many as the income classes are, involving transition probabilities. Numerical solutions of these systems are constructed under different assumptions on the law of production and in the presence of different fiscal systems, which provides an example of the versatility of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

In this paper, the vehicle ISD (inerter-spring-damper) suspension and power-driven-damper control strategy are combined to the suspension design, and the power-driven-damper semiactive ISD suspension is proposed. The dynamic models of the passive suspension S1 and two semiactive ISD suspensions S2 and S3 are established. Based on the port-controlled Hamiltonian theory, the power-driven-damper semiactive control strategy is designed by analyzing the power transfer of suspension S3. Then, the parameters of the two models are optimized by the particle swarm optimization algorithm, and the optimization results show that the suspension S3 has better performance. The influence of the semiactive damping coefficient, the spring stiffness, and the inertance on the vibration suppression performance is investigated based on the suspension S3. The effect of parameter perturbation on power-driven-damper semiactive vehicle ISD suspension illustrates that the designed semiactive vehicle ISD suspension has better ride comfort in a wider range frequency and good robust performance. [ABSTRACT FROM AUTHOR]

A ship equipment pedestal is a structure that connects power equipment to a hull. It must have a high load-bearing capacity and the ability to withstand large impact loads. In this paper, a novel structure with a positive-negative Poisson's ratio is proposed. The deformation mechanism and mode of this structure under quasistatic compression loading are analysed via numerical simulation. Based on this new structure, a multicellular pedestal is designed, and its bearing capacity and impact resistance are analysed. The structural parameters of the pedestal are optimized. An experiment is conducted to evaluate the impact resistance of the pedestal model, which confirms that the proposed multicellular pedestal exhibits excellent impact resistance. [ABSTRACT FROM AUTHOR]

In order to further study the cushioning performance of concave hexagonal cores (CHCs) and expand their application range, the in-plane finite element model of CHCs is established in this paper. A dynamic cushioning coefficient method was proposed to characterize the cushioning performance of CHCs. The dynamic cushioning coefficient curve and minimum dynamic cushioning coefficient (MDCC) of CHCs with different impact velocities and structural parameters are obtained. The influence rules of structural parameters and impact velocities on the MDCC are analyzed; the deformation mode and transformation empirical formula are also obtained. The results show that when other parameters are constant, the MDCC of CHCs decreases with the increase of impact velocity, increases with the increase of wall thickness and side length ratio, and decreases with the increase of expansion angle. The theoretical analysis is consistent with the finite element results, which further verifies the reliability of the model. This paper provides a solid theoretical basis for the industrial application of the cushioning performance of CHCs and forms a key technical support. [ABSTRACT FROM AUTHOR]

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%. [ABSTRACT FROM AUTHOR]

When a super-large diameter shield tunnel passes through a strongly karst-developed area, in order to ensure the stability of the tunnel and economy of karst reinforcement, the reasonable treatment range for a huge number of karst caves needs to be researched. Based on a karst treatment project of a shield tunnel with a diameter of 14.5 m, this paper studies the influence of intrusive fully filled karst caves on the stability of the lining and excavation face by using theoretical calculation. In addition, the safe distance between the tunnel and karst caves outside the tunnel is proposed through numerical simulation, and the corresponding treatment range is obtained. The results show that the bearing capacity of the lining has a certain reserve when the intrusive cave is smaller than a certain size, and the caves filled with plastic or hard plastic clay have sufficient antifracturing ability. Therefore, some small fully filled caves inside the tunnel profile can be left unreinforced. On the other side, the critical safe distance is only 1–2 m for the caves with a size of 3 m, so the outside caves with a size less than 3 m can be left unreinforced in the treatment range farther away from the tunnel. The proposed treatment range is close to similar projects that have been built, indicating that analysis results are reasonable. [ABSTRACT FROM AUTHOR]

Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing. [ABSTRACT FROM AUTHOR]

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC). [ABSTRACT FROM AUTHOR]

In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual's ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific. [ABSTRACT FROM AUTHOR]

This paper presents, for the first time, the implementation of a pattern reconfigurable Yagi-Uda antenna utilizing radio frequency identification (RFID) technology for remote pattern control. The proposed scheme emphasizes the use of wireless communication instead of long metal cables, resulting in improved stability of the antenna's pattern and return loss. Two low-power consumption single-pole-double-throw (SPDT) switches are employed on a passive resonator, enabling it to function as a director or reflector under the long-range control (up to 25 m) of an RFID reader. Measurement results demonstrate a −10 dB impedance bandwidth of 4.2% and a gain of 7 dBi at 2.41 GHz. The entire system operates with an ultra-low power consumption of 12 μW. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

Grain is extremely vulnerable to external loads during production and processing, resulting in the deterioration of grain quality. Deteriorated grain not only affects the economic value of grain but also affects the safety of storage. This has a very important relationship with the biomechanical properties of grains. It is of great significance to explore the mechanical properties of grain under different conditions and analyze the relationship between its physical and chemical properties and mechanical properties for improving its processing and eating quality. In this paper, the research methods of the mechanical properties of grains are reviewed. Various factors influencing the mechanical properties of cereals were analyzed. The relationship between the internal organizational structure of grain and its mechanical properties was discussed. This paper puts forward the shortcomings in the current research on the mechanical properties of grains and puts forward the prospect and analysis of its importance in future development in order to provide a reference for reducing crushing in the grain processing process. [ABSTRACT FROM AUTHOR]

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal. [ABSTRACT FROM AUTHOR]

The development of smart elderly industry is an inevitable way to cope with the aging of the population. This research takes the smart elderly care service supply chain as the object, combines the social emotional choice theory for the first time, and uses the time perception to refine the elderly care demand into future-orientation demand and present-orientation demand. This paper analyzes the coordination effect of suppliers' efforts to meet the needs of the elderly under the two conditions of no contract and benefit-sharing contract on the dual-channel supply chain of smart elderly care services. The results show that the benefit-sharing contract is more conducive to maximizing the profit of the supply chain, and the segmentation of elderly demand is conducive to giving full play to the advantages of dual-channel differentiated services, which is conducive to forming a win-win situation of improving the service efficiency of the smart elderly service supply chain and increasing the happiness index of elderly users. The main contributions of this paper are: Using geriatric behavioral psychology to analyze the motivation of the elderly and designing the service effort level index considering the needs of the elderly. Match online and offline channels with personalized services to give full play to the "smart" effect. Based on the game method of Hotelling and Stackelberg, the coordination and optimization of the smart elderly care services dual-channel supply chain, considering the needs of the elderly are realized. This is of great research significance for maximizing the benefits of the smart elderly service supply chain and promoting the development of the smart elderly industry. [ABSTRACT FROM AUTHOR]

Edible coatings and active packaging have become more prevalent in response to changing consumption patterns and market trends to enhance the quality and safety of fresh products. In this work, we investigated the effect of aloe vera gel (AVG) coating and paraffin wax-coated paperboard (PWB) packaging on the postharvest quality attributes of both grapes and apples during storage. The fruits were coated with 50% AVG concentrations, and the inner wall of the corrugated paperboard was coated with paraffin wax emulsion. The grapes and apples were stored for 12 and 35 days, respectively, at ambient conditions (25 ± 3°C and 80–85% relative humidity). The physicochemical properties, microbiological attributes, and decay incidence of the fruits were analyzed at intervals during storage. Both fruits treated with AVG and PWB packaging retained better qualities than the control at the final day of the storage period. Particularly, PWB packaging provided considerably superior quality from the control sample in terms of weight loss (≈54% and 32%), firmness (≈48% and 68%), and color difference (≈30% and 28%) for both grapes and apples. These findings would introduce a novel approach for preserving the quality attributes of both climacteric and nonclimacteric fruits for a prolonged storage period at ambient temperature by PWB packaging and AVG coating. [ABSTRACT FROM AUTHOR]