Search Results (1,873)

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Fear is a transdiagnostic construct implicated in multiple psychiatric disorders, reflecting a partial dissociation between clinical phenotypes and underlying neurobiological mechanisms. Converging evidence suggests that aberrant fear processing plays a central role in cognitive and psychopathological models of psychosis. In this narrative review, we synthesize evidence on the neurobiological mechanisms of aberrant fear modulation in schizophrenia from a translational perspective, integrating findings from neuroimaging, preclinical models, pharmacological interventions, and psychotherapy. Schizophrenia is characterized by aberrant emotional processing and inappropriate neural responses to stimuli with reduced or absent objective salience, reflecting impaired discrimination of relevant environmental information. At the system level, evidence implicates dysregulation of cortico-limbic and salience-processing networks in altered fear learning, threat appraisal, and emotional prediction. Neurochemical findings indicate that dopamine–glutamate dysregulation and associated intracellular signaling pathways act as upstream modulatory mechanisms contributing to these network-level abnormalities. Therapeutic interventions, including antipsychotic drugs and psychotherapeutic approaches, partially modulate these systems, although effects remain heterogeneous. Overall, the evidence supports a hierarchical model in which aberrant fear processing in schizophrenia arises from disrupted salience attribution and impaired integration across cognitive, affective, and neurobiological levels. This intermediate dysfunction links molecular alterations to large-scale network disturbances and clinical symptom expression, providing a framework for more mechanism-based therapeutic strategies. Full article

This paper examines leadership characteristics within local self-governments in the Republic of Serbia through a comparative analysis of leaders’ self-assessments and associates’ evaluations. Drawing on the Johari Window framework, the study explores differences in the perception of leadership attributes from two complementary perspectives. The sample consisted of 150 participants occupying managerial positions within different municipal administrations. The findings indicate that capability is the dominant leadership attribute across both respondent groups, followed by energy, reliability, intelligence, and responsibility. However, notable discrepancies were identified between self-perception and external evaluation, particularly regarding adaptive and interpersonal characteristics. The results further suggest that leadership perception in transitional institutional environments is strongly influenced by organizational uncertainty and institutional instability. Emotional and relational attributes appear to be less emphasized, whereas functional competencies and managerial effectiveness remain highly valued. The study contributes to contemporary leadership research by highlighting the importance of contextual and relational dimensions in the interpretation of leadership characteristics. In addition, the findings offer practical implications for leadership development within public administration systems. Full article

The advancing fluency of large language models (LLMs) raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether state-of-the-art LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced language with unique linguistic and cultural characteristics. We conduct two tasks across six models: Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek. First, we evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts. Second, we assess the extent to which LLM-generated texts exhibit emotional or personality traits comparable to those of humans. Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1 > 0.95), though classification performance deteriorates on paraphrased samples, indicating reliance on superficial stylistic cues. Emotion and personality classification experiments reveal significant generalization gaps: classifiers trained on human data perform poorly on AI-generated texts and vice versa, suggesting LLMs encode affective signals differently from humans. Importantly, augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages. Model-specific analyses show that GPT-4o and Gemini exhibit superior affective coherence, while LLaMA performs worse. Linguistic and psycholinguistic analyses reveal measurable divergences in tone, authenticity, and textual complexity between human and AI texts. These findings have significant implications for affective computing, authorship attribution, and responsible AI deployment, particularly within under-resourced language contexts where generative AI detection and alignment pose unique challenges. Full article

Multirotor unmanned aerial vehicles (UAVs) rely on electronic speed controllers (ESCs) that decode motor commands from pulse-width modulation (PWM) signals, making the flight-controller-to-ESC command path a physical-layer attack surface for intentional electromagnetic interference (IEMI). This paper presents a mechanism-based analysis of IEMI attacks that induce motor stoppage in UAV brushless DC motor controllers. We develop a timing-error model in which a sinusoidal disturbance on the PWM line shifts the detected edge instants and drives the decoded pulse width into stop-equivalent regimes, and we show that the disturbance reaching the ESC’s thresholding node is shaped by a frequency-selective cascade of the PWM cable’s coupling response and the ESC’s input-path transfer function. We experimentally characterize this model on five commercial ESCs through conducted and radiated injection. The measured thresholds differ by more than an order of magnitude across ESCs and are reordered between frequency bands and injection modes; comparing conducted and radiated results allows us to attribute these differences primarily to the cable coupling response and reveals cases where it either hides or amplifies an ESC’s susceptibility. The susceptible frequency also shifts with PWM cable length in qualitative agreement with transmission-line resonance, confirming that observed radiated susceptibility reflects the joint design of ESC and cable rather than a single intrinsic property. The cable lengths examined here (45–125 cm) are longer than those of compact multirotors and were chosen to place resonances within our antenna’s band; we discuss the implications of this choice and identify shorter, deployment-realistic cables as a priority for future work. Full article

This study examines how aquarium visitors’ perceived restorative environmental attributes influence leisure life satisfaction, positive emotional experience, and visit intention. Drawing on Attention Restoration Theory, aquariums are conceptualized not merely as indoor exhibition facilities but as restorative indoor blue space leisure settings. Using survey data from 452 Korean adults who had visited major aquariums within the previous 12 months, this study employed structural equation modeling and multi-group analysis. The results show that being away, fascination, and compatibility positively affected leisure life satisfaction, while fascination and compatibility significantly enhanced positive emotional experience. Both leisure life satisfaction and positive emotional experience were found to increase visit intention. Multi-group analysis revealed a significant difference only in the relationship between compatibility and positive emotional experience. Specifically, compatibility had a stronger effect on positive emotional experience among repeat visitors. In this study, Attention Restoration Theory is extended to aquarium-based indoor blue space settings, and restorative environmental perceptions are shown to influence and shape visitor responses through both cognitive and affective pathways. Although the outcome variable primarily captures visitors’ intention to revisit and recommend aquariums rather than direct pro-environmental behavior, the findings offer implications for sustainable marine leisure by showing how restorative and emotionally meaningful aquarium experiences can support conservation-oriented communication and longer-term visitor engagement. Practically, the findings suggest that aquarium managers should move beyond short-term price-oriented strategies and design restorative experiences that enhance fascination and compatibility, thereby strengthening emotionally meaningful and longer-term visitor engagement in sustainability-relevant leisure contexts. Full article

This research investigates a vertically structured Ge-on-Si avalanche photodetector (APD) fabricated in a separate absorption, charge, and multiplication configuration. The application of ramp gating enables reverse bias beyond the punch-through voltage, allowing the device to operate in linear avalanche mode. A significant dark avalanche current is observed under steady conditions, exhibiting linear multiplication approximately proportional to the input gating and thermal generation rate. Notably, this linear behavior persists even at voltages beyond the Geiger mode. The observed results are attributed to Ge/Si interface traps caused by the 4.18% lattice mismatch and deep-level traps introduced during fabrication. Under 1550 nm short-wave infrared normal-incidence pulsed illumination, the device exhibits negative differential resistance, attributed to illumination-induced self-quenching of electric field in multiplication region and modification of the barrier at the Ge/Si interface. A light-induced slow transient decrease in the absolute dark-state current is followed by a sustained inverse quenching effect, restoring the large dark-state current. These findings offer insights into the dynamic behavior of Ge-on-Si APDs, with potential implications for advanced optoelectronic applications. Full article

Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

This study examines how green bond financing costs in China are jointly shaped by market pricing mechanisms and institutional investor behavior. It develops an integrated two-level framework linking issuance-level bond characteristics with investor decision-making to explain green bond pricing in an emerging market. Using a comprehensive dataset of Chinese green bond issuances, the results show that financing costs are driven mainly by conventional credit-related signals, including issuer and bond ratings, guarantee structures, issuance size, and maturity. However, market frictions such as liquidity constraints and rating inertia weaken the capitalization of environmental attributes in yields. A survey-based Logit analysis of institutional investors in Shanghai further shows that green bond investment is influenced more by trading activity, information transparency, and risk management than by environmental awareness alone. Institutional heterogeneity also suggests that securities firms display stronger participation than investment companies, reflecting differences in bond-market exposure, product familiarity, and institutional investment mandates. Overall, the findings reveal a feedback mechanism in which market signals shape investor behavior, which in turn reinforces or moderates pricing dynamics. The study clarifies the structural and behavioral drivers of green bond pricing and offers policy implications for improving transparency, liquidity, and investor incentives. Full article

The global regulatory landscape is shifting from voluntary corporate social responsibility (CSR) reporting to mandatory Environmental, Social, and Governance (ESG) disclosure, yet whether this transition drives substantive corporate environmental change or merely symbolic compliance remains empirically contested. This study investigates the causal impact of mandatory ESG disclosure on firm value and operational carbon intensity, drawing on an unbalanced panel of 9682 firm-year observations for 1626 listed firms from the European Union (EU-27) and Japan covering the period 2018 to 2024. The EU serves as the treatment group, where mandatory disclosure requirements escalated substantially from 2021 onward through the Sustainable Finance Disclosure Regulation and the Corporate Sustainability Reporting Directive proposal. Japan serves as the control group, representing a developed economy with sophisticated capital markets and high ESG awareness that maintained a voluntary disclosure environment throughout the study period. A Difference-in-Differences framework with firm- and year-fixed effects is employed, and causal identification is validated through a dynamic event study analysis. Three principal findings emerge. First, mandatory ESG disclosure is not associated with a statistically significant improvement in firm value in the EU–Japan comparative context, a result that is interpreted as descriptive rather than causal given evidence of pre-existing valuation divergence between the two groups. Second, mandatory disclosure is associated with a significant and progressive reduction in Scope 1 and 2 carbon intensity, indicating substantive operational decarbonization rather than symbolic compliance. Third, this emissions-reducing effect is significantly amplified among firms with dedicated CSR sustainability committees, while the board independence policy indicator yields no significant moderating effect, a finding attributed to data limitations. These results carry direct implications for policymakers designing climate-related disclosure frameworks and for scholars examining the boundary conditions under which mandatory transparency translates into genuine environmental performance. Full article

Against the backdrop of deep digital economic integration, the synergistic agglomeration of new quality productive forces (NQPFs) and digital transformation (DT) has become a key engine for regional high-quality development. Based on data from 31 Chinese provinces during 2011–2023, this study measured the synergistic level of NQPF and DT. Using a modified gravity model, we convert attribute data into relational data and analyze driving mechanisms via social network analysis and quadratic assignment procedures. The results show that the synergistic agglomeration network presents club convergence rather than homogeneous dispersion, forming a structure comprising “polar-core absorption, hub transmission, hinterland integration, and peripheral marginalization.” Eastern regions act as net beneficiaries; Guangdong, Fujian, and other hubs become net-spillover brokers; central and western regions achieve element equilibrium, yet traditional industrial bases face a widening digital divide. Targeted policy implications are proposed. This study provides references for breaking regional digital barriers and optimizing the spatial layout of high-quality development. Full article

Risk assessment tools are used in criminal justice to evaluate an individual’s likelihood of reoffending. There is a growing discussion around the use of artificial intelligence (AI) and machine learning (ML) in algorithmic risk assessment (ARA). This survey examines the use of and the potential for bias in the use of ARA in criminal justice. Through a structured interdisciplinary review of recent research on the impact of ARA, this investigation examines the tools currently being used and whether there is evidence that ARA tools contribute to bias. Included papers were collected from Google Scholar and the ACM Digital Library and have been published since 2015, discuss AI, and focus on the adult justice system in the US, yielding 56 studies. In total, 79% of the surveyed literature concluded that AI and ML can or do contribute to biased performance in risk assessment. The two most recorded sources of bias were the use of historical court records as training data and the use of variables or features that correlate strongly with race, gender, age, or other protected attributes, while noting that this result relies heavily on a small number of real-world observations, most notably the COMPAS dataset collected in Broward County. The recorded benefits of ARA included efficiency and resource utilization. The use of AI-derived risk assessment tools is growing and holds the potential to affect a lot of lives. It is important to examine and consider the implications of their use, especially involving bias and fairness in criminal justice decision-making. Full article

This study examines the moderating effect of firm attributes on the value relevance of accounting information in Saudi Arabia. Using a sample of 630 firm-year observations from 126 Saudi listed firms over 2018–2022, the research evaluates whether audit quality, size, leverage, growth potential, board diversity, and profitability complement the valuation role of earnings per share (EPS) and book value per share (BVPS) and if so then which direction of the attribute gave greater value relevance. Results reveal that all the firm attributes tested have a significant moderating effect on value relevance. Lower leverage, higher growth potential, greater board diversity, and profitability all lead to higher predicted market value for given EPS and BVPS. Big 4 audit quality and larger firm size are found to moderate the value relevance of accounting information rather than to influence share price directly. Both attributes strengthen the value relevance of earnings per share (EPS)—the EPS coefficient is significantly higher for firms audited by a Big 4 firm and for larger firms—while weakening the value relevance of book value per share (BVPS), with the BVPS coefficient being significantly lower in both cases. The combined effect is that earnings carry greater pricing weight, and book values carry lesser pricing weight, when audit quality is high and when firms are larger. Results also reveal that cohorts with Big 4 auditor, larger size, lower leverage, higher growth potential, more diverse boards, and profitability all have greater value relevance (higher R²) than cohorts with the alternative for each attribute. Hence, tests provide evidence that these attributes strengthen the association between selective accounting figures (EPS and BVPS) and share prices. The findings contribute to agency, information asymmetry, and value-relevance theory by showing that firm attributes condition the EPS and BVPS pricing weights rather than affecting price directly. The results have implications for regulators and firms seeking to improve financial reporting credibility and usefulness amid concentrated ownership. This study contributes timely empirical evidence on the multifaceted drivers of value relevance in an under-researched Middle Eastern emerging market. Full article

Purpose: This study aims at the modification and psychometric evaluation of the “revised Illness Perception Questionnaire” (IPQ-R) for occupational dermatological rehabilitation. Methods: First, the questionnaire was modified for application in occupational dermatology. Subsequently, 254 patients of an inpatient rehabilitation programme participated in a cross-sectional survey. Afterwards, the dimensional analysis of the IPQ-R was conducted using principal component analysis. Separate analyses were conducted for the illness representations and the causal attribution scale. Results: A total of 228 participants were included in the analysis (age: M = 48.2 years; SD = 12.0; 53.9% female). The patient acceptance of the questionnaire was high (response rate 87.3%; rate of completion between 92.5% and 98.4%, N = 254). The IPQ-R for occupational dermatology consists of 29 items in the domain of illness representations, which include seven factors (illness coherence, emotional representations, consequences: implications for the structuring of own life, consequences: financial and social impacts, treatment control, personal control, and timeline acute/chronic). Six of these scales have acceptable-to-good internal consistency (Cronbach’s α 0.72–0.84); for one scale, the internal consistency is Cronbach’s α = 0.66. A separate analysis of the causes resulted in eight factors (psychological causes at work and during leisure time, attributions outside the workplace, skin cleansing and skin protection measures, behaviour-related risk factors, causes at work, other risk factors, external factors that cannot be influenced by the person, and climatic influences) with a total of 30 items. Five of the eight scales have an acceptable-to-good internal consistency (Cronbach’s α 0.71–0.83), and three scales are just below the acceptable range (Cronbach’s α 0.63–0.66). Conclusion: Overall, the initial psychometric results of the IPQ-R for occupational dermatology were satisfactory. However, additional validation steps are still required. The following differences to the original model should be considered when interpreting the available results: the factor “timeline cyclical” could not be replicated in this field of application. Additionally, two factors with different thematic emphases in the “consequences” section, besides effects on the personal way of life, social and financial consequences, became visible as well. Full article