Search Results (1,517)

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

Background: Intrahepatic cholangiocarcinoma (ihCC) is a rare aggressive liver malignancy with rising incidence. For resectable cases, surgery is the only curative approach, but recurrence rates remain high. These challenges highlight the need for personalized, evidence-based clinical decision-making to improve patient outcomes. To address this, we developed the Multiphasic Evidential Decision-making Matrix (MedMax) to support systematic, individualized therapeutic strategies for ihCC. Methods: In this retrospective single-center study, between 2010 and 2020, we assessed the ability of the MedMax matrix to make treatment decisions in 489 consecutive patients with ihCC or suspected ihCC. Patients were divided into two cohorts depending on whether their tumor was operable (surgical cohort, n = 335) or non-operable (non-surgical cohort, n = 154). We assessed the accuracy of diagnostic confirmation and treatment allocation by MedMax and evaluated how the model’s recommendations corresponded to those made by the tumor board. Results: In the surgical cohort, MedMax achieved 100% accuracy in diagnostic confirmation and 97.9% accuracy in treatment allocation. There was 74.3% concordance between the resection type proposed by MedMax and actual extent of resection. This discrepancy was caused by deviations from the preoperative plan based on intraoperative findings, which could not have been predicted preoperatively. In the non-surgical cohort, MedMax again achieved 100% accuracy in diagnostic confirmation and 98.7% accuracy in treatment allocation. All discrepancies between the decisions made by MedMax and those made by the tumor board were attributed complex, high-risk patient profiles. MedMax reliably identified risk factors (such as advanced comorbidities and multifocal disease) in both cohorts. Conclusions: The MedMax matrix can make accurate, reliable and transparent decisions about the diagnosis and treatment of patients with ihCC thanks to its modular, evidence-based approach. It can also stratify and document risks in both surgical and non-surgical settings. Full article

Fibroin-based films represent a promising platform for sustainable and bio-derived materials. Existing literature has mainly focused on isolated molecules, plasticizers, or chemical cross-linkers, and the function of complex, multi-component natural extracts as structure-modulating agents in fibroin films remains poorly understood. In this study, edible films containing mulberry leaf extract (MLE; 2–8 wt%) and fibroin (8 wt%) were prepared by solution casting, and their structures were investigated using spectroscopic, morphological, thermal, mechanical, and barrier property analyses. The results reveal that MLE induces concentration-dependent changes in film performance through multicomponent, non-covalent interactions with the fibroin. An approximately 187% increase in tensile strength was achieved at high MLE concentration, confirming effective physical reinforcement. The water vapor transmission rate decreased markedly from 0.888 to 0.170 g·h⁻¹·m⁻², indicating an enhanced moisture barrier, whereas oxygen permeability increased at higher extract loadings, suggesting localized chain rearrangements. High optical transparency in the visible region was maintained (79.95–83.77%), while UV response was selectively altered with extract concentration. Overall, the 8MLE formulation exhibited the most balanced performance. This study demonstrates that plant-derived extracts can serve as effective natural modifiers for tailoring fibroin film properties without inducing crystallization, offering a sustainable strategy for designing bio-based and edible protein film systems. Full article

Art therapy assessment benefits from analytical clarity while preserving non-directive, process-sensitive practice. Although the Expressive Therapies Continuum (ETC) is widely used to conceptualize sensory, affective, cognitive, and symbolic processes in art-making, ETC-informed assessment often relies on implicit clinical reasoning, limiting transparency and interdisciplinary communication. This article presents the developmental stage of a protocol-based Expressive Therapies Continuum Assessment Profile (ETC-AP) developed at Rīga Stradiņš University. The ETC-AP differentiates activation and inhibition patterns around integration midpoints and organizes observation in a defined five-step interpretive sequence without positioning the method as a psychometrically validated test. It combines (i) a uniform three-task, non-directive administration with a brief post-task inquiry; (ii) criteria-guided coding of observable features across three artworks and process notes; and (iii) 0–100 descriptive profile indicators to support within-case pattern description and professional dialogue. An illustrative case vignette shows how the ETC-AP can generate trauma-informed, regulation-oriented hypotheses about channel accessibility and cautious regulation-oriented sequencing, while remaining subordinate to clinical judgment and context. Key boundaries include incomplete operational coverage in some inhibition ranges, limits of static documentation for process-dependent markers, and the need for structured training materials and programmatic studies of reliability, feasibility, and sensitivity to change. Full article

Background/Objectives: Diabetes is a chronic metabolic disorder affecting global health, where early prediction can significantly reduce disease severity. Methods: This research proposes an interpretable multi-metric fuzzy distance-based ensemble (MMFDE) that integrates multi-variant gradient-boosting classifiers (GBM, LightGBM, XGBoost, and AdaBoost) through a novel fuzzy fusion mechanism designed for intrinsic interpretability. Unlike conventional ensembles relying on opaque averaging or voting, MMFDE transforms base classifier predictions into a high-dimensional fuzzy space quantified via a weighted hybrid distance incorporating Euclidean, Manhattan, Chebyshev, and cosine metrics against ideal diabetic and non-diabetic reference vectors. These distances are translated into membership degrees with the help of exponentially decaying functions, which give clinicians calibrated confidence scores for every prediction. Comprehensive SHAP analysis identifies important clinical risk factors (glucose, BMI, and diabetes pedigree function), which show concordance with the medical literature, thereby giving greater clinical trust. Results: Experimental evaluations on two publicly available datasets, Hospital Frankfurt Germany Diabetes Dataset (HFGDD) and Pima Indians Diabetes Dataset (PIDD), show that MMFDE outperforms all base models with a significant accuracy of 94.83% and Area Under the Curve (AUC) of 97.66% on HFGDD and three different levels of interpretability: geometric transparency via distance-based decisions, confidence-calibrated uncertainty estimates, and feature-level explanations via SHAP. The confidence thresholds enabled in the framework support risk stratification clinical workflows with high-confidence predictions for automated screening and cases with moderate/low confidence flagged out for review by the clinician. Conclusions: By demonstrating that high performance and interpretability need not be mutually exclusive, MMFDE advances trustworthy AI for clinical decision support, addressing the critical need for transparent and clinically actionable diabetes prediction systems. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

Accurate distinction between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) remains the key local staging problem in bladder cancer because treatment intensity, timing of radical therapy, and suitability for bladder-preserving strategies all depend on it. Multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) now provide a standardized imaging framework for local staging and increasingly support MRI-first clinical pathways. Artificial intelligence (AI) has emerged as an additional decision-support layer, but the evidence base remains methodologically uneven. In this structured narrative review, we synthesized peer-reviewed literature from January 2020 to March 2026, while retaining foundational VI-RADS studies from 2018 to 2019, and prioritized guideline documents, meta-analyses, prospective cohorts, multicenter and externally validated AI studies, response-assessment studies, and papers addressing implementation and reporting quality. Current evidence shows that radiomics and deep learning models can achieve high discrimination for MIBC detection on MRI, and that the most plausible incremental value of AI lies in equivocal VI-RADS lesions, reader support outside high-volume expert settings, and multimodal risk stratification. However, most studies remain retrospective, highly selected, segmentation-dependent, and vulnerable to reference-standard bias, domain shift, and poor calibration. This review therefore emphasizes several translational issues that are often underreported: lesion-level versus patient-level inference, the distortive effect of TURBT-based labels, the need to evaluate false-negative consequences in VI-RADS 3 tumors, and the distinction between diagnostic support and broader pathway redesign. We also discuss response assessment, nacVI-RADS, segmentation automation, multicenter and federated infrastructure, workflow ownership, and the limits of imaging-only models in a biologically heterogeneous disease. The most credible near-term role of AI is not autonomous diagnosis, but augmentation of standardized mpMRI and VI-RADS within multidisciplinary care. Future progress will depend on prospective utility studies, site-held-out validation, transparent reporting, and the integration of imaging with molecular and cellular heterogeneity through radiogenomic and multi-omics approaches. Full article

This study investigates the influence of blockchain-enabled supply chain traceability (BESCT) on sustainable supply chain practices (SSCP) in the context of small and medium-sized enterprises (SMEs) in the Turkish manufacturing sector. Grounded in the Resource-Based View (RBV), the research further examines the mediating roles of perceived information transparency (PIT) and supply chain collaboration (SCC) and the moderating effect of environmental orientation (EO). The study employs a quantitative research design using data collected from 652 managers representing various manufacturing SMEs. Structural equation modeling via SmartPLS 4.0 is applied to test a moderated mediation model and assess the relationships among the constructs. The results indicate that BESCT is positively associated with SSCP both directly and through PIT and SCC as mediating mechanisms. PIT is linked to improved visibility and information integrity, while SCC is associated with joint sustainability efforts across supply chain partners. Moreover, EO strengthens the positive associations between BESCT and PIT with SSCP, while its effect on collaboration is more nuanced. Given the cross-sectional design, these findings should be interpreted as associative rather than causal. In addition, the use of a non-probability convenience sampling approach may limit generalizability, and the results should be interpreted with caution. This study contributes to the RBV literature by conceptualizing blockchain as a traceability-enabled dynamic capability that supports sustainability-oriented practices in SMEs. Full article

In response to corrosion challenges encountered during the gathering and transportation of wet natural gas, this study systematically investigates the corrosion behavior of L245NCS steel in environments containing O₂, H₂S, CO₂ and simulated oilfield-produced water. The research employs a combined approach involving high-pressure autoclave experiments and transparent flow loop simulations. Autoclave tests reproduce gas phase, liquid phase, and gas–liquid interface conditions under a controlled O₂-H₂S-CO₂ mixture, while a visual flow loop equipped with elbows and undulating sections is used to examine liquid accumulation behavior and flow characteristics under dynamic, real-world operating conditions. Results indicate that corrosion is most severe at the gas–liquid interface. H₂S is identified as the primary corrosive agent, exerting a stronger influence than CO₂ or O₂. Liquid accumulation is the main factor leading to non-uniform corrosion distribution, and its formation is influenced by water content, pressure, temperature difference, and pipeline shutdown and restart operations. Critical areas such as low-lying sections, downhill bottoms, and the beginning of uphill sections exhibit localized corrosion rates up to 61.4% higher than areas without liquid accumulation. This integrated methodology bridges mechanistic understanding with engineering practice, providing a basis for corrosion risk assessment, optimal monitoring point placement, and integrity management of wet gas pipelines. Full article

While Environmental, Social, and Governance (ESG) rating divergence poses a barrier to accurate sustainability measurement and sustainable investment, how internal managerial cognition addresses this external market misalignment remains underexplored. To address the research question of how executive focus shapes market consensus on corporate sustainability, this study integrates the Attention-Based View and Signaling Theory to examine the potential mitigating role of Top Management Team (TMT) environmental attention on ESG rating divergence. Utilizing high-dimensional fixed-effects regressions and textual analysis, we analyze a sample of Chinese A-share non-financial listed firms from 2015 to 2023. Empirical results indicate that a transparent and forthcoming managerial environmental focus helps reduce rating divergence, thereby partially aligning informational baselines. This cognitive alignment can act as an information calibrator, particularly when environmental issues match the firm’s core industry materiality, and this association appears more pronounced in regions with stringent environmental regulations. Robustness checks support the notion that substantive, quantitative sustainability disclosures driven by executive attention assist in alleviating informational misalignment among external rating agencies. These findings offer socio-economic and policy insights for advancing sustainable development, suggesting that regulators could consider encouraging structured sustainability reporting to support the role of executive cognition in standardizing ESG measurements. Full article

Social washing refers to the strategic exaggeration or misrepresentation of an organisation’s commitment to social responsibility, ethical governance, or social impact without corresponding substantive action. It typically operates through selective disclosure, symbolic initiatives, or performative communication that aligns the organisation with socially desirable values—such as equity, human rights, community development, or inclusion—while underlying practices remain unchanged, weakly evidenced, or contradictory. The concept belongs to the wider family of “washing” phenomena associated with corporate social responsibility (CSR) and environmental, social, and governance (ESG) frameworks, especially the difficult-to-measure social (“S”) pillar. By contrast, authentic accountability refers to governance and reporting practices that connect institutional commitments to verifiable social outcomes and discernible improvements in human well-being. The institutionalisation of ESG frameworks has raised expectations of corporate responsibility while also enlarging the scope for reputational manipulation. Within this setting, social washing has become relevant not only to social policy and sustainable development debates, but also to corporate governance, ESG evaluation, and cross-sector partnership practice. This entry examines how organisations construct narratives of social responsibility that do not necessarily correspond to substantive social outcomes. It also argues that such distortions matter both for welfare systems and civil-society actors and for ESG assessment, reputational signalling, and the interpretation of social performance in market settings. Full article

Exercise adaptation and training maladaptation arise from overlapping metabolic, redox, inflammatory, endocrine, and tissue-remodeling processes, so the translational question is not whether biomarkers change but when, where, and for which decision they become informative. This narrative review develops a decision-linked framework for minimally invasive biomarkers across the recovery–overload continuum and treats biomarker meaning as a molecule–matrix–time–decision relationship rather than as a stand-alone peak. The framework is organized around five coupled layers: stimulus architecture, signaling and release biology, sampling matrix and pre-analytics, bout-relative kinetics, and the monitoring decision to be supported. Current evidence indicates that no single biomarker reliably separates productive remodeling from delayed recovery, tissue strain, non-functional overreaching, or early maladaptation. Classical chemistry remains useful for bounded tasks, especially delayed tissue strain and stress reactivity; cfDNA appears promising for rapid load sensitivity; targeted metabolite panels are strongest for recovery phenotyping; and circulating RNAs and extracellular-vesicle cargo add mechanistic depth but remain constrained by pre-analytical fragility and incomplete standardization. The central practical implication is that overload is better interpreted as progressive loss of signal resolution than as threshold-crossing and that sparse temporally staggered panels are more likely to aid monitoring decisions than isolated markers or untimed high-dimensional profiles. Progress will depend on purpose-specific panels, transparent analytical standards, and prospective validation against symptoms, performance, and established measures across sex, hormonal, circadian, and training contexts. Full article

With the recent ban on Diversity, Equity and Inclusion (DEI) efforts, this study seeks to address an under-researched area—the lived experience of workplace training participation (WTP)/non-participation of Black administrative employees at Canadian universities. While research in academia has focused on faculty and students, there is a lack of research on administrative employee participation within university settings in Canada. This gap is especially significant as this group composes a significant ratio of the entire workforce in Canadian universities. It is also important to understand the implications of WTP/non-participation for employees who identify as Black. This study used Stephen Billett’s conceptualization of affordance and access as a starting point to identify gaps in the workplace training (WT) literature, specifically as it pertains to Black employees. Data was collected from 26 Black administrative employees who discussed their lived experience with WTP in these settings. A qualitative approach from a phenomenological perspective was used to better understand the participants’ lived experiences with WTP. The results revealed a lack of transparency in the WT approval process, experiences of time pressure, managers’ discretion and racism as factors that influence WTP. Full article

The need for Arabic Aspect-Based Sentiment Analysis (ABSA) has grown steadily alongside the expansion of digital content, while the linguistic complexity of Modern Standard Arabic and its diverse dialects introduces significant challenges. However, progress in the field remains constrained by methodological fragmentation, inconsistent task definitions, heterogeneous datasets, and non-standardized evaluation practices. Based on a systematic analysis of 57 studies, this work presents an analytical and interpretive review that moves beyond performance-oriented surveys to examine the methodological foundations of Arabic ABSA research. The review follows a rigorous and transparent study selection process and applies a structured analytical framework to analyze task formulations, dataset characteristics, modeling approaches and evaluation strategies. Our findings reveal persistent challenges, including ambiguous aspect definitions, insufficiently documented annotation protocols, structural annotation biases, and limited robustness across domains and dialects. A heavy reliance on Transformer-based architectures and new Arabic foundation models can create an illusion of progress. Researchers often evaluate these models on small and homogeneous datasets. Consequently, strong in-domain performance obscures limited cross-domain and cross-dialectal generalizability. This study concludes by outlining actionable research directions, emphasizing clearer task standardization, more rigorous annotation guidelines, unified evaluation, and broader dialectal coverage to enhance reproducibility and scalability in Arabic ABSA systems. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications. Full article