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China's heritage protection programme has gradually included the defence industry, and many industrial buildings related to defence have been included in the national list of cultural relics for protection. The relevant research mainly concerns the value of these heritage sites, lacking of research on the site selection and construction of buildings. This field also ignores the significant historical background of the Second Sino-Japanese War. This paper researches the National Resources Commission (NRC) during the Second Sino-Japanese War. It studies its site selection strategy for the 28 factories built by the organization in Hunan and Chongqing. First, the NRC-related historical data were sorted, including the list of factories built before and during the war, and their own historical construction database was established. Then, "Historical Position Finding ‐ Site Feature Construction ‐ Site Selection Factor Evaluation" is used to analyse each site. Finally, the site selection strategy of the NRC before and after the war was analysed. The innovation of this research is to put forward a research method combining field investigation and historical archives based on the perspective of institutional history. The study determined the spatial location of each plant and provided a direct basis for subsequent protection and utilization. [ABSTRACT FROM AUTHOR]

Shared decision making (SDM) improves health outcomes by providing individualized nursing interventions to patients and educating and empowering them to actively participate in health. However, hypertensive patients who require self-management do not actively participate in the SDM process. This scoping review aims to investigate the available information on SDM to self- management programs for hypertensive patients. The proposed scoping review protocol will be conducted using the Arksey and O'Malley methodological framework, following the Joanna Briggs Institute's methodology for scoping reviews. Multiple databases will be searched, including the MEDLINE (PubMed), Embase, Cochrane Library. Papers' titles, abstracts, and full text will predominantly be screened by one researcher with a second researcher. Data will be extracted using a customized table developed in collaboration with the research team, and presented in tabular format, arranged thematically. In the scoping review, studies involving the self- management program applied for hypertensive patients with two or more of the components of the intervention are included. And in the SDM process, studies in which healthcare providers provide various options for patients' self- management and set health goals in consideration of patients' preferences and values will be included. [ABSTRACT FROM AUTHOR]

This study presents the case of China's Jiangsu Province. The spatial-temporal pattern evolution of different manufacturing sectors is discussed using spatial analysis technology (spatial autocorrelation and standard deviation ellipses). The Granger test is used to analyze the relationship between the change in the manufacturing industry spatial agglomeration and regional economic differences. The following conclusions are drawn: 1) The spatial agglomeration trend of most manufacturing sectors is weakening. Much of the manufacturing sector, like the rubber and plastic product industries, has been transferred from southern to northern Jiangsu. 2) From the scale, only a minority of these enterprises possess substantial registered capital. The capital injection scale of more manufacturing enterprises is insignificant. At the same time, manufacturing companies with substantial financial resources are increasingly inclined to choose less-concentrated areas when choosing new investment areas. 3) The reduction of regional economic differences is considered to be the Granger-cause for the decline of the spatial agglomeration degree of the manufacturing industry in Jiangsu Province. Analyzing the spatiotemporal pattern of the manufacturing industry in Jiangsu Province will provide specific policy reference values for the manufacturing industry and economic development of Jiangsu province. In addition, for companies of different sizes, the findings of this paper also provide valuable references on how they can choose suitable investment locations according to their size in the future. [ABSTRACT FROM AUTHOR]

Conventional power systems are almost at the verge of their existence and are quickly being replaced by modern, especially smarter alternatives. Rapid and wide deployment of Load side management, which is a smarter and modern way to ensure efficient and economical use of power, is a classic example. However, there are several challenges which need to be addressed before it can be realized as a commercially viable solution. Suitable communication method, called "Home Area Networks", is a mandatory requirement which should be cost effective, robust and with ability to simultaneously handle large number of devices. Due to various inherent issues, communication protocols commonly used now-a-days, are not fully capable to address the requirements of HANs. Power Line Carrier Communications (PLCC) can be a suitable and efficient alternative of quick realization of HANs. In this paper, various aspects of PLCC have been analyzed for use in HANs. Modulation methods, materials, physical parameters and effect of noise have been analyzed. The paper evaluates performance and effectiveness of FSK modulation with aluminum transmission medium for PLC within a residential environment, focusing on the tradeoffs between distance, transmission medium diameter, and type of transmission medium, and cable gauge, which can degrade the performance of PLCC. A novel simulation model has been presented incorporating RLCG (Resistance, Inductance, Capacitance, and Conductance) tools and additive white Gaussian noise (AWGN). Performance parameter have been comprehensively analyzed of implementation using aluminum as a communication medium. This paper discusses cost effectiveness of PLCC by replacing copper by aluminum as the transmission medium. The study reveals that aluminum cables must be twice the size of copper cable in order to achieve same communication distances. Higher carrier frequencies in FSK modulation increase noise susceptibility, consequently reducing achievable communication distances. Cables with smaller diameters results in lower transmission ranges. These results highlight the trade-offs involved in implementing FSK for in-home PLCC (iPLCC). The study highlights how crucial it is to take these trade-offs into account while developing and refining FSK-based iPLCC systems for use in smart homes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe a protocol for a systematic review (SR) and meta-analysis examining the associations between an episode of COVID-19 and trajectory as well as the risk of frailty. The protocol for this SR has been registered in the PROSPERO database (CRD42023468297) and conforms to the guidelines proposed by the Preferred Reporting Items for Systematic Reviews (PRISMA). The search strategy will involve retrieving literature from six different databases and will be guided by keywords encompassing population (community-dwelling or institution-dwelling adults), exposure (episode of COVID-19), and outcome (frailty). The citations retrieved from the search process will be screened for their eligibility. The risk of bias for the articles identified to be eligible for the review will be examined using the Quality In Prognosis Studies (QUIPS) tool. The Metafor package in R will be used for quantitative data synthesis of the literature. Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be used to assess the quality and certainty of the body of evidence. This systematic review will provide crucial information to determine whether an episode of COVID-19 is associated with an increased risk of frailty. The results of this review will have significant clinical implications in mitigating the risk of frailty after COVID-19. [ABSTRACT FROM AUTHOR]

This paper considers message delivery through direct communication between drones in an epidemic manner. One method of increasing communication opportunities is to enlarge the communication range. However, this approach is not always possible due to power consumption or equipment limitations. As a novel alternative to this method, we consider setting a virtually deployed road network (VDRN) for drones. By making drones move in a distributed manner along a VDRN, they pass close to each other more frequently, increasing communication opportunities. Through theoretical analysis, we clarify how much a VDRN can improve contact performance. We also investigate how much the use of a VDRN can increase travel time, since moving along such a network can cause detours. By comparing the numerical results for the above two methods of increasing communication opportunities, we examine the required duration of detours for VDRNs to achieve the same level of contact performance as enlarging the communication range. [ABSTRACT FROM AUTHOR]

High-resolution computed tomography (HRCT) has become a widely used tool for studying the inner ear morphology of vertebrates. Amphisbaenians are one of the most specialized groups of fossorial reptiles but are poorly understood relative to other squamate reptile. In this paper we survey the anatomy of the inner and middle ear of these fossorial reptiles using HRCT models and we describe qualitatively and quantitatively (using 3D morphometrics) the anatomy of the inner ear. Amphisbaenians are diverse in skull anatomy, especially in the configuration of the snout, which correlates with digging modes. We demonstrate that the ear also exhibits a diversity of configurations, which are independent of phylogenetic relationships. Results from morphological analyses also allow us to describe 11 new potentially informative phylogenetic characters including some that help to diagnose amphisbaenians, such as: 1) the globular vestibule, ii) semicircular canals arranged in a circular trajectory, and iii) an extensive area of interaction between the columella footplate and the lagenar recess. Among extant amphisbaenians, Rhineura floridana has the most unusual inner ear configuration, including a horizontal semicircular canal that is in the same orientation as the inclined snout. The new morphological information helps us to better understand the morphology of headfirst-burrowing fossorial reptiles and contributes new data for resolution of phylogenetic relationships among amphisbaenians. [ABSTRACT FROM AUTHOR]

This study examines differences in benefit receipt using an intersectional approach. Intersectionality theory emphasizes the importance of the interplay of multiple social dimensions. Taking this as a starting point, the paper investigates how different combinations of three demographic variables plus education buffer or amplify benefit receipt and thereby create relatively advantaged and disadvantaged groups. Administrative data were used, sourced from Dutch registers that provide accurate and detailed information on benefit receipt for the entire Dutch population, including small and hard-to-reach segments. Multilevel Analyses of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) are performed to assess which intersectional groups are relatively advantaged or disadvantaged with respect to benefit receipt. Intersectional group differences are more pronounced for social assistance than for unemployment insurance. Complex combinations of education, gender, age and migration background are required to better understand differences in benefit receipt, especially for unemployment insurance. [ABSTRACT FROM AUTHOR]

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry. [ABSTRACT FROM AUTHOR]

In many countries, public transport (PT) services are offered by multiple operators with different modes, such as trains, metros, and buses, which are interchangeable across a single journey. This paper presents a model with one origin (O)-destination(D) path operated by two operators, each of which is responsible for different parts of the OD journey. One operator competes with potential third-party transport companies by offering discount incentives. Such an abstract settings has not been discussed in the literature. We explore how prices, demand, profits, and social welfare change with discounts through a theoretical analysis and numerical simulations under five scenarios. The results indicate that in all the scenarios the operator offering a discount incentive can always attract more passengers and increase its profits. Moreover, reducing the service time of operators offering discounts contributes to an increase in social welfare. Notably, this paper deduces for the first time that the demand scenario aimed at maximizing social welfare is twice as high as that aimed at maximizing total profit. However, in the scenario of maximum social welfare, the profitability of operators becomes challenging. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this scoping review is to provide an overview of the available evidence on the effectiveness of web-based interventions for fear of cancer recurrence (FCR) and a discussion of drawbacks and possible improvements for web-based interventions identified in the reviewed studies. These steps fulfil the aim of this review, which is to offer suggestions for developing future web-based interventions based on the reviewed studies. Methods: Five databases (PubMed, MEDLINE, EMBASE, SCOPUS and Web of Science) were searched. Original peer-reviewed articles, written in English, on web-based interventions for FCR were included for review. The data from the included studies was synthesised thematically. Results: We included 34 papers reporting on 28 interventions. Most of the studies in the papers were quantitative and mixed quantitative studies with a qualitative element, e.g. an interview post-intervention. Interventions were most commonly trialled with women breast cancer patients. Top three countries where studies were conducted were USA, Australia and the Netherlands. The most common theoretical framework for interventions is cognitive behavioural therapy (CBT), followed by mindfulness-based and mixed CBT, mindfulness, acceptance and commitment therapy (ACT), relaxation approaches. FCR was the primary focus/measure in 19 Studies, in 9 studies FCR was a secondary/related outcome/measure. Overall, the evidence of efficacy of web-based interventions on FCR is mixed. Conclusions: The existing research suggests several key points for producing more robust evidence about the effectiveness of web-based interventions for FCR. First, the studies suggest that it is a priority to better define eligibility criteria to proactively include people with higher levels of FCR. Second, there is a need for longer-term follow-up and outcome measuring period. Third, research examining the reasons for dropout from web-based interventions for FCR is critical to improve the effectiveness of web-based interventions. Fourth, while web-based interventions do not involve the costs of transportation, traveling time, space, equipment, cleaning, and other expenses, further cost utility analyses should be performed. Finally, future studies should assess how intervention accessibility, adherence, and effectiveness can be improved across different intervention designs, varying from intensive synchronous individual therapist-assisted web-based programme to blended designs combining the advantages of face-to-face and internet-based elements, to entirely self-managed programmes. Implications for cancer survivors: Developing and evaluating more accessible FCR treatments have been identified among top international FCR research priorities (Shaw et al. 2021). While there is some evidence that web-based interventions can be as effective as face-to-face interventions, currently there is a dearth of systematic data about the ways in which the web-based modality specifically can enhance supportive care for FCR. Developing knowledge about effective web-based interventions has implications for cancer survivors as they can be presented with more accessible, low-cost and low-burden options for managing fear of cancer recurrence. [ABSTRACT FROM AUTHOR]

This paper aims to investigate trade openness's effects on total factor productivity (TFP) using monthly data from December 1991 to March 2024. The analysis also incorporated absorptive capacity to examine the behavior of TFP components. Our findings from a multivariate VECM model indicated that absorptive capacity did not significantly impact TFP, even in the short term. Conversely, the increase in openness contributes to raising TFP by about 26 and 0.16 percentage points in the short and long term, respectively. Additionally, absorptive capacity and trade openness Granger-Cause short- and long-term components of TFP. These results are statistically significant at conventional significance levels. Policymakers should consider the dynamic effects of their policy actions on other sectors of the economy that were not initially the focus of the policy. Policymakers should develop concrete policies that improve the efficient use of resources in production chains to potentialize the productive impact of trade liberalization, including investment in human capital, ICT, and research and development. [ABSTRACT FROM AUTHOR]

It is estimated that disruptions to life caused by the COVID-19 pandemic have led to an increase in the number of children and young people suffering from mental health issues globally. In England one in four children experienced poor mental health in 2022. Social prescribing is gaining traction as a systems-based approach, which builds upon person-centered methods, to refer children and young people with non-clinical mental health issues to appropriate community assets. Recognition of social prescribing benefits for children's mental health is increasing, yet evidence is limited. Inconsistent terminology and variation of terms used to describe social prescribing practices across the literature hinders understanding and assessment of social prescribing's impact on children's mental health. This scoping review thus aims to systematically identify and analyse the various terms, concepts and language used to describe social prescribing with children and young people across the wider health and social care literature base. The scoping review will be undertaken using a six-stage framework which includes: identifying the research question, identifying relevant studies, study selection, charting the data, collating, summarising and reporting the results, and consultation. Electronic databases (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health, PsychInfo, Social Policy Practice, Scopus, Science Direct, Cochrane library and Joanna Briggs), alongside evidence from grey literature, hand search, citation tracking, and use of expert correspondence will be included in the review to ensure published and unpublished literature is captured. Data extraction will be carried out by two reviewers using a predefined form to capture study characteristics, intervention descriptions, outcomes, and key terms used to report social prescribing for children and young people. No formal quality appraisal or risk of bias evaluation will be performed, as this scoping review aims to map and describe the literature. Data will be stored and managed using the Rayaan.ai platform and a critical narrative of the common themes found will be included. [ABSTRACT FROM AUTHOR]

Current mobility trend indicates that the number of private cars will decline in the near future. One of the reasons for this trend is the development of Mobility as a Service (MaaS), which in conjunction with information and communication technologies (ICT) drive the application of transport services in smart city, respond to environmental issues, and provide users with reliable mobility. Electric vehicle sharing (EVS) travel has been regarded as a feasible mainstream model of sustainable mobility services in the future, which can effectively improve the utilization rate of motor vehicles, solve the problems of traffic congestion, environmental pollution and urban land, and promote low-carbon and sustainable development. To help electric vehicle operators improve service quality, the establishment of EVS program service performance evaluation is an urgent problem to be solved. Based on this, this paper firstly constructs the evaluation index system from 5 aspects: electric vehicle, charge station, user experience, payment and intelligent services through literature review and Delphi method. Secondly, the criteria importance though intercriteria correlation (CRITIC) and the improved G1 method are introduced to overcome the shortcomings of the single method, and the combined weights are calculated by the multiplication normalization method. Finally, a decision model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIse Kriterijumski Optimizacioni Racun (VIKOR) method is constructed to select the best service performance of EVS program, and its feasibility and effectiveness are verified by sensitivity analysis and comparative analysis. The result shows that EVCARD is the best performing EVS program, and shared electric vehicle and charge station are the key factors to be considered in the selection. This study provides scientific and feasible guidance for the optimal service performance selection of EVS programs, which is of great significance for users to choose EVS programs. [ABSTRACT FROM AUTHOR]

This study aimed to identify the factors affecting the gripping force and stiffness of 25-gauge and 27-gauge (25G and 27G, respectively) internal limiting membrane (ILM) forceps and to compare the effect of these factors on various ILM forceps manufactured by different companies. This study evaluated 25G and 27G ILM forceps with two different types of tip shapes, Eckardt and Maxgrip, manufactured by Alcon (A), DORC (B), VitreQ (C), and Katalyst (D). The gripping force was defined as the force required to move the ILM forceps away from a thin paper by pulling the paper. Shaft stiffness was determined by measuring the shaft displacement under a known force. Multiple regression analysis revealed that the gripping force showed significant correlations with the gauge (P<0.001), type of shaft tip (Eckardt/Maxgrip) (P<0.001), and contact area of the tip (P<0.001). The shaft stiffness showed significant correlations with the gauge (P<0.001), length of the base (P<0.001), thickness of the metal of the shaft (P = 0.05), and lumen area of the shaft (P = 0.01). The gripping force and shaft stiffness differed for each product. Thus, vitreoretinal surgeons must select the appropriate type of ILM forceps based on their characteristics. [ABSTRACT FROM AUTHOR]

The real-time and accurate detection and ranging of ships play a pivotal role in ensuring navigation safety, this study aims to enhance the navigation safety and environmental perception capabilities of inland waterway vessels. In the ship detection stage, addressing challenges such as large parameters, high computational complexity, and poor real-time performance in existing ship detection models, this paper proposes the MS-YOLOv5s ship target detection algorithm. This algorithm, based on YOLOv5s, utilizes the lightweight MobileNetV3-Small network to replace the original YOLOv5s feature extraction backbone network, thereby improving the detection speed. The results indicate that the parameter size of the MS-YOLOv5s model is 3.55M, only 50.49% of YOLOv5s. Achieving a detection rate of 50.28 FPS, the precision is 96.80%, and the mAP is 98.40%, striking a balance between high accuracy and low computational demand. In the depth estimation stage, influenced by the environment, leading to unstable measurement data, this paper proposes a binocular Kalman filter fusion ranging algorithm. The standard deviation of the ranging results is minimized to 6.032μm, which is one order of magnitude smaller than traditional ranging algorithms, significantly enhancing the robustness of the measurement results. Within a distance of 20m from the ship target, the error can be controlled within 3%, showcasing the applicability of the method proposed in this paper in complex inland waterway environments contributes to the enhancement of ships' environmental perception capabilities and navigation safety, holding positive implications for the development of intelligent vessels. [ABSTRACT FROM AUTHOR]

Nowadays, electronic computers use a "binary" numbering system, as opposed to "ternary" logic, which is closer to the way the human brain thinks. In this paper, the symmetric ternary system is applied to membrane computing for the first time. By combining the symmetric ternary system with membrane computing, this paper provides a more suitable arithmetic operation method for bio-computers, which breaks through the limitations of the traditional binary system in complex operations, and has a great potential for application in artificial intelligence and automatic learning in particular. The P System we designed include: Π+ for symmetric ternary addition, Π* for symmetric ternary multiplication, and Π/ for symmetric ternary division. The operation process of each P System was explained through examples, and their feasibility and effectiveness were verified through simulation software, UPSimulator. The system we designed can be further applied to symmetric ternary applications. [ABSTRACT FROM AUTHOR]

Semantic feature combination/parsing issue is one of the key problems in sound event classification for acoustic scene analysis, environmental sound monitoring, and urban soundscape analysis. The input audio signal in the acoustic scene classification is composed of multiple acoustic events, which usually leads to low recognition rate in complex environments. To address this issue, this paper proposes the Hierarchical-Concatenate Fusion(HCF)-TDNN model by adding HCF Module to ECAPA-TDNN model for sound event classification. In the HCF module, firstly, the audio signal is converted into two-dimensional time-frequency features for segmentation. Then, the segmented features are convolved one by one for improving the small receptive field in perceiving details. Finally, after the convolution is completed, the two adjacent parts are combined before proceeding with the next convolution for enlarging the receptive field in capturing large targets. Therefore, the improved model further enhances the scalability by emphasizing channel attention and efficient propagation and aggregation of feature information. The proposed model is trained and validated on the Urbansound8K dataset. The experimental results show that the proposed model can achieve the best classification accuracy of 95.83%, which is an approximate improvement of 5% (relatively) over the ECAPA-TDNN model. [ABSTRACT FROM AUTHOR]

With the rise of the Internet of things, the limitations of traditional PKI certificate authentication technology have progressively emerged. Hence, identity-based public key algorithms have been proposed. In this paper, we propose a new approach to generate an identity key, named identity public key (IPK). IPK identity key generation protocol is based on SM2 elliptic curve cryptography and random matrix theory. It builds upon the concept of combined public key (CPK) and resolves the linear collusion issue of CPK as well as the authenticity verification problem of the declared public key of the simplified TF-CPK by enhancing the identity mapping approach. Another main aim of this paper is to prove the security of the IPK identity key generation protocol. Roughly speaking, we verify the security of each part of the private key and the security of the composite private key. We also increase the function and performance comparison with other schemes, such as IBC(SM9) and TF-CPK. Our scheme has the lowest computation cost, which demonstrates its rationality. [ABSTRACT FROM AUTHOR]

Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging. This paper proposes an innovative approach using the stable Paretian model to analyze the price return of jet fuel in large samples. It comprehensively compares the fitting effect of the stable Paretian distribution with that of the normal distribution based on specific criteria and non-parametric significance tests. Furthermore, it investigates the accuracy of risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) predicted by both models. In addition to comparing differences in VaR between predicted values and actual values, this paper provides a more comprehensive comparison of risk measures under rolling window forecast situation. Results suggest that despite indistinguishable results in VaR backtest, the stable Paretian distribution has a overall better fitting effect as well as a less biased predicted CVaR based on the AIC of -14099.46, BIC of -14110.98, p = 0.58 in Kolmogorov-Smirnov test and p = 0.46(0.92) in the 0.01(0.05) significance level of Expected Shortfall Regression Test. This might be explained by its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions. its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions. [ABSTRACT FROM AUTHOR]

Research on the SSI effect on flat sites has yielded many valuable conclusions. However, current research on the impacts of various special local terrains on structural dynamics remains limited. For mountainous areas, it is common to construct houses in a multi-step, climbing, and laterally staggered architectural form that follows the mountain terrain. Only through the analysis of the combined action of the upper and lower parts can the seismic performance of this type of structural form be better revealed; considering the influence of SSI effects will be closer to the actual seismic effects. Therefore, to identify the damage factors of the mid-story isolated stilted structures under earthquakes and provide optimized design plans for the structures, six models are established considering three slopes and two types of foundations based on the engineering case in Chongqing, China. Through the elastic-plastic time-history analysis under earthquakes in the down and transverse-slope directions, concludes, compared with not considering SSI, the seismic response of the mid-story isolated stilted structures considering SSI in mountainous areas is amplified. With the increase of the mountain slope, the seismic response of the structures considering SSI increases, and the amplification coefficients are between 1–1.8. The amplification coefficients of the structures without SSI are concentrated around 1, which is less influenced by the slope. The damage to the stilted isolated layer is mainly concentrated in the column and the beam end, and the maximum seismic response appears in the short columns. The foundation soil stress increases with the increase of the mountain slope. [ABSTRACT FROM AUTHOR]

Background: Scholarly publications are important indicators of research productivity and investigator development in Centers of Biomedical Research Excellence (COBREs). However, no information is available to describe implementation and evaluation of writing development programs within COBREs. Therefore, this paper aimed to evaluate the first year of a campus-wide COBRE-supported writing program. Methods: A convergent parallel mixed-methods design (QUAN + QUAL) was used. All writing program participants were invited to complete post-participation surveys, and a subgroup was selected using purposive sampling to complete individual semi-structured interviews. Descriptive statistics were used to characterize survey data, and qualitative content analysis was employed to analyze interview data. Self-determination theory served as the theoretical framework by which themes were developed and interpreted. Results: Professional staff, post-doctoral fellows, and faculty from all academic ranks (n = 29) participated in the writing program during its first year. Survey respondents (n = 18, response rate 62%) rated social support (89%), group accountability (89%), hearing group members' writing goals (78%), receiving group advice (67%), and setting a weekly writing schedule (56%) as beneficial program components. Participants rated program benefits such as breaking away from other responsibilities, staying on task with writing goals, and receiving social support as most beneficial. During interviews, participants (n = 14) described five major themes related to the benefits received: 1) belonging to a community of writers; 2) managing writing-related emotions; 3) improved productivity; 4) establishing helpful writing habits; and 5) improved motivation for scholarly writing. Conclusions: This first-year programmatic evaluation demonstrates the writing program's effectiveness as a campus-level development resource supported by a research center. Both survey and interview data affirmed that participants perceived autonomy, competence, and relatedness were supported through participation in the writing program. Participants placed particular emphasis on the writing program's successful development of a community of scholarly writers. [ABSTRACT FROM AUTHOR]

This paper presents and elaborates on empirical methods and approaches used to identify Zero-Dose (ZD) and Under-immunized (UI) children as well as the communities that these children reside in within Bangladesh. This paper also describes demand- and supply side-barriers that lead to children being ZD and UI in the country. Time period for the study was December 2022—May 2023. The study methodology encompassed secondary data analysis using data from national surveys, primary data collection and analysis via a lot quality assurance sampling (LQAS) survey and also, qualitative data collection and analysis. Study population included caregivers of children aged 4.5 months (4 months 15 days) to 23 months for the LQAS survey. The qualitative component included policymakers, program managers and service providers working in immunization as well as mothers in the selected study areas who had a living child aged less than 2 years as the study population. Our data analysis confirms existence of ZD and UI children in areas which were categorized into haor (wetlands), hilly, char (sandy/silty land surrounded by water), coastal, plain land and urban slums. Determinant analysis showed that the mother's level of education, antenatal visits made, and access to media were significantly associated with children being ZD or UI. Reproductive autonomy emerged as a key factor that had prominent impact on a child being ZD. The qualitative analysis indicated the importance of population migration, health workforce shortages and lack of access to transportation as prominent barriers to immunization. Notably, the methods and approaches used in this study are both effective and easily replicable to identify ZD and UI children. The drivers of ZD and UI along with the barriers to immunization provide potential areas for intervention by policy-makers and can apprise about interventions to be tested in future implementation research. [ABSTRACT FROM AUTHOR]

Cavitation may occur in water-jet pump during operation of water-jet propulsion vessel, and once cavitation occurs, the tip clearance pulsating pressure of the impeller may be intensified, resulting in increased vibration of the water-jet propulsion unit. In this paper, the cavitation pulsating pressure characteristics at different positions in the pump are studied by experiment and numerical simulation, and the pulsating pressure characteristics in tip clearance are mainly researched. Based on Star-CCM+ commercial software, unsteady Reynolds-averaged Navier-Stokes equations(RANS) numerical simulation is carried out, and the feasibility of the numerical simulation method is verified by uncertainty analysis. The results show that the cavitation pulsating pressure near the leading edge of the impeller in the tip clearance is the largest. The variation of the tip clearance pulsating pressure with the intensification of cavitation is studied by numerical simulation, and its mechanism is revealed. A dimensionless coefficient of net positive suction head (CNPSH) is proposed, and the study shows that the cavitation pulsation pressure coefficients of pumps of different scales are equal when the working conditions are similar and the CNPSH are equal, which indicates that the cavitation pulsating pressure performance of full scale pump can be predicted by model scale. It is of great significance to evaluate the vibration performance of the full scale water-jet propulsion. [ABSTRACT FROM AUTHOR]

In the field of bearing defect detection, Aiming at the problem of low efficiency in manual inspection and prone to missed detections in scenarios with small target defects, and overlapping targets, an improved YOLOv5-based object detection method is proposed. Firstly, in terms of feature extraction, the C3 modules in the original backbone of YOLOv5 are replaced with the finer-grained Res2Block modules to improve the model's feature extraction ability. Secondly, in terms of feature fusion, a Bidirectional Feature Pyramid Network (BiFPN) is added to the original neck of YOLOv5 to enhance the fusion ability of shallow graphic features and deep semantic features. Finally, the performance of the improved YOLOv5 algorithm is validated through ablation experiments and comparative experiments with other defect detection algorithms, including the Small_obj algorithm the existing method of adding a small target detection head for identifying small target defects. The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection. [ABSTRACT FROM AUTHOR]

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare. [ABSTRACT FROM AUTHOR]

Despite the widespread influence of the burgeoning digital economy on agricultural productivity in recent years, China's food system confronts numerous challenges. Notably, research exploring the digital economy's impact on food system resilience remains scarce, and the pivotal role of industrial agglomeration in this context remains unclear. Therefore, this article is based on the panel data of 30 provinces in China from 2011 to 2021, this paper empirically examines the direct effect, the mechanism of action, and the spatial spillover effect of the resilience of a digital economy-enabled food system using a double fixed-effects model, mediated-effects model, and spatial econometric model. The results show the following: (1) The resilience of China's urban food system shows obvious spatial differences, but the overall trend in improvement requires attention. (2) The development of a digital economy has a facilitating effect on the level of resilience of the food system, and industrial agglomeration induces an intermediary effect. (3) The digital economy has a significant positive spatial spillover effect on the resilience of the food system; i.e., the digital economy can improve the resilience of the food system in the region and the neighboring regions. Accordingly, policy recommendations have been put forward to improve infrastructure construction and promote the development of digital villages; strengthen the construction of industrial agglomerations and promote the enhancement of the quality and efficiency of industries; and promote the development of regional linkages and build a solid foundation for food security. [ABSTRACT FROM AUTHOR]

To examine the resource utilization in different phases such as development and sales within China's real estate industry, this paper employs a two-stage Data Envelopment Analysis (DEA) model to measure the production efficiency of the real estate industry across 31 provinces, municipalities, and autonomous regions of China from 2014 to 2022. By examining both overall and phase-specific trends, the study utilizes a panel Tobit model to explore the factors affecting efficiency. Empirical results indicate that the leverage ratio of companies, per capita GDP of regions, and real estate regulatory policies significantly impact production efficiency. Further analysis of regional heterogeneity and its effect on production efficiency revealed that the per capita residential building area, which reflects the housing stock configuration in different regions, exhibits a significant single threshold effect. This not only objectively assesses the utilization of real estate resources in different areas but also delves deeper into the principal factors and their mechanisms affecting the production efficiency of the real estate industry, thus providing theoretical support and policy recommendations for effectively enhancing production efficiency. [ABSTRACT FROM AUTHOR]

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction. We emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-specific characteristics. Our deep learning models utilize trajectory data sourced from the Automatic Identification System (AIS) to train and learn regular patterns within ship trajectory data, enabling them to predict trajectories for the next hour. Experimental results reveal that CNN has substantially reduced the Mean Absolute Error (MAE) and Mean Square Error (MSE) of ship trajectory prediction, showcasing superior performance compared to other deep learning algorithms. Additionally, a comparative analysis with other models—Recurrent Neural Network (RNN), GRU, LSTM, and DBS-LSTM—using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Non-Linear ADE (NL-ADE), demonstrates our method's robustness and accuracy. Our approach not only cleans the data but also enriches it, providing a robust foundation for subsequent deep learning applications in ship trajectory prediction. This improvement effectively enhances the accuracy of trajectory prediction, promising advancements in maritime traffic safety. [ABSTRACT FROM AUTHOR]

Background: Climate change is widely recognised to threaten human health, wellbeing and livelihoods, including through its effects on the emergence, spread and burdens of climate–and water-sensitive infectious diseases. However, the scale and mechanisms of the impacts are uncertain and it is unclear whether existing forecasting capacities will foster successful local-level adaptation planning, particularly in climate vulnerable regions in developing countries. The purpose of this scoping review was to characterise and map priority climate- and water-sensitive diseases, map existing forecasting and surveillance systems in climate and health sectors and scope out the needs and potential to develop integrated climate-driven early warning forecasting systems for long-term adaptation planning and interventions in the south Asia region. Methods: We searched Web of Science Core Collection, Scopus and PubMed using title, abstract and keywords only for papers focussing on climate-and water-sensitive diseases and explicit mention of either forecasting or surveillance systems in south Asia. We conducted further internet search of relevant national climate adaptation plans and health policies affecting disease management. We identified 187 studies reporting on climate-sensitive diseases and information systems in the south Asia context published between 1992 and 2024. Results: We found very few robust, evidenced-based forecasting systems for climate- and water- sensitive infectious diseases, which suggests limited operationalisation of decision-support tools that could inform actions to reduce disease burdens in the region. Many of the information systems platforms identified focussed on climate-sensitive vector-borne disease systems, with limited tools for water-sensitive diseases. This reveals an opportunity to develop tools for these neglected disease groups. Of the 34 operational platforms identified across the focal countries, only 13 (representing 38.2%) are freely available online and all were developed and implemented by the human health sector. Tools are needed for other south Asian countries (Afghanistan, Sri Lanka, Bhutan) where the risks of infectious diseases are predicted to increase substantially due to climate change, drought and shifts in human demography and use of ecosystems. Conclusion: Altogether, the findings highlight clear opportunities to invest in the co-development and implementation of contextually relevant climate-driven early warning tools and research priorities for disease control and adaptation planning. [ABSTRACT FROM AUTHOR]

With the rapid development of Industrial Internet of Things (IIoT), network security issues have become increasingly severe, making intrusion detection one of the key technologies for ensuring IIoT security. However, existing intrusion detection systems face challenges such as incomplete data features, missing labels, parameter leakage, and high communication overhead. To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. Additionally, the paper introduces the Gradient Similarity Model Aggregation (GSA) algorithm, which dynamically selects and weights updates from different models to reduce communication overhead. Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. Experimental validation on the Edge-IIoTset and CIC IoT 2023 datasets achieves accuracies of 94.5% and 99.2%, respectively. The results demonstrate that the NIDS-FGPA model possesses the ability to identify and capture complex network attacks, significantly enhancing the overall security of the network. [ABSTRACT FROM AUTHOR]

The protection system is crucial for grid stability and safeguarding essential components, including generators, transformers, transmission systems, and power connections. The smart grid system increases the flexibility and complexity of the power system, making fault detection and isolation the primary challenges for the protection system. This paper presents an optimal protection solution using an adaptive electronic relay to enhance reliability and enable self-healing. The proposed protection algorithm quickly detects faults and automatically isolates them from the rest of the healthy system in 25ms. The relay operation algorithm has been validated using MATLAB SIMULINK software. The results confirm the effectiveness of the proposed smart electronic relay in various sections of the smart grid system, including transformers, transmission and distribution [ABSTRACT FROM AUTHOR]

This paper looks into the MaxEnt model in a trial to comprehend the ecological and environmental conditions that propagate and drive the spread of Ebola Virus Disease in Africa. We use the MaxEnt model to assess risk determinants associated with the occurrence and distribution of EVD, taking into account non-correlated variables such as neighborhood mean temperature, rainfall, and human population density. Our findings indicate that among the factors that significantly shape the geographical distribution of EVD risk are human population density, annual rainfall, temperature variability, and seasonality. The model used is both reliable and accurate (the average value for training AUC was 0.987); it can be used as a valuable approach for the prediction of infectious disease outbreaks. High-risk areas are primarily identified in the western and central regions of Africa, with some of the others in the east also vulnerable. This further calls for specified public health interventions and enhanced surveillance in specified hotspots, contributing to global efforts to predict and mitigate risks associated with EVD outbreaks more adequately. The findings further support that it remains imperative to conduct additional research, including socio-economic and cultural variables, to enhance the understanding of how environmental factors contribute to the emergence and transmission of Ebola. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes. [ABSTRACT FROM AUTHOR]

Immigrant newcomers and refugees (INRs) are two migrant categories that experience consistent systemic barriers to settlement and integration in Canada as older adults. This paper explores the challenges experienced by Arabic-speaking INR older adults in Edmonton, Canada, during settlement and discusses policy and service implications. A qualitative description study using community-based participatory research principles was implemented to evaluate and support digital literacy in Arabic-speaking INR older adults. We included men and women aged 55 and older who identified as immigrants or refugees and spoke Arabic. Experiences of settlement challenges were consistently identified during data collection and engagement of INR older adult participants. A thematic sub-analysis of interviews with (10 individuals and one couple) of participants' narratives was completed in 2022 and was used to identify themes related to settlement barriers for this population. Two main themes were identified: (1) Limited English skills and digital literacy gaps create service barriers for INR older adults, and (2) Gaps in services and policies as basic needs remain unmet. We describe key challenges experienced by INR older adults, such as language barriers, precarious finances, poor access to health care services and lack of transportation and employment opportunities, which hinder successful integration into the new society. This study showcases the ongoing challenges with early settlement and integration that continue despite Canada's well-developed immigration settlement landscape. INR older adults often remain invisible in policy, and understanding their experiences is a first step to addressing their needs for resources that support healthy aging in the post-migration context. [ABSTRACT FROM AUTHOR]

Nuclear power's role as a reliable, baseload, low-carbon source and its importance in achieving clean energy goals are being increasingly recognized with growing urgency around decarbonization of the global energy systems. However, to deliver a long-term sustainable solution, it is essential to develop innovative nuclear technologies for improving the fuel utilization and reducing the nuclear waste disposal challenge. Zero Power Reactors (ZPR) are an essential initial step for developing new nuclear technologies because they allow for testing and refinement in a safe environment before large-scale deployment. This paper discusses the design of a ZPR experiments for the development of iMAGINE, a novel chloride-based molten salt reactor technology. The paper presents a detailed analysis of the neutronic design for the shutdown and control systems of an experimental ZPR based on the iMAGINE molten salt reactor technology. The study concludes that a split-core design with a lower corner reflector as an extension of the lower annular reflector offers the most robust ZPR configuration, offering optimum operational margins and maneuverability. This design ensures safety, regulatory compliance, and sufficient control and shutdown performance for the successful development of the iMAGINE technology. [ABSTRACT FROM AUTHOR]

How much information does a dataset contain about an outcome of interest? To answer this question, estimates are generated for a given dataset, representing the minimum possible absolute prediction error for an outcome variable that any model could achieve. The estimate is produced using a constrained omniscient model that mandates only that identical observations receive identical predictions, and that observations which are very similar to each other receive predictions that are alike. It is demonstrated that the resulting prediction accuracy bounds function effectively on both simulated data and real-world datasets. This method generates bounds on predictive performance typically within 10% of the performance of the true model, and performs well across a range of simulated and real datasets. Three applications of the methodology are discussed: measuring data quality, model evaluation, and quantifying the amount of irreducible error in a prediction problem. [ABSTRACT FROM AUTHOR]

Background: Nepal established the health partner coordination for COVID-19 response based on national and international plans and framework to support information-sharing for decision- making and course correction. This paper aims to assess the performance of COVID-19 Health Partner Coordination response in Nepal by adopting coordination best practices and tools from the international humanitarian cluster system. Methods: Secondary data from unpublished documents on health partner coordination for COVID-19 response in Nepal was collected and analysed from April 2023 to May 2023. The secondary data were from the review of the health partner coordination meetings conducted for COVID-19 response using a researcher-developed document review tool, responses of health partners on a survey tool adapted from the cluster coordination performance monitoring tool from the Global Health Cluster, and transcript of Focused Group Discussion among health partners. Descriptive analysis of quantitative information and thematic analysis with predefined themes of qualitative information were performed using MS Excel and MS Word respectively. A written approval from the Ministry of Health and Population and an ethical clearance from the Nepal Health Research Council was obtained before conducting the study. Results: More than three-fifths of the meetings showed good results in conduction, process, participation, and documentation of meeting action points with improvement required for follow-up (22.2%). Assessment of health partner coordination subfunctions resulted in either 'good' (>75.0%) or 'satisfactory' (50.1%-75.0%) except for prioritization based on analyses, which was 'unsatisfactory' (<50%). Partners admired good practices of health partner coordination, pointed out some issues, and provided recommendations. Conclusion: COVID-19 has highlighted the importance of effective coordination of health sector for response to a pandemic. Continuation of this practice after addressing the areas for improvement will contribute to health preparedness and readiness for future disasters or public health emergencies. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the digital economy on urban environmental pollution by analyzing panel data from 283 prefecture-level cities in China from 2011 to 2019 and using the digital technology comprehensive pilot zone of China as a natural experiment. The results demonstrate that digital technology has a significant effect in reducing pollutant emissions and empowering urban environmental governance. The findings are proven to be robust based on various tests, including parallel trend, PSM-DID, and placebo tests. Our analysis further shows that digital technology is particularly effective in controlling pollution in old industrial areas, high digital areas, and low energy efficiency areas. We also find that the national digital technology integrated pilot zone can mitigate environmental pollution in prefecture-level cities by increasing public environmental awareness and encouraging green technology innovation. Moreover, our research indicates that digital technology-enabled urban pollution control can contribute to the formation of a new urbanization pattern in China. These findings provide valuable insights for promoting the digital economy and achieving the goal of carbon reduction in China. [ABSTRACT FROM AUTHOR]

Progress in health outcomes across Africa has been uneven, marked by significant disparities among countries, which not only challenges the global health security but impede progress towards achieving the United Nations' Sustainable Development Goals 3 and 10 (SDG 3 and SDG 10) and Universal Health Coverage (UHC). This paper examines the progress of African countries in reducing intra-country health outcome disparities between 2000 and 2019. In other words, the paper investigates the convergence hypothesis in health outcome using a panel data from 40 African countries. Data were sourced from the World Development Indicators, the World Governance Indicators, and the World Health Organization database. Employing a non-linear dynamic factor model, the study focused on three health outcomes: infant mortality rate, under-5 mortality rate, and life expectancy at birth. The findings indicate that while the hypothesis of convergence is not supported for the selected countries, evidence of convergence clubs is observed for the three health outcome variables. The paper further examine the factors contributing to club formation by using the marginal effects of the ordered logit regression model. The findings indicate that the overall impact of the control variables aligns with existing research. Moreover, governance quality and domestic government health expenditure emerge as significant determinants influencing the probability of membership in specific clubs for the child mortality rate models. In the life expectancy model, governance quality significantly drives club formation. The results suggest that there is a need for common health policies for the different convergence clubs, while country-specific policies should be implemented for the divergent countries. For instance, policies and strategies promoting health prioritization in national budget allocation and reallocation should be encouraged within each final club. Efforts to promote good governance policies by emphasizing anti-corruption measures and government effectiveness should also be encouraged. Moreover, there is a need to implement regional monitoring mechanisms to ensure progress in meeting health commitments, while prioritizing urbanization plans in countries with poorer health outcomes to enhance sanitation access. [ABSTRACT FROM AUTHOR]

The paper introduces a new class of convexity named strongly modified (p, h)-convex functions and establishes various properties of these functions, providing a comprehensive understanding of their behavior and characteristics. Additionally, the paper investigates Schur inequality and Hermite-Hadamard (H-H) inequalities for this new class of convexity. Also, H-H inequalities are proved within context of Riemann-Liouville integrals and Caputo Fractional derivatives. The efficiency and feasibility of Schur inequality and H-H inequalities are supported by incorporating multiple illustrations, that demonstrate the applicability of strongly modified (p, h)-convex functions. The results contribute to the field of mathematical analysis and provide valuable insights into the properties and applications of strongly modified (p, h)-convex functions. [ABSTRACT FROM AUTHOR]

With the improvement of education level and the expansion of higher education, more students can have the opportunities to obtain better education, and the pressure of employment competition is also increasing. How to improve students' employment competitiveness, comprehensive quality and the ability to explore paths for career planning and employment strategies has become a common concern in today's society. Under the background of today's informatization, the paths of career planning and employment strategies are becoming more and more informatized. The support of Internet is essential for obtaining more employment information. As a representative product of the information age, deep learning provides people with a better path. This paper conducts an in-depth study of the career planning and employment strategy paths based on deep learning in the information age. Research has shown that in the current information age, deep learning through career planning and employment strategy paths can help students solve the main problems they face in career planning education and better meet the needs of today's society. Career awareness increased by 35% and self-improvement by 15%. This indicated that in the information age, career planning and employment strategies based on deep learning are a way to conform to the trend of the times, which can better help college students improve their understanding, promote employment, and promote self-development.This study combines quantitative and qualitative methods, collects data through questionnaires, and uses deep learning model for analysis. Control group and experimental group were set up to evaluate the effect of career planning education. Descriptive statistics and correlation analysis were used to ensure the accuracy and reliability of the results. [ABSTRACT FROM AUTHOR]

This paper sets out to investigate the alignment between the logistics architecture and resources across China's nine logistics regions, while also examining the individual advantages these regions possess. With the goal of establishing an evaluative framework for sustainable logistics development, the research introduces the principle of logistics equilibrium to augment traditional evaluation metrics, thus forming an assessment index system designed to quantify the congruence between logistics architecture and resources.Utilizing a democratic evaluation approach that emphasizes individual regional advantages, and leveraging empirical data from the decade spanning 2011 to 2020, this paper reveals a general upward trajectory in the matching level of logistics resources to architecture albeit with notable regional disparities. It is observed that while certain areas have made significant strides, others lag, underscoring the varied pace of development among different logistics regions.The research also brings to light the distinct advantage characteristics that emerge as each region evolves, suggesting that these attributes can be harnessed to foster a more environmentally conscious and forward-thinking approach to logistics. By implementing a democratic evaluation to rank these regions, this paper aims to identify exemplars that are not only recognized for their achievements but also serve as models for the formulation of green and sustainable logistics strategies tailored to each region's specific needs and potential. [ABSTRACT FROM AUTHOR]

The succession mode of interim CEO is highly uncertain. The smooth transition function of interim CEO is particularly important in the process of enterprise reform. We divide interim CEOs into potential successors and transition successors. Then we empirically analyze the heterogeneous impact and mechanism of interim CEOs on firm performance. The results show that compared with transition CEO, potential CEO has less negative impact on corporate performance. Interim CEO has a negative impact on enterprise performance by reducing agency efficiency. The mechanism analysis of this paper are as follows. For the potential CEO, strong incentive constraints can effectively mitigate the negative impact of the succession on corporate performance. For a transitional CEO, superior personal capabilities coupled with an effective oversight mechanism represent more optimal solutions. In today's corporate landscape, the frequencies of executive changes and interim successions are on the rise annually. Our research is tailored to aid enterprises in navigating these transitions seamlessly and in expediting their recovery processes. [ABSTRACT FROM AUTHOR]

Tourism consumption as a typical representative of service consumption, has strong comprehensiveness and driving force, continuously expanding new consumption upgrade space. In the context of the digital economy, the development of the mobile internet has opened up new opportunities for the tourism industry acting as an important factor influencing the consumer behaviour of the residents. Based on 9007 resident survey data from the China General Social Survey (CGSS) in 2017, this paper explores the relationship between Internet use, access to information and residents' travel consumption behavior by means of probit model and mediating effect model. First, according to the findings, Internet use has a positive impact on residents' travel consumption behavior, increasing residents' tourism expenditure. Second, the mechanism suggests that access to information plays a partially mediating role between Internet use and travel consumption behavior. Third, further analysis revealed that, for the retired population, the more frequently they use the Internet, the more likely they are to spend on travel. Compared to those with lower incomes, those with higher incomes will spend less on travel after retirement. With this in mind, in order to promote residents' tourism consumption, speed and fee reduction should be continuously promoted, urban and rural information infrastructure should be strengthened, and accessibility of tourism information for residents should be improved. At the same time, there is a need to innovate the way tourism products are promoted, improve the types of tourism products and launch diversified tourism products. [ABSTRACT FROM AUTHOR]

The malicious use of deepfake videos seriously affects information security and brings great harm to society. Currently, deepfake videos are mainly generated based on deep learning methods, which are difficult to be recognized by the naked eye, therefore, it is of great significance to study accurate and efficient deepfake video detection techniques. Most of the existing detection methods focus on analyzing the discriminative information in a specific feature domain for classification from a local or global perspective. Such detection methods based on a single type feature have certain limitations in practical applications. In this paper, we propose a deepfake detection method with the ability to comprehensively analyze the forgery face features, which integrates features in the space domain, noise domain, and frequency domain, and uses the Inception Transformer to learn the mix of global and local information dynamically. We evaluate the proposed method on the DFDC, Celeb-DF, and FaceForensic++ benchmark datasets. Extensive experiments verify the effectiveness and good generalization of the proposed method. Compared with the optimal model, the proposed method with a small number of parameters does not use pre-training, distillation, or assembly, but still achieves competitive performance. The ablation experiments evaluate the role of each component. [ABSTRACT FROM AUTHOR]

Textbooks play a critical role in schooling around the world. Small sample studies show that many books continue to under-represent women and girls, and to portray men and women in stereotypical gendered roles. In this paper, we use quantitative text analysis to assess the degree of gender bias in a newly assembled corpus of 1,255 English language school textbooks from 34 countries that are publicly available online. We find consistent patterns of under-representation of female characters and portrayal of stereotypical gendered roles. Women and girls appear less frequently, are portrayed as more passive, are less likely to be associated with work or achievement, and are more likely to be associated with the home and traditionally female occupations. Comparing across countries, female representation in books is correlated with higher GDP and more legal rights for women. Under-representation and stereotypes are a particular problem in South Asia. [ABSTRACT FROM AUTHOR]

With the implementation of the "Rural Revitalization Strategy" in China, it is common for enterprises to go to the countryside to develop business. However, enterprises often neglect the local environmental protection in rural areas while developing the economy to pursue profits. As the end of the national administrative system and the villagers' autonomous organization, the village committee needs to participate in monitoring enterprises' environmental behavior. With this in mind, this paper builds a game model of enterprises, grass-roots governments, farmers, and village committees and analyzes the impact of village committees, grass-roots governments, and farmers on enterprise environmental behavior. The conclusions are as follows: (i) it is difficult for the village committee to promote the positive environmental behavior of enterprises, which needs the supervision of the grass-roots government; (ii) Improving the coordination ability of village committees is conducive to reducing the burden of government supervision; (iii) Farmers' awareness of environmental protection can affect the environmental behavior of enterprises through the rights protection mechanism and reputation mechanism. [ABSTRACT FROM AUTHOR]

Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model. [ABSTRACT FROM AUTHOR]

Studies of macroscopic speed modeling of bidirectional pedestrian cross-flows have relied heavily on scenario experiments, but the data itself may be deficient because large-scale scenario experiments are not easy to organize and subjects may not be walking under normal conditions. In order to explore the possibility of using microscopic pedestrian flow simulations for macroscopic speed modeling of pedestrian flows, a series of two-way pedestrian cross-flow simulation experiments were designed. Bidirectional pedestrian flows are defined as Peds1 and Peds2. The crossing angle and pedestrian flow rate are used as variables, and a bidirectional pedestrian flows simulation is designed as an orthogonal experiment. The crossing angles range from 15 to 165 degrees, and bidirectional pedestrian flow rate range from 1 ped/s to 8 ped/s. A series of simulations are built and performed on the GIS agent-based modeling architecture (GAMA) platform. By analyzing the flow data of bidirectional flows in the crossing area, it is found that when the Peds1 density falls below a threshold, Peds1 speed is determined by pedestrians themselves and mainly remains in a free flow state; otherwise, the Peds1 speed decreases with density. The clear effects such as Peds2 density on the Peds1 speed cannot be determined. A piecewise function combined with a linear function and an exponential function is constructed as the Peds1 speed model considering the influence of the crossing angle. The calibration results show that the piecewise function should be better than the non-piecewise function. Compared to the results of established studies, the results in this paper have some differences. Therefore, the simulation method cannot completely replace the scene experiments. However, this approach can provide suggestions for subsequent refinement of the experimental program, as well as a feasible direction for the construction of a speed relationship for bidirectional pedestrian flows. [ABSTRACT FROM AUTHOR]