Search Results (144)

Remote work, as a technologically possible and widely applicable working mode, gained renewed attention during lockdowns amidst the COVID-19 pandemic. On one hand, remote work ensured that working remained sustainable; on the other hand, the unexpected and widespread nature of the immediate shift to remote work led to issues in terms of practicing and adapting to the process. Moreover, remote work can have strong social, economic, and environmental effects that have to be comprehensively understood. The high interest of employees in continuing with full or hybrid remote work calls for effective coping strategies at the individual and organizational levels in the future. This article focuses on academic studies documenting the peculiarities of remote work during the COVID-19 lockdowns. The aim is to identify the issues relating to remote work during the COVID-19 lockdowns that are documented in academic studies and thematically classify them into a range of factors. In this study, bibliometric and content analyses were employed, leading to comprehensive insights into the following areas: (1) remote work as a cause for changes in physical and psychological health; (2) remote work as a cause for changes in daily behavior, routine, and lifestyle; (3) factors that affect the process and productivity of remote work; (4) societal, economic, and environmental consequences of remote work; and (5) the distribution of the effects of remote work on individuals, economic subjects, and sectors. In conclusion, this study on working practices during the COVID-19 lockdowns that were documented in academic studies offers several benefits and areas of novelty: first, a comprehensive overview of the widespread process of adjusting to this new working mode; second, a classification of factors that affected the process at different stages and in different areas; and third, common factors that had more widespread effects during the remote working period. The findings also offer the following theoretical and practical implications: For researchers, this article can be a reference offering a holistic view of remote working during these lockdowns. For practitioners, it can provide an understanding of the impacting factors and their contextualization in terms of health, sociodemographic, and sectoral aspects can allow for more accurate human resource management strategies. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

Introduction: Nurses are the largest group of healthcare workers, and healthcare managers should pay attention to the quality of work life and the health and working capacity of nurses in order to ensure a sufficient number of nurses and a stable workforce. Aim: The present study aimed to determine nurses’ quality of work life, work ability index and intention to leave the nursing profession and to examine the associations between nurses’ quality of work life, work ability index and intention to leave the nursing profession. Methods: An online cross-sectional study was conducted. A total of 498 nurses completed the instrument, consisting of demographic data, Brooks’ Quality of Nursing Work Life Survey (BQNWL), Work Ability Index Questionnaire (WAIQ) and questions on their intention to leave their current job or the nursing profession. Results: Most nurses had a moderate quality of work life (QWL) (73.7%) and a good work ability index (WAI) (43.78%). Men (p = 0.047), nurses who study (p = 0.021), nurses who do not have children (p = 0.000) and nurses who do not take care of their parents (p = 0.000) have a statistically significantly higher total WAIQ score. Most nurses (61.1%) had considered changing jobs in the last 12 months, and 36.9% had considered leaving the nursing profession. A statistically significant positive correlation was found between the total BQNWL and the total WAI. The study found no correlation between QWL, WAI and intention to change jobs or leave the profession, which was unexpected. Conclusions: To ensure the provision of necessary nursing care and a healthy working environment for nurses, it is necessary to regularly monitor QWL and WAI and take measures to ensure the highest quality of working life. Further longitudinal and mixed-methods research is needed to understand the relationship between QWL, WAI and intention to leave. Full article

Accurate traffic forecasting is challenging due to the complex spatial-temporal interdependencies of large road networks and sudden speed changes caused by unexpected events. Traditional models often struggle with the non-stationary and volatile characteristics of traffic time series. While existing sequence decomposition methods can capture stable long-term trends and periodic information, they fail to address complex fluctuation patterns. To tackle this issue, we propose the Spatial-Temporal traffic flow prediction with residual and trend Decomposition Transformer (STDformer), which decomposes time series into different components, thus enabling more accurate modeling of both short-term and long-term dependencies. Our method processes the time series in parallel using the Trend Decomposition Block and the Spatial-Temporal Relation Attention. The Spatial-Temporal Relation Attention captures dynamic spatial correlations across the road network, while the Trend Decomposition Block decomposes the series into trend, seasonal, and residual components. Each component is then independently modeled by the Temporal Modeling Block to capture its unique temporal dynamics. Finally, the outputs from the Temporal Modeling Block are fused through a selective gating mechanism, combined with the Spatial-Temporal Relation Attention output to produce the final prediction. Extensive experiments on PEMS traffic datasets demonstrate that STDformer consistently outperforms state-of-the-art traffic flow prediction methods, particularly under volatile conditions. These results validate STDformer’s practical utility in real-world traffic management, highlighting its potential to assist traffic managers in making informed decisions and optimizing traffic efficiency. Full article

Introduction: Preterm birth is the leading cause of perinatal mortality worldwide, with prevalence rates showing little reduction. Although mortality rates have decreased, morbidity rates remain concerningly high. In recent years, there has been a surge in studies examining the etiology, risk factors, and management of preterm birth. The use of vaginal probiotics in pregnant women at risk of preterm birth has garnered attention as a potential approach for improving perinatal outcomes and modulating the vaginal microbiota. However, the efficacy of this intervention remains unclear. Therefore, this study explored the impact of vaginal probiotics on perinatal outcomes and vaginal microbiota composition in pregnant women at risk of preterm birth. Materials and Methods: This was a randomized, prospective, longitudinal, double-blind, placebo-controlled, multicentric trial conducted across seven maternities in Spain from October 2017 to August 2022 in pregnant women at risk of preterm birth. Participants were randomly assigned to receive vaginal probiotics containing four lactobacilli strains or a placebo. The primary outcome was to explore a potential correlation between probiotic use among pregnant women at risk of preterm birth and the actual rate of preterm birth before 37 gestational weeks. Secondary outcomes included an evaluation of preterm birth rates, neonatal morbidity, the vaginal microbiota, and changes in the vaginal microbiota after receiving probiotics. Other secondary outcomes were identifying vaginal microbiota patterns associated with preterm birth and exploring potential therapeutic mechanisms involving probiotics. Trial registration: Clinicaltrials.gov, identifier: NCT03689166. Results: A total of 200 participants were included. Of those, birth data were obtained for 181 women. Demographics were similar between both groups. An analysis of perinatal outcomes found no significant differences in preterm birth rates, prematurity rates, gestational weeks at delivery, neonatal complications, time to birth, or latency time to delivery. Microbiota analysis showed no significant differences in vaginal microbiota changes between groups. No serious or unexpected adverse reactions were reported. Conclusions: There were no statistically significant differences for spontaneous preterm birth between pregnant women receiving probiotics and pregnant women receiving the placebo. Full article

Understanding historical climatic extremes and variability is crucial for effective climate change adaptation, particularly for urban flood management in developing countries. This study investigates historical precipitation trends in the Kathmandu Valley, Nepal, focusing on precipitation frequency, intensity, and the influence of the El Niño–Southern Oscillation (ENSO), using extreme precipitation indices and the precipitation concentration index (PCI). The results reveal sharply fluctuating short-term precipitation from 1980 to 2022, with the exception of an increasing trend during spring (1.17 mm/year) and a decreasing trend in November and December. Trends in extreme precipitation indices are mixed: RX7day shows an increasing trend of 0.1 mm/year, with decadal analysis (1980–2001 and 2002–2022) indicating similar upward patterns. In contrast, RX1day, RX3day, RX5day, and R95pTOT exhibit inconsistent trends, while R99pTOT demonstrates a decreasing trend over the full period (1980–2022). Although the number of days with precipitation ≥ 35 mm has declined, the increasing trend in 7-day maximum precipitation, coupled with no significant change in total annual precipitation and highly variable short-term rainfall, points to a rising risk of unexpected extreme precipitation events. Precipitation patterns in the Kathmandu Valley remain highly irregular across seasons, except during summer. ENSO exhibits a negative correlation with annual precipitation, extreme precipitation indices, and the PCI but shows a positive correlation with the annual and summer PCI as well as 1-day maximum precipitation, emphasizing its significant influence on precipitation variability. These findings highlight the urgent need for targeted climate adaptation strategies and provide valuable insights for hydrologists, meteorologists, policymakers, and urban planners to enhance climate resilience and improve flood management in the Kathmandu Valley. Full article

The COVID-19 pandemic has led to significant shifts in global water consumption, particularly affecting wastewater treatment plants (WWTPs). In Kuwait, where high residential water usage exists, the lockdowns resulting from the pandemic created a unique opportunity to evaluate the effects of altered human activity on wastewater characteristics. This study examines the effects of the lockdown on key wastewater parameters, including flow rate, COD, BOD, TSS, total Kjeldahl nitrogen (TKN), and total phosphorus (TP). Data were collected from four WWTPs in Kuwait over 4 to 8 years, with the Kabd WWTP providing continuous daily data for an 8-year period. A comparative analysis was conducted between pre-lockdown, lockdown, and post-lockdown periods using statistical methods such as paired t-tests. The study also integrates a global comparison to relate Kuwait’s findings. Results indicate a significant increase in wastewater flow (7.6%) during the lockdown, rising from 165,486 m³/d to 178,033 m³/d. COD and BOD levels increased by 27.1% and 18.9%, respectively, while TSS showed the largest rise at 29.9%. TKN increased by 20.1%, indicating higher nitrogenous waste contributions from residential sources. These findings highlight the pandemic’s impact on wastewater characteristics in Kuwait, driven primarily by increased domestic water consumption. The study underscores the necessity of adaptive wastewater management strategies, especially in semi-arid regions, where WWTPs must be equipped to handle unexpected changes in wastewater composition. This research provides essential insights for improving the flexibility of wastewater systems with future disruptions, contributing to both environmental management and public health awareness. Full article

The synthetic cortisol analog budesonide (BUD) is an essential drug employed to manage chronic inflammatory diseases in humans, mainly those involving gastroenteric and airway mucosa, such as rhinitis, laryngitis, bronchitis, esophagitis, gastritis, and colitis, with high levels of success. As a glucocorticoid, BUD prevents the expression of pro-inflammatory cytokines/chemokines and the recruitment of immune cells into the inflamed mucosa. However, emerging evidence indicates that BUD, unlike classical glucocorticoids, is also a potent modulator of stem and cancer cell behavior/plasticity. Certainly, BUD stabilizes cell–cell adhesions, preventing embryonic stem cell differentiation and inhibiting the development of 3D gastruloids. In addition, BUD inhibits the motile/invasive propensity of different cancer cells, including breast, lung, and pancreatic cancer. Finally, it prevents the infection of positive single-stranded human-infecting RNA viruses such as SARS-CoV-2. At a molecular level, BUD induces epigenetic changes and modifies the transcriptome of epithelial, stem, and cancer cells, providing molecular support to the immune cell-independent activity of BUD. Here, we performed an in-depth review of these unexpected activities of BUD, identified by unbiased drug screening programs, and we emphasize the molecular mechanisms modulated by this efficacious drug that deserve further research. Full article

This study explores the passengers’ accessibility dilemma in Valparaíso, Chile, through field observations and laboratory experiments. The aim is to investigate the accessibility in metro stations based on the users’ emotional experience. Perceptions were reported through the emotions of passengers according to a circumplex psychological model and an accessibility ranking. Passengers reported their emotions (e.g., stress, sadness, relaxation, and happiness) during different trip moments. Results indicate that rearranging train seats parallel to movement creates a more spacious aisle, enhancing mobility and evoking positive emotions such as happiness. However, an unexpected rise in sadness suggests that social dynamics may influence emotional responses, warranting further investigation. Overcrowding increases stress and sadness, emphasizing the need for capacity management to improve passengers’ emotional experiences. Field observations reveal that early journey stages, such as walking to the station or waiting on the platform, are associated with unpleasant experiences due to poor infrastructure and accessibility barriers. In contrast, train rides foster more positive emotions, credited to better accessibility onboard. Passenger dissatisfaction arises from issues such as elevator malfunctions, inconsistent train schedules, and inadequate station accessibility. This study could help to understand passenger behavior when the accessibility conditions of metro stations and their surroundings are changed. Further studies will expand the concept of emotions by considering social and psychological factors and explore different types of stations and their surroundings considering a larger sample size in laboratory experiments and field studies. Full article

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments. Full article

This study assessed the toxicity and therapeutic potential of Angelica dahurica and Angelica pubescens using Danio rerio (zebrafish) larvae. Toxicity was evaluated through mortality, malformations, and gene expression changes related to stress and the HPA axis. A. dahurica demonstrated low toxicity (LD50 (50% lethal dose) >200 µg/mL), with no significant malformations at 15–30 µg/mL, although higher doses caused edemas and heart defects. A. pubescens exhibited higher toxicity, with 100% mortality at 200 µg/mL and severe malformations. Both species showed potential cardiotoxicity, slowing heart rates after prolonged exposure. Gene expression studies suggested A. dahurica had stress-protective effects, increasing nr3c1 expression, while A. pubescens had dose-dependent effects, with lower concentrations having anxiolytic properties and higher concentrations increasing stress. Interestingly, diazepam showed unexpected gene expression changes, highlighting the influence of environmental and dosage factors. In conclusion, both species show therapeutic potential for anxiety, with A. dahurica showing promising effects at lower concentrations. However, A. pubescens requires careful dosage management due to its higher toxicity risks. Further studies are needed to optimize therapeutic applications and fully understand mechanisms of action. Full article

Micro- and small enterprises, despite their effective and significant role in strengthening the economy, especially in developing countries, continue to struggle, particularly in adverse conditions and unstable governments. Accordingly, there is a need to understand the key factors that can internally enhance micro- and small enterprises and support them in standing strong and becoming more resilient during adverse times, ultimately ensuring better economic contribution. This research investigates how coping with unexpected challenges, described as the ability to manage and adapt to unexpected challenges, and defining core purpose, defined as the ability to define core vision and values for the business, enhances micro- and small enterprises’ resilience during adverse conditions. This study further investigates whether business resilience, described as the ability of a business to adapt effectively to changing unstable environments, positively influences business economic sustainability. This study also examined whether business resilience can positively mediate the relationship between coping with unexpected challenges, defining core purpose and having business economic sustainability. Accordingly, a sample of 303 respondents was collected from micro- and small entrepreneurs operating different types of activities. This study’s findings reported that coping with unexpected challenges and defining core purposes positively influenced business resilience and economic sustainability. This study also revealed that business resilience can directly and significantly influence business economic sustainability and could partially mediate the connection between coping with unexpected challenges, defining core purpose and having business economic sustainability. This study concluded by offering theoretical and practical implications to entrepreneurs, policymakers and stakeholders. Full article

This paper is dedicated to studying the optimal investment proportions of three types of assets with symmetry, namely, risky assets, risk-free assets, and wealth management products, when the stochastic expenditure process follows a jump-diffusion model. The stochastic expenditure process is treated as an exogenous cash flow and is assumed to follow a stochastic differential process with jumps. Under the Cox–Ingersoll–Ross interest rate term structure, it is presumed that the prices of multiple risky assets evolve according to a multi-dimensional geometric Brownian motion. By employing stochastic control theory, the Hamilton–Jacobi–Bellman (HJB) equation for the household portfolio problem is formulated. Considering various risk-preference functions, particularly the Hyperbolic Absolute Risk Aversion (HARA) function, and given the algebraic form of the objective function through the terminal-value maximization condition, an explicit solution for the optimal investment strategy is derived. The findings indicate that when household investment behavior is characterized by random expenditures and symmetry, as the risk-free interest rate rises, the optimal proportion of investment in wealth-management products also increases, whereas the proportion of investment in risky assets continually declines. As the expected future expenditure increases, households will decrease their acquisition of risky assets, and the proportion of risky-asset purchases is sensitive to changes in the expectation of unexpected expenditures. Full article

Amid escalating climate change concerns, methane—a greenhouse gas with a global warming potential far exceeding that of carbon dioxide—demands urgent attention. The Canadian dairy industry significantly contributes to methane emissions through cattle enteric fermentation and manure management practices. Precise benchmarking of these emissions is critical for developing effective mitigation strategies. This study ingeniously integrates eight years of Sentinel-5P satellite data with advanced machine learning techniques to establish a methane concentration benchmark and predict future emission trends in the Canadian dairy sector. By meticulously analyzing weekly methane concentration data from 575 dairy farms and 384 dairy processors, we uncovered intriguing patterns: methane levels peak during autumn, and Ontario exhibits the highest concentrations among all provinces. The COVID-19 pandemic introduced unexpected shifts in methane emissions due to altered production methods and disrupted supply chains. Our Long Short-Term Memory (LSTM) neural network model adeptly captures methane concentration trends, providing a powerful tool for planning and reducing emissions from dairy operations. This pioneering approach not only demonstrates the untapped potential of combining satellite data with machine learning for environmental monitoring but also paves the way for informed emission reduction strategies in the dairy industry. Future endeavors will focus on enhancing satellite data accuracy, integrating more granular farm and processor variables, and refining machine learning models to bolster prediction precision. Full article

Accurate estimation of construction costs is of foremost importance in construction management processes. Considering the changes and unexpected situations, cost estimations should be revised during the construction process. This study investigates the predictability of earned value management (EVM)-based approaches using machine learning (ML) methods. A total of 2318 data points via 19 EVM-based cost estimation methods were created and six ML methods were used for the analyses. The planned and actual project data of the rough construction activities of a housing project completed in Türkiye were used. The ML methods considered consisted of adaptive neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), Gaussian process regression (GPR), long-short-term memory (LSTM), M5 model trees (M5TREEs), and support vector machines (SVMs). The created models were compared using performance criteria such as mean absolute percentage error (MAPE), relative root means square error (RRMSE), coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and overall index of model performance (OI). Moreover, radar charts, trend graphs, Taylor diagrams, violin plots, and error boxplots were used to evaluate the performance of the estimation models. The results revealed that the classical ANN model outperforms EVM-based cost methods that utilize current ML methods. Full article