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Objectives. This study aimed to investigate 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry, and assess the contribution of age, gender, body mass index (BMI) and physical activity (PA). Methods. The sample comprised 904 workers. Musculoskeletal symptoms were assessed using the Nordic musculoskeletal questionnaire. χ2 tests assessed the association between musculoskeletal symptoms and age/gender/BMI/PA, and multivariate logistic regression models predicted the 12-month occurrence of symptoms in the five most prevalent regions. Results. The five most affected body regions were the lower back, shoulders, neck, knees and wrists/hands. Older workers presented more symptoms in all body regions, except dorsal; female gender was associated with neck symptoms (p = 0.001); overweight was associated with a higher prevalence of lower back (p = 0.01), knee (p = 0.017) and ankle/foot (p = 0.037) symptoms; and a moderate PA level was associated with thigh/hip symptoms (p = 0.006). Age was a significant predictor in all five most affected regions; gender was a significant predictor for shoulders, neck and wrists/hands; and BMI was a significant predictor for lower back symptoms. Conclusion. The 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry is higher in the lower back, shoulders, neck, knees and wrists/hands. [ABSTRACT FROM AUTHOR]

Objective: The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed. Methods: Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures. Results: First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures. Conclusion: Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry. [ABSTRACT FROM AUTHOR]

Lower-limb strength measures can enhance falls risk assessment but due to the lack of clinically applicable methods, such measures are not included in current screening. The enhanced paper grip test (EPGT) is a simple-to-use and cost-effective test that could fill this gap. However, its outcome measure (EPGT force) has not yet been directly linked to the risk of falling. Is the EPGT a good candidate for falls risk screening in older people in the community? Seventy-one older people living independently in the community were recruited for this prospective observational study (median age 69 y, range 65y-79y). Lower-limb and whole-body strength were assessed at baseline using the EPGT and a standardised hand-grip method respectively. Incident falls were recorded monthly for a year through follow-up telephone conversations. The capacity of individual strength measures to predict falls and to enhance an established falls risk assessment tool (FRAT) commonly used by UK's national health service (NHS) was assessed using binomial logistic regression. The analysis was repeated for the subset of participants without history of falling at baseline (prediction of first-ever falls). Increased EPGT force and increased symmetry in strength between limbs were significantly associated with reduced risk of falling. Compared to the NHS-FRAT, the EPGT correctly classified more people (73% vs 69%), it achieved higher sensitivity (56% vs 26%) and higher negative predictive value (76% vs 68%). Complementing the NHS-FRAT with the EPGT produced a more comprehensive model that correctly classified 91% of participants and achieved 98% specificity, 81% sensitivity, 89% negative and 96% positive predictive value. Replacing the EPGT with hand-grip strength consistently undermined prediction accuracy. The EPGT remained highly accurate when focused on the prediction of first-ever falls. The EPGT can substantially enhance falls screening in the community. These results can also inform effective personalised strength exercise interventions. • The enhanced paper grip test (EPGT) accurately predicted the risk of falling. • The EPGT can enhance community screening for falls (recurrent and first-ever). • Higher EPGT force is significantly associated with reduced risk of falling. • Higher strength asymmetry between limbs also decreases the risk of falling. • These results can guide effective personalised strength exercise interventions. [ABSTRACT FROM AUTHOR]

Introduction: Gender-transformative approaches (GTAs) have been successfully carried out to address harmful gender norms and power imbalances to promote more gender equitability. However, to improve the health and wellbeing of young people, it is necessary to involve household heads by positively transforming their beliefs on gender equity and norms. Methods: This study was cross-sectional quantitative research undertaken in six local government areas in Ebonyi State, Nigeria. The study population consisted of household heads in households with young people aged 15-24 years. Data were collected for 15 days using paper and electronic copies of the questionnaire. Descriptive, bivariate, and logistic regression analyses were performed using Stata. Results: The results showed that 46.32% of male and 62.81% of female heads of households disagreed with the statement "a good woman never questions her husband's opinions, even if she is not sure she agrees with them." Female heads of households aged 50years and below with an odds ratio of 0.47 (p-value= 0.02) suggest they were 0.47 times more likely to have a positive attitude toward the rights and privileges of young girls. Male heads of households aged 50years and below with an odds ratio of 1.05 (p-value= 0.84) suggest that they were 1.05 times more likely to have a positive attitude toward the rights and privileges of young girls. Conclusion: This paper provides new knowledge on the gender norm attitude of male and female heads of households on the rights, privileges, and equity promotion of young boys and young girls, as well as its associated factors. [ABSTRACT FROM AUTHOR]

Pharmacists are expected to demonstrate their expertise in clinical practice and conduct research activities to generate new evidence. However, the factors promoting research activities among pharmacists remain unclear. Therefore, we investigated the research activities of Japanese pharmacists through a questionnaire survey and examined the factors contributing to the promotion of research activities. A web-based questionnaire using Google Forms was disseminated across pharmacists working in community pharmacies, drugstores, hospitals, and clinics. The questionnaire included respondents' backgrounds, research activities, and research environments. Logistic regression analysis was used to examine the factors promoting pharmacists' research activities, with experience in research paper acceptance as the objective variable. In total, 401 responses were included in the analysis. Of the respondents, 54.1% were hospital pharmacists, and 77.1% were pharmacists with > 5 years of pharmacist experience. Furthermore, 50.4% of the pharmacists had presented at conferences, and 22.2% had experience in research paper acceptance. The influential factors were "master's degree or higher," "number of affiliated academic societies," "acquisition of specialists/certified pharmacists," and "daily availability of a consultant for writing research papers." This study revealed the factors contributing to the promotion of research activities among pharmacists. We believe that our findings will help promote research among pharmacists. [ABSTRACT FROM AUTHOR]

A corrigendum has been issued for an article published in Acta Paediatrica titled "Short-term neurological injury in newborn infants with overcooling in passive hypothermia and transferred to reference hospital in Colombia." The corrigendum addresses an error in Table 3, specifically regarding the use of symbols ">" and "<" in the group "Age of Onset of Hypothermia (hours)." The revised table rectifies the inaccuracies and ensures the precision of the findings. The online version of the article has been corrected accordingly. [Extracted from the article]

The photovoltaic (PV) industry experienced a drastic overcapacity, leading to heightened market competition in 2009. After the overcapacity, the EU and the USA initiated trade barriers targeting Chinese PV goods in 2011, stimulating China to seek alternative markets. In 2013, China announced the Belt and Road Initiative (BRI). Although the BRI is a framework of international cooperation instead of a specific strategy that aims at PV industry, its role in helping Chinese PV firms deal with the trade sanction should be addressed. Based on the data from Chinese Custom Trade Statistics spanning 2009 to 2016, this paper precisely quantifies the extent of PV trade deflection from China to BRI markets that can be ascribed to the BRI effects. Employing spatial analysis, we manifest the trade deflection on maps. Moreover, we adopt the linear probability models and panel logit models to investigate the impact of BRI on China's PV export expansion. The results show that BRI markets provide a buffer area for Chinese PV enterprises to alleviate their loss caused by trade sanctions. The BRI facilitated 4751 million more PV products conveyed from China to BRI markets from 2013 to 2016, occupying about 33.25% of the total export from China to BRI markets. Chinese PV firms prefer to explore the BRI markets than the rest of the world. Compared to state-owned enterprises (SOEs), non-SOEs are more active in entering BRI countries. However, no significant difference had been detected in BRI effects on SOEs and non-SOEs. It was noted that a robust local industrial basement related to the PV industry can potentially ease firms' transition to BRI markets. Comparatively, firms situated in central China have benefited more from the BRI than their counterparts in the coastal and western regions. Furthermore, the BRI encourages Chinese PV enterprises to venture into countries with low economic complexity. This paper provides novel insights into BRI by validating BRI's effects on Chinese PV firms' export expansion. On the one hand, unlike previous BRI research that rarely considers the unstable external environment, we validate that the BRI can provide a spare market for China when it competes with developed countries. On the other hand, by investigating the roles of firms, regions and destinations, our results are enlightening for renewable energy industrial and trade policymakers. [ABSTRACT FROM AUTHOR]

Introduction: Peer-reviewed literature is commonly used to assess academic progress and research excellency. However, representation in authorship of global health publications is biased and unfair. In order to shed light on current gaps towards attaining gender equality in scientific production and shift power asymmetries in global health research, we conducted an assessment of authorship trends from 1972 to 2021 with a focus on gender and geographic representation in scientific articles authored or co-authored by researchers affiliated with UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP). Methods: We searched PubMed, Web of Science, and HRP public reports for publications where at least one author was affiliated with HRP. Our main outcome measures were author gender and location of author affiliation, classified by region and country income group. We used descriptive statistics to characterize the publications under analysis as well as the total number of authors from the included papers. We applied a logistic regression model to explore associations between author gender and other characteristics of published articles and a time series analysis to assess how time can influence the inclusion of women as authors in a publication. Python and R were used for all analyses. Results: A total of 1,484 publications with 14,424 listed authors representing 5,950 unique authors were included in our analysis: 42.5% were female, 35.1% male, and 22.4% unknown (p<0.0001). First authorship was more likely female (56.9%) and from a high-income country (74.6%, p<0.0001) while last authorship was mostly male (53.7%) also from a high-income country (82.5%, p<0.0001). Females more frequently published papers using qualitative data (61.4%) and reviews/estimates (59.4%) while men published more case control (70.7%) and randomised controlled studies (53.0%), p<0.0001. The adjusted odds of there being a female author increased 4% for every additional year that passed. Conclusion: While there are more females authoring articles as compared to the past, they are still lagging behind with regards to seniority and prestige. Likewise, female representation is closely tied to what institution they are affiliated with and where that institution is located. Global health research institutions need to actively promote change by ensuring women are included in research and research outputs, giving them opportunities to lead. [ABSTRACT FROM AUTHOR]

In today's fast-paced technological landscape, advancements are happening at the speed of light. Every aspect of the surroundings is intricately linked to development and technology. AI, cloud computing, and the IoT reign supreme in the realm of technology. In the realm of cyberspace, everything is interconnected through computers and networking devices. With the increasing reliance on computers and the internet, ensuring safety becomes ever more critical. Safeguarding network architecture and the proper use of networking devices and tools play a vital role in cybersecurity. This study has developed a system called Network Intrusion Detection System using ML, which is suitable for home network environments. This paper has created an application that makes a significant impact on real-time network environments by providing security for a particular home network. Leveraging ML in networks has improved the results by providing accuracy and efficiency. The algorithm of Logistic Regression is used to demonstrate network behavior and classify network traffic as either in an "Attack" or "Benign" state. This helps in detecting suspicious activities across the network and can prevent them at a later stage. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation. [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]

In some parts of the Vhembe District in Limpopo Province of South Africa, on-farm vandalism is becoming a serious problem, and solutions are not easy to find. Vandalism has had a variety of negative impacts on livestock farmers. A few studies have studied the concept of vandalism, but studies employing empirical models to determine the factors that are of significance to vandalism are conspicuously rare, if not absent. This paper investigated factors of significance to on-farm infrastructural vandalism in the Musekwa Valley of the Vhembe District, Limpopo Province. The study used a mixed method approach and collected data using a cross-cultural semi structured questionnaire instrument translated from English into the native Tshivenda. Data were collected from fifty-five (n=55) purposively selected primary participants who were interviewed using face to face approach. Additional data were collected through key informant interviews administered on purposively selected key informants (n=4), focus group discussions were done, and transect walks were employed for observation purposes. A binary logistic regression model was fitted to the data, and the following factors emerged as significant to vandalism: age, gender, education level, ownership of cattle, exposure to information, household expenditure, and reporting of incidents of vandalism to authorities. It is recommended that a community-based approach is adopted to find solutions to the problem of vandalism, to avoid a top-down approach that residents might undermine. [ABSTRACT FROM AUTHOR]

The widespread and substantial effect of the global financial crisis in history underlines the importance of forecasting financial crisis effectively. In this paper, we propose temporal convolutional network (TCN), which based on a convolutional neural network, to construct an early warning system for financial crises. The proposed TCN is compared with logit model and other deep learning models. The Shapley value decomposition is calculated for the interpretability of the early warning system. Experimental results show that the proposed TCN outperforms other models, and the stock price and the real GDP growth have the largest contributions in the crises prediction. [ABSTRACT FROM AUTHOR]

Aim: To assess the prevalence of non‐communicable disease risk factors among the nursing staff and educate them on prevention. Background: Nursing staff is integral to the Indian community healthcare systems. Recent studies report a high prevalence of non‐communicable diseases in Indian nursing staff. Therefore, data on the prevalence of non‐communicable disease risk factors among nursing staff are crucial for education on prevention. Design: A cross‐sectional digital survey‐based study. Method: We invited 4435 nursing staff to attend our online survey. We used a customized questionnaire for data collection, including a digitized version of the Community‐Based Assessment Checklist form. A score of >4 was considered high risk and warranted screening. Result: Among 682 nursing staff who attended, 70% had never undergone screening for non‐communicable diseases. The prevalence of non‐communicable disease risk factors was significantly higher in male nursing staff. In addition, logistic regression analysis showed that age, tobacco and alcohol use, increased waist circumference, physical inactivity and family history of non‐communicable diseases were significant risk factors among nursing staff. Conclusion: The study findings suggest that the nursing staff have suboptimal self‐health concerns on non‐communicable diseases. This situation warrants continued medical education, awareness campaigns on adopting a healthy lifestyle and health promotion. Summary statement: What is already known about this topic? The prevalence of non‐communicable diseases (NCDs) among Indian nursing staff is a growing concern, underscoring the need for comprehensive research and targeted interventions.Despite the significant health risks faced by this demographic, there is a notable lack of data on NCD risk factors specific to Indian nursing professionals. What this paper adds? This paper adds information on the prevalence of NCD risk factors chiefly among nursing staff in the Delhi National Capital Region.Male nursing staff have particularly high NCD risk factors. The implications of this paper: Findings may help develop routine screening programmes for nursing staff, continued nursing education and good, safe nursing practice. [ABSTRACT FROM AUTHOR]

Aims: This study describes the incidence of fatigue in kidney transplant recipients and analyses the relationship between physiological factors, psychological factors, situational factors and fatigue in kidney transplant recipients. Background: Fatigue, as a common symptom after kidney transplantation, is affected by many factors, but the influence of some factors on the fatigue of kidney transplant recipients is still controversial. Design: This cross‐sectional study was designed based on the theory of unpleasant symptoms. Methods: Our survey involved 307 participants attending the kidney transplant outpatient clinic of a tertiary Class A hospital (Changsha, Hunan, China). Data were collected between February and April 2021 using a structured questionnaire and electronic medical records. Data were analysed using IBM SPSS 25.0 (SPSS Inc.) Results: It was found that the incidence of fatigue in kidney transplant recipients was 53.1%. According to the binary logistic regression analysis, sleep quality, hypokalemia, anxiety, depression and education level were independent risk factors for fatigue in kidney transplant recipients. Conclusion: The incidence of fatigue in kidney transplant recipients was high and was influenced by physical, psychological and situational factors. Clinical nurses should assess fatigue levels in a timely and multidimensional manner in clinical practice and provide effective and scientific guidance about fatigue self‐coping and symptom management for kidney transplant recipients. Summary statement: What is already known about this topic? Fatigue is a common symptom of kidney transplant recipients and can impair postoperative recovery, reemployment, quality of life, social activities and physical and mental states, causing the body to be in a state of chronic energy consumption.Owing to the imperceptible physical injury upon kidney transplant recipients, fatigue is often underappreciated or even disregarded. What this paper adds? It was found that the rate of fatigue in kidney transplant recipients was high.The risk factors for fatigue in kidney transplant recipients based on the theory of unpleasant symptoms were analysed in three ways: physiological, psychological and situational. The results showed that sleep quality, duration after transplantation, serum potassium level, anxiety, depression, education level, financial burden and social support were associated with fatigue in kidney transplant recipients. The implications of this paper: Clinical nurses should conduct timely and multidimensional fatigue evaluations of kidney transplant recipients and pay more attention to those with a high level of fatigue.The findings can provide a scientific basis for kidney transplant specialist nurses to conduct targeted interventions and facilitate fatigue management for kidney transplant recipients. They can also help to develop fatigue intervention models based on the displeasure symptom theory to improve fatigue in kidney transplant recipients. [ABSTRACT FROM AUTHOR]

Maintaining user privacy in machine learning is a critical concern due to the implications of data collection. Federated learning (FL) has emerged as a promising solution by sharing trained models rather than user data. However, FL still faces several challenges, particularly in terms of security and privacy, such as vulnerability to inference attacks. There is an inherent trade-off between communication traffic across the network and computational costs on the server or client, which this paper aims to address by maneuvering between these performance parameters. To tackle these issues, this paper proposes two complementary frameworks: PolyFLAM ("Polymorphic Federated Learning Aggregation of Models") and PolyFLAP ("Polymorphic Federated Learning Aggregation of Parameters"). These frameworks provide two options to suit the needs of users, depending on whether they prioritize reducing communication across the network or lowering computational costs on the server or client. PolyFLAM reduces computational costs by exchanging entire models, eliminating the need to rebuild models from parameters. In contrast, PolyFLAP reduces communication costs by transmitting only model parameters, which are smaller in size compared to entire models. Both frameworks are supported by polymorphic encryption, ensuring privacy is maintained even in cases of key leakage. Furthermore, these frameworks offer five different machine learning models, including support vector machines, logistic regression, Gaussian naïve Bayes, stochastic gradient descent, and multi-layer perceptron, to cover as many real-life problems as possible. The evaluation of these frameworks with simulated and real-life datasets demonstrated that they can effectively withstand various attacks, including inference attacks that aim to compromise user privacy by capturing exchanged models or parameters. [ABSTRACT FROM AUTHOR]

Introduction: The North-eastern region of India has a relatively higher prevalence of substance use, which together with poor dietary practices and a lack of physical activity is one of the key risk factors for NCDs among older adults in the region. Understanding the prevalence of NCDs and their relationship to substance use can help develop preventive strategies and sensitization in North-eastern India. Objective: To assess the prevalence of NCDs and the strength of the association of substance abuse among the geriatric population of North-eastern states in India, for the development of preventive strategies. Methods: Data from the Longitudinal Ageing Study in India (LASI Wave-I, 2017–18) were drawn to develop this paper. The bi-variate and binary logistic regression analyses were carried out to predict the association between non-communicable diseases and substance use adjusting select socio-demographic characteristics. Results: The paper revealed the prevalence of NCDs among urban people (61.45%) is higher than among rural people (42.45%). Hypertension (37.29%) can be seen as the most prevalent disease among the following given NCDs followed by Diabetes (8.94%). The chances of having Cancer are nineteen times higher (OR = 19.8; C.I. = 18.82–20.83) if an individual has past smoking behaviour after controlling for socio-demographic and physical activity variables. Conclusion: Since, the high prevalence of hypertension correlated with the high level of substance abuse, require immediate attention to develop appropriate intervention strategies for its control (substance abuse) and prevention of hypertension. In a lower middle-income country like India, preventive measures, rather than curative measures will be cost-effective and helpful. [ABSTRACT FROM AUTHOR]

Introduction: Despite a growing interest in the measurement and conceptualization of wellbeing, the integration within sustainability research, and the understanding of how different wellbeing outcomes relate, is limited. Many studies focus on singular, often objectively measured, outcomes, without acknowledging the breadth of available measures. This approach can result in crucial subjective information, which can be explored to understand actors' behaviors and responses, being omitted from research and policy. This study explores objective and subjective wellbeing outcomes, and how they relate, within an environmentally vulnerable context. Wellbeing and environmental services are intrinsically interlinked, therefore, appropriate policy solutions are required to address human needs and pressures on supporting ecosystems. Methods: This paper uses binary logistic regression modelling, and qualitative participatory rural appraisal methods, to understand the environmental conditions, including climatic hazards and landscape characteristics, associated with households experiencing different objective/subjective wellbeing outcomes within Volta Delta, Ghana. Results: The mixed method approach highlights a differing relationship between inland agricultural areas impacted by drought and erosion, and coastal/riverine, peri-urban landscapes exposed to flooding and salinization. Agricultural areas associate with "poor but happy" outcomes, whereas peri-urban landscapes associate with being "non-poor but unhappy." Drawing on existing literature, and both quantitative and qualitative results, these varying outcomes are hypothesized to be driven by differences in livelihood vulnerability, relative comparisons to others, responses to climatic hazards, and individualistic/collective wellbeing conceptualizations. Discussion: Our study concludes that environmental conditions influence objective and subjective wellbeing through different mechanisms. Sustainable development research should incorporate both objective and subjective measures when implementing and monitoring policy to more comprehensibly capture, and improve, wellbeing in environmentally vulnerable locations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Pilots are frequently exposed to thrombotic risk as a result of immobility from air travel. As hypoxemia is associated with secondary polycythemia, and polycythemia increases the risk of thrombosis, intermittent exposure to high-altitude hypoxic environments could escalate the risk of thrombosis in pilots. Our objectives were to find the prevalence of polycythemia in airplane pilots (primary outcome) and to assess associated risk factors of polycythemia (secondary outcome). METHODS: This study is a cross-sectional descriptive study. Data was collected from paper-based and computerized medical records of airplane pilots who applied for Class 1 Aviation Medical Certificate renewal at the Institute of Aviation Medicine, Royal Thai Air Force, Bangkok, Thailand, in 2018. The data was sampled by a simple random sampling technique. RESULTS: A total of 386 paper-based records were sampled. Of those, 29 (7.5%) of the pilots met polycythemia criteria. Spearman's correlation analysis showed a significant positive correlation between Body Mass Index (BMI) and hemoglobin (correlation coefficient = 0.127) and between BMI and hematocrit (correlation coefficient = 0.105). In multivariate logistic regression of each variable on polycythemia as defined by hemoglobin alone, piloting a non-pressurized aircraft was found to be an independent predictor of polycythemia (odds ratio = 4.3). DISCUSSION: The prevalence of polycythemia in airplane pilots was 7.5%. Operating a non-pressurized aircraft was a significant risk factor of polycythemia, and pilots with higher BMI were more likely to have increased red blood cell parameters. [ABSTRACT FROM AUTHOR]

Research on small and medium-sized enterprises (SMEs) access to bank finance is vital for the euro area economy. SMEs heavily represent the European business sector, employing around 100 million people and accounting for more than half of the Gross Domestic Product. Research studies in the field often rely on the ECB/EC Survey on the Access to Finance of Enterprises (SAFE). Many studies employ probit or logit models with categorical dependent variables derived from SAFE. The research findings show that hardly any study employs the simpler linear probability model (LPM), with a dominant lack of research providing evidence that justifies the model selection process and suitability. However, it is well known that different econometrics models can lack consistency and frequently yield different results. Yet, the literature has no consensus on the best econometric approach. In addition, there is a lack of robustness tests in the literature to ensure model validity, underlining the need for a comprehensive review of the methodological framework that dominates SAFE data use. This paper addresses the identified research gap by introducing a robust methodological framework that helps researchers identify and choose an appropriate categorical model when using SAFE data. The study adds significant value to the extant literature by identifying four criteria that need to be considered when selecting the appropriate model among three common binary dependent models: LPM, probit and logit models. The findings show that the probit model was appropriate is all cases but that the LPM should not be disregarded, as it can be used in two cases: when considering the interaction between monetary policy and debt to assets and monetary policy and innovation. The use of the LPM is justified as a less complex econometric model, allowing for clearer communication of the results. This innovative, robust approach to choosing the appropriate econometric categorical dependent model when employing SAFE data contributes to support policy effectively. [ABSTRACT FROM AUTHOR]

Road traffic crashes are becoming thorny issues being faced worldwide. Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out. However, the impact of built environment on traffic crash spatiotemporal trends has not received much attention. Moreover, the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected. To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes, this paper proposed a method of "ST-GWLR" for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression (GWLR) modeling methods. Firstly, the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model, hotspot analysis, and Mann-Kendall trend test. Then, the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression (GLR) model, to improve the accuracy of the model estimation. GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors, to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes. The performance of the GWLR models and GLR models was examined and compared sufficiently. The results showed that the proposed ST-GWLR, which captured spatial non-stationarity, performed better than the classic GLR combined with spatiotemporal analysis, and improved the prediction accuracy of the models by 14.9%, 13.9%, and 15.1%, respectively. There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors. The findings of this paper have positive implications for traffic safety management and urban built environment planning. [ABSTRACT FROM AUTHOR]

Objectives: To explore the rate of NUPs and associated factors in the PED of the 'Hospital Universitario y Politécnico La Fe' in Valencia (Spain) using Andersen's Behavioural Model. Methods: We conducted a descriptive cross‐sectional study using Andersen's Behavioural Model in parents visiting the PED with their children at the 'Hospital Universitario y Politécnico La Fe' in Valencia (Spain). Results: The study involved a total of 530 participants, of whom 419 (79%) had made an NUP. The predisposing factors identified were: (I) paediatric patients brought in by their fathers (OR = 0.460; p = 0.005), (II) lower educational attainment (OR = 3.841; p = 0.000), (III) first‐time parenthood (OR = 2.335; p = 0.000) and (IV) higher parental stress (OR = 1.974; p = 0.023). The enabling factors included: (I) responsibility for a significant part of the childcare shared with others (OR = 0.348; p = 0.041) and (II) the perception that PEDs provide better care than primary care (PC) services (OR = 1.628; p = 0.005). The need factors were: (I) existing chronic illness in the child seeking care (OR = 0.343; p = 0.000) and (II) the perceived severity of the urgency (OR = 0.440; p = 0.031). Conclusions: The NUP rates found in this study are similar to those found internationally. In accordance with Andersen's Behavioural Model, we identify predisposing, enabling and need factors to explain the multifactorial nature of NUPs in PEDs. Implications for Practice: Identifying the factors associated with NUPs enables interventions to be targeted at those groups most likely to engage in NUPs, thereby optimising the functioning of the PED and improving the well‐being of children and families. These interventions should focus on improving parental health literacy, providing education on making appropriate decisions about accessing health services and recognising severe symptoms in children, as well as improving access to high‐quality PC services. Providing support to parents during the transition to parenthood would also be beneficial. Reporting Method: This paper adheres to the STROBE initiative guidelines. Contribution from patients or members of the public: Participants, who voluntarily agreed to take part, contributed to the study by completing a paper‐based questionnaire containing all the study variables as prepared by the research team. [ABSTRACT FROM AUTHOR]

In a construction project, especially in risky environments, subcontractors are considered as important as the main contractor since most work is carried out by them. A crucial step in building projects is choosing the finest subcontractor. When choosing subcontractors, a variety of considerations must be taken into account. A poor choice of subcontractors might cause a lot of issues when the job is being done. These include subpar work and project length delays. Many things influence this process. From earlier studies, variables that affect the choice of a subcontractor have been compiled. The main aim of the paper is to predict the failure of the subcontractor by using a mix of several advanced technology as BIM, machine leaning and building a system. The methodology of the paper is first to use BIM to see the mistakes and unqualified quantity estimation by the subcontractors which lead to increase the cost of the projects and hence failures of the projects. The second step is to identify primary reasons for subcontractors' failure and identify and evaluate the major reasons why contractors fail on industrial projects in Iraq. The third step is to predict the performance of the subcontractor using three algorithms (logistic regression, decision tree and Naive Bayes) with data collected from 234 projects and see how the wrong selection of the subcontractors leads to incomplete projects in the future. The variables that used in the paper include the most effective one, as change order effect directly on the projects performance as well as cost, time, deviation, type of projects and the rate of project completion, the low the percentage of completion the worse the performance. The final step is to develop a system to manage the subcontract in different projects stage. The results show that there is a different in the quantity estimated between the actual and the estimated range between (5% to 15%) in several item of the building, which consider high. the best algorithms for the is Naive Bayes as give accuracy about 97%. [ABSTRACT FROM AUTHOR]

Cryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification accuracy of ECB and CBC encryption modes in block cryptosystem is significantly higher than that of the other two classification models by 35.2% and 36.1%. In single division, the identification accuracy is higher than HGBDTLR with a maximum of 9.8%, and higher than HKNNRF with a maximum of 7.5%. At the same time, the scheme proposed in this paper has significantly improved the identification effect compared with the single division identification accuracy of 17 cryptosystem directly and the 17 classification accuracy of 5.9% compared with random classification, which indicates that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has significant advantages in the accuracy of identifying multiple cryptographic algorithms. [ABSTRACT FROM AUTHOR]

Simple Summary: Achieving improved rates of overall survival remains a significant challenge in advanced-stage ovarian cancer; even if complete debulking is achieved, certain patients report poor results in terms of survival. Therefore, the aim of this paper is to investigate new prognostic markers that could point out which cases could benefit most from debulking surgery and which ones should be rather submitted to neoadjuvant therapies followed by debulking surgery. Background: Despite improving surgical techniques and achieving more often complete debulking procedures, certain patients with advanced-stage ovarian cancer still have a very poor prognosis. The aim of the current paper is to investigate whether inflammatory and nutritional status can predict the long-term outcomes of ovarian cancer patients. Methods: A retrospective analysis of 57 cases diagnosed with advanced-stage ovarian cancer submitted to surgery as first intent therapy was carried out. In all cases, the preoperative status was determined by calculating the CRP/albumin ratio, as well as the Glasgow score, the modified Glasgow score and the prognostic nutritional index. Results: Patients presenting higher values of the CRP/albumin ratio, with a higher Glasgow score, modified Glasgow score and prognostic nutritional index (PNI), were more frequently associated with incomplete debulking surgery, a higher peritoneal carcinomatosis index and poorer overall survival (20 months versus 9 months for the CRP/albumin ratio p = 0.011, 42 versus 27 versus 12 months for the Glasgow score p = 0.042, 50 versus 19 versus 12 months for the modified Glasgow score, p = 0.001, and 54 months versus 21 months, p = 0.011 for the prognostic nutritional index). Conclusions: A strong relationship between the nutritional and inflammatory status in advanced-stage ovarian cancer seems to exist. [ABSTRACT FROM AUTHOR]

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product, and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria. Then, the logistic regression algorithm is employed to deal with the reference samples, establish the machine discrimination model, and realize the discrimination of fault types. Finally, simulation test results and experimental results verify the accuracy of the proposed method. The comparison analysis shows that the proposed method has higher recognition accuracy, especially when the arc dissipation power is smaller than 2 x 10³ W, the zero-off period is not obvious. In conclusion, the proposed method expands the arc fault identification theory. [ABSTRACT FROM AUTHOR]

Purpose: While the literature has highlighted the impacts of COVID-19, there is limited evidence on the gendered determinants of the impact of COVID-19 among small-scale rural traders in developing and emerging economies. Design/methodology/approach: Cross-border fish traders who had operated before and during the COVID-19 pandemic were interviewed in a survey conducted in Zambia and Malawi. Logistic regressions among male and female traders were employed to assess the gendered predictors. Findings: Heterogeneous effects in geographical location, skills, and knowledge were reported among male cross-border traders. Effects of household structure and composition significantly influenced the impact of COVID-19 among female traders. Surprisingly, membership in trade associations was associated with the high impact of COVID-19. Research limitations/implications: Due to the COVID-19 pandemic and the migratory nature of cross-border fish traders, the population of cross-border fish traders at the time of the study was unknown and difficult to establish, cross-border fish traders (CBFT) at the landing sites and market areas were targeted for the survey without bias. Originality/value: This paper addresses a gap in the literature on understanding gendered predictors of the impacts of COVID-19 among small-scale cross-border traders. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation. Design/methodology/approach: The study analyzes data from 1,002 farmers' surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA). Findings: The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA. Originality/value: This study shows that in the context of DMU, SN's promotion effect on farmers' adoption of CCMM is strengthened. [ABSTRACT FROM AUTHOR]

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the "is_arrested" class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification. [ABSTRACT FROM AUTHOR]

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention. [ABSTRACT FROM AUTHOR]

Background: Ghana was one of the few countries commended to have halved extreme poverty after the MDG era yet the under-five mortality rate (U5MR) was dire. Ghana's U5MR is still above the national and global SDG target. As a result, the government has over the years implemented other complementary social and policy interventions to improve the socioeconomic status of households to ensure better child health outcomes. Yet key gaps exist and threaten child health outcome sustainability, and child health-related SDG targets. In this regard, we hypothesize that household wealth should therefore not influence under-five child deaths significantly. Methods: The paper first reports the under-five mortality trend over the past 30 years in Ghana. It uniquely analyses the levels and trends in wealth as measured by the Gross National Income per capita at purchasing power parity (ln GNI/p, ppp) as a proxy of the mean Comparative Wealth Index (CWI) against under-five child deaths in Ghana. Using data from the 2014 Ghana Demographic and Health Survey and employing the logistic regression estimation technique, the paper further estimates the effect of household wealth and other key covariates on under-five child deaths. Implemented government poverty-alleviating policies and intervention programs to reduce childhood mortality in Ghana were reviewed to identify risk gaps to child deaths. Results: The 30 year trend analysis from 1990 to 2020 shows that the under-five mortality rate in Ghana is still high, falling short of the rate of decline that is expected toward achieving the 2030 SDG target for U5MR of 25 deaths per 1000 live births. Our empirical estimations show that household wealth still has a negative and significant relationship with under-five child deaths despite the government's existing poverty-alleviating and pro-poor health policies. In addition, the results show that regional differences, maternal age, the number of children alive, and attendance to postnatal healthcare services are crucial for child survival. Conclusion: The results imply that the government should consolidate universal programmes with intensified targeted interventions toward improving household wealth, especially in the Northern Savannah regions. A new and dynamic child health policy is imminent to reflect strategies towards achieving the SDG target on U5MR. Additionally, intensive education on active maternal participation in postnatal care should be given critical consideration to improve child survival. [ABSTRACT FROM AUTHOR]

Current electricity sectors will be unable to keep up with commercial and residential customers' increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1. [ABSTRACT FROM AUTHOR]

Aims: Dehydration is one of the common complications of dysphagia and poses significant risks including hospitalization and mortality, but the relationship between dysphagia and dehydration has received little attention. This study aims to determine the prevalence and risk factors for dehydration of patients with dysphagia in eastern China, and to provide reference for early identification and prevention of dehydration. Methods: A descriptive, cross‐sectional design was conducted. Three hundred and thirty‐seven (n = 337) patients with dysphagia participated in the study between August and December 2022. Information relating to participants' demographic variables, nutrition, cognition, functional, hydration status and fluid intake was collected. Univariate analysis was used to examine related impact factors, and then binary logistic regression analysis was conducted to determine reliable impact factors. Results: Among 337 patients with dysphagia, the average age was 63.47 ± 16.96, most participants were male (72.1%) and married (91.7%). The prevalence of dehydration was calculated to be 43.9%, the mean plasma osmolality score was 293.53 mmol/L. Diseases with the highest prevalence were stroke (78.3%), followed by hypertension (63.5%). The risk for dehydration increased with older age, usage of more medicines such as diuretics and beta‐blockers, worse functional status and lower fluid intake. Conclusion: This study found a high percentage of dehydration in patients with dysphagia. Findings can provide a basis for targeted nursing interventions for clinical prevention and treatment of dehydration. Summary statement: What is already known about this topic? Dehydration is one of the adverse consequences of dysphagia, and it causes physical stress to sufferers and enormous costs to the health care system.Previous studies have identified that personal factors (age, gender, health condition etc.), living environment, and social support may affect dehydration What this paper adds? Results showed high percentages of dehydration among patients with dysphagia; older age, greater use of medicines such as diuretics and beta‐blockers, worse functional status and lower fluid intake played a crucial role in hospital patients with dysphagia, leading to dehydration. The implications of this paper: This paper reveals prevalence and risk factors associated with dehydration of patients with dysphagia in hospital.Findings could provide reference data for development of nursing preventive interventions, to reduce the incidence of dehydration and improve the health level of patients. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care. Background: With shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization. Design: This is a single‐centre retrospective descriptive study. Methods: Logistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April–September 2017 as risk factors. Results: Unplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04–4.77), malignancy (OR: 2.16, 95% CI: 1.44–3.24), non‐surgical admission (OR: 1.76, 95% CI: 1.07–2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14–2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06–2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non‐malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days. Conclusions: Patients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care. Summary statement: What is already known about this topic? Although the characteristics of patients at high risk of rehospitalization include malignancy, heart failure, conditions such as chronic obstructive pulmonary disease and other diseases prone to recurrence, old age, reduced activities of daily living and emergency hospitalization, few studies have reported on cases with multiple risks. What this paper adds? The most frequent risk types for unplanned readmissions within a month were emergency admissions for malignant tumours, emergency admissions for non‐malignant tumours with a hospital stay of 2 weeks or more and scheduled admissions for non‐surgical purposes with a length of stay of 2 weeks or more. The implications of this paper: The risk of unplanned readmission within 1 month of discharge increased due to a combination of multiple risks.This study helps us identify patients who are at high risk for readmission early in the hospitalization process and in providing nursing support for transition to their homes. [ABSTRACT FROM AUTHOR]

Despite the large evidence of the recent globalization phenomenon at national level, very little is known about the involvement of regional economies in Global Value Chains (GVCs). Instead, the regional dimension of GVCs is of primary importance since regions require an absolute advantage to be part of an international production chain. It can in fact be easily the case that within the same country both the participation and the gains from GVCs strongly differ among regions. In going to the territorial roots of GVCs, the paper aims to conceptualize a taxonomy of different modes in which regions can be involved in GVCs, based on different intensity of participation and rewarding conditions. Based on regional trade in value added matrices, the taxonomy is applied to the manufacturing sector at NUTS2 regions in Europe, combining two indicators of regional participation and 'terms of trade' imposed in the chain. Although national patterns are visible, and a clear divide between Eastern and Western Europe emerges, the modes of involvement are highly diversified within countries. Moreover, through a multinomial logit model, the local characteristics associated with the different roles that regions can have within GVCs are looked for. Their identification has far-reaching normative consequences that intervene in the capacity of regions to gain from participation in GVCs, and to mitigate the interregional income distribution effects that this involvement may cause. [ABSTRACT FROM AUTHOR]

Consumer price index has been in the centre of interest for many years, since being published in 1990s in the Czech Republic but recent price growth raised more questions on methodology and data sources used in price statistics. Users are interested not only in the figures itself but also in statistical issue influencing interpretation and the quality of consumer price index that is often used as an approximation of inflation rate. The paper introduces price statistics compiled by the Czech Statistical Office and it specifically focuses on data sources and in particular scanner data. The paper explains how advanced statistical methods such as machine learning are implemented in official statistical production. We think that the official statistics is being on the historical junction where modern methods are going to be implemented. Our paper shows the usage of machine learning procedures applied on scanner data within consumer price index. Used method is based on logistic regression and powerful Python solution and that provides fast and high quality results. [ABSTRACT FROM AUTHOR]

Non-take-up, i.e. individuals not applying for a benefit they are eligible for, is a widespread problem limiting the reach of welfare and protection systems. This paper seeks to understand it by means of a theoretical framework comprising two levels of analysis: the claimants' individual characteristics in relation to the information barriers they face, and the administrative logic and functioning regarding the communications strategy used by public institutions. To test the hypotheses of these two levels of analysis, the paper analyses the B-MINCOME pilot scheme, a cash transfer programme implemented in the city of Barcelona between 2017 and 2019. Findings indicate that, although claimants' characteristics may play a significant role, the administrative functioning and the communications strategy are fundamental in determining take-up rates. The conclusions briefly address some of the technical and moral concerns raised by non-take-up. [ABSTRACT FROM AUTHOR]

This paper assesses empirically the COVID-19 effect on businesses and the potential dynamic changes regarding post-COVID-19 automation and technology penetration using various logistic regression models. A field survey was used to collect the necessary data for testing various hypotheses. This study demonstrates the severity of the pandemic on businesses and how it has changed their perspectives on technology as a critical aspect of survival and future success. The results showed that capital-intensive firms are more resilient to the crisis. In addition, the firms that were affected severely in terms of employment due to the pandemic believe that technology will significantly impact hiring, investment, and value added. This paper investigates a unique phenomenon represented by COVID-19, its impact on businesses in a resource-rich context and their responsiveness concerning technology deployment and automation. [ABSTRACT FROM AUTHOR]

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one's behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service. [ABSTRACT FROM AUTHOR]

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Understanding visitors' spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors' experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include "cultural attraction seekers", "selective sightseers", and "city-life lovers". Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors' intentions to revisit the destination in the future. This research found that "cultural attraction seekers" and "selective sightseers" display a higher likelihood of considering a return visit to the city. [ABSTRACT FROM AUTHOR]

Purpose: The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO2 equivalent (CO2e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers. Design/methodology/approach: To describe customers' preferences, we design a multinomial logit model based on recipients' travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs. Findings: Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO2e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups. Originality/value: This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients' availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to explore the association between medication literacy and blood pressure control among hypertensive patients. Background: Blood pressure control is a challenge for global health systems. Medication literacy is essential for medication self‐management in hypertensive patients and a basis for managers to develop comprehensive intervention strategies for hypertension medication use. Design: A cross‐sectional observational study was conducted. Methods: A total of 378 hypertensive patients was selected by convenience sampling from two tertiary hospitals and four community health service centres from December 2021 to January 2022 in Changsha, China. Associations between medication literacy and blood pressure control were identified with chi‐square, independent samples t‐tests and logistic regression analyses. Results: The average medication literacy score of the hypertensive patients investigated was low. Over a third of patients had uncontrolled blood pressure. Logistic regression analysis showed that medication literacy was an influencing factor for blood pressure control rate in hypertensive patients. Conclusions: Medication literacy and blood pressure control among hypertensive patients was poor. Medication literacy was a facilitator of blood pressure control so improving medication literacy may be of value to improve blood pressure control in hypertensive patients. Summary statement: What is already known about this topic? Medication literacy is essential for medication self‐management in hypertensive patientsThe association between medication literacy and blood pressure control in hypertensive patients is unclear. What this paper adds? Medication literacy and blood pressure control rates among hypertensive patients were poor and in need of urgent improvement.Medication literacy was a facilitator of blood pressure control in hypertensive patients. The implications of this paper: Clinical nurses may enhance hypertension management by improving medication literacy.Findings provide a base for implementing medication literacy intervention strategies and promoting blood pressure management. [ABSTRACT FROM AUTHOR]

This paper uses non-traditional approaches to predict why volunteers remain in or quit a non-governmental organisation position. A questionnaire featuring 55 predictors was conducted via an online survey mechanism from March to May 2021. A total of 250 responses were received. The subsequent data analysis compared logistic regression and artificial neural network results, using machine-learning interpreters to explain the features which determined decisions. The results indicate greater accuracy for neural networks. According to the logistic regression results, intrinsic motivation, volunteering through an NGO and the age of volunteers influenced the intention to remain. Moreover, NGOs that offered online volunteering opportunities during the COVID-19 pandemic had higher rates of intention to remain. However, the neural network analysis, performed using the Local Interpretable Model-Agnostic Explanations (LIME) method, indicated the need to consider different predictors to those identified by the logistic regression. The LIME method also enables the individualisation of the explanations of predictions, indicating the importance of considering the role of volunteers' feelings in both quit and remain decisions, which is something that is not provided by traditional methods such as logistic regression. Furthermore, the LIME approach demonstrates that NGOs must address both volunteer management and experience to retain volunteers. Nonetheless, volunteer management is more critical to stop volunteers quitting, suggesting that volunteer integration is crucial. [ABSTRACT FROM AUTHOR]

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel 'FeatureBoostThyro' (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. [ABSTRACT FROM AUTHOR]

Green agricultural development opens up a feasible way for China to construct sustainable communities and promote rural revitalization strategies. However, a clear gap on the subject sets apart various regions in China, resulting in off-balance development. This paper, based on the panel data from 31 provinces (municipalities and autonomous regions) in China from 2013 to 2020, established an evaluation index system for China's green agricultural development levels, measured the green agricultural development in different regions, and applied the log(t) model, the Ordered Probit model, and the Ordered Logit model to identify convergence trends and causes of China's green agricultural development level club convergence, respectively. Our research found the following: (1) The green agricultural development levels in various regions in China are on the rise, and regional gaps are still significant. The output of green agricultural products is the focus of various regions in promoting green agricultural development. (2) The green agricultural development levels in China's 31 provinces (municipalities and autonomous regions) have converged to four clubs. China is gradually forming a convergence pattern of green agricultural development levels, with the main agricultural production areas as the core. (3) The regional gap at the level of environmental regulation for green agricultural development first narrows, then expands, and then narrows. The intensity of environmental regulation in most areas is in the first stage. This indicates that China should continue to maintain the current intensity and promote green agricultural development levels to converge towards high-level clubs. In addition, the GDP, grain production area, fiscal support for agriculture, and rural human capital can narrow the gaps in green agricultural development levels between regions. [ABSTRACT FROM AUTHOR]

The generalized multinomial logit (GMNL) model accommodates scale heterogeneity to the random parameters logit (RPL) model. It has been often used to study people's preferences and predict people's decisions in many areas, such as health economics, marketing, agricultural studies, transportation research and public policy. However, there are few works studying the efficiency of this model estimator and the corresponding estimation and prediction risks. In this paper, we use a frequentist model averaging (FMA) estimator to reduce the estimation and prediction risks of the GMNL model estimator. We show that the asymptotic squared error risk of the FMA estimator dominates that of the GMNL model estimator, and it is consistent with the results of our Monte Carlo experiments. The accuracy of the predicted choices is also higher based on the FMA estimates compared to the results based on the GMNL estimates. In the empirical analyses, using the FMA estimator improves the percentage of correct predicted choices by 10% compared to the results with GMNL estimates. This paper provides a more efficient alternative to the GMNL model to capture people's preferences and predict people's choices. [ABSTRACT FROM AUTHOR]

Although the Chinese government has implemented a variety of measures, the gender wage gap in 21st century China has not decreased. A significant body of literature has studied this phenomenon using sector segmentation theory, but these studies have overlooked the importance of the collective economy beyond the public and private sectors. Moreover, they have lacked assessment of the gender wage gap across different wage groups, hindering an accurate estimation of the gender wage gap in China, and the formulation of appropriate recommendations. Utilizing micro-level data from 2004, 2008, and 2013, this paper examines trends in the gender wage gap within the public sector, private sector, and collective economy. Employing a selection bias correction based on the multinomial logit model, this study finds that the gender wage gap is smallest and most stable within the public sector. Furthermore, the private sector surpasses the collective economy in this period, becoming the sector with the largest gender wage gap. Meanwhile, a recentered influence function regression reveals a substantial gender wage gap among the low-wage population in all three sectors, as well as among the high-wage population in the private sector. Additionally, employing Brown wage decomposition, this study concludes that inter-sector, rather than intra-sector, differences account for the largest share of the gender wage gap, with gender discrimination in certain sectors identified as the primary cause. Finally, this paper provides policy recommendations aimed at addressing the gender wage gap among low-wage groups and within the private sector. [ABSTRACT FROM AUTHOR]

Background: With the emergence of numerous scientific outputs, growing attention is paid to research misconduct. This study aimed to investigate knowledge, attitudes and practices about research misconduct among medical residents in southwest China. Methods: A cross-sectional study was conducted in southwest China from November 2022 through March 2023. The links to the questionnaire were sent to the directors of the teaching management department in 17 tertiary hospitals. Answers were collected and analyzed. Logistic regression analysis was performed to explore the factors associated with research misconduct among residents. Results: 6200 residents were enrolled in the study, and 88.5% of participants attended a course on research integrity, but 53.7% of participants admitted to having committed at least one form of research misconduct. Having a postgraduate or above, publishing papers as the first author or corresponding author, attending a course on research integrity, lower self-reported knowledge on research integrity and lower perceived consequences for research misconduct were positively correlated to research misconduct. Serving as a primary investigator for a research project was negatively associated with research misconduct. Most residents (66.3%) agreed that the reason for research misconduct is that researchers lack research ability. Conclusions: The high self-reported rate of research misconduct among residents in southwest China underscores a universal necessity for enhancing research integrity courses in residency programs. The ineffectiveness of current training in China suggests a possible global need for reevaluating and improving educational approaches to foster research integrity. Addressing these challenges is imperative not only for the credibility of medical research and patient care in China but also for maintaining the highest ethical standards in medical education worldwide. Policymakers, educators, and healthcare leaders on a global scale should collaborate to establish comprehensive strategies that ensure the responsible conduct of research, ultimately safeguarding the integrity of medical advancements and promoting trust in scientific endeavors across borders. [ABSTRACT FROM AUTHOR]

Consumer participation is critical to achieving successful food safety governance. However, in the field of food safety governance, consumer participation faces the dilemma of collective action. Based on social capital theory, this study introduces a total of 1229 questionnaires from online food consumers in China were collected by randomly distributing electronic questionnaires to online shoppers, and tests the effect and mechanism of social capital on consumer participation in food safety governance. By using ordered regression and multinomial logit models, the empirical results show that social capital can reduce the adverse effects of free-riding on consumers' participation in food safety governance and in fact will ultimately encourage consumers to participate in food safety governance. Furthermore, social capital will enhance people's participation in food safety governance through two paths: promoting consumers' sharing of food safety information and promoting consumers' direct supply of safe food. The conclusion of this paper provides inspiration for the promotion of consumer participation in the public affairs related to food safety governance in developing countries. [ABSTRACT FROM AUTHOR]