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Digital transformation has emerged as a powerful force in reshaping the business landscape and enabling organizations to enhance their capabilities. One critical aspect of this change is how it impacts an enterprise’s innovation ability. To explore this question, we select data regarding China’s A-share listed enterprises from 2007 to 2021 as the research sample. We employ crawler technology to gather keywords related to “digital transformation” from annual reports, portraying detailed journeys of enterprises’ digital transformation. Through descriptive statistics and multiple covariance tests, a linear relationship is established between digital transformation and innovation ability. Benchmark regression is conducted and a robustness test is utilized to determine the robustness of the benchmark regression. The mechanism, heterogeneity, and moderating effects of this study are also tested. The results reveal that digital transformation makes a significant positive contribution to the innovation capability of enterprises. Meanwhile, among different types of enterprises, the impact of digital transformation on enterprise innovation capability shows heterogeneity. In terms of the impact mechanism, digital transformation can enhance the innovation output of enterprises by reducing the agency cost and improving the risk-taking level of enterprises, so as to further improve the innovation capability of enterprises. The research results of this paper provide essential theoretical support for the digital transformation of enterprises and the government’s formulation of enterprises’ digitalization strategies. More profoundly, it provides significant reference for how to further promote the digital transformation of Chinese enterprises. [ABSTRACT FROM AUTHOR]

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions. [ABSTRACT FROM AUTHOR]

The traditional public art education model has many drawbacks. After all, this teaching model is the most common teaching model. Most colleges and universities still rely on the traditional teaching mode. This teaching mode is not attractive, boring, and low cost, so the popularity rate is high. The digital interactive design with multimedia courseware as the main body has played a great role in promoting the teaching of public art education in colleges and universities. In this paper, when teachers use multimedia courseware for teaching, because the computer cannot process physical signals, part of the hardware is a converter that converts physical signals such as light and pictures received by the CMOS sensor into digital signals and inputs them to digital signals and analog signals. Through the converter, the two-way transmission of teacher and student information in the teaching process of public art majors from the perspective of diversified public art was realized. At the same time, the information elements of sound, image and text were integrated into the interactive works of public art majors from the perspective of diverse public art, so as to stimulate students' interest in public art learning. Combined with the application of CMOS image sensor technology in diversified public art teaching, a questionnaire survey was conducted to explore the satisfaction of students in public art education and teaching of colleges and universities by CMOS image sensor technology, 50% of the students agreed with the proposal in this article. In this paper, the diversified public art teaching based on CMOS image sensor technology was derived outside the classroom, and the teaching was related to students' life, so as to improve the artistic atmosphere of the campus and truly improve the teaching effect of public art education in colleges and universities. [ABSTRACT FROM AUTHOR]

This paper realizes infrared image denoising, recognition, and semantic segmentation for complex electrical equipment and proposes a thermal fault diagnosis method that incorporates temperature differences. We introduce a deformable convolution module into the Denoising Convolutional Neural Network (DeDn-CNN) and propose an image denoising algorithm based on this improved network. By replacing Gaussian wrap-around filtering with anisotropic diffusion filtering, we suggest an image enhancement algorithm that employs Weighted Guided Filtering (WGF) with an enhanced Single-Scale Retinex (Ani-SSR) technique to prevent strong edge halos. Furthermore, we propose a refined detection algorithm for electrical equipment that builds upon an improved RetinaNet. This algorithm incorporates a rotating rectangular frame and an attention module, addressing the challenge of precise detection in scenarios where electrical equipment is densely arranged or tilted. We also introduce a thermal fault diagnosis approach that combines temperature differences with DeeplabV3 + semantic segmentation. The improved RetinaNet's recognition results are fed into the DeeplabV3 + model to further segment structures prone to thermal faults. The accuracy of component recognition in this paper achieved 87.23%, 86.54%, and 90.91%, with respective false alarm rates of 7.50%, 8.20%, and 7.89%. We propose a comprehensive method spanning from preprocessing through target recognition to thermal fault diagnosis for infrared images of complex electrical equipment, providing practical insights and robust solutions for future automation of electrical equipment inspections. [ABSTRACT FROM AUTHOR]

Urban green space is a direct way to improve the carbon sink capacity of urban ecosystems. The carbon storage assessment of megacity green spaces is of great significance to the service function of urban ecosystems and the management of urban carbon zoning in the future. Based on multi-period remote sensing image data, this paper used the CASA model and the InVEST model to analyze the spatio-temporal variation and driving mechanism of carbon storage in Shenzhen green space and discussed the applicability of the two models to the estimation of carbon storage in urban green space. The research results showed that, from 2008 to 2022, in addition to the rapid expansion of construction land, the area of green space and other land types in Shenzhen showed a significant decrease trend. The estimation results of the carbon storage model showed that the carbon storage of green space shows a significant trend of reduction from 2008 to 2022, and the reduction amounts are 0.8 × 106 t (CASA model) and 0.64 × 106 t (InVEST model), respectively. The evaluation results of the model show that, in megacities, the spatial applicability of InVEST model is lower than that of CASA model, and the CASA model is more accurate in estimating the carbon storage of urban green space. The research results can provide a scientific basis for the assessment of the carbon sink capacity of megacity ecosystems with the goal of "dual carbon". [ABSTRACT FROM AUTHOR]

Understand the dynamics of cancer stem cells (CSCs), prevent the non-recurrence of cancers and develop therapeutic strategies to destroy both cancer cells and CSCs remain a challenge topic. In this paper, we study both analytically and numerically the dynamics of CSCs under radiotherapy effects. The dynamical model takes into account the diffusion of cells, the de-differentiation (or plasticity) mechanism of differentiated cancer cells (DCs) and the time delay on the interaction between microRNAs molecules (microRNAs) with DCs. The stability of the model system is studied by using a Hopf bifurcation analysis. We mainly investigate on the critical time delay τ c , that represents the time for DCs to transform into CSCs after the interaction of microRNAs with DCs. Using the system parameters, we calculate the value of τ c for prostate, lung and breast cancers. To confirm the analytical predictions, the numerical simulations are performed and show the formation of spatiotemporal circular patterns. Such patterns have been found as promising diagnostic and therapeutic value in management of cancer and various diseases. The radiotherapy is applied in the particular case of prostate model. We calculate the optimum dose of radiation and determine the probability of avoiding local cancer recurrence after radiotherapy treatment. We find numerically a complete eradication of patterns when the radiotherapy is applied before a time t < τ c . This scenario induces microRNAs to act as suppressors as experimentally observed in prostate cancer. The results obtained in this paper will provide a better concept for the clinicians and oncologists to understand the complex dynamics of CSCs and to design more efficacious therapeutic strategies to prevent the non-recurrence of cancers. [ABSTRACT FROM AUTHOR]

It is of interest to develop wide-temperature domain damped hydrophobic materials. In this paper, we designed incorporating bio-based phenolic resin into the IIR matrix and introducing dibenzyl fork acetone (DBA) into the main chain structure with sodium hydroxide activation to construct three-dimensional network. In this paper, we designed incorporating bio-based phenolic resin into the IIR matrix and introducing dibenzyl fork acetone (DBA) into the main chain structure with sodium hydroxide activation to construct three-dimensional network. The added bio-based phenolic resin has reticulated structure blended with butyl rubber, combined with sodium hydride activation-modified IIR. The results show that sodium hydride activated modification of DBA is introduced into the main chain structure of IIR by infrared and 1H NMR analysis. The material hydrophobic is realized by the introduction of DBA with static water contact angle of 103.5°. The addition of 10phr lignin-based phenolic resin (LPF) is compatible with IIR, and the torque can reach 7.0 N-m. The tensile elongation of the modified butyl rubber composite can reach 2400% with tensile strength up to 11.43 MPa, while the damping factor can reach 0.37 even at 70 °C. The thermal stability of the composites is enhanced with mass retention rate of 28%. The bio-based PF/NaH activation-modified butyl rubber damping material has potential applications in damping hydrophobicity with wide temperature range. [ABSTRACT FROM AUTHOR]

Star dunes are the tallest dunes on Earth and are amongst the larger and more spectacular aeolian landforms. Although they are widespread in modern sandy deserts, star dunes are rarely recognised in the rock record probably due to a lack of suitable sedimentary models. This paper presents a new sedimentary model for the structure of a star dune at Erg Chebbi in Morocco (Sahara Desert) on the basis of ground-penetrating radar (GPR) surveys. Individual sedimentary structures in star dunes are similar to those in linear or barchanoid dunes, likely leading to misidentification in the rock record. However, the suite of features described in this paper will permit identification of star dunes in future studies of the rock record. Optically stimulated luminescence (OSL) dating shows that accumulation of the Erg Chebbi star dune post-dates the end of the African Humid Period (AHP). At the base of the dune, there is an ~ 8000-year hiatus in the record. Since then, the dune has grown rapidly to create a 100 m high dune within the past 1000 years and is migrating towards the west. Changes in the cross-strata support the idea that star dune construction was accompanied by a change in the wind directions. [ABSTRACT FROM AUTHOR]

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent. [ABSTRACT FROM AUTHOR]

In this study, the deep learning algorithm of Convolutional Neural Network long short-term memory (CNN–LSTM) is used to classify various jewelry rocks such as agate, turquoise, calcites, and azure from various historical periods and styles related to Shahr-e Sokhteh. Here, the CNN–LSTM architecture includes utilizing CNN layers for the extraction of features from input data mixed with LSTMs for supporting sequence forecasting. It should be mentioned that interpretable deep learning-assisted laser induced breakdown spectroscopy helped achieve excellent performance. For the first time, this paper interprets the Convolutional LSTM effectiveness layer by layer in self-adaptively obtaining LIBS features and the quantitative data of major chemical elements in jewelry rocks. Moreover, Lasso method is applied on data as a factor for investigation of interoperability. The results demonstrated that LIBS can be essentially combined with a deep learning algorithm for the classification of different jewelry songs. The proposed methodology yielded high accuracy, confirming the effectiveness and suitability of the approach in the discrimination process. [ABSTRACT FROM AUTHOR]

With the continuous construction of urban traffic roads, more and more new roads are cut off by existing roads to form "dead end roads". There is an urgent need for a trenchless method suitable for urban ultra-shallow overburden to build the undercrossing tunnel. To solve this problem, this paper proposed the micro pipe jacking and joint assembly structure (MPJ & JAS) method, which has the characteristics of shallow burial depth, low cost, short construction time, flexible cross-section setting and high space utilization. The MPJ & JAS method construct a large cross-section tunnel through assembling small cross-section elements, quite different from traditional methods. Therefore, this paper designed a CT-shaped integrated joint, the mechanical performance of which was verified and clarified by tensile test. The bending test and finite element (FE) analysis proved the reliability of MPJ & JAS tunnel structure, and confirmed the structure performances such as the failure models, crack behaviors, load–deflection response and stress–strain distribution. Moreover, the influences of the steel plate thickness, concrete strength and shear connector spacing were determined by the FE analysis. On the basis of test results and reasonable assumptions, a theoretical design method considering the influence of the CT-shaped integrated joint was proposed, which can effectively predict the bending strength of the MPJ & JAS tunnel structure with an error of less than 10%. Finally, in view of the characteristics of the MPJ & JAS method, the suitable micro pipe jacking machine, soil reinforcement measure, hydraulic traction construction technology, high-precision guidance system and concrete construction quality detection method based on the phased array ultrasonic imaging technology were developed, supporting the accurate and efficient construction of the MPJ & JAS tunnel. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

In electron optics, calculation of the electric field plays a major role in all computations and simulations. Accurate field calculation methods such as the finite element method (FEM), boundary element method and finite difference method, have been used for years. However, such methods are computationally very expensive and make the computer simulation challenging or even infeasible when trying to apply automated design of electrostatic lens systems with many free parameters. Hence, for years, electron optics scientists have been searching for a fast and accurate method of field calculation to tackle the aforementioned problem in the design and optimization of electrostatic electron lens systems. This paper presents a novel method for fast electric field calculation in electrostatic electron lens systems with reasonably high accuracy to enable the electron-optical designers to design and optimize an electrostatic lens system with many free parameters in a reasonably short time. The essence of the method is to express the off-axis potential in an axially symmetrical coordinate system in terms of derivatives of the axial potential up to the fourth order, and equate this to the potential of the electrode at that axial position. Doing this for a limited number of axial positions, we get a set of equations that can be solved to obtain the axial potential, necessary for calculating the lens properties. We name this method the fourth-order electrode method because we take the axial derivatives up to the fourth order. To solve the equations, a quintic spline approximation of the axial potential is calculated by solving three sets of linear equations simultaneously. The sets of equations are extracted from the Laplace equation and the fundamental equations that describe a quintic spline. The accuracy and speed of this method is compared with other field calculation methods, such as the FEM and second order electrode method (SOEM). The new field calculation method is implemented in design/optimization of electrostatic lens systems by using a genetic algorithm based optimization program for electrostatic lens systems developed by the authors. The effectiveness of this new field calculation method in optimizing optical parameters of electrostatic lens systems is compared with FEM and SOEM and the results are presented. It should be noted that the formulation is derived for general axis symmetrical electrostatic electron lens systems, however the examples shown in this paper are with cylindrical electrodes due to the simplicity of the implementation in the software. [ABSTRACT FROM AUTHOR]

Aiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods. [ABSTRACT FROM AUTHOR]

The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373. [ABSTRACT FROM AUTHOR]

In recent years, the power conversion efficiency (PCE (%)) of perovskite solar cells (PSCs) has improved to over 26%. To enhance the photovoltaic properties of PSCs, several materials for the electron transport layer (ETL) have been investigated. Zinc oxide (ZnO) is a significant ETL due to its high electron mobility and optical transparency in PSCs. As a result of various deposition methods, ZnO ETL can be processed at low temperatures. On the other hand, based on several studies, metal-doped ZnO can facilitate electron transfer, thereby improving the performance of un-doped ZnO ETL-based PSCs. Here, to improve the PCE (%) and long-term stability of un-doped ZnO ETL-PSCs, silver (Ag)-doped ZnO 1wt% as a buffer layer is examined. In this paper, with the addition of an organic solvent (ethanol) to the dispersion of Ag-doped ZnO 1 wt% nanoparticles (NPs) in deionized (DI) water, the morphology of the buffer layer (Ag-doped ZnO 1 wt%) can be controlled. This approach focuses on reducing the wettability of the ZnO/Ag-doped ZnO 1 wt% bilayer ETLs and enhancing the stability of un-doped ZnO ETL-PSCs. According to the results, the ZnO/H2O-ethanol mixtures-Ag-doped ZnO 1 wt% bilayer ETL leads to the formation of high-quality perovskite with low defects, reducing the recombination rate, and long-term stability of un-doped ZnO ETL-PSCs in ambient conditions. [ABSTRACT FROM AUTHOR]

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults. [ABSTRACT FROM AUTHOR]

In this work, the electrochemical generation of phenothiazin-5-ium (PTZox) from the direct oxidation of phenothiazine (PTZ) in a water/acetonitrile mixture using a commercial carbon anode and conventional stainless steel cathode is reported. PTZox is a reactive intermediate with high potential synthetic applications, which is used in this paper for the synthesis of new phenothiazine derivatives. In this work a novel and simple electrochemical methodology for the synthesis of some bis(phenylsulfonyl)-10H-phenothiazine derivatives was established. In this paper, a mechanism for PTZ oxidation in the presence of arylsulfinic acids has been proposed based on the results obtained from voltammetric and coulometric experiments as well as spectroscopic data of the products. These syntheses are performed in a simple cell by applying constant current under mild conditions and at room temperature with high atom economy. [ABSTRACT FROM AUTHOR]

Close-distance coal seams (CDCS) are widely distributed, and the layout of the upper and lower panels can be divided into "=" type and "+" type. The "+" superposition of upper and lower coal pillars in CDCS caused strong mine pressure, but there are few studies on the panel crossing residual coal pillars (RCP) when the upper and lower coal seams are "+" type layout. In view of the special spatial position ("+" type layout), this paper takes the typical panel 4-301 of a particular mine as the project indagation background and studies mining and crossing the overlying coal pillars by dint of field measurement, numerical simulation, indoor test, and engineering application. Compared with vertical stress or horizontal stress alone, the indexes of deviatoric stress and plastic zone can reflect the failure evolution of surrounding rock more comprehensively. Hence, this paper analyzes the expansion form of the plastic zone and the variation law of deviatoric stress before and after mining influence in the underlying mining roadway. The research results show that: (1) There is a sub-peak zone of deviatoric stress under the RCP. The deviatoric stress is bimodal in the range of 9 m below. After the peak value decays to 7.4 MPa, it changes to a single peak located in the area directly below the middle of the RCP. (2) The maximum plastic zones of the roof and two ribs of the roadway below the RCP are 3.4 m and 5 m, respectively. The crest value of deviatoric stress reaches 10 MPa. As the distance between the panel and the RCP decreases, the shape of the high deviatoric stress area presents the evolution law from the "ellipse" of the roof → the "crescent" of two ribs → the "cochlea" of the tips of the ribs. (3) When the mining of the underlying panel is 10 m, 0 m, or − 10 m away from the RCP (without passing through the RCP). The crest value of deviatoric stress within 5–10 m in advance of the roadway increases in turn. However, the peak value is significantly reduced when it is − 20 m away from the RCP (through the RCP). The crest value of deviatoric stress of two ribs decreases in turn along the panel rib → section coal pillar rib → solid coal rib. Based on this, the underlying 45 m of the RCP is divided into area I (10 m), area II (overlapping area 20 m), and area III (15 m) based on the degree of disturbance. And propose the technical scheme of asymmetric combined control in different zones by using asymmetric channel steel truss anchor cable for the top-ribs of areas I and III, and top-ribs asymmetric channel steel truss anchor cable + door-type support in area II. On-site project practice shows that the partitioned control technology successfully resisted the roadway instability and failure caused by the dynamic-static superimposed stress disturbance under the RCP and realized the primary support of the sectional coal roadway. The conclusion provides technical support and scheme design for the partitioning support of roadways under similar "+" type cross‐panels. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy. [ABSTRACT FROM AUTHOR]

Old urban community renovation is an important task of urban renewal in China. In order to ensure the quality and efficiency of renovation work, the government requires a method to assess the priority for old urban community renovation. This paper proposes an evaluation model from a more comprehensive perspective. It establishes the evaluation index system with CIPP model. The method of Cloud-VIKOR is selected to construct the evaluation model. Finally, selects nine case communities to verify the evaluation model. The results show that the evaluation index system covers the whole process of renovation project and the evaluation indexes proposed in the existing research. The priority decision result of 9 case communities is basically consistent with the actual renovation sequence and does not change greatly due to the fluctuation of decision-making mechanism coefficient or the evaluation index weight. This evaluation model can help decision-makers diagnose and optimize the renovation project. [ABSTRACT FROM AUTHOR]

In the realm of air combat, autonomous decision-making in regard to Unmanned Aerial Vehicle (UAV) has emerged as a critical force. However, prevailing autonomous decision-making algorithms in this domain predominantly rely on rule-based methods, proving challenging to design and implement optimal solutions in complex multi-UAV combat environments. This paper proposes a novel approach to multi-UAV air combat decision-making utilizing hierarchical reinforcement learning. First, a hierarchical decision-making network is designed based on tactical action types to streamline the complexity of the maneuver decision-making space. Second, the high-quality combat experience gained from training is decomposed, with the aim of augmenting the quantity of valuable experiences and alleviating the intricacies of strategy learning. Finally, the performance of the algorithm is validated using the advanced UAV simulation platform JSBSim. Through comparisons with various baseline algorithms, our experiments demonstrate the superior performance of the proposed method in both even and disadvantaged air combat environments. [ABSTRACT FROM AUTHOR]

The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain. [ABSTRACT FROM AUTHOR]

The Helmholtz equation plays a crucial role in the study of wave propagation, underwater acoustics, and the behavior of waves in the ocean environment. The Helmholtz equation is also used to describe propagation through ocean waves, such as sound waves or electromagnetic waves. This paper presents the Elzaki transform residual power series method (E T-RPSM) for the analytical treatment of fractional-order Helmholtz equation. To develop this scheme, we combine Elzaki transform (E T) with residual power series method (RPSM). The fractional derivatives are described in Caputo sense. The E T is capable of handling the fractional order and turning the problem into a recurrence form, which is the novelty of our paper. We implement RPSM in such a way that this recurrence relation generates the results in the form of an iterative series. Two numerical applications are considered to demonstrate the efficiency and authenticity of this scheme. The obtained series are determined very quickly and converge to the exact solution only after a few iterations. Graphical plots and absolute error are shown to observe the authenticity of this suggested approach. [ABSTRACT FROM AUTHOR]

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%. [ABSTRACT FROM AUTHOR]

This paper is an attempt to provide a dual-source inverter, an intelligent inverter topology that links two isolated DC sources to a single three-phase output through single-stage conversion. The converter is designed to be utilized in hybrid photovoltaic fuel cell systems, among other renewable energy applications. The proposed dual-source inverter employs a single DC-AC converter, as opposed to conventional dual-source hybrid inverters which make use of several input DC-DC modules to obtain the voltage formed across the inverter's input DC-link. In the proposed topology, the semiconductor count is low, which leads to improved efficiency, cost, complexity, and reliability. The proposed topology makes use of two impedance networks connected by transformers, diodes, and capacitors. The regulation of the electrical power generated by primary sources and the independence of the converter on key factors like voltage and frequency are essential parameters in multi-input converters. This feature becomes highly prominent when the control algorithm is implemented by conventional processors. Viewed from this perspective, the control method described in this paper is worthy of consideration. The research work describes a 220-W/50 Hz prototype that employs Simple Boost-SPWM. Experimental results verify the analyses and corroborate the satisfactory performance of the suggested converter. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) processes are known as revolutionary manufacturing processes that fabricate a part using a 3D model layer upon layer. These techniques gained more attention from various industries due to their advantages like low waste material. Also, these processes can produce any part with high degrees of complexity in a short period of time. The Fused Deposition Modeling (FDM) process is a material extrusion-based technique which works by extruding a fine molten polymeric filament through a heated nozzle on the heated platform named printer bed. In this method, some important manufacturing parameters play a crucial role in controlling the mechanical properties and quality of the final fabricated part. However, all printed specimens through the FDM process should be tested based on the standards under some critical circumstances. Thus, in the current research paper, five and three test speeds are considered in tensile and fracture testing procedures, respectively to evaluate how these speeds can affect the mechanical and mode I fracture properties. Also, as the FDM specimens present elastic–plastic behavior, the critical value of J-integral is assumed as a fracture assessment and calculated from the finite element analysis. Among the mechanical properties, ultimate tensile strength is affected significantly by the test speed. For instance, the ultimate tensile strength of FDM specimens is 39.02, 38.58, 42.33, 48.09, and 52.11 for test speeds of 2, 4, 6, 8, and 10 mm/min, respectively. But vice-versa results are detected for the mode I fracture behavior and corresponding values of J for the FDM-PLA specimens. Finally, experimental and numerical results together with comprehensive discussions about the considered speeds and obtained results are reported. [ABSTRACT FROM AUTHOR]

Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh–Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection. [ABSTRACT FROM AUTHOR]

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level. [ABSTRACT FROM AUTHOR]

The paramount concern of highly accurate energy-efficient computing in machines with significant cognitive capabilities aims to enhance the accuracy and efficiency of bio-inspired Spiking Neural Networks (SNNs). This paper addresses this main objective by introducing a novel spatial power spike-timing-dependent plasticity (Spatial-Pow-STDP) learning rule as a digital block with high accuracy in a bio-inspired SNN model. Motivated by the demand for precise and accelerated computation that reduces high-cost resources in neural network applications, this paper presents a methodology based on COordinate Rotation DIgital Computer (CORDIC) definitions. The proposed designs of CORDIC algorithms for exponential (Exp CORDIC), natural logarithm (Ln CORDIC), and arbitrary power function (Pow CORDIC) are meticulously detailed and evaluated to ensure optimal acceleration and accuracy, which respectively show average errors near 10–9, 10–6, and 10–5 with 4, 4, and 6 iterations. The engineered architectures for the Exp, Ln, and Pow CORDIC implementations are illustrated and assessed, showcasing the efficiency achieved through high frequency, leading to the introduction of a Spatial-Pow-STDP learning block design based on Pow CORDIC that facilitates efficient and accurate hardware computation with 6.93 × 10–3 average error with 9 iterations. The proposed learning mechanism integrates this structure into a large-scale spatiotemporal SNN consisting of three layers with reduced hyper-parameters, enabling unsupervised training in an event-based paradigm using excitatory and inhibitory synapses. As a result, the application of the developed methodology and equations in the computational SNN model for image classification reveals superior accuracy and convergence speed compared to existing spiking networks by achieving up to 97.5%, 97.6%, 93.4%, and 93% accuracy, respectively, when trained on the MNIST, EMNIST digits, EMNIST letters, and CIFAR10 datasets with 6, 2, 2, and 6 training epochs. [ABSTRACT FROM AUTHOR]

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck's component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck's engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system. [ABSTRACT FROM AUTHOR]

Standard laparoscopes, which are widely used in minimally invasive surgery, have significant handling limitations due to their rigid design. This paper presents an approach for a bending section for laparoscopes based on a standard semi-finished tube made of Nitinol with laser-cut flexure hinges. Flexure hinges simply created from a semi-finished product are a key element for realizing low-cost compliant structures with minimal design space. Superelastic materials such as Nitinol allow the reversible strain required for this purpose while maintaining sufficient strength in abuse load cases. This paper focuses on the development of a bending section for single use laparoscopic devices (OD 10 mm) with a bending angle of 100°, which enables the application of 100 µm diameter Nitinol actuator wires. For this purpose, constructive measures to realise a required bending curvature and Finite Element Analysis for determining the strain distribution in the flexural region are applied and described for the design of the flexure hinges. In parallel, the influence of the laser-based manufacturing process on the microstructure is investigated and evaluated using micrographs. The deformation behavior of the bending section is experimentally determined using Digital Image Correlation. The required actuation forces and the failure load of the monolithic bending section is measured and compared to a state of the art riveted bending section made of stainless steel. With the developed monolothic bending section the actuation force could be reduced by 50% and the available inner diameter could be increased by 10% while avoiding the need of any assembly step. [ABSTRACT FROM AUTHOR]

This paper introduces the design of a reconfigurable transmitarray operating within the D-band frequency range (110–170 GHz). The transmitarray unit cell is composed of three metal layers and two quartz dielectric substrates. It achieves a 1-bit phase shift resolution through the alternating states of two innovative switches integrated into the active transmitting patch of the unit cell. To address the challenge of miniaturization in the D-band, compact switches compatible with the proposed unit cell dimensions are introduced. These switches are constructed using phase change materials (PCM) that change between amorphous and crystalline states when exposed to heat. The paper includes a full-wave simulation of the unit cell, demonstrating an insertion loss below 1.5 dB across a wide frequency band of 27%. Additionally, a 10 × 10 elements transmitarray is synthesized using a numerical tool and its theoretical results are compared to full-wave electromagnetic simulations for validation purposes. The results indicate that by incorporating the proposed switches into the unit cell, the transmitarray achieves promising reconfiguration capabilities within the D-band. Moreover, the paper presents the architecture of a command line designed to bias the PCM switches. Notably, this command line represents a novel approach, as it enables individual biasing of each PCM switch using direct current (DC). The influence of these command lines on the transmitarray’s performance is thoroughly investigated. Although there is a compromise in the 1-dB gain bandwidth, the overall behavior of the transmitarray remains encouraging. [ABSTRACT FROM AUTHOR]

Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges. [ABSTRACT FROM AUTHOR]

The TRIZ theory was used to accurately discover the problems to be solved in the design of roadway surrounding rock control technology. This paper tried to solve the complex issue of surrounding rock control in deep roadways from a new perspective. Based on the functional component analysis and causal axis analysis of the problem's primary reason, simultaneously, the surrounding rock control technology was optimized through technical contradiction analysis, physical contradiction analysis, and substance and field model analysis. As a result, a fully enclosed wire-shell support technology was proposed. Finally, taking the typical soft rock roadway engineering of Pansan Coal Mine in Huainan Mining Area, Anhui Province, China, as the engineering background, the engineering application and effect evaluation were completed. This paper provides a reference for controlling the instability of deep soft rock roadways in coal mines. A new idea of optimizing roadway support engineering based on TRIZ theory was proposed. [ABSTRACT FROM AUTHOR]

In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers' decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions. [ABSTRACT FROM AUTHOR]

In this paper, a new topology is introduced for capacitor-based multi-level inverters. The proposed topology is based on combination of two Cross-Square-Switched T-Type inverters. This structure can be generalized in two modular and cascaded modes. In the cascaded mode, higher voltage levels are produced with low power switches. The main features of the proposed topology include the level generation without the utilization of the H-bridge module, the low number of switching components, a lower number of DC voltage sources, and low total blocking voltage. Besides, in the proposed topology, the number of conducting switches in the current path for each different voltage level is low, which leads to a conduction loss decrement. The loss simulations are performed, and the results are presented. A study provides a detailed comparison of the proposed topology in terms of various parameters. In this paper, the nearest level modulation switching, which is low-frequency switching, is utilized to generate voltage levels. To confirm the performance of the proposed topology, a simulation was performed with MATLAB/Simulink software, and a laboratory sample was implemented. Comparative results, simulation results, and implementation results indicate the appropriate performance of the proposed structure in different steady-state and dynamic conditions. [ABSTRACT FROM AUTHOR]

This paper deals with a class of nonparametric two-sample location-scale tests. The purpose of this paper is to approximate the exact p-value of the considered class under a randomized block design. The exact p-value of the considered class is approximated by the saddlepoint approximation method, also by the traditional method which is the normal approximation method. The saddlepoint approximation method is more accurate than the normal approximation method in approximating the exact p-value, and does not take a lot of time like the simulation method. This accuracy is proved by applying the mentioned methods to two real data sets and a simulation study. [ABSTRACT FROM AUTHOR]

The congruency sequence effect (CSE) is one of the most investigated effects in the cognitive control literature. The conflict monitoring theory suggests that the CSE is the result of adjustments in cognitive control based on perceived conflict. A recent paper by Zhang and colleagues, has investigated whether the manipulation of conflict level by changing distractor incompatibility in a flanker task affects the amount of adjustments in cognitive control. Their study produced mixed findings, somewhat supporting the original conflict monitoring theory, but also suggesting other explanations, such as the repetition expectancy account. We replicated the experimental design in a multisite online study (N = 347), with Hungarian, Italian, and Singaporean participants. Our results supported the prediction that changes in the level of conflict trigger conflict adaptation, revealing that increasing conflict levels induced stronger adaptive control. Bayesian hypothesis testing indicated a monotonic reduction in congruency effects as a function of previous conflict strength. This finding is in line with the extension of the traditional conflict monitoring theory, as well as other theories like affective signaling and expected value of control, implying that the relationship between conflict and interference effects is gradual, rather than a binary function. [ABSTRACT FROM AUTHOR]

In this paper, an experimental evaluation of a newly developed flat plate double pass solar air heater combined with aluminum cans for drying red pepper was presented. The proposed solar dryer system was designed, modeled, and evaluated. Solar air heater trials were carried out using the absorber's top and bottom plate and aluminum cans for red pepper drying at Bahir Dar, Ethiopia. To test the solar dryer, 100 pieces of red paper were obtained from the Bahir Dar region of Ethiopia for the purpose of experimentation. Microsoft Excel was used to perform statistical analysis of eleven mathematical models. The results show that the mixed-mode solar greenhouse dryer takes less time to dry red pepper than the open solar dryer. In the midday, the solar insolation reached 973 W/m2 and the minimum solar insolation was 220 W/m2 and air is expelled at a rate of 0.0383 kg/s. According to the experimental results, the dryers chamber temperature ranged from 30.9 to 54 °C, while the ambient temperature was between 22.6 and 28.2 °C. The mixed-mode double pass achieves up to 46% and 28% efficiency when used with aluminum can dryers and conventional open sun dryers, respectively. A drying rate of 0.0003395 kg/s was achieved for the open sun dryer system and 0.0000365 kg/s for the mixed mode solar dryer. Using mixed-mode and open-sun solar dryers, the logarithmic model was found to be most effective in explaining the red pepper behavior. Furthermore, a comparison was made between the experimental and predicted moisture ratios through the calculation of the coefficient of determination (R2), the reduced chi-square (X2), and the root mean square error (RMSE). The results show that the logarithmic model achieved the highest value of the correlation coefficient (R2), which was determined to be 0.9978 and 0.9989, while the logarithmic model achieved the lowest value of Chi-square (X2). [ABSTRACT FROM AUTHOR]

This paper aimed to develop a coefficient of variation (CV) control chart utilizing the generalized multiple dependent state (GMDS) sampling approach for CV monitoring. We conducted a comprehensive examination of this designed control chart in comparison to existing control charts based on multiple dependent state sampling (MDS) and the Shewhart-type CV control chart, with a focus on average run lengths. The results were then compared to run-rule control charts available in the existing literature. Additionally, we elucidated the implementation of the proposed control chart through concrete examples and a simulation study. The findings clearly demonstrated that the GMDS sampling control chart shows significantly superior accuracy in detecting process shifts when compared to the MDS sampling control chart. As a result, the control chart approach presented in this paper holds significant potential for applications in textile and medical industries, particularly when researchers seek to identify minor to moderate shifts in the CV, contributing to enhanced quality control and process monitoring in these domains. [ABSTRACT FROM AUTHOR]

Data centers are usually characterized by high energy loads, which raises increasing sustainability concerns in both academic and daily usage. To mitigate the uncertainty and high volatility of distributed wind energy generation, this paper proposes a hybrid energy storage allocation strategy by means of the Empirical Mode Decomposition (EMD) technique and the two-stage robust method. First, this paper conducts the evolution analyses for the over- and under-evaluated uncertainty of wind power fluctuation under different time scales. Second, we employ the EMD technique to configure a high-frequency flywheel energy storage device, realizing the wind power transformation from large fluctuations to small fluctuations and the convergence of the wind power fluctuation curves in minute- and hour levels. Finally, based on the hour-level wind energy stable power curves, we carry out two-stage robust planning for the equipment capacity of low-frequency cold storage tanks and lithium bromide chillers. The case study on a data center microgrid in northeastern China confirms the effectiveness of our proposed strategy. [ABSTRACT FROM AUTHOR]

Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on parallel data. A wide range of methods have been proposed in the literature to address this complex problem. This paper presents an in-depth investigation of semi-supervised neural machine translation specifically focusing on translating Arabic dialects, particularly Egyptian, to Modern Standard Arabic. The study employs two distinct datasets: one parallel dataset containing aligned sentences in both dialects, and a monolingual dataset where the source dialect is not directly connected to the target language in the training data. Three different translation systems are explored in this study. The first is an attention-based sequence-to-sequence model that benefits from the shared vocabulary between the Egyptian dialect and Modern Arabic to learn word embeddings. The second is an unsupervised transformer model that depends solely on monolingual data, without any parallel data. The third system starts with the parallel dataset for an initial supervised learning phase and then incorporates the monolingual data during the training process. [ABSTRACT FROM AUTHOR]

To investigate the influence of a geogrid-reinforced tailings dam on the seepage stability of the dam body, this paper was based on the field test of a reinforced tailings accumulation dam. The study utilized the finite element strength reduction method to simulate the stability of the main dam of the Fengshuigou tailings reservoir under different seepage conditions using ABAQUS software. Additionally, the paper discussed the impact of conventional heightening, dry beach length, and geogrid reinforcement on the position and safety factor of the saturation line of the dam body. The results showed that when the dam body was raised, the saturation line rose by 2.8–5.3 m, resulting in a decrease in the safety factor. The geogrid effectively reduced the height of the saturation line in the tailings dam. In comparison to the unreinforced condition (dam heightening), the saturation line of the tailings dam decreased by 0.9–2.8 m under the local reinforcement condition and by 3.2–12.5 m under the overall reinforcement condition. The geogrid significantly improved the stability of the tailings dam. Furthermore, under the local reinforcement condition, the safety factor of the dam increased by 3.8–5.5%, and under the overall reinforcement condition, it increased by 35.9–42.9%, when compared to the unreinforced condition. Increasing the dry beach length improved the stability of the tailings dam, and under normal working conditions, the safety of the tailings dam was much higher than under the minimum dry beach condition. These results served as a reference for the design of the dam and the new tailings reservoir, laying a foundation for the sustainable development and environmental protection of the tailings pond. [ABSTRACT FROM AUTHOR]

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks. [ABSTRACT FROM AUTHOR]

Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values. [ABSTRACT FROM AUTHOR]

Coal mining machine drums are prone to damage and malfunction under extremely complex working conditions, which seriously affects the efficiency and safety of coal production. In this paper, based on the theory of coal rock cutting and virtual simulation technology, finite element models of drum cutting coal rock were established and then verified by physical experiments. Through simulation analysis, the dynamic reliability of the drum was studied from three aspects: load, stress and wear, and a mathematical model of drum load was established with respect to the traction speed and drum rotation speed; based on the orthogonal test, the optimal working parameters to improve the wear resistance of the drum were derived. The results of the study found that when the traction speed increases, the load on the drum increases, and when the drum rotation speed increases, the load on the drum decreases; when the traction speed is increased from 2 to 6 m/min, the stress on the pick body under different rotation speeds increases to different degrees, with an average increase rate of 27.394%; when the drum rotation speed is 90 r/min, the traction speed is 3 m/min, and the coal loading mode is projectile loading, the wear depth of the picks and spiral blades is relatively small. The research method and results of this paper can provide a reference for the selection of the drum working parameters. [ABSTRACT FROM AUTHOR]

Probiotics have gained a significant attention as a promising way to improve gut health and overall well-being. The increasing recognition of the potential health advantages associated with functional food products, leading to a specific emphasis on co-encapsulating probiotic bacteria and bioactive compounds within a unified matrix. To further explore this concept, a meta-analysis was performed to assess the effects of probiotics encapsulated in nanoparticles. A comprehensive meta-analysis was conducted, encompassing 10 papers published from 2017 to 2022, focusing on the encapsulation of probiotics within nanoparticles and their viability in various gastrointestinal conditions. The selection of these papers was based on their direct relevance to the research topic. Random-effect models were used to aggregate study-specific risk estimates. In the majority of studies, it was observed that nano-encapsulated nanoparticles showed improved viability over time compared to their free state counterparts. At various time intervals, the odds ratios (OR) with 95% confidence intervals (CI) were estimated using fixed and random effect models. At 0 min, the OR (95%CI) was 2.79 (2.79; 2.80) and 2.38 (2.14; 2.64) for. At 30 and 60 min observation was at similar rate of 2.23 (2.23; 2.24) and 2.05 (1.73; 2.43). However, at 90 min it was 1.39 (1.39; 1.39) and 1.66 (1.29; 2.14) and at 120 min 2.41 (2.41; 2.42) and 2.03 (1.63; 2.52). Overall evaluation of encapsulation revealed an improvement in probiotic bacterial viability in simulated the gastrointestinal environments. [ABSTRACT FROM AUTHOR]