Search Results (952)

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Hyperspectral imaging (HSI) is a state-of-the-art analytical technique that combines the use of conventional digital imaging and spectroscopy to capture both spatial and spectral information simultaneously in hundreds of narrow, adjacent wavelength bands. In recent decades, the progress in HSI has been rapid, and the technique has been increasingly utilized in forensic sciences, demonstrating its superiority to standard analytical techniques with respect to being non-invasive and contact-free. Although numerous forensic HSI articles have appeared in the literature in recent years, there has yet to emerge a systematic comparison of HSI performance, instrumentation, and cross-domain translational difficulties within forensic science. This review fills this important gap by analyzing the principles, instrumentations, methods of HSI data processing, and potential applications of HSI in forensics in the context of nine important fields: blood stain analysis and estimation of blood age; document authentication; fingerprint detection and enhancement; gunshot residue (GSR) analysis; analysis of trace evidences; detection of biological fluids; postmortem interval (PMI) estimation; determination of bruise age; and multidisciplinary applications. Comparative analysis of over fifty peer-reviewed articles published from 2010 to 2026 in HSI-based forensic sciences is provided herein, with classification accuracies between 81% and 100%. The use of chemometric and machine-learning methods, such as principal component analysis (PCA), support vector machines (SVM), partial least square discriminant analysis (PLS-DA), and Convolutional Neural Networks (CNNs), is carefully analyzed. Some problems concerning standardization, legal acceptance, data sets available, and forensic application are considered alongside future developments of HSI technology. Full article

Antimicrobial resistance is outpacing antibacterial innovation, yet most explanations for the shrinking pipeline emphasize downstream economic and regulatory attrition. This review identifies an underexamined upstream bottleneck: the unreliable recognition of genuine chemical novelty when a natural-product hit is first encountered. It advances a unifying framework—false novelty—that treats two conventionally separate failures—the rediscovery of known metabolites and the structural misassignment of genuinely new ones—as coordinate manifestations of a single diagnostic failure, an integration rarely made explicit. Eleven independently verified cases from 2019 to 2025, spanning three rediscoveries and eight misassignments, demonstrate that both failures persist under modern instrumentation. Their persistence, therefore, appears to reflect inconsistent workflow adoption more than a lack of technical capability, since dereplication and computational structure-verification tools mature enough to intercept both are already available. The review translates this diagnosis into a resource-conscious, three-checkpoint verification workflow that matches each failure mode to an appropriate remedy at the discovery stage, where correction is cheapest. Recognizing novelty earlier offers a tractable route to conserving scarce discovery-stage resources. Full article

Carbon price instability within the EU Emissions Trading System (EU ETS) is associated with financial stress that propagates across green finance markets, yet the system-level dynamics linking carbon allowance instruments, clean energy equities, green bonds, and oil volatility remain insufficiently characterized over the turbulent 2021–2026 period. This paper applies the DCC-GARCH $R^2$ decomposed connectedness framework to five exchange-traded funds and one volatility index spanning the principal channels through which EU ETS regulatory shocks propagate to financial markets, and derives a novel Connectedness-Based Hierarchy Index (CBHI) that translates the transmitter–receiver hierarchy into a time-varying portfolio desirability index. Five key findings emerge. First, the carbon allowance futures ETF (KRBN) and the Paris-aligned equity ETF (CARB) form a near-closed systemic bloc within this asset universe: bilateral connectedness reaches $0.867$, with to and from directional connectedness values both approaching 80%. Second, this co-transmitter structure is highly contingent on the joint inclusion of both instruments; excluding CARB raises KRBN’s CBHI from $0.251$ to $22.204$, reclassifying it as a structural diversifier and reducing the mean Total Connectedness Index (TCI) from 52.18% to 30.81%. Third, system-wide connectedness averages 52.18% but surges to nearly 73% during EU ETS regulatory crises. Fourth, connectedness-aware portfolios outperform the minimum-variance benchmark across all risk-adjusted metrics, yielding an annualized Sharpe ratio of $0.148$ vs. $-1.066$ ($T=1035$ observations, daily rebalancing, zero transaction costs); this improvement is driven by reallocation toward crude oil volatility (OVX), the sole non-ETF and most peripheral instrument (to = 5.55%, $\mathrm{CBHI}=14.310$), which the CBHI identifies as the system’s dominant structural diversifier and whose weight rises from 0.4% in the benchmark to 31.6%. Fifth, the CBHI uncovers a structural tension in Paris-aligned mandates: CARB records the second-lowest CBHI ($0.254$), indicating that climate alignment and systemic risk minimization are partially conflicting objectives. Full article

Climate-resilient land-use planning increasingly requires methods that can translate heterogeneous climate-risk knowledge into actionable territorial recommendations. This study develops the Hazard–Sector Translation Framework, a planning-oriented method for linking regional hazard pathways with affected territorial systems, land-use domains, recommendation families, planning instruments, and resilience functions. The framework was developed through a qualitative cross-regional synthesis of five Southern European regions: Sicily, Costa del Sol, Osijek-Baranja County, Central Greece, and the Troodos Mountain Range. The analysis identifies how diverse hazard pathways, including heat, drought, water scarcity, pluvial flooding, wildfire, hail, frost risk, and tourism climate-suitability pressures, become actionable through recurring land-use domains. Results show that heterogeneous regional risks converge around five main land-use resilience domains: buildings, agriculture, blue-green infrastructure, transportation, and protected-area conservation, while governance and capacity are treated separately as cross-cutting implementation conditions. The operational matrix demonstrates how region-specific hazard pathways can be converted into traceable recommendation structures without reducing local complexity. The framework does not replace detailed hazard modeling or climate-risk assessment; rather, it provides an intermediate methodological bridge between climate-risk evidence and land-use planning action. The approach is transferable to other regions seeking to organize stakeholder-derived needs and adaptation recommendations into coherent, sector-specific planning responses. Full article

This narrative review explores the interconnected relationship between quality of life (QoL) and satisfaction with nursing care (SNC) among cancer patients, recognizing that cancer’s burden extends beyond survival and significantly affects physical, psychological, and social well-being. A non-systematic search of PubMed, CINAHL, Scopus, and Google Scholar (2000–2025) identified relevant literature using keywords related to oncology nursing, QoL, SNC and patient satisfaction. Inclusion criteria encompassed peer-reviewed studies (quantitative, qualitative, and reviews) involving adult cancer populations and validated assessment instruments. Pediatric and non-nursing studies were excluded. Both QoL and SNC are critical outcomes in cancer care. QoL is an individual’s subjective perception and SNC reflects the patient’s subjective evaluation of how well nursing services align with their expectations, encompassing empathy, competence, and responsiveness. Research consistently demonstrates a strong positive association between high SNC and superior QoL outcomes, particularly through effective pain and symptom management, reducing psychological distress, and fostering patient empowerment. This relationship, however, is moderated by factors such as the patient’s disease stage, the type of care (hospital vs. palliative/home care), and organizational deficits like high nurse workload, which can undermine the delivery of high-quality care. Ultimately, ethical nursing practices are crucial for translating professional duty into positive patient experiences that are favorably linked with both SNC and QoL outcomes for cancer patients. Full article

Electric vehicle manufacturing firms face increasing supply chain vulnerability due to component shortages, technological interdependence, raw material volatility, and demand uncertainty. This study aims to examine whether digital transformation can be translated into a resilience-building capability and to identify the transmission channels. Using panel data from Chinese A-share listed electric vehicle manufacturing firms from 2014 to 2023, we employ a double machine learning framework to estimate the relationship between digital transformation and supply chain resilience while accounting for high-dimensional firm-level controls. The results show that digital transformation is positively associated with supply chain resilience. This finding remains robust across alternative sample-splitting ratios, different machine learning algorithms, alternative winsorization thresholds, and sample restrictions. It also holds after addressing potential endogeneity using instrumental variable estimation. Mechanism tests indicate that digital transformation contributes to resilience by promoting technological innovation and reducing managerial transaction costs. Heterogeneity analysis further shows that the effect is more pronounced among firms with stronger market positions, larger firms, and vehicle manufacturers. These findings suggest that digital transformation is not merely a tool for operational upgrading but also an important organizational capability for strengthening supply chain resilience in electric vehicle manufacturing. Full article

Starting from a semiotic understanding of the city as a stratified and continuously rewritten palimpsest, this paper investigates the relationship between urban heritage, graphic representation, and cultural translation within the current digital scenario. The study introduces and develops a theoretical framework, the “translation triangle” (TT), articulated around three mutually co-determining domains: the cognitive, concerned with the critical identification of relevant signs within the urban text; the technical–digital, concerned with their formalisation through survey instruments, three-dimensional modelling, and visualisation pipelines; and the socio-anthropological, concerned with the mediation of translated content towards its recipient. The framework is tested through two urban contexts: Pavia, Italy, and Gdańsk, Poland, different in scale and research advancement. Both demonstrate that the quality of urban heritage representation depends not solely on geometric accuracy or visual effectiveness, but on the capacity to make explicit the interpretative choices embedded in each translational act and to produce forms of knowledge that are comprehensible, interrogable, and culturally shareable. The paper concludes that every digital representation of urban heritage constitutes a semiotic act, not a neutral technical operation, and that the future of the discipline lies in constructing translations that are conscious, verifiable, and culturally grounded, capable of preserving the historical meaning of heritage without reducing it to spectacle or data infrastructure. Full article

This study asks whether, and through which firm-level channels, the green data center (GDC) pilot improves corporate sustainability, and it aims to quantify the policy effect and identify its transmission pathways. Under the dual shift in digital transformation and low-carbon transition, the energy demand and emissions generated by data centers have become important constraints on the sustainability performance of firms. This study regards the 2015 “National Green Data Center Pilot Work Plan” as a quasi-experimental policy shock. In terms of methods, using panel observations of Chinese A-share companies listed in Shanghai or Shenzhen during 2010–2024, a difference-in-differences (DID) strategy is employed to estimate the effect of GDC policy and to identify its transmission pathways for corporate sustainability. In terms of results, the empirical estimates indicate that the GDC pilot improves corporate sustainability, and the effect is robust to fixed-effects specifications, an instrumental-variable strategy, propensity-score matching, and a placebo test. Mechanism tests show that the pilot works through three channels: greater green innovation output and technical value, reduced financing frictions, and enhanced green governance capacity. Heterogeneity tests further show that the effect is stronger among firms with a higher level of digital transformation, a larger share of skilled technical personnel, stronger internal control, and greater executive green awareness, and that it is clearer where regional environmental regulation is stricter and market competition is more intense. In terms of conclusions, by integrating institutional pressure theory with the resource-based view, this study explains how the GDC pilot is translated into a firm-level sustainability advantage, and it offers evidence for refining GDC policy design and advancing the coordinated digital and green transformation of enterprises. The novelty of the study lies in providing firm-level causal evidence within a unified “pressure-to-capability” framework, in opening the three transmission channels, and in specifying the technological, organizational, and environmental conditions under which the effect is stronger. Full article

The global energy system is undergoing a structural transformation as countries pursue decarbonization while safeguarding energy security and economic resilience. However, renewable energy transition performance remains difficult to assess because existing evidence is often fragmented across single indicators, capacity-based measures, or isolated policy dimensions, while transition outcomes are shaped by multiple environmental, economic, institutional, technological, and cost-related factors. This study addresses this gap by benchmarking renewable energy transition performance across G7 economies through an integrated multi-criteria decision-making framework. The proposed methodology combines distance-based CRITIC (D-CRITIC) for objective criteria weighting and COBRA for compromise-based ranking to translate heterogeneous transition indicators into transparent cross-country comparisons. Using a secondary dataset covering fourteen indicators and seven G7 economies, the framework first derives objective criterion weights from cross-country dispersion and distance-based inter-criteria dependence, and then ranks countries by measuring their compromise distances from positive ideal, negative ideal, and average solutions. Using fourteen indicators spanning renewable energy penetration, environmental pressure, economic conditions, governance capacity, technology cost feasibility, and innovation capability, the D-CRITIC results identify CO₂ emissions per capita as the most informative criterion, followed by the share of electricity generated by bioenergy, the share of primary energy consumption from renewable sources, and solar LCOE. The COBRA-based assessment ranks Canada first, followed by the United Kingdom, Italy, Germany, Japan, the United States, and France. Robustness and sensitivity analyses show broad consistency in ranking patterns across alternative MCDM methods, while weight perturbation tests confirm that the ranking remains unchanged under ±5% and ±10% relative changes in criterion weights. These findings indicate that stronger renewable energy transition performance is associated with balanced progress across emissions reduction, renewable energy penetration, technology cost feasibility, institutional capacity, and innovation-related conditions, rather than superiority in a single indicator. The proposed framework offers a transparent and replicable tool for renewable energy transition benchmarking and evidence-based policy learning, while the results should be interpreted as cross-sectional comparative performance under the selected dataset and criteria rather than as a causal evaluation of specific policy instruments. Full article

X-ray reflection spectroscopy provides one of the most effective electromagnetic routes to measuring the dimensionless spin parameter of accreting black holes. The method has produced spin constraints for both stellar-mass black holes in X-ray binaries and supermassive black holes in active galactic nuclei, and it is central to the science objectives of present and future X-ray telescopes. At the same time, this method is vulnerable to a set of coupled observational and modeling systematics: continuum-reflection degeneracy, insufficient passband, unresolved distant reflection or absorption, detector effects, source variability, accretion-state dependence, and assumptions built into the reflection model itself. This article synthesizes the talks and discussions held at the 2025 Wake Forest workshop Recent Progress on Black Hole Spin Measurements Across the Electromagnetic and Gravitational Spectra; motivated by them, we propose a practical quality-control framework for assessing whether a published reflection-based spin measurement should be treated as robust, provisional, or not assessable from the published information alone. We organize the problem around three pillars: detectability, meaning that the relativistic reflection signal is unambiguously present in the data; uniqueness, meaning that the relativistic component can be separated from the continuum, from distant reflection, absorption, and instrumental effects; and robustness, meaning that the inferred spin is stable against plausible changes in model assumptions, data selection, and accretion-state treatment. We then translate these principles into applicable criteria, a tiered quality-classification scheme, and a reporting checklist for future analyses. The quantitative calibration of each criterion requires a dedicated campaign of simulations over realistic scenarios, which we outline here and defer to a future publication. We aim to define a reproducible path toward a curated, community-maintained compilation of reliable spin constraints and to guide the implementation of reflection spectroscopy in the high-throughput, high-resolution era. Full article

Experience has become a central source of competitiveness in wellness tourism, yet natural features in resorts often remain passive scenery rather than deliberately designed service content. This prospective, four-group randomised controlled service-evaluation study examined whether brief nature-based wellness micro-interventions developed from natural elements already present in a coastal health and wellness resort were associated with more favourable immediate evaluations of experience quality, satisfaction, perceived innovativeness, added value, and recommendation intention. Of 120 adult hotel guests randomly allocated in equal proportions to cloud noticing, forest therapy elements, active control, or standard stay, 114 completed the final evaluation. A contextually adapted 22-item instrument based on service-performance logic and tourism-experience research was used. The forest-therapy-elements and cloud-noticing conditions showed the most favourable overall evaluation profiles, followed by active control and standard stay. The clearest differentiation concerned perceived innovativeness, while recommendation intention was most strongly associated with perceived added value and satisfaction. These findings are relevant to wellness-tourism development because they indicate that natural elements already present in a resort may acquire additional perceived service value when translated into structured, facilitated, and place-sensitive guest activities. Because outcomes were immediate self-reports obtained in a single resort and participants and facilitators could not be blinded, the findings should not be interpreted as proof of lasting behavioural effects. They suggest a promising low-infrastructure service-development pathway whose effects on actual recommendation, repeat visitation, willingness to pay, loyalty, and destination outcomes require confirmation in multi-site and longitudinal research. Full article

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

Background/Objectives: Workplace violence (WPV) is a major occupational health hazard and psychosocial safety concern in psychiatric nursing, yet evidence integrating exposure, worker well-being, and workforce stability within a single theory-informed model remains limited in Saudi Arabia. This study examined associations between WPV and proximal occupational outcomes (burnout, job satisfaction, and absenteeism) and distal outcomes (quality of life [QoL] and turnover intentions) among psychiatric nurses and tested burnout as a statistical mediator of the WPV–turnover intentions association. Methods: A multicentre cross-sectional census survey was conducted across three psychiatric facilities in northern Saudi Arabia between June and August 2025 (N = 171). Participants reported WPV exposure over the preceding 12 months using Arabic versions of standardised instruments, including validated Arabic versions where available and a forward–backward-translated WVSQ. Linear, negative binomial, and logistic regression models examined outcome associations and factors associated with WPV exposure; the PROCESS macro (Model 4, 5000 bootstrap resamples) tested statistical mediation. Results: Most nurses reported at least one WPV incident in the preceding 12 months, most commonly verbal abuse. Younger age, male sex, shorter psychiatric nursing experience, lower perceived staffing adequacy, and inadequate security were associated with higher WPV exposure. WPV exposure was associated with lower QoL across all WHOQOL-BREF domains, higher burnout, lower job satisfaction, higher absenteeism, and stronger turnover intentions. Burnout showed a partial statistical mediation pattern in the WPV–turnover intentions association. Because the study was not specifically powered for the indirect effect, the mediation result should be interpreted with caution. Conclusions: In this cross-sectional study, WPV was a pervasive occupational exposure among Saudi psychiatric nurses and was associated with poorer QoL and higher turnover intentions, with burnout showing a partial statistical mediation pattern. Multilevel prevention, safer staffing, and structured post-incident psychological support are needed to support psychiatric nurses’ well-being and workforce retention. Full article

Teachers’ socio-emotional competencies are increasingly recognized as essential for professional well-being, adaptive teaching, and effective participation in digitally transforming educational systems. However, valid and culturally appropriate instruments for assessing these competencies among teachers remain limited, particularly in multilingual and underrepresented educational contexts. This study aimed to adapt the Behavioral, Emotional, and Social Skills Inventory (BESSI) for teachers in Kazakhstan and to examine its preliminary psychometric properties and socio-emotional competency profiles. The instrument was translated and culturally adapted into Kazakh and Russian using a TRAPD-based (Translation, Review, Adjudication, Pretesting, and Documentation) procedure involving translation, review, adjudication, pretesting, and documentation. Data were collected from 1458 school teachers across Kazakhstan representing different subject areas and language groups. After data-quality screening, the final analytic sample included 918 valid observations. Reliability analyses demonstrated excellent total-scale internal consistency (Cronbach’s α = 0.92; McDonald’s ω = 0.94), while domain-level reliability ranged from moderate to acceptable (α = 0.60–0.76; ω = 0.73–0.81). Exploratory factor analysis suggested an empirical eight-factor structure rather than a direct replication of the original BESSI framework, indicating that socio-emotional competencies may be organized differently in teacher populations. Model-based clustering identified four exploratory teacher profiles reflecting different configurations of self-management, emotional regulation, interpersonal, and innovation-related competencies. The profiles differed not only in overall competency levels but also in specific patterns of strengths and developmental needs. The findings provide preliminary evidence supporting the use of the adapted BESSI as a promising research and diagnostic instrument for assessing teachers’ socio-emotional competencies in Kazakhstan. At the same time, the results indicate the need for further confirmatory validation, measurement invariance testing, and external validity evidence before broader application. Full article