Search Results (409)

With the rise in rural tourism, public space use has become more complex, causing significant weekday-weekend vitality imbalances. However, the factors shaping these dynamics in traditional villages remain unclear. This study uses Wi-Fi sensing method to analyze vitality variations across weekdays and weekends, and it develops a 13-metric evaluation framework to examine how built environment factors, from both internal and external dimensions, differentially influence the vitality of public spaces in traditional villages across various time periods. Using 17 public spaces in Yantou Village, Lishui, China, as a case, it finds: (1) Historical Element Proximity consistently and significantly drives public space vitality across all periods; (2) Leisure Facility Count and Decorative Element Count demonstrate strong positive effects during weekend morning peaks. (3) Retail Facility Count significantly reduces vitality during weekend morning peak but enhances it during midday off-peak, whereas Street Vendor Count shows the opposite pattern—increasing vitality in morning peak and decreasing it in midday off-peak. Using Wi-Fi sensing’s high-resolution, real-time, and non-invasive capabilities, this study provides a scientific method to accurately assess the variations in public space vitality and their impact factors between weekdays and weekends in traditional villages, offering technical support for enhancing public space vitality and sustainably revitalizing rural heritage. Full article

Despite the growing impacts of climate change worldwide, achieving consensus on climate action remains a challenge partly because of heterogeneity in perceptions of climate risks within and across countries. Lack of consensus has hindered global collective action. We use a system dynamics approach to examine how interactions among cultural, socio-political, psychological, and institutional factors shape public support or opposition for climate mitigation policy. We investigate the conditions under which the dominant public opinion about climate policy can shift within a 20-year time frame. We observed opinion shifts in 20% of simulations, primarily in individualistic cultural contexts with high perceived climate risk. Changing the dominant opinion was especially difficult to achieve in collectivistic cultures, as we observed no shifts in dominant opinion within the parameter ranges examined. Our study underscores the importance of understanding how cultural context mediates the approaches needed to effectively mobilize collective climate action. Full article

Background: This narrative review aims to assess multiple strategies available to evaluate and manage corneal astigmatism in the context of cataract surgery, with a focus on the surgical techniques, intraocular lens (IOL) selection, and the integration of advanced new technologies. Methods: A narrative review based on a literature search in PubMed/MEDLINE and the Cochrane Library, covering publications from 1990 to 2025, was conducted. Eligible studies included randomized controlled trials, observational studies, prospective and retrospective analyses, and systematic reviews. Key search terms included “astigmatism”, “cataract surgery”, “keratometry”, and “refraction.” Studies were screened and selected by two independent reviewers. Results: Corneal astigmatism is the most common form of astigmatism. While the anterior corneal astigmatism plays a more important role, the posterior corneal astigmatism and the posterior-to-anterior corneal ratio (Gullstrand ratio) can impact the postoperative refractive results in a very important way. While planning the cataract surgery, surgically induced astigmatism (SIA), especially on the posterior cornea, must be taken into consideration. Various approaches, such as opposite clear corneal incisions (OCCIs), toric intraocular lens (IOLs), intraoperative aberrometry, and the integration of artificial intelligence and robotic-assisted surgery, are increasing the precision of astigmatism correction and surgical outcomes. Conclusions: Individualized surgical planning and precise measurement are key factors in reducing residual astigmatism and obtaining the best visual outcomes in patients with corneal astigmatism undergoing cataract surgery. By taking into consideration the posterior corneal data, refining IOL calculations, and embracing the rapidly developing technological innovations, patient satisfaction and visual quality can be substantially improved, and the predictability of the surgical outcome can be enhanced. Full article

In the context of low-growth urban development, the interaction between the supply–demand structure of community public service facilities and the housing market has increasingly become a key research concern. Yet, systematic investigations into how supply–demand dynamics influence market value remain limited. To fill this gap, this study takes Wuchang District of Wuhan as the empirical case and establishes an integrated framework of “supply–demand evaluation—value effects” to assess both the equity of facility allocation and its capitalization effects. The results indicate that: (1) all categories of public service facilities in Wuchang District have Gini coefficients above 0.6, indicating substantial imbalance. Among them, elderly care, infant care, and child recreation facilities exceed 0.7, reflecting particularly severe inequality. (2) The “accessibility–housing price” quadrant model further reveals typical mismatch patterns, with “low accessibility–high price” and “high accessibility–low price” zones together accounting for 45.08%, suggesting that mismatches are widespread in the study area. (3) MGWR results show that different facility types exert differentiated effects across locations, with some even displaying opposite positive and negative effects, underscoring significant spatial heterogeneity. Overall, this study uncovers the intrinsic links between facility supply–demand structures and market value, clarifies the differentiated roles of facility types in shaping spatial value, and provides empirical evidence to support improvements in urban public service systems. Full article

Assessing the externalities of nature conservation policies, conceived as unintended socio-economic and ecological effects, is essential for evaluating societal costs and improving conservation strategies. This is particularly relevant in the case of wolf conservation and its interaction with traditional pastoralism, an animal farming system that provides valuable ecosystem services but is rapidly declining across Europe. We used structured questionnaires with Spanish herders to evaluate the environmental and societal impacts of livestock-guarding dogs (LGDs) as a measure to prevent wolf attacks. On average, farms with 750 sheep employed five mastiffs and three sheepdogs, with LGD numbers increasing in wolf-abundant areas. The number of mastiffs rose proportionally with herd size (0.6 mastiffs per 100 sheep), whereas sheepdog numbers plateaued. The estimated annual cost per LGD was €364, with LGDs contributing approximately 7% of a farm’s carbon emissions. Sixty-one percent of herders reported minor societal conflicts involving LGDs, primarily dogs chasing pedestrians or cyclists, while 1% involved minor bites. The likelihood of societal conflict increased with the number of LGDs on a farm. Additionally, each LGD caused an estimated 0.71 wildlife fatalities per year, mostly involving small mammals such as rabbits, hares, and young ungulates. These findings highlight the need for urgent coexistence strategies to balance effective wolf conservation with the sustainability of traditional pastoralism. Without targeted intervention, the continued expansion of Spain’s wolf population may increase costs to herders and foster growing public opposition to wolf conservation efforts. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

The hepatitis B virus (HBV) is a DNA virus of major public health concern whose error-prone polymerase has driven the emergence of ten distinct genotypes and a multitude of resistance-associated mutations (RAMs). Herein, we conducted a retrospective observational study analyzing 8152 hepatitis B virus (HBV) sequences from 27 regions across the Americas, retrieved from GenBank, to construct a database and examine associations among HBV genotypes/subtypes, geographic distribution, resistance-associated mutations (RAMs), and resistance to nucleos(t)ide analogs (NAs) used in the treatment of chronic infection. Following phylogenetic analysis, mutations at clinically relevant sites in the reverse transcriptase domain were identified and classified by resistance to NAs. Genotypes A (21.1% A2 and 14.7% A1) and D predominated across the retrieved database, whereas genotypes E, G, H, and I each accounted for fewer than 3% of the sequences. Among the sequences in the database, 10.6% harbored RAMs, with genotypes G, A, and H predominating in this category. The most frequently observed RAM was L180M + M204V/I, which is associated with resistance to LMV, ETV, and TBV, whereas resistance to ADV and TDF remained rare. Genotypes G and A2 were significantly associated with a higher likelihood of harboring multiple RAMs (as evaluated by logistic regression), along with an increased risk of resistance to LMV, ETV, and TBV; the opposite was true for subtype A1. Notably, genotypes H and B5 were associated with an elevated risk of TDF resistance. A comprehensive understanding of RAMs and circulating genotypes in the Americas is essential for identifying high-risk populations and establishing geographically targeted therapeutic strategies. Full article

Renewable energy sources, particularly solar photovoltaics (PVs), are rapidly expanding to achieve carbon neutrality. Integrated photovoltaic (IPV) solutions in underutilized spaces offer a viable option for countries with land constraints and public opposition. Vertical PV (VPV) systems, featuring bifacial solar modules installed vertically, facing east and west, present a promising alternative. In contrast to conventional tilted PV (CPV) systems, which peak around midday, VPV systems generate more power in the morning and afternoon. This mitigates issues such as the duck curve and curtailment caused by midday overgeneration. Moreover, combining VPV and CPV systems can increase the solar hosting capacity of a distribution line (DL) for PV-system interconnections, driving research interest. This study assessed the hosting-capacity improvements from VPV systems by analyzing voltage fluctuations and thermal constraints using OpenDSS software (Version 9.1.1.1). The potential substitution effect of a smart inverter-based energy-storage system (ESS) was also explored. The analysis, based on real-grid conditions in South Korea, incorporated actual DL data, generation and demand profiles, and operational data from both VPV and CPV systems. Worst-case scenarios were simulated to evaluate their impact on grid stability. The results demonstrate that VPV systems can increase hosting capacity by up to 23% and ensure stable grid operation by reducing power-generation uncertainties. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly correlate with the diagnosis of multidimensional depression, this method provides a more precise and objective representation of the problem. The application of the method to the 2019 Brazilian Health Survey data demonstrated its effectiveness, resulting in a composite indicator that separates individuals who self-declare as having depression from individuals who self-declare as not having depression. The results highlight individuals who have a limiting chronic condition, high levels of education, less support from friends and family, perform unhealthy work, and are male. In contrast, individuals with the opposite characteristics are associated with a negative multidimensional depression diagnosis. The proposed composite indicator not only improves the measurement accuracy but also offers a new means of visualizing and understanding the multidimensional nature of depression diagnosis, providing valuable information for the formulation of targeted public health policies aimed at reducing the time for which people show symptoms of depression. Full article

This article focuses on an advocacy campaign for a complete ban on vaping in Kazakhstan led by the Smokefree Kazakhstan Coalition. Initiated in 2021, the campaign aimed to address the growing public health concerns about vaping among adolescents, which was reflected in a sharp increase in vape use among young people—from 1.6% in 2014 to 5.8% in 2022. Despite facing strong opposition from the vaping industry and political lobbyists, the Coalition gained support from key political figures, public health leaders, and NGOs. Over 32 months, the campaign achieved several key milestones, including the inclusion of criminal liabilities for those involved in the vaping industry, and, in 2024, it eventually joined a number of countries such as Thailand, Qatar, Japan, Singapore, and India in approving a vaping ban. The advocacy efforts relied on public engagement, social media, and coordinated civil society mobilization—including petitions, public meetings, awareness campaigns, and coalition-building among NGOs and health advocacy groups—to overcome industry resistance toward official vaping market ban approvals. This article uses the case study approach with the Power Prism framework to describe and evaluate the advocacy campaign’s strategic plan, its political challenges, and the significant impact of public health advocacy in shaping national health policy. The significance of the article lies in the success of the vaping ban in the Kazakhstani context, which may serve as a model for other countries facing similar public health issues, political instability, and industry resistance. Full article

Interpretation of the New Testament (NT) in general and the Pauline corpus in particular still appears to be at a crossroads. Scholars continue to publish articles and monographs in binary opposition to one another. The terminology used to designate the overarching perspectives of these binary publications sharply contrasts a “traditional” perspective (Protestant in general, and Lutheran in particular) with a variously named “new” or “radical new” perspective. Most recently, beyond the imprecise “new” terminology, the non-traditional perspective is being referred to as the “post-supersessionist”, “nonsupersessionist”, or “within Judaism” perspective and is still strongly being contested. Historically speaking, these antithetical perspectives cannot both be completely correct. Arguably, then, the time has come to explore what the study of Kuhnian paradigms might reveal about this state of affairs in NT scholarship. Most important, in proffering a twofold hermeneutical way forward that is focused on better understanding the emic perspective of the texts that we interpret—to the extent humanly possible—it is hoped that we might become more keenly aware of the ethical implications of our paradigms, terminology, and exegesis for those who rely on our work for their understanding and appropriation of the Scriptures in their everyday living. Full article

Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D image data includes two main tasks: One is to build an environment map, and the other is to simultaneously track the position and movement of visual odometry estimation (VOE). Visual SLAM and VOE are used in many applications, such as robot systems, autonomous mobile robots, assistance systems for the blind, human–machine interaction, industry, etc. To solve the computer vision problems in Visual SLAM and VOE from RGB-D images, deep learning (DL) is an approach that gives very convincing results. This manuscript examines the results, advantages, difficulties, and challenges of the problem of Visual SLAM and VOE based on DL. In this paper, the taxonomy is proposed to conduct a complete survey based on three methods to construct Visual SLAM and VOE from RGB-D images (1) using DL for the modules of the Visual SLAM and VOE systems; (2) using DL to supplement the modules of Visual SLAM and VOE systems; and (3) using end-to-end DL to build Visual SLAM and VOE systems. The 220 scientific publications on Visual SLAM, VOE, and related issues were surveyed. The studies were surveyed based on the order of methods, datasets, evaluation measures, and detailed results. In particular, studies on using DL to build Visual SLAM and VOE systems have analyzed the challenges, advantages, and disadvantages. We also proposed and published the TQU-SLAM benchmark dataset, and a comparative study on fine-tuning the VOE model using a Multi-Layer Fusion network (MLF-VO) framework was performed. The comparison results of VOE on the TQU-SLAM benchmark dataset range from 16.97 m to 57.61 m. This is a huge error compared to the VOE methods on the KITTI, TUM RGB-D SLAM, and ICL-NUIM datasets. Therefore, the dataset we publish is very challenging, especially in the opposite direction (OP-D) when collecting and annotation data. The results of the comparative study are also presented in detail and available. Full article

Background/Objectives: Oppositional defiant disorder (ODD) and conduct disorder (CD) are important behavior disorders in children and adolescents, often linked with long-term psychosocial problems. Antipsychotics are frequently prescribed to manage severe symptoms and improve behavior, but their efficacy in this population is still unclear and a lot of physicians are remittent in prescribing them. This systematic review aims to assess the effectiveness of antipsychotic treatment in reducing symptoms associated with ODD and CD in children and adolescents. Methods: Studies that investigated how effective antipsychotic treatments are for children and teens diagnosed with oppositional defiant disorder (ODD) and conduct disorder (CD) were reviewed. Only studies that met a few main criteria were included: participants were between 5 and 18 years old with an ODD or CD diagnosis; the treatment could be any type of antipsychotic, whether typical or atypical; the accepted study designs were randomized controlled trials (RCTs), cohort studies, systematic reviews with meta-analysis, or observational studies. The outcomes of interest were reductions in aggressive or defiant behaviors, improvements in social functioning, and the occurrence of any adverse effects from the medications. There was no restriction on the language of publication, and studies published from 2000 to 2024 were considered. Studies that focused only on non-antipsychotic drugs or behavioral therapies, as well as case reports, expert opinions, and non-peer-reviewed articles did not meet the inclusion criteria. Results: The review consisted of 13 studies. The results suggest that some antipsychotic drugs—especially atypical antipsychotics—can substantially reduce aggressive and defiant behavior in children and adolescents who have oppositional defiant disorder (ODD) or conduct disorder (CD). Common side effects of these medications include weight gain, sedation, and metabolic problems. Conclusions: Although adverse effects are a concern, the potential of these medications to manage disruptive behaviors should not be overlooked. When used in combination with behavioral therapy and other forms of treatment, antipsychotics can markedly improve the outcomes of these very difficult-to-treat patients. Clinicians who treat these patients need to consider antipsychotics as a serious option. If they do not, they are denying their patients medication that could greatly benefit them. Full article