Search Results (626)

We introduce I-fp convergence (ideal convergence in fuzzy paranormed spaces) and develop its core theory, including stability results and an equivalence to $I^{*}$-fp convergence under the AP Property. Building on this foundation, we design an adaptive base-stock policy for a single-echelon inventory system in which weekly demand is expressed as triangular fuzzy numbers while holiday or promotion weeks are treated as ideal-small anomalies. The policy is updated by a simple learning rule that can be implemented in any spreadsheet, requires no optimisation software, and remains insensitive to tuning choices. Extensive simulation confirms that the method simultaneously lowers cost, reduces average inventory and raises service level relative to a crisp benchmark, all while filtering sparse demand spikes in a principled way. These findings position I-fp convergence as a lightweight yet rigorous tool for blending linguistic uncertainty with anomaly-aware decision making in supply-chain analytics. Full article

The study relies on an extensive dataset (≈1.86 million news headlines) to investigate the heterogeneity and predictive power of explicit sentiment signals (TextBlob, VADER, and FinBERT) and implied sentiment (VIX) for stock market trends. We find that news content predominantly consists of objective or neutral information, with only a small portion carrying subjective or emotive weight. There is a structural market bias toward upswings (bullish market states). Market behavior appears anticipatory rather than reactive: forward-looking implied sentiment captures a substantial share (≈45–50%) of the variation in stock returns. By contrast, sentiment scores, even when disaggregated into firm- and non-firm-specific subscores, lack robust predictive power. However, weekend and holiday sentiment contains modest yet valuable market signals. Algorithm-wise, Gradient Boosting Machine (GBM) stands out in both classification (bullish vs. bearish) and regression tasks. Neither FinBERT news sentiment, historical returns, nor implied volatility offer a consistently exploitable edge over market efficiency. Thus, our findings lend empirical support to both the weak-form and semi-strong forms of the Efficient Market Hypothesis. In the realm of exploitable trading strategies, markets remain an enigma against systematic alpha. Full article

In response to the challenges faced by traditional short-term electricity load forecasting models during holiday load predictions, a novel approach based on multifactor analysis and the Backpropagation–Gated Recurrent Unit (BP-GRU) model is proposed. Firstly, the impacts of temperature, humidity, and the type of date on the electricity load curve are examined. The study clarifies the positive and negative effects of temperature at different times on load forecasting and reveals the non-dependence characteristics of historical data on holiday load curves. Secondly, the performance of various model weight parameter optimization algorithms is assessed, leading to the establishment of a combined GRU and BP model. The operational workflow of the BP-GRU model is introduced. Lastly, the effectiveness of the proposed forecasting model is verified using actual load data. Compared to existing models, the proposed approach exhibits a significant advantage in achieving higher accuracy in holiday load predictions. Full article

Osteoporosis is a chronic disease associated with significant morbidity and mortality and requires long-term therapy. Efficacious and well-tolerated treatments are available, but their effect is either short-lived or lost following their discontinuation. The exception is bisphosphonates that reduce bone resorption and turnover, can be administered in regimens ranging from once-daily to once-yearly, and have been shown in randomized clinical trials to reduce the incidence of all osteoporotic fractures, but their effect persists following their discontinuation. This is due to their property of being taken-up selectively by the skeleton and being slowly released following treatment arrest. This property allows the discontinuation of bisphosphonate treatment for different periods of time, the so-called drug holiday, which reduces the risk of rare adverse events while maintaining the effect; an action particularly important for patients at very high risk of fractures for whom sequential therapy with different agents is currently advised. Thus, bisphosphonates, apart from being the treatment of choice for certain groups of patients, are also indispensable for the consolidation and maintenance of the gains of all other treatments, providing, in addition, the opportunity of temporary treatment arrest. Most patients with postmenopausal osteoporosis will, therefore, receive bisphosphonate at some stage during therapy of their disease, regardless of their initial fracture risk. Full article

Compared to multiple other European countries, Germany still lists among those countries restricting the operation of most retail activities on Sundays as well as public holidays. For a long time, the German populace backed this decision. The COVID-19 crisis had distinct effects on retail behavior, expectations, and perceptions among broad strata of German society. To quantify these changes, this study implements the results of two surveys from 2018 and 2025. Both samples were drawn from among the population of the fourth-largest German city of Cologne and visitors to the city. The results of t-tests and multiple multivariate regression analyses indicate that visitors still attend retail Sundays for hedonistic motives, i.e., related events, but in 2025 utilitarian motives have become more essential. While the amount of money spent during retail Sundays increased, this development is primarily driven by visitors not native to Cologne. However, city events continue to draw visitors and should be continued by city management. The financial potential for retailers by abolishing the German Shop Closing Act consequently remains limited, and its abolishment would only increase the ease of shoppers. Thus, legal changes to the act will have only limited potential for urban economic development. Full article

When Wittgenstein (2009, § 116) writes that “[w]hat we do is to bring words back from their metaphysical to their everyday use”, many will presumably read this is an anti-religious statement. According to the everyday use of the word “metaphysical”, religion is metaphysical, one would then argue. However, the target of Wittgensteinian investigations is language “on holiday” (2009, § 38), that is, when language does not do any job. This explains why Wittgenstein can claim that philosophy “leaves everything as it is” (2009, § 124): removing “a wheel that can be turned though nothing else moves with it” means removing something that is “not part of the mechanism” and therefore does not change the mechanism (2009, § 271). In other words, “metaphysics” does here not stand for a specific subject matter, such as religion. But what does it mean that something is “not part of the mechanism”? How does one show that something is not? Is it possible that religion is an instance of language on holiday? These questions are far more complex than they may seem at first, and the answer to the last question is not a general one—ultimately, it has to be answered in the first person. How do these difficulties manifest themselves? After a methodological discussion of the above points (sec. 1), two sections (2–3) discuss two examples of contexts in which these difficulties come into view. The discussions of these two examples thus aim to clarify what the distinction between the metaphysical and the non-metaphysical amounts to in two specific cases. In particular, the difference between a relation to a belief and to a person is highlighted. Paying attention to this difference is one way of getting out of the difficult constellation of religious belief and metaphysics. Full article

This study investigates the nonlinear effects of air pollution on urban entertainment consumption by analyzing daily PM₁₀ levels and movie box office data across 334 Chinese cities from 2012 to 2022, resulting in a total of 1,250,339 observations. Utilizing a two-way fixed effects model and threshold regression framework, we identify three key findings: (1) elevated PM₁₀ concentrations significantly reduce movie attendance, with a 1-unit increase decreasing consumption by 0.0797 units; (2) the inhibitory effect intensifies during weekends and holidays, reflecting heightened sensitivity to pollution during leisure periods; (3) threshold effects emerge, where PM₁₀ exceeding 0.0229 μg/m³ triggers a sharp decline in attendance, while temperature moderates this relationship, amplifying pollution’s negative impact. By integrating meteorological, environmental, and socioeconomic datasets, this research reveals substitution patterns between digital and offline entertainment under pollution stress. The results underscore the necessity for region-specific pollution mitigation strategies, cinema infrastructure upgrades, and dynamic pricing policies to counteract environmental disruptions. These insights advance the interdisciplinary nexus of environmental economics and cultural consumption, offering actionable pathways for sustainable urban development. Full article

Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. Using three years of highway traffic flow data during Labor Day from Shandong, China, ST-Cross-Attn outperformed eight state-of-the-art benchmarks, achieving an average improvement of 4.34% in inbound flow prediction and 2.3% in outbound flow prediction. Extensive ablation studies further confirmed the effectiveness of the model’s components and multi-task learning framework, demonstrating its potential for reliable holiday traffic forecasting. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

Returning to work after extended holidays poses significant challenges to employees’ psychological adjustment, yet this phenomenon remains underexplored in organizational research. Drawing on the Conservation of Resources (COR) theory, this study develops and tests a moderated mediation model to examine how pre-holiday work-related irritation influences post-holiday workplace vigor through heightened need for recovery, and how perceived organizational support buffers this process. Data were collected through a four-wave time-lagged design surrounding the Chinese Spring Festival, with a final sample of 349 employees across diverse industries. Results show that pre-holiday emotional strain increases employees’ recovery needs, which in turn undermines their workplace vigor. Moreover, boundary strength at home and perceived organizational support buffer the indirect negative pathway, highlighting the critical roles of both personal and organizational resources in the recovery process. By shifting attention from burnout to positive energy states such as vigor, this study advances theoretical understanding of post-holiday adjustment dynamics and offers practical insights for organizations seeking to foster employee resilience and sustained engagement after structured breaks. Full article

This study investigates the effectiveness of two cybersecurity awareness interventions—phishing simulations and organized online training—in enhancing end-user resilience to phishing attacks in a Croatian university setting. Three controlled phishing simulations and one targeted instructional module were executed across several organizational departments. This study assesses behavioral responses, compromise rates, and statistical associations with demographic variables, including age, department, and educational background. Despite educational instruction yielding a marginally reduced number of compromised users, statistical analysis revealed no meaningful difference between the two methods. The third phishing simulation, executed over a pre-holiday timeframe, demonstrated a significantly elevated compromising rate, underscoring the influence of temporal and organizational context on employee alertness. These findings highlight the shortcomings of standalone awareness assessments and stress the necessity for ongoing, contextualized, and integrated cybersecurity training approaches. The findings offer practical guidance for developing more effective phishing defense strategies within organizational environments. Full article

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights. Full article

Objectives: This study aimed to describe the profile and identify the clinical and sociodemographic factors associated with emergency department (ED) use among patients aged ≥65 years. Methods: This was a retrospective cross-sectional study of patients aged ≥65 years who were seen in the emergency department of the Hospital de Mérida (Spain) in 2019, the year before the Coronavirus Disease 2019 (COVID-19) pandemic. Descriptive statistics were calculated for dependent variables such as hours of ED stay, total number of visits, reasons for discharge, and diagnostic specialty, disaggregated by gender, season, age group, day type (work or holiday), shift, and population area (rural or urban). Results: Significant differences in ED hours were found according to gender (p < 0.001), season (p = 0.024), age group (p < 0.001), attention shift (p < 0.001), and population area (p = 0.003). Discharge to home was the most common destination (63.5%), followed by hospital admission (25.7%). Admissions for gastrointestinal surgery and neurology were predominant in men, and admissions for internal medicine and trauma were predominant in women. Patients aged 65–79 years were admitted to internal medicine, and those over 80 years were admitted to cardiology. Among patients who presented to the hospital’s emergency department and required admission, 51.5% were men aged ≥ 75 years, rising to 53.3% among those aged 65 to 74 years. The clinical areas were related to cardiology (27.67%) and pneumology (20.63%). Conclusions: Demands for ED care in those over 65 years of age are associated with sociodemographic and clinical characteristics, which can be used to better plan and manage resources and improve user satisfaction. Full article

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities. Full article

Daylight Saving Time (DST), involving clock shifts forward in spring and backward in autumn, was introduced to promote energy savings. However, its effectiveness remains controversial, especially in buildings with temporary occupancy like academic institutions, which have high daytime use but low summer occupancy. This study investigates the impact of DST transitions on energy consumption across seven campuses of two higher education institutions (HEIs) in northern Portugal and Spain, located in different time zones, using measured data from 2023. The analysis accounted for the structural and operational characteristics of each campus to contextualize consumption patterns. Weekly electricity consumption before and after DST changes were compared using independent samples t-tests to assess statistical significance. Results show that the spring transition to DST led to an average energy saving of 1.7%, while the autumn return to standard time caused an average increase of 1.2%. Significant differences (p < 0.05) were found in five of the seven campuses. Descriptive statistics and confidence intervals indicated that only sites with intervals excluding zero exhibited consistent changes. Seasonal energy demand appeared more influenced by academic schedules and thermal comfort needs—particularly heating—than by DST alone. Higher consumption coincided with periods of intense academic activity and extreme temperatures, while lower demand aligned with holidays and longer daylight months. Although DST yielded modest energy savings, its overall impact on academic campus energy use is limited and highly dependent on local conditions. The findings highlight the need to consider regional climate, institutional policies, user behavior, and smart technology integration in future energy efficiency analyses in academic settings. Full article