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Developing cost-effective and stable materials for the electrocatalysis of hydrogen evolution reaction (HER) remains challenging. In this study, efficient catalysts for HER were synthesized by integrating the cobalt and molybdenum oxides via electrodeposition, followed by subsequent sulfurization of the as-prepared oxides using chemical vapor deposition (CVD). This methodology allowed the incorporation of both cobalt and molybdenum components into the catalyst in a single step. The as-synthesized CoSx@MoSx/MoOx-based catalysts exhibited excellent hydrogen production performance in acidic media owing to the presence of Co-S and Mo-S bonds in the hybrid structure. Particularly, CoSx@MoSx/MoOx(90@360) and MoSx@CoOx(180@180) displayed the best HER performances with low overpotentials of 80 mV and 150 mV, respectively. The catalysts were highly stable, with their stability preserved for over 1000 cycles with marginal reduction in overall efficiency. Therefore, these findings suggest the potential of CoSx@MoSx/MoOx and MoSx@CoOx composites as ideal candidates for developing low-cost catalysts for electrochemical hydrogen production. [ABSTRACT FROM AUTHOR]

The aim of this work is a synthesis of suitable hydrogel to produce slow‐release fertilizer using recycled cellulose which is obtained from waste paper. For this purpose, initially, we extracted alpha cellulose from waste paper and modified it to obtain carboxymethyl cellulose (CMC). Then, the CMC was converted to a suitable hydrogel through in situ graft copolymerization of acrylic acid and vinyl acetate in the presence of methylene bisacrylamide as a crosslinker. The various factors that affect hydrogel synthesis, such as the amounts of CMC, monomers, initiator, and crosslinker, were evaluated. In the optimized formulation, the weight ratio of monomers to CMC is 7, the molar ratio of monomers to each other is 1, and the crosslinker is used as 3 molar percent of monomers. The products were characterized using Fourier transform infrared, thermal gravimetric analysis, and scanning electron microscope analyses. The swelling behavior of the synthesized hydrogels was evaluated in different environments, such as distilled water, tap water, salt water, and different pH levels. The swelling ratio increases with an increase in pH level. Between the synthesized hydrogels, the best one was selected for slow‐release fertilizer production and loaded with 20‐20‐20 fertilizer (NPK), and the release behavior was evaluated. In an alkaline pH, there was a long time for NPK release within a slow‐release medium and even after 361 h, the release process was continued. Also, the performance of the fertilizer‐loaded hydrogel in soil using water holding capacity and water retention ratio tests were evaluated. [ABSTRACT FROM AUTHOR]

Integrating more active components into a catalyst material could facilitate the development of multifunctional electrocatalysts for energy conversion and storage applications. In this study, we developed a multifunctional electrocatalyst, namely, Pd alloyed with Co-Fe deposited on N-doped mesoporous carbon derived from tissue paper (Pd@Co-Fe/N-TDC). The synergism in Pd@Co-Fe/N-TDC, stemming from the interatomic alloy between Pd and Co-Fe, N-doped mesoporous carbon with defective surfaces, distribution of polyhedral Pd nanoparticles, and strong metal-support interfacial interaction, resulted in significantly high electrocatalytic performance for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Pd@Co-Fe/N-TDC was found to be an efficient bifunctional oxygen electrocatalyst, and this was evidenced by a high onset potential (1.01 V) and kinetic current density (2.6 mA/cm2) for the ORR and by a low overpotential (296 mV) and a low Tafel slope value (38 mV/dec) for the OER, along with a small Δ E of 736 mV. The catalyst also exhibited high durability for both ORR and OER, even after 10000 and 5000 cycles, respectively. Theoretical assessment provides an insight into the synergism of active metal sites in Pd@Co-Fe/N-TDC, which showed its potential for use as a non-Pt electrocatalyst for energy applications. [ABSTRACT FROM AUTHOR]

The clarity of radio frequency fingerprint (RFF) features is an important factor affecting the recognition effect of the classifier. Recognition of RFF signals is essential in radio frequency (RF) perception. However, the RF signal is easily affected by interference and noise during transmission, which makes the distribution boundary of the characteristics of the RFF unclear. It then affects the recognition effect of the classifier. The common preprocessing algorithms, such as denoising and channel compensation, can improve the performance of communication demodulation but seriously affect the unique signal distortion of RFF characteristics. The spectral overlap between the useful signal and the noise is serious when the spectrum is broad, so it is difficult to separate the signal from the noise by traditional methods effectively. Therefore, this paper proposes a data enhancement technology for RF signals named random railings to increase the diversity of input samples and improve the overall performance of the model. At the same time, this paper also proposes a bilateral‐branch signal feature enhancement network. The dual branch structure of BBSEN extracts the features of the enhanced time‐domain signal and the spectrum obtained by the short‐time Fourier transform (STFT) of the signal and then achieves feature fusion through the improved self‐attention mechanism to obtain the RFF features, which are more suitable for the interference environment. Experiments show that the algorithm proposed in this paper can effectively improve the antijamming environment of RF signals and even can be used after preprocessing algorithms such as denoising and further improve the recognition rate of high signal‐to‐noise ratio signals. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device's accuracy. [ABSTRACT FROM AUTHOR]

The increasing environmental consideration and the growth of instability of the energy market call for methods that can process the organic fraction of municipal solid waste (OFMSW) into fuel instead of disposal. Due to the pyrolysis and gasification that are efficient procedures to achieve valuable products, gasification of OFMSW with different particle sizes and compositions was carried out in the presence of Ni and Ni-Ce-loaded Y-zeolite in a multizone tubular kiln reactor. During the experiments, 500°C was applied in the first reactor zone, while the 2nd zone was at an elevated temperature of 600°C or 900°C in the presence of steam. The combined pyrolysis and gasification experiments were also carried out without a catalyst with the same operating conditions. The feedstock was collected from the organic fraction of a Hungarian mechanical-biological treatment (MBT) plant and was separated into four different fractions based on particle size: <1 cm dry biomass and fine particles, 1-2 cm paper-rich, 2-6 cm paper and plastic-rich feedstocks, and <6 cm mixture of these fractions. During the experimental work, product yields; gaseous product composition; the ratio of H2/CO, CO2/CO, and CH4/CO; and lower heating value were determined in the function of feedstock composition, the applied temperature, and catalysts. It was found that the hydrogen content and H2/CO ratio of gaseous products were increased due to catalyst application and temperature elevation in the 2nd reactor zone. The addition of Ce to the Ni/Y-zeolite catalyst was advantageous in the case of hydrogen formation at a lower temperature (600°C). The hydrogen and methane content of products obtained from the catalytic pyrolysis-gasification of paper and plastic-rich OFMSWs on elevated temperatures were higher, which increased the lower heating value of those products. Based on the elemental analysis of the obtained solid residues, it was found that paper/plastic-rich feedstock released hydrogen and carbon with a higher extent. 1-2 cm feedstock-related solid residues had the highest H/C ratio which caused a 12.5-12.8 MJ/kg gross heating value. As a result, combined pyrolysis and gasification appear to be an efficient method to attain valuable outputs from OFMSW not only in gas but also in solid products. [ABSTRACT FROM AUTHOR]

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to examine the characteristics of scientific production related to the use of filtered water in the field of dentistry. Material and Methods. A quantitative and descriptive observational study was carried out with a scientometric approach. Data were collected from the Web of Science (WOS) database during the period January 1991 to December 2023. A search strategy incorporating a combination of MeSH terms, including terms and thesauri related to "filtered water" and "dentistry", was used. R Studio version 4.3.2 and CiteSpace 6.2.R7 were used for data analysis. Results. Over the 32-year study period, 227 scholarly papers from 134 different sources were reviewed. The literature in this field has shown an annual growth rate of 10.44%. During the year 2010, a steady movement in the number of publications and authors was observed, with considerable collaborative interaction. In the year 2020, a large interaction between publications and their citations was found. The "Citation Burst" graph identified three references that have experienced the largest "burst" of citations in an evaluated period. Lotka's law described the productivity of authors, finding that most authors have published only one paper, while a smaller number of authors have published two papers. Most authors contributed a small number of articles, while a few authors contributed a large amount of the existing literature. Conclusion. A comprehensive overview of the scientific production related to the use of filtered water in dentistry over a span of 32 years is provided. The results highlight the growing interdisciplinarity and international collaboration in this field. Finally, the importance of filtered water in dentistry and its growing relevance in the scientific literature are emphasized. [ABSTRACT FROM AUTHOR]

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real‐time data anomaly detection system for small hydropower clusters (RDADS‐SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real‐time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real‐time data anomaly detection algorithm for small hydropower stations integrated with the Z‐score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived‐based hot‐standby RDADS‐SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision‐making. [ABSTRACT FROM AUTHOR]

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Aim. To systematically map and identify key knowledge on organizational support for nurses' career planning and development. Design. Scoping review. Methods. Systematic electronic searches were carried out with the CINAHL, PubMed, Scopus, and Web of Science databases in May 2022. The searches were limited to scientific, peer-review papers that were published in English from January 2012 to May 2022. Data were extracted and synthetized and are presented in tables and text. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results. We identified 1,400 papers and 28 met the inclusion criteria. Organizations recognized nurses' career planning and development in relation to the individual's professional development and the organization's need to promote high-quality services and workforce engagement. The organizational support included strategic work to ensure there were adequate resources and purposeful vacancies and a structured framework based on objective qualification criteria and equal assessment. Organizations focused on sharing knowledge, structured career planning, and interpersonal support. Support within the nursing profession and multilayered interprofessional collaboration were also important. Conclusion. Nurses' career planning and development was linked to their personal development and the organization's aims and required support from both fellow nurses and other professionals. Implications for the Nursing Management. Identifying the organizational structures and methods that are needed to support nurses' career planning and development can help nursing management to evaluate and develop strategies that improve the attractiveness of a nursing career and nurses' engagement. [ABSTRACT FROM AUTHOR]

Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport. [ABSTRACT FROM AUTHOR]

Modelica is a simulation language that is able to model systems from many different fields of scientific and engineering contexts, which can coexist in individual models. Modelica Standard Library is an open‐source library that contains many ready‐to‐use open‐source models built in Modelica language but does not have any code to determine transmission line parameters from its geometry, thus limiting its usability for power system studies. This paper first describes important issues arising when modelling power transmission lines, then presents how they can be solved using Modelica code. Readers of this paper will, therefore, be able to model power lines from the geometry of conductors using any of the existing (some freely available) Modelica‐capable simulation tools. [ABSTRACT FROM AUTHOR]

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study is to explore the scientific literature between ultraprocessed foods (UPFs) and cancer using a scientometric mapping. Materials and Methods: A Scopus search was conducted on February 4, 2024, limited to papers published between 2018 and 2023. We found 662 articles, 189 reviews, 68 book chapters, 13 conference papers, and 9 notes. The whole analysis included the evaluation of scholarly output by country/region, the number of scholarly papers produced (scholarly output), the number of views (view count), the field‐weighted citation impact (FWCI). Results: In the analysis conducted for the period 2018–2023, a dataset was examined where the annual growth rate was 5.96%, indicating a sustained expansion of the literature. The average number of citations per paper was 18.56, underlining the impact and relevance of the publications. Sixty‐six single‐authored papers were identified, and international collaborations accounted for 27.23% of the collaborative efforts. The most prominent authors were Inge Huybrechts, Marc J.R. Gunter, and Edward Luciano Giovannucci. In terms of impact and visibility, Harvard University leads with 52 contributions and a field‐weighted impact of 3.39. Conclusions: The literature in the field of UPFs and cancer has experienced a sustained expansion. The scientometric indicators reveal a high activity of recent academic contributions with significant impact. [ABSTRACT FROM AUTHOR]

The shift towards green aviation necessitates the adoption of all‐electric aircraft, marking an essential trend in the aviation industry. Distributed propulsion systems enhance aircraft flexibility and reliability, synchronously reducing performance demands and costs of single propulsion units compared to centralized propulsion system. The high reliability and lightweight characteristics of brushless DC motors have made them increasingly popular in distributed propulsion systems. Despite its advantage which helps to build efficient aircraft aerodynamic layout, there are more cables in the distributed propulsion system, which accounts for most of the weight of the aircraft. To address this, all‐electric aircraft utilizes high‐voltage power supply to reduce the current, so it uses SiC MOSFETs as power devices to improve its efficiency and heat dissipation. The speed controller in all‐electric aircraft determines the efficiency and reliability of the propulsion unit. However, the traditional high‐voltage speed controller poses challenges for aircraft maintenance because of high cost. Therefore, this paper emphasizes the significance of designing a low‐cost, high‐performance speed controller for all‐electric aircraft. Challenges such as circuit interference, battery voltage fluctuation, and sampling noise affecting the high‐voltage controller during operation are solved, respectively. First, to address the likelihood of mutual interference in the bridge arm, a self‐bootstrapping voltage reduction drive mode is designed to turn off SiC MOSFET and enhance circuit anti‐interference. Second, to solve the challenge of wide battery voltage fluctuation range and inconsistent controller output, an adaptive pulse width modulation (PWM) regulation mode for battery voltage is proposed, ensuring stable controller output. Finally, to counteract the impact of the comparator threshold on zero‐crossing sampling, an electrical angle delay method based on delay compensation is designed to improve the efficiency of the brushless DC motor during operation. Experimental results show that the three solutions proposed in this paper are helpful to improve the anti‐interference, stability, and efficiency of the controller in the high‐voltage distributed electric propulsion system and contribute to reduce the cost and weight in the propulsion unit. [ABSTRACT FROM AUTHOR]

Malicious encrypted traffic detection is a critical component of network security management. Previous detection methods can be categorized into two classes as follows: one is to use the feature engineering method to construct traffic features for classification and the other is to use the end‐to‐end method that directly inputs the original traffic to obtain traffic features for classification. Both of the abovementioned two methods have the problem that the obtained features cannot fully characterize the traffic. To this end, this paper proposes a hierarchical multimodal deep learning model (HMMED) for malicious encrypted traffic detection. This model adopts the abovementioned two feature generation methods to learn the features of payload and header, respectively, then fuses the features to get the final traffic features, and finally inputs the final traffic features into the softmax classifier for classification. In addition, since traditional deep learning is highly dependent on the training set size and data distribution, resulting in a model that is not very generalizable and difficult to adapt to unseen encrypted traffic, the model proposed in this paper uses a large amount of unlabeled encrypted traffic in the pretraining layer to pretrain a submodel used to obtain a generic packet payload representation. The test results on the USTC‐TFC2016 dataset show that the proposed model can effectively solve the problem of insufficient feature extraction of traditional detection methods and improve the ACC of malicious encrypted traffic detection. [ABSTRACT FROM AUTHOR]

Today's societies need a sustainable supply of energy more than ever, whereas unseen events challenge the electric power supply. The coordinated operation of a power‐gas‐transportation system can decrease the effects of such faults in the distribution level. This paper proposes a model predictive control approach for dynamic load restoration in an integrated energy system. The coordination of electric and gas networks besides the mobile energy storage (MES) units provides a novel resilient alternative to serve active and reactive loads. First, the location and time of drastic events are identified as the initialization phase in this paper using a vulnerability analysis by a master–slave problem to find the weak points of the electric grid. Then, a rolling horizon model predictive control‐based approach is proposed to make corrective decisions to serve loads. Meanwhile, a new linearization approach for the Weymouth equation has been proposed based on the binary expansion method. The proposed linear resiliency‐oriented scheduling problem is tested on IEEE 33‐bus and IEEE 69‐bus integrated with a seven‐node gas distribution grid and a six‐station transportation railway. The analysis revealed that the coordination between electric and gas resources minimizes the not‐supplied load; MES units act as certain emergency tools to serve the critical loads. [ABSTRACT FROM AUTHOR]

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low‐frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low‐frequency oscillation phenomena. To address matter that the traditional S‐transform time‐frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S‐transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time‐frequency characterization. Secondly, the PMU data waveform with implicit low‐frequency oscillation information is converted into a two‐dimensional time‐frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low‐frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time‐frequency resolution of PMU active power data signal and has effective noise immunity. [ABSTRACT FROM AUTHOR]

In this paper, we substituted the ordinary derivative with the covariant derivative in simple translation, that is built by applying the ordinary derivative and we investigate the new display of the simple translation. We have introduced a new translation operator in which its generator is exactly covariant derivative. To know the translation of a quantum particle around a space where the effect of gravity is significant, such as a black hole, it is enough to put the metric of that space in the translation derived in this paper. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain. [ABSTRACT FROM AUTHOR]

Energy consumption has risen much more while the economy has expanded so quickly. As a result, significant environmental contamination is created, which causes public worry. Under this background, enhancing energy efficiency and lowering pollution is particularly crucial for the fulfillment of sustainable economic development. Efficiency evaluation for energy and environment is an effective measure to achieve both energy saving and emission reduction as well as sustainable social development. In this paper, energy and environmental efficiency consists of energy utilization efficiency and pollution treatment efficiency. An improved two‐stage DEA model is proposed, which takes into account how stochastic and environmental factors affect decision‐making units (DMUs). Finally, the author analyses the panel data of 15 cities in Southwest China from 2010 to 2018, which proves the feasibility and effectiveness of this study. The results show Southwest China's poor energy and environmental efficiency as well as the stark differences among its cities. The scientific value revealed by this paper is as follows: (1) for the study of energy and environmental efficiency, this paper is more targeted at the differences between cities and (2) using an improved two‐stage DEA model to consider the effects of stochastic factors on DMUs. [ABSTRACT FROM AUTHOR]

Traffic accident data‐based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on traffic drivers' measurement of penalty, the relationship among drivers, cars, and dates, etc. How to use multisource data based on deep learning, especially based on the Chinese recent unstructured data and structured data to establish accident portrait for individual and groups of accident drivers, still lacks. Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. This paper proposes a multisource accident datasets‐driven deep learning‐based traffic accident portrait method. Our multisource accident datasets‐driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature‐driven bidirectional long short‐term memory (Bi‐LSTM) and accident feature‐driven bidirectional conditional random field (Bi‐CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature‐driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. This paper uses the NER dataset of the People's Daily and a manually labeled dataset to test the Bi‐LSTM + Bi‐CRF model, and it acquires the highest scores of 0.9562 and 0.9779 compared with several other models. This paper uses the DuIE dataset and a manually labeled dataset to test the PCNN model, and it acquires the highest scores of 0.9674 and 0.9108 compared with several other models. Experiments verified our model's merits than other models in regards to accident label extraction, accident identity identification, and accident relationship extraction. [ABSTRACT FROM AUTHOR]

Combining the super‐efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super‐efficiency model and the truck‐ restricted conditions, using the super‐efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super‐efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super‐efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency. [ABSTRACT FROM AUTHOR]

As a critical component of the energy sector, the high‐temperature gas‐cooled reactor (HTR) system plays an important role on the road to carbon neutrality. As a flagship of the safety analysis codes, the HTR code package (HCP) has been developed to provide a comprehensive modeling and simulation platform of the HTR system under operational and accidental conditions, especially for pebble bed reactors. A variety of individual legacy HTR computer codes were integrated into a consistent code package using flexible and efficient programing techniques and standards, and the know‐how gained over decades in HTR safety studies was preserved. This paper gives a state‐of‐the‐art overview in HTR studies and presents the new flexible curtain‐based control rod system that has been recently implemented. This method involves flexible manipulation of isotope concentrations and utilizes C++ object‐oriented programing principles while incorporating the legacy codes. Comparative analyses with established codes such as Serpent and ATHLET underscore the precision and reliability of the HCP, thereby enhancing its applicability in HTR design and safety evaluations. Finally, the paper outlines prospective avenues for further advancing the HCP, underscoring its evolving role in shaping the future of HTR development and analysis. [ABSTRACT FROM AUTHOR]

Review articles present comprehensive overview of relevant literature on specific themes and synthesise the studies related to these themes, with the aim of strengthening the foundation of knowledge and facilitating theory development. The significance of review articles in science is immeasurable as both students and researchers rely on these articles as the starting point for their research. Interestingly, many postgraduate students are expected to write review articles for journal publications as a way of demonstrating their ability to contribute to new knowledge in their respective fields. However, there is no comprehensive instructional framework to guide them on how to analyse and synthesise the literature in their niches into publishable review articles. The dearth of ample guidance or explicit training results in students having to learn all by themselves, usually by trial and error, which often leads to high rejection rates from publishing houses. Therefore, this article seeks to identify these challenges from a beginner's perspective and strives to plug the identified gaps and discrepancies. Thus, the purpose of this paper is to serve as a systematic guide for emerging scientists and to summarise the most important information on how to write and structure a publishable review article. [ABSTRACT FROM AUTHOR]

Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper's scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy. [ABSTRACT FROM AUTHOR]

Background. Lactobacillus acidophilus is lactic acid bacteria that produce bacteriocins. Bacteriocins are antimicrobial peptides or proteins that exhibit activity against closely related bacteria. The aim of this study was to determine the effect of L. acidophilus ATCC 4356 bacteriocin against Staphylococcus aureus. Material and Methods. We used four different phenotypic methods for antimicrobial activities against two standard strains: methicillin-resistant S. aureus (MRSA) ATCC 33591 and methicillin-susceptible S. aureus (MSSA) ATCC 25923. The methods were (1) agar well diffusion, (2) overlay soft agar, (3) paper disk, and (4) modification of punch hole. The ammonium sulfate method was used to concentrate crude bacteriocin, and ultrafiltration and dialysis tubes were used to remove ammonium sulfate from the bacteriocins. Each method was repeated in triplicate. Result. L. acidophilus ATCC 4356 showed antimicrobial activity against both MRSA and MSSA standard strains only by the overlay soft agar method and not by the agar well diffusion, punch hole modification, and paper disk methods. No antimicrobial effects were observed in crude bacteriocins concentrated. Conclusion. The growth inhibition of S. aureus in overlay soft agar method may be due to the production of bacteriocin-like substances. The overlay soft agar method is a qualitative test, so there is a need for further study to optimize the conditions for the production of bacteriocin-like substances in the culture supernatant and precise comparison between the inhibitory activity and pheromone secretion of different strains. [ABSTRACT FROM AUTHOR]

Image advertising is widely used by companies to advertise their products and increase awareness of their brands. With the constant development of image generation techniques, automatic compositing of advertisement images has also been widely studied. However, the existing algorithms cannot synthesise consistent-looking advertisement images for a given product. The key challenge is to stitch a given product into a scene that matches the style of the product while maintaining a consistent-looking. To solve this problem, this paper proposes a new two-stage automatic advertisement image generation model, called Advertisement Synthesis Network (ASNet), which explores a two-stage generation framework to synthesise consistent-looking product advertisement images. Specifically, ASNet first generates a preliminary target product scene using Pre-Synthesis and then extracts scene features using Pseudo-Target Object Encoder (PTOE) and true target features using Real Target Object Encoder (RTOE), respectively. Finally, we inject the acquired features into the pretrained diffusion model and reconstruct them in the preliminary generated target goods scene. Extensive experiments have shown that the method achieves better results in all three performance metrics related to the quality of the synthesised image compared to other methods. In addition, we have done a simple and preliminary study on the effect of synthetic advertisement images on real consumers' purchase intention and brand perception. The results of the study show that the advertisement images synthesised by the model proposed in this paper have a positive impact on consumer purchase intention and brand perception. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication. [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

As a commonly used mode of transportation in people's daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries (LIBs) have wide‐ranging applications in areas such as electric vehicles and mobile devices. Accurate estimation of the state of health (SOH) of batteries is an important aspect of battery state estimation. Battery capacity cannot be precisely measured due to negative factors such as aging effects. To address this issue, this paper proposes a LIB's SOH estimation method based on incremental energy analysis (IEA) and transformer. First, data collected during the constant‐current (CC) charging phase of the battery are used to create and analyze the IEA curve. Then, the peaks and areas of the curve are proposed as health characteristics of the LIB. Cosine similarity analysis (CSA) is employed to determine the correlation between each health characteristic and SOH, as well as the correlations between different health characteristics. Finally, an accurate estimation model of battery health was developed using the Bayesian‐transformer model by plotting the relationship between health characteristics and battery health. To validate the reliability of the model, comparisons with regression evaluation metrics of other models such as support vector regression (SVR) and recurrent neural network (RNN) are conducted under different charging rates. The conclusion is drawn that this model exhibits an R2 greater than 98%, MAE less than 0.22%, RMSE less than 0.26%, and MAPE less than 0.0026%. Its accuracy is significantly improved compared to the same type of methods, and it can be used for high accuracy estimation of SOH in LIBs. The multistep prediction of a single step adopted by the model can effectively overcome the capacity regeneration problem in the field of SOH estimation, which will inspire future experts and scholars to improve the accuracy of SOH estimation. [ABSTRACT FROM AUTHOR]

F– ions (fluoride ions) are crucial in various chemical waste and environmental safety contexts. However, excessive fluoride exposure can pose a threat to human well‐being. In this study, a simple 4‐substituted pyrene derivative known as 4‐hydroxypyrene (4-PyOH) was designed as a colorimetric probe for detecting F– through the formation of hydrogen bonds between F– and a hydroxyl group. The probe 4-PyOH exhibited exceptional sensitivity and selectivity towards F– ions and was successfully utilized as test strips for detecting F– ions in organic solvents. The detection limit reached an impressively low level of 3.06 × 10−7 M in the organic solvent. The recognition mechanism was confirmed through 1H NMR titration. [ABSTRACT FROM AUTHOR]

Solving the Diophantine equation has fascinated mathematicians from various civilizations. In this paper, we propose the resolution of quadratic Diophantine equations with integer coefficients. Our contribution consists of generalizing certain results which have already been developed in the literature. This paper also proposes the criterion on the solvability of the quadratic Diophantine equation here studied. [ABSTRACT FROM AUTHOR]

Enterprise financial risk refers to the uncertainty of financial status caused by various internal and external factors during investment, financing, operations, and other activities, which may result in losses for the enterprise. Agricultural group enterprises engage in diversified businesses and have a large number of transactions. Characteristics such as natural disasters, financing difficulties, and strong seasonality of income and expenditure make agricultural enterprises face greater financial risks. Based on the perspective of evidence fusion, this paper takes the JS Agricultural Reclamation Group as the research object, puts forward a financial risk early warning model based on evidence theory, and innovatively puts forward the basic probability assignment of evidence obtained by whitening weight function, and uses evidence theory to fuse uncertain risk information. This paper makes an empirical study of 420 subsidiaries of the group and selects 10 subsidiaries as case study objects. The results show that the model proposed in this study can improve the prediction accuracy of the group's intelligent financial decision system and the existing literature and reduce the subjective factors and information loss in the risk assessment process. In addition, this study provides the probability interval of early warning results, which can expose small probability risks and provide a more reliable basis for regulators, enterprise managers, and investors to make efficient and correct decisions. [ABSTRACT FROM AUTHOR]

This article proposes a novel Mahjong game model, LsAc ∗‐MJ, designed to address challenges posed by data scarcity, difficulty in leveraging contextual information, and the computational resource‐intensive nature of self‐play zero‐shot learning. The model is applied to Japanese Mahjong for experiments. LsAc ∗‐MJ employs long short‐term memory (LSTM) neural networks, utilizing hidden nodes to store and propagate contextual historical information, thereby enhancing decision accuracy. Additionally, the paper introduces an optimized Advantage Actor‐Critic (A2C) algorithm incorporating an experience replay mechanism to enhance the model's decision‐making capabilities and mitigate convergence difficulties arising from strong data correlations. Furthermore, the paper presents a two‐stage training approach for self‐play deep reinforcement learning models guided by expert knowledge, thereby improving training efficiency. Extensive ablation experiments and performance comparisons demonstrate that, in contrast to other typical deep reinforcement learning models on the RLcard platform, the LsAc ∗‐MJ model consumes lower computational and time resources, has higher training efficiency, faster average decision time, higher win‐rate, and stronger decision‐making ability. [ABSTRACT FROM AUTHOR]

This paper focuses on the differences in crowding perception among different types of passengers in trains, aiming to optimize passenger experience and improve the level of service of urban rail transit. Based on data from a passenger survey on the Beijing subway, this paper introduces the concept of Crowding Perception Threshold (CPT) and analyzes the principle of passenger spatiotemporal crowding. Considering factors such as gender, age, travel purpose, and standing density of passengers, the paper constructs a quantitative model of crowding perception using the ordered logit model and proposes a method for classifying the level of service accordingly. The study results indicate that the CPTs for all types of passengers range from 91.8% to 101.6%, with the females, elderly individuals, and noncommuters showing greater sensitivity to crowding. In the temporal dimension, all types of passengers have higher CPTs during peak hours than during off‐peak hours, influenced by passengers' crowding expectations. In the spatial dimension, the level of service for most types of passengers is considered crowded at standing densities of 6‐7 pax/m2 during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8 pax/m2, at which point additional passengers are not recommended. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated frequency regulation strategy for grid‐forming (GFM) type‐4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid‐connected (GC) and stand‐alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series‐connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady‐state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small‐signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a novel parameterized frequency‐domain modal parameter identification method, called direct modal variational mode decomposition (DMVMD), based on the multivariate variational mode decomposition (MVMD) framework and the principle of modal superposition. Under the constraint of normalized mode shapes, this paper theoretically derives the relationship between multivariate variational mode decomposition and the natural frequencies and mode shapes of structural systems. The aim is to extract K response modes and their corresponding mode shapes from the excited C‐dimensional vibration signals of the measured component's response. First, the measured multichannel vibration signals are decomposed into IMFs aligned with K‐order natural frequencies using multivariate variational mode decomposition (MVMD). Then, the Hilbert equations and mode shape normalization constraints are used to solve the structural natural frequencies and mode shapes. Furthermore, the proposed multimodal identification algorithm has been validated through numerical simulations and experimental examples, demonstrating its high accuracy and robustness in modal identification. Compared to the existing multimodal algorithms related to variational mode decomposition, the proposed method is more direct and elegant. This method has been successfully applied to the modal parameter identification of subway tunnel structures, enabling accurate determination of the location of tunnel damage through analysis of the identified modal parameters. [ABSTRACT FROM AUTHOR]

Background. A timely diagnosis of cancer is important for patient outcomes. The delay in the patient interval (time from symptom interpretation to seeking help) is often the longest throughout the cancer patient pathway. Factors extending this interval include vague symptom profiles increasing the difficulty of symptom appraisal and individual demographics influencing help‐seeking behaviours. An underexplored and potential source of delay in cancer diagnosis is associated with managing symptoms using self‐care activities prior to presentation to healthcare. Methods. This study aimed to characterise the use of self‐care activities in the context of managing nonspecific symptoms, prior to cancer diagnosis and their effect on the length of the patient interval. Eligible publications were identified using a rapid systematic review, and their qualitative self‐care data were extracted and analysed using thematic synthesis. Results. Forty‐five qualitative research papers between 2009 and 2024 were included in the final review. Self‐care was used as part of an iterative process, often resulting in delayed presentation to healthcare, if methods were effective in managing nonspecific symptoms. Across the literature, varying types of self‐care activities were reported across all cancers with nonspecific symptoms, including the use of over‐the‐counter or alternative medications, lifestyle changes, and watchful waiting. The individual's decision to self‐care was either prompted externally by a healthcare professional (HCP) (e.g., community pharmacists) or prompted by the individual depending on the availability of home remedies and medication. Patients used self‐care when there was a low perceived need to seek healthcare, to determine whether healthcare was required, or to avoid the use of healthcare. However, across the literature, there is limited evidence to understand the variation by cancer type, symptoms, and individual characteristics. Conclusions. The findings of this rapid review demonstrate that self‐care activities could hinder prompt help‐seeking and delay cancer diagnosis among people who are experiencing nonspecific cancer symptoms. However, more evidence is needed to understand which individual factors facilitate the adoption of self‐care behaviours over prompt help‐seeking for nonspecific cancer symptoms. [ABSTRACT FROM AUTHOR]

Telephone triage services are becoming increasingly commonplace in modern healthcare. Despite this widespread adoption, health researchers and practitioners seeking to understand evidence-based best practice face several challenges. Firstly, the few systematic reviews available yield small sample sizes, suggesting a small amount of research. Secondly, the rapid pace of development of telephone triage technologies means that there may be temporal validity issues with the available research, given some of this research is relatively old. Thirdly, researchers use different terminologies to describe telephone triage, meaning evidence may be more difficult to find than if consistent terminology was used. This bibliometric analysis therefore is aimed at providing a macroscopic overview of telephone triage, to understand the scale and scope of the available evidence (i.e., where, when, and by whom research is conducted), for interested researchers and practitioners. Additionally, it is aimed at quantifying the prevalence of terms used to describe telephone triage, to recommend consistent terminology for future use, and to improve accessibility of research. To address these aims, literature searches using three different key terms: "telephone triage," "remote triage," and "teletriage" were conducted in Scopus and PubMed. Corresponding bibliometric data was visualised and analysed using VOSviewer. This bibliometric review identified 784 papers since the term "telephone triage" first appears in 1980, confirming a paucity of literature in the field. An overview of telephone triage research up to 2021 is provided, which should serve as a useful foundation for future research and application of evidence-based practice. Gaps in telephone triage research are identified, and the use of consistent terminology is encouraged, with the aim of supporting telephone triage researchers in determining research priorities and improving the impact of future studies. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

This paper proposes an anime audio retrieval method based on audio separation and feature recognition techniques, aiming to help users conveniently locate their desired audio segments and enhance the overall user experience. Additionally, by establishing an audio fingerprint database and a corresponding copyright information management system, it becomes possible to track and manage the audio content within anime, effectively preventing piracy and unauthorized use, thereby improving the management and protection of audio resources. Traditional methods for anime audio feature recognition suffer from issues like low efficiency and subjective factors. In contrast, the proposed approach overcomes these limitations by automatically separating and extracting audio fingerprints from different audio sources within anime and creating an anime audio fingerprint database for fast retrieval. The paper utilizes an improved audio separation model based on the efficient channel attention mechanism to separate the anime audio. Subsequently, feature recognition is performed on the separated anime audio, employing a contrastive learning-based audio fingerprint retrieval method for anime audio fingerprinting. Experimental results demonstrate that the proposed algorithm effectively alleviates the issue of poor audio separation performance in anime audio, while also improving retrieval efficiency and accuracy, meeting the demands for anime audio content retrieval. [ABSTRACT FROM AUTHOR]

Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems. [ABSTRACT FROM AUTHOR]

There is a lack of study on fault detection methods of medical equipment at home and abroad. The main reason is that the research of fault features is diverse and not systematic. This paper aims to propose a fault recognition method for medical equipment combining the electrical performance parameter features with fault events. First, it treats the equipment as a whole system, setting up the analysis model. Then, we are going to analyze the signal for indicator. This paper chooses the multi-index electrical performance parameters (MEPP) method for the fault identification an indicator. It is proved that the electrical performance signal can evaluate the status of equipment. Thus, it can also be used to recognize the fault or other working statuses. Then, the features of current, voltage, and power are studied exhaustively using a mathematical model. After that, the weight of each parameter feature in any specific event will be determined according to the influence of each parameter feature on fault events. At that time, the recognition method basically realizes the correlation between multi-index features and fault events through weight. Next, the above method needs to be verified in the experiment. This paper chooses six monitors for setting the rules of normal status. The normal status is the baseline for fault identification. Then, feature intervals of other faults are established around this reference. Finally, each feature interval will be constantly adjusted to meet the preset recognition rate and updated to the rules in the subsequent measurement. In this paper, 10 monitors are selected as samples to update a set of basic fault judgment rules based on MEPP, and by adjusting the overlapping interval, the fault recognition rate reaches more than 90% in this study. To sum up, this paper uses the MEPP method to find out the relationship of features of current, voltage, and power with fault events. It will become a new direction for fault recognition studies on electrical medical equipment and other device. [ABSTRACT FROM AUTHOR]

Objective. Currently, the summaries of research on periodontal ligament stem cells (PDLSCs) are mainly reviews, and the systematic evaluation of all relevant studies is lacking. The aim of our study was to reveal the research status and developmental trends of PDLSCs using bibliometric analyses. Methods. Publications on PDLSC from 2004 to 2023 in the PubMed database were searched and then screened according to certain inclusion and exclusion criteria. Two researchers browsed the included papers and recorded information such as the research type and research model. The VOSviewer software was used to analyze the distribution of authors, journals, and institutions. The contents and directions of PDLSC research were summarized by analyzing high‐frequency keywords. The CiteSpace software was used to monitor burst words, determine hot factors, and indicate developmental trends. Results. During the past two decades, the number of studies on PDLSCs increased. China published the most related papers. The primary type of article was basic research. Among core journals, the Journal of Periodontal Research had the highest number of publications. The Fourth Military Medical University (China) was leading in the number of articles on PDLSCs. Research topics mainly included mechanism of periodontal diseases, tissue engineering and regeneration, biological characteristics of PDLSCs, and comparison with other stem cells. Infectious inflammation and mechanical stimulation were important pathological conditions and research topics. Conclusion. The research of PDLSCs is still in a rapid development stage. Our study provides new insights into the current research status and future trend in this field. [ABSTRACT FROM AUTHOR]