Search Results (3,151)

Air pollution is monitored worldwide through networks of sensors. They provide information on local air pollution, which also provides a basis for a multitude of research. To reduce health hazards caused by air pollution, the concentrations of pollutants as measured by sensors need to be apportioned to particular sources. Although several methods to achieve this have been developed, only a few works on the contributions of pollution sources to health hazards are available in the literature. In this work, a simple scheme is proposed to compare health hazards from each of the main sources of air pollution in a given country, region, or area. The comparison involves the main air pollutants of PM_2.5, NO₂, and O₃ for chronic exposures and PM_2.5, NO₂, O₃, and SO₂ for acute exposures. The actual health hazard from each substance is determined from concentrations measured by sensors and the concentration–response functions found in the literature. The apportionment of substances to sources is based on emission inventories, thus avoiding costly methods of source apportionment. This method has been applied to the entire country, i.e., Poland, yielding the average proportion of health hazards from particular sources. The example demonstrates the flexibility and ease of application of the scheme. Uncertainties in the results were subjected to discussion. The key advantage of the scheme lies in its ability to provide an indication of the most harmful sources of pollution, thus highlighting efficient interventions. Full article

Global climate change results in increasing challenges to the structural security of China’s food system, while pronounced spatial heterogeneities in provincial production and consumption intensify the risk of supply-demand imbalance. This study examines the resilience of China’s inter-provincial staple food flow network from a systemic perspective and identifies its key drivers. Inter-provincial food flows are first inferred using a cost-minimization optimization model. Network resilience is then evaluated by integrating complex network analysis with ecological network resilience theory. Finally, econometric analysis is applied to quantify the relative contributions of multiple structural factors to resilience dynamics. The results reveal an overall decline in the resilience of aggregated staple food, alongside persistently low resilience in soybeans network, indicating heightened structural vulnerability. Substantial heterogeneity is observed across staples in both resilience levels and underlying mechanisms. In general, greater connectivity and diversity of flow paths enhance system resilience, although this effect is markedly weaker for soybeans due to concentrated and import-dependent supply structures. By explicitly linking flow, network structure, and resilience, this study provides system-level insights into the functioning of inter-provincial food flow networks. The proposed analytical framework offers a transferable tool for assessing interregional food flow resilience and supports evidence-based strategies for validating system robustness under uncertainties. Full article

Many complex systems requires the use of controllers to allow an automated, self-adaptive management of components and resources. Controllers are software components that observe a system, analyse its quality, and recommend and enact decisions to maintain or improve quality. While controllers have been for many years, recently Artificial Intelligence (AI) techniques such as Machine Learning (ML) and specifically reinforcement learning (RL) are used to construct these controllers, causing uncertainties about the quality of them due to their construction. We investigate quality metrics for RL-constructed software-based controllers that allow for their continuous quality control, which is particularly motivated by increasing automation and also the usage of artificial intelligence and control theoretic solutions for controller construction and operation. We introduce self-adaptation and control principles and define a quality-oriented controller reference architecture for controllers for self-adaptive systems. This forms the basis for the central contribution, a quality analysis metrics framework for controllers themselves. Full article

This paper advances a unified theoretical framework that synthesises Shane and Eckhardt’s individual–opportunity nexus, Ramoglou and Tsang’s opportunities-as-propensities perspective, and Davidsson’s tripartite model of new venture ideas, external enablers, and opportunity confidence. Building on these foundations, the paper develops an entrepreneurial entanglement model that explains how opportunities emerge as probabilistic propensities within dynamic configurations of agents, artefacts, distributed agencies, and spatiotemporal conditions. The model clarifies how material artefacts, socio-cognitive processes, and environmental shifts jointly shape the emergence, visibility, and realisation of entrepreneurial possibilities. By situating opportunity formation within an entangled field—rather than within isolated acts of discovery or creation—the framework deepens understanding of how entrepreneurial actions give rise to potentialities and how these potentialities become actualised under conditions of uncertainty. The analysis contributes to both theory and practice by offering a relational, mechanism-based account of how entrepreneurial behaviour and environmental factors intersect to structure the formation and realisation of opportunities. Full article

In the context of a high proportion of renewable energy integration, active splitting section search—one of the “three defense lines” of a power system—is crucial for the security, stability, and long-term sustainability of islanded grids. Addressing the random fluctuations of high-penetration wind power and the weakened voltage support capability caused by multi-infeed HVDC, this paper proposes a slow-coherency-based active splitting section optimization model that explicitly accounts for wind power uncertainty and multi-infeed DC stability constraints. First, a GMM-K-means method is applied to historical wind data to model, sample, and cluster scenarios, efficiently generating and reducing a representative set of typical wind outputs; this accurately captures wind uncertainty while lowering computational burden. Subsequently, an improved particle swarm optimizer enhanced by genetic operators is used to optimize a multi-dimensional coherency fitness function that incorporates a refined equivalent power index, frequency constraints, and connectivity requirements. Simulations on a modified New England 39-bus system demonstrate that the proposed model markedly enlarges the post-split voltage stability margin and effectively reduces power-flow shocks and power imbalance compared with existing methods. This research contributes to enhancing the sustainability and operational resilience of power systems under energy transition. Full article

Rifampicin-resistant tuberculosis (RR-TB) remains a major threat to global TB control, primarily driven by mutations in the rpoB gene of Mycobacterium tuberculosis (Mtb). Most resistance-conferring mutations occur within the 81-base pair RIF resistance determining region (RRDR), particularly at codons S450L, H445Y/D, and D435V, which are strongly associated with high level resistance. However, increasing evidence of low-frequency and disputed variants both within and beyond the RRDR reveals a broader genetic spectrum that contributes to diagnostic uncertainty and variable phenotypic outcomes. This review summarizes current knowledge of high frequency, low frequency, and disputed rpoB mutations and their implications for molecular detection of RIF resistance. Structural analyses show that specific amino acid substitutions alter key hydrogen bonds or create steric hindrance in the RIF-binding pocket, leading to diverse resistance levels. Despite the success of molecular platforms such as Xpert MTB/RIF and line probe assays, their hotspot-based detection limits sensitivity to noncanonical variants. Lowering the minimum inhibitory concentration (MIC) breakpoint and integrating sequencing-based approaches, such as targeted and whole-genome sequencing, can enhance detection accuracy. A combined genomic and phenotypic framework will be essential to close existing diagnostic gaps and advance precision guided management of RIF-resistant and multidrug-resistant tuberculosis. Full article

Background: Children with congenital heart disease (CHD) are at substantially increased risk for respiratory infections, which occur more frequently and with greater severity than in healthy peers. This heightened vulnerability stems from multifactorial immune impairment, including defects in innate and adaptive immunity, chronic inflammation related to abnormal hemodynamics and hypoxia, reduced thymic function, and genetic syndromes affecting both cardiac and immune development. Viral pathogens—particularly respiratory syncytial virus (RSV), influenza viruses, and SARS-CoV-2—account for most infections, although bacterial pathogens remain relevant, especially in postoperative settings. Methods: This narrative review summarizes current evidence on infection susceptibility in children with CHD, the epidemiology and clinical relevance of major respiratory pathogens, and the effectiveness of available preventive measures. Literature evaluating immunological mechanisms, infection burden, vaccine effectiveness, and passive immunization strategies was examined, along with existing national and international immunization guidelines. Results: Children with CHD consistently exhibit higher rates of hospitalization, intensive care unit admission, mechanical ventilation, and mortality following respiratory infections. RSV, influenza, and SARS-CoV-2 infections are particularly severe in this population, while bacterial infections, though less common, contribute substantially to postoperative morbidity. Preventive options—including routine childhood vaccines, pneumococcal and Haemophilus influenzae type b vaccines, influenza vaccines, COVID-19 mRNA vaccines, and RSV monoclonal antibodies—demonstrate strong protective effects. New long-acting RSV monoclonal antibodies and maternal vaccination markedly enhance prevention in early infancy. However, vaccine coverage remains insufficient due to parental hesitancy, provider uncertainty, delayed immunization, and limited CHD-specific evidence. Conclusions: Respiratory infections pose a significant and preventable health burden in children with CHD. Enhancing the use of both active and passive immunization is essential to reduce morbidity and mortality. Strengthening evidence-based guidelines, improving coordination between specialists and primary care providers, integrating immunization checks into routine CHD management, and providing clear, condition-specific counseling to families can substantially improve vaccine uptake and clinical outcomes in this vulnerable population. Full article

In response to the growing complexity and uncertainty in real-world decision-making, this study introduces a novel framework based on the polytopic fuzzy tensor (PFT) model, which unifies the geometric structure of polytopes with the representational power of fuzzy tensors. The PFT framework is specifically designed to handle high-dimensional, imprecise, and ambiguous information commonly encountered in multi-criteria group decision-making scenarios. To support this framework, we define a suite of algebraic operations, aggregation mechanisms, and theoretical properties tailored to the PFT environment, with comprehensive mathematical formulations and illustrative validations. The effectiveness of the proposed method is demonstrated through a real-world application involving the evaluation of six pancreatic cancer treatment strategies. These alternatives are assessed against five key criteria: quality of life, side effects, treatment accessibility, cost, and duration. Our results reveal that the PFT-based approach outperforms traditional fuzzy decision-making techniques by delivering more consistent, interpretable, and reliable outcomes under uncertainty. Moreover, comparative analysis confirms the model’s superior ability to handle multidimensional expert evaluations and integrate conflicting information. This research contributes a significant advancement in the field of fuzzy decision science by offering a flexible, theoretically sound, and practically applicable tool for complex decision problems. Future work will focus on improving computational performance, adapting the model for real-time data, and exploring broader interdisciplinary applications. Full article

This article presents an experimental and statistical investigation of how optical magnification influences calibration constants, measurement results, and uncertainty in a digital optical microscope. Measurements were performed on reference gauge blocks with nominal lengths from 1.0 mm to 1.5 mm at five magnification levels (1×–5×) to quantify the effect of magnification on dimensional accuracy. A combined statistical methodology integrating non-parametric hypothesis testing and bootstrap-based uncertainty analysis was developed to evaluate data distributions and validate the use of a normal coverage factor (k = 2) for expanded uncertainty. The results showed that magnification has a statistically significant effect on the measured lengths for most standards, with the smallest combined standard uncertainty achieved at approximately 4× magnification. The uncertainty budget analysis revealed that the dominant component arises from the microscope’s declared Maximum Permissible Error (MPE), while type A and reference-standard components contribute only marginally. All expanded uncertainties remained within the declared MPE limits, confirming the reliability and traceability of the measurement process. Practical recommendations were proposed for selecting optimal magnification and for implementing calibration verification procedures at each zoom level. The presented methodology provides a validated framework for minimizing uncertainty in image-based dimensional measurements using digital optical microscopes. Full article

The strong uncertainty of renewable energy poses significant reliability and safety challenges for the coordinated operation of multi-integrated energy systems (MIES). To address this, a data-driven two-stage distributed robust collaborative optimization scheduling model for MIES is proposed, based on a spatiotemporal fusion conditional diffusion model (STF-CDM). First, to more accurately capture the uncertainty in renewable energy output, the model utilizes a scenario set generated by the STF-CDM model and reduced via the K-means clustering algorithm as the initial renewable energy scenarios for the distributed robust optimization set. The STF-CDM model employs a Temporal module component (TMC) unit composed of Transformer time-series modules and a Spatial module component (SMC) unit composed of CNN neural networks for feature extraction and fusion of time-series and spatial-series data. Second, a benefit allocation method based on multi-energy trading contribution rates is proposed to achieve equitable distribution of cooperative gains. Finally, to protect participant privacy and enhance computational efficiency, an alternating direction multiplier method (ADMM) coupled with parallelizable column and constraint generation (C&CG) is employed to solve the energy trading problem. The case analysis results demonstrate that the STF-CDM model proposed in this study exhibits superior performance in addressing the uncertainty of renewable energy output. Concurrently, the asymmetric Nash game mechanism and the ADMM-C&CG solution algorithm proposed in this study achieve a fair and reasonable distribution of benefits among all participants when handling energy transactions and cooperative gains. This is accomplished while ensuring system robustness, economic efficiency, and privacy. Full article

Detecting low-altitude, slow-speed, small (LSS) UAVs is especially challenging in low-visibility scenes (low light, haze, motion blur), where inherent uncertainties in sensor data and object appearance dominate. We propose GAME-YOLO, a novel detector that integrates a Bayesian-inspired probabilistic reasoning framework with Global Attention and Multi-Scale Enhancement to improve small-object perception and sub-pixel-level localization. Built on YOLOv11, our framework comprises: (i) a visibility restoration front-end that probabilistically infers and enhances latent image clarity; (ii) a global-attention-augmented backbone that performs context-aware feature selection; (iii) an adaptive multi-scale fusion neck that dynamically weights feature contributions; (iv) a sub-pixel-aware small-object detection head (SOH) that leverages high-resolution feature grids to model sub-pixel offsets; and (v) a novel Shape-Aware IoU loss combined with focal loss. Extensive experiments on the LSS2025-DET dataset demonstrate that GAME-YOLO achieves state-of-the-art performance, with an AP@50 of 52.0% and AP@[0.50:0.95] of 32.0%, significantly outperforming strong baselines such as LEAF-YOLO (48.3% AP@50) and YOLOv11 (36.2% AP@50). The model maintains high efficiency, operating at 48 FPS with only 7.6 M parameters and 19.6 GFLOPs. Ablation studies confirm the complementary gains from our probabilistic design choices, including a +10.5 pp improvement in AP@50 over the baseline. Cross-dataset evaluation on VisDrone-DET2021 further validates its generalization capability, achieving 39.2% AP@50. These results indicate that GAME-YOLO offers a practical and reliable solution for vision-based UAV surveillance by effectively marrying the efficiency of deterministic detectors with the robustness principles of Bayesian inference. Full article

Treatment adherence in chronic diseases is addressed here as a measurable construct that can be formally defined and computed from heterogeneous IoT data streams. The central contribution of this work lies in establishing a mathematical formulation of adherence that integrates both explicit treatment-related activities and behavioural indicators derived from sensor observations. The methodology specifies how raw data from wearables, BLE beacons, and ambient devices can be transformed into clinically meaningful activities through fuzzy logic, enabling the representation of uncertainty, temporal variability, and partial evidence. This framework also accommodates activity labels generated by machine learning models, providing a mechanism to adapt their outputs—originally expressed as probabilistic or categorical predictions—into fuzzy memberships suitable for adherence computation. By unifying sensor-driven activity extraction and model-based activity recognition under a common fuzzy representation, the proposed formulation delivers a coherent pathway for calculating adherence across multiple dimensions and contexts, thereby supporting robust and interpretable evaluation of patient behaviour. By integrating these elements, the methodology provides a comprehensive and interpretable profile of adherence, moving from isolated measures to a unified characterisation of patient behaviour. The framework enables healthcare professionals and patients to better monitor progress, anticipate risks, and support long-term disease management. Full article

This study presents an integrated stochastic framework for assessing and forecasting drought dynamics in the western Bhagirathi–Hooghly River Basin, encompassing the districts of Bankura, Birbhum, Burdwan, Medinipur, and Purulia. Employing multiple probabilistic and statistical techniques, including the gamma-based standardized precipitation index (SPI), effective drought index (EDI), rainfall anomaly index (RAI), and the auto-regressive integrated moving average (ARIMA) model, the research quantifies spatio-temporal variability and projects drought risk under non-stationary climatic conditions. The analysis of century-long rainfall records (1905–2023), coupled with LANDSAT-derived vegetation and moisture indices, reveals escalating drought frequency and severity, particularly in Purulia, where recurrent droughts occur at roughly four-year intervals. Stochastic evaluation of rainfall anomalies and SPI distributions indicates significant inter-annual variability and complex temporal dependencies across all districts. ARIMA-based forecasts (2025–2045) suggest persistent negative SPI trends, with Bankura and Purulia exhibiting heightened drought probability and reduced predictability at longer timescales. The integration of remote sensing and time-series modelling enhances the robustness of drought prediction by combining climatic stochasticity with land-surface responses. The findings demonstrate that a hybrid stochastic modelling approach effectively captures uncertainty in drought evolution and supports climate-resilient water resource management. This research contributes a novel, region-specific stochastic framework that advances risk-based drought assessment, aligning with the broader goal of developing adaptive and probabilistic environmental management strategies under changing climatic regimes. Full article

Integrating environmental, social, and governance (ESG) reporting with the Sustainable Development Goals (SDGs) is important for achieving corporate sustainability. The rapid evolution of regulations like the Corporate Sustainability Reporting Directive (CSRD), and the fragmented research landscape create uncertainty for strategic planning. This paper addresses the critical gap related to the lack of predictive data into future research trends at the ESG-SDG nexus. The research begins with a bibliometric analysis using two software programs R-Biblioshiny 5.2.0 and VOSviewer 1.6.20, to process data extracted from the Web of Science (Clarivate). Selected key terms regarding sustainability reporting concepts and reporting standards, as well as the engagements of auditors were used to filter the database information. Starting from the bibliometric analysis of 361 publications completed during January 2015–September 2025, the study performs further a quantitative measurement bibliometrics using RStudio 4.5.2 and provides a novel ensemble forecasting model (AutoRegressive Integrated Moving Average, Error, Trend, Seasonal Components, and Linear regression with SDG factors) that cartograph the alignment of the current research field and forecast its evolution. The results reveal that terms regarding reporting “CSRD” and sustainability assurance, “ISSA 5000” are the most dominant research fronts, strongly aligned with SDG 12, 13 and 17. The forecasting model predicts sustained growth in this area. The study contributes by providing a forward-thinking strategic map for researchers, policymakers and businesses, transforming sustainability integration from a compliance task into systematic, data-driven approach for priority setting strategy. Full article

Global crises, from pandemics to geopolitical instability, intensify societal anxiety. Paradoxically, these periods of collective threat often witness surges in luxury consumption. Drawing on Terror Management Theory (TMT), we propose this behavior is a psychological response to the deindividuating nature of such threats. We argue that a collective crisis increases intentions to purchase luxury goods via an intensified need for uniqueness, which functions as a self-affirming mechanism against a threatened sense of personal identity. We test this model using the COVID-19 pandemic as a salient operationalization of a collective threat. We further propose that this effect is counterintuitively moderated by independent self-construal. Findings from an experimental study (N = 276) show that perceived crisis risk increases luxury purchase intention, and this effect is serially mediated by the need for uniqueness. Critically, this indirect effect is strongest for individuals low in independent self-construal, who are prompted to engage in compensatory uniqueness-seeking when their primary buffer of social connection is disrupted. Our findings contribute to consumer behavior research by identifying a novel psychological pathway linking collective threats to consumption and offer insights for brands navigating consumer behavior during periods of widespread uncertainty. Full article