Search Results (4,974)

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. Full article

B-cell maturation antigen (BCMA), G protein-coupled receptor class C group 5 member D (GPRC5D)-directed immunotherapies, chimeric antigen receptor T-cell (CAR-T) products, bispecific T-cell engagers (BsAbs), and antibody–drug conjugates (ADCs), have transformed the management of MM. Their adoption is now extending beyond tertiary centers following FDA modifications for CAR-T safety and the rapid uptake of off-the-shelf bispecifics suitable for community delivery. Clinicians outside specialist hubs must therefore be conversant with the full toxicity spectrum, including rare but high-consequence events, both for informed consent and for the work-up of post-therapy complications. In this narrative review, we report on the published literature around toxicities of approved and investigational BCMA- and GPRC5D-directed therapies, drawing on pivotal trial data, real-world cohorts, pharmacovigilance studies, and consensus management recommendations, with emphasis on practical recognition and risk mitigation. This review presents toxicities by a temporal pattern including acute (CRS, ICANS, infection, ocular, mucocutaneous), subacute (cranial nerve palsies, parkinsonism, myelitis, peripheral neuropathies IEC-associated enterocolitis and cardiovascular events), and long-term (prolonged cytopenias, second primary malignancies). We discuss validated risk stratification tools, such as the CAR-HEMATOTOX score, EASIX index, and multidisciplinary geriatric assessment, which predicts severe ICANS, infection, and resource utilization, supporting individualized pre-treatment planning. Safe delivery of immune therapies in community settings requires infrastructure for acute critical care, neurology, ophthalmology, infectious disease and long-term surveillance, but is achievable when paired with validated risk stratification and clear referral pathways. Full article

Carbon dioxide (CO₂) capture through adsorption using porous materials has emerged as a promising strategy for mitigating industrial greenhouse gas emissions. However, selecting an optimal adsorbent material under varying operating conditions remains a complex and time-consuming process when relying solely on experimental studies. In this project, a machine-learning-based framework is developed to predict CO₂ adsorption capacity and identify the most suitable adsorbent material using process and material parameters. A comprehensive dataset was constructed comprising multiple classes of adsorbent materials including activated carbon, zeolites, metal–organic frameworks (MOFs), porous organic polymers (POPs), alumina/silica, and amine-functionalized sorbents. The dataset includes key parameters such as temperature, pressure, CO₂ mole fraction, humidity, BET surface area, micropore characteristics, amine loading, heat of adsorption, particle density, pellet diameter, and bed void fraction. Two machine learning models based on the XGBoost algorithm were implemented. An XGBoost Regressor was used to predict the experimental CO₂ adsorption capacity, while an XGBoost Classifier was trained to identify the type of adsorbent used based on the input parameters. The models were trained and validated using a train–test split approach to ensure reliable performance evaluation. The results demonstrate that gradient boosting models can accurately capture complex nonlinear relationships between adsorption conditions, material properties, and adsorption performance. The developed framework provides a fast and efficient predictive tool that can assist researchers and engineers in screening adsorbent materials and optimizing CO₂ capture systems for industrial applications. Using this model, one can predict the adsorption capacity of any adsorbent used in the training dataset and predict its type with 95% accuracy. Full article

Film-forming antitranspirants may help potatoes tolerate moderate drought, but their effects on early tuberisation and tuber size distribution remain unclear. Two pot experiments were conducted in a polytunnel (late summer) and a glasshouse (winter–spring), with moderate drought imposed during tuber initiation and early bulking, alone (DT) or combined with an antitranspirant (di-1-p-menthene; VGDT). Leaf relative water content (RWC), stolon traits, and tuber yield and size distribution were measured. Moderate drought reduced RWC, stolon number, and tuber set, which indicates the sensitivity of early tuber development to water deficit. VGDT increased leaf RWC under drought from 55% to 71% in Experiment 1 and from 62% to 73% in Experiment 2, while the total tuber number under moderate drought increased from 5.2 to 11.7 tubers plant⁻¹ in Experiment 1 and from 6.1 to 10.7 tubers plant⁻¹ in Experiment 2. VGDT also increased the number of large (≥9 cm) tubers, shifting size distribution towards marketable classes. Although Vapor Gard improved plant water status and tuber number under drought, it did not restore performance to irrigated levels. These findings indicate its value as a complementary tool to mitigate drought-related losses during tuberisation, not a substitute for irrigation. Full article

Background/Objectives: Asthma’s preventable burden is heavily driven by severe exacerbations (SE). Replacing standalone short-acting β2-agonists (SABAs), which risk reducing patient tolerance, with inhaled corticosteroid/long-acting β2-agonist combinations optimizes care through maintenance, reliever, and maintenance-and-reliever therapy (MART). This systematic review and meta-analysis evaluated the efficacy, effectiveness, and safety of Budesonide/Formoterol (B/F). Methods: PubMed, Cochrane, and Embase were searched for randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) through May 15, 2026. Bias was assessed via the Cochrane Risk of Bias tool version 2 (RoB 2) and the Risk ff Bias in Non-randomized Studies of Interventions (ROBINS-I) version 2.0. Primary outcomes (time to first SE, annual SE rate) were pooled using a random-effects meta-analysis, yielding hazard ratios (HRs) and rate ratios (RRs). Results: We included 19 studies (15 RCTs, 4 non-RCTs) comprising 104,600 patients (primarily aged ≥12 years with mild-to-severe asthma). Most RCTs had a low risk of bias, whereas the non-RCTs had a high risk of bias. B/F MART significantly delayed the first SE and reduced annual rates versus Budesonide + SABA (HR = 0.57; RR = 0.55), B/F + SABA (HR = 0.62; RR = 0.58), and Fluticasone/Salmeterol + SABA (HR = 0.75; RR = 0.72). As-needed B/F reduced first SE hazard and annual rates versus SABA alone (HR = 0.43; RR = 0.42). Compared with Budesonide + SABA, it delayed the first SE (HR = 0.85) but showed non-significant rate reductions (RR = 0.90). Adverse events were balanced between groups over 12–52 weeks. Conclusions: B/F MART demonstrates high efficacy in mitigating the risk of the first SE. However, limited trial data leave the evidence for maintenance or reliever regimens controversial. Across all regimens, B/F is well-tolerated within 6 to 12 months. Full article

Transitioning to sustainable, plant-forward diets, such as the Planetary Health Diet is a global priority; however, the palatability gap remains a formidable barrier, as consumers often perceive low-sodium, plant-centric diets as sensory-deficient. While aromatic herbs could bridge this gap, the current literature rarely integrates their sensory, ecological, phytochemical, and cultural dimensions. This narrative review explores how Mediterranean aromatic plants indigenous to the Maltese Islands function as sensory and molecular catalysts to bridge this gap. Through a thematic synthesis (2005–2026) integrating ethnobotanical evidence with molecular nutrition and sensory science, the Maltese archipelago is examined as a small-island ecological model. Chronic abiotic stressors, including high salinity and intense solar exposure, induce phytochemical priming, significantly enhancing secondary metabolites like polyphenols and terpenoids. These compounds establish a folk–medicine bridge, where traditional culinary practices align with modern biochemical validation. These bioactives demonstrate a capacity to modulate the NF-κB inflammatory axis, mitigate systemic inflammaging, and support the gut–microbiome–brain axis. Furthermore, these aromatics serve as translational tools for EAT-Lancet 2025 targets by facilitating cross-modal sensory compensation for sodium reduction and improving nutrient bioaccessibility via the culinary entourage effect. The TASTE-MED framework positions culinary nutrition as a vital translational bridge, asserting that flavour is a prerequisite for dietary sustainability and aligning individual molecular resilience with broader planetary health goals. Full article

Background: The administration of intravenous contrast media using automated power injectors is fundamental for high-quality computed tomography (CT), particularly in CT angiography (CTA). The selection of an appropriate vascular access device (VAD) and adherence to technical safety standards are critical for ensuring diagnostic efficacy and patient safety. This systematic review aims to synthesize current scientific literature regarding the efficacy and safety of contrast media infusion across various vascular access routes, including peripheral intravenous cannulas (PIVC), central venous catheters (CVC), and totally implantable venous access devices (TIVAD). Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in PubMed, CINAHL, Web of Science, and Scopus for studies published between 2000 and 2026. A total of 19 studies—including randomized controlled trials (RCTs), cohort studies, and systematic reviews—were analyzed. Methodological quality was assessed using Joanna Briggs Institute (JBI) appraisal tools. Results: Modern PIVCs utilizing diffuser technology (side holes) significantly reduce distal jet pressure, minimizing vessel wall damage during high-flow injections. For patients with difficult vascular access, “power-injectable” certified devices (e.g., PICCs or TIVADs) serve as a safe alternative. Standard, non-power-injectable central lines must be avoided due to the risk of catheter rupture. The selection of an appropriate vascular access device is particularly challenging in patients of older age or those with chronic conditions, such as peripheral vascular disease, obesity, or chemotherapy-related venous damage, which often lead to difficult intravenous access (DIVA). Conclusions: Utilizing certified power-injectable devices and advanced cannula designs improves the safety of high-pressure contrast administration. Adherence to technical protocols and the identification of high-risk patients are essential for mitigating complications such as contrast extravasation. Full article

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design. Full article

As hybrid work arrangements become more established in organisations, the need for effective design and implementation strategies has grown significantly. Evidence indicates that employee wellbeing and engagement in hybrid work environments are declining and this presents a critical challenge for human resource management (HRM) professionals. This presents HRM professionals with a critical imperative of improving wellbeing, while maintaining engagement and productivity at work. This aligns closely with the United Nations’ 17 Sustainable Development Goals, particularly those that promote wellbeing and decent work. Through a systematic synthesis of 78 studies, this research investigates the key determinants of employee engagement and wellbeing in hybrid work contexts. The conceptual framework for this study is grounded in existing theoretical perspectives from the Job Demands–Resources model, Saks Frameworks and wellbeing perspective presented by Guest. The analysis identifies five critical factors that influence engagement and wellbeing outcomes in hybrid work, accompanied by evidence-based propositions for practice. These recommendations encompass: establishing well-equipped workspaces with appropriate flexibility in both location and time; developing organisational culture and leadership through enhanced communication and collaboration mechanisms; strategically allocating jobs and tasks whilst fostering effective networks and collaboration tools and implementing targeted training interventions to mitigate technostress and burnout associated with digital workloads. We advocate for future research to develop comprehensive models, frameworks and wellbeing interventions to guide HRM professionals in addressing these challenges at both the local and global levels. Full article

Large Language Models (LLMs) have emerged as transformative tools across various domains, exhibiting remarkable capabilities in natural language processing and generation. However, their reliance on static pre-training data limits their ability to access up-to-date and domain-specific information. The existing research often treats augmentation strategies in isolation, and limited efforts have been made to systematically compare them through the lens of information integrity. This review focuses specifically on Retrieval-Augmented Generation (RAG) and fine-tuning, identifying them as the two dominant paradigms for integrating external knowledge: RAG for retrieval-based context injection and fine-tuning for parametric knowledge adaptation. While existing surveys predominantly focus on performance metrics like accuracy or latency, this paper addresses the critical gap of data fidelity—the preservation of truthfulness, integrity, and fairness during augmentation. We systematically synthesize empirical findings from diverse methodologies to determine how each approach mitigates hallucinations and bias. By comparing the trade-offs between retrieval-based context injection and parametric knowledge adaptation, this survey provides unique value to readers by providing a structured taxonomy, a unified evaluation framework, and actionable insights to guide future research and practical deployment of robust, high-fidelity LLMs. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

The feasibility of employing the dynamic substructure approach for seismic response analysis of complex structures has been widely recognized. However, the analytical accuracy of this method is affected by several factors, including the element type, the structural configuration, and the analysis method. To address these issues, four types of reticulated shell structures were designed and analyzed using the mode superposition response spectrum method (MSRSM) and the time history analysis method (THAM). The displacements of the key nodes and all member stresses were extracted to compare the simplified finite element models with the original models. The relative errors of nodal displacements calculated by the models with reduced degree of freedom (DOF) were within 1.62%. For the member stresses of the single-layer reticulated shells, the relative errors of the simplified models were within 14.35%. In the simplified models of double-layer reticulated shells, several members exhibited a relative error greater than 30%; however, these members were mainly located near the substructure boundaries and accounted for less than 0.62% of the entire structure. Three strategies are proposed to mitigate the influence of the member stress errors on the structural analysis conclusions for double-layer reticulated shell structures. In addition, the dynamic substructure method was extended to the coupled system of large-span spatial structures and point-supported glass facades. The seismic response results confirmed that this method effectively reduces computational costs while maintaining satisfactory accuracy, indicating that it is a useful tool for simplifying large-span spatial structures in extensive numerical analyses. Full article

Practical socioeconomic systems commonly contain imprecise and subjective data, while existing uncertain regression methods perform poorly for highly multicollinear variables. Uncertain least squares is susceptible to multicollinearity and outliers, and uncertain LASSO fails to stably select correlated variables. To address these issues, this paper proposes an uncertain elastic net regression model targeting multicollinear uncertain data. Based on the minimum uncertain expectation framework, the model adopts combined regularization to realize sparse variable screening and grouping effect, which mitigates multicollinearity and enhances estimation stability. We verify the model via numerical examples and an empirical study on Shandong’s domestic tourism data, taking two classic uncertain regression methods as benchmarks. The results show that our model outperforms competitors in fitting accuracy, coefficient stability and variable selection. This method provides a reliable, interpretable tool for regression modeling under uncertainty and multicollinearity, and can be applied to tourism and socioeconomic research. Full article

Background: Spinocerebellar ataxia type 2 (SCA2) is an inherited neurodegenerative disorder characterized by progressive cerebellar degeneration. One difficulty in treating this disease lies in identifying preclinical carriers: individuals who carry the pathogenic ATXN2 mutation but remain asymptomatic with respect to motor manifestations. Though magnetic resonance imaging (MRI) has proven valuable in supporting the diagnosis of ataxia, traditional univariate approaches using linear measurements have shown limited ability to capture the complex anatomical changes that occur across the disease spectrum, particularly during the preclinical phase. Methods: This study employed a comprehensive multivariate approach to improve the classification of individuals across the SCA2 spectrum. We developed a multinomial logistic regression model incorporating multiple linear measurements derived from magnetic resonance imaging to discriminate between healthy controls (n = 72), preclinical carriers (n = 17), and patients with manifest SCA2 (n = 61). To mitigate inherent class imbalance, particularly in the smaller preclinical subgroup, we implemented the Synthetic Minority Over-sampling Technique (SMOTE), generating a balanced dataset that enhances the model’s ability to discern the distinctive anatomical features. This was compared to the model applied to the unbalanced data. An improvement was observed when applying SMOTE. Results: The multivariate model demonstrated discriminatory performance, achieving an overall accuracy of 80.7%. The ability to identify healthy controls (AUC: 0.96), preclinical individuals (AUC: 0.75), and clinical individuals (AUC: 95%). This represents an advance over previous univariate approaches, which have had difficulty capturing the neurodegenerative changes characteristic of the preclinical stage. Conclusions: By integrating multiple neuroimaging biomarkers into a multivariable model, this study provides a tool for early identification of preclinical SCA2 carriers. The ability to accurately classify these individuals opens an opportunity for early therapeutic intervention before irreversible neurological deterioration occurs. This approach shows promise for optimizing clinical trial design and personalized care in SCA2. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article