Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the bac...
Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional...
Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage e...
Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a s...
The increasing penetration of photovoltaic (PV) and hydroelectric power generation and their coupling uncertainties have brought great challenges to multi-energy’s coordinated dispatch. Traditional methods such as stochastic optimization (SO) a...
Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge...
Precise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming...
Traditional multi-criteria decision-making methods often operate on the assumption of symmetry, presupposing that the positive impact of a criterion’s presence is perfectly complementary to the negative impact of its absence. However, in real-w...
Industrial parks serve as the core carriers for low-carbon transition in the industrial sector, while multi-energy complementary integrated energy systems (IESs) represent the key technological pathway for achieving efficient decarbonization in such...
This paper proposes a Multi-strategy Improved Love Evolution Algorithm, named MSILEA, to overcome the limitations of the original Love Evolution Algorithm (LEA) in complex optimization tasks. Although LEA has a distinctive stimulus–value–...
The textile and clothing value chain faces increasing pressure to improve reuse and recycling rates, particularly in post-consumer scenarios, where garments must be rapidly assessed, classified, and routed toward appropriate end-of-life pathways. Pos...
High penetration of distributed energy resources (DERs) is reshaping radial distribution networks, yet weather-dependent generation, variable demand, and feeder-level surplus–deficit imbalance can compromise voltage quality and coordinated oper...
Skin cancer, particularly melanoma, represents a major public health concern due to its steadily increasing incidence. Conventional clinical diagnostic methods present several limitations, including inter-observer variability and the risk of misdiagn...
The randomness and volatility of large-scale clean energy output represented by wind power and photovoltaic lead to difficulties in grid connection. The problems of abandoned wind, light, and water become increasingly prominent. The adjustment capaci...
Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage abilit...
The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction s...
Finding diabetic foot ulcers (DFUs) early and accurately is essential for improving patients’ quality of life and lowering the risk of amputation. RGB images, commonly used in automated DFU detection, have limitations such as lighting variation...
Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and...
Arc-driven powder bed fusion represents a low-cost alternative to beam-based powder bed systems, yet the morphological stability regimes governing single-track formation and the relative influence of process parameters on regime transitions have not...
Groundwater overexploitation poses a major challenge to sustainable agricultural water management, yet the behavioral mechanisms through which different groundwater policies affect farmers’ irrigation decisions remain poorly understood. The obj...
The growing global reliance on solar photovoltaic (PV) systems requires robust, automated inspection techniques to ensure reliability and efficiency. Thermal infrared (IR) imaging is widely used for detecting PV faults; however, accurate classificati...
Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximi...
Background/Objectives: Hospital pharmacies must maintain medication availability despite heterogeneous and time-varying demand. Statistical fit alone may not yield well-calibrated future scenarios or the best replenishment decision. This study prospe...
Background and Objectives: Colorectal cancer is a significant global health burden, with lung metastasis contributing substantially to mortality. Accurate risk stratification of synchronous lung metastasis (sLM) in patients with T1 colorectal cancer...
An extensive literature on decision theory has been developed by both subjective Bayesians and Neyman–Pearson (NP) theorists, with more recent contributions to it from evidential decision theorists. The last-mentioned, however, have often been...
In this paper, we propose a literature review regarding two deep learning architectures, namely Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets), applied to medical images, in order to analyze them to help in medical decision supp...
Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The f...
Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading...
Molecular docking is among the most widely used techniques in structure-based drug discovery and has become integral to natural-product research. Despite substantial advances in computational algorithms and artificial intelligence, the methodological...
Open innovation makes IT investment decisions dependent on knowledge, technologies, and evidence that cross organizational boundaries. Yet these decisions are evaluated through disconnected logics: technical teams assess maturity; executives evaluate...
Cost control in engineering, procurement, and construction (EPC) public works is shaped by interacting drivers, nonlinear feedback, and qualitative judgments. Existing studies usually apply the three relevant method families separately: DEMATEL-ISM m...
Early-season crop yield prediction is critical for food security assessment and timely agricultural decision-making, yet large-scale remote-sensing applications are often constrained by a mismatch between pixel-level observations and county-level yie...
Conventional laboratory fatigue tests are often limited to single-stage loading. As a result, they do not fully capture the progressive damage caused by repeated traffic loading. To address this limitation, this study proposes an integrated multi-sta...
Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-...
Dynamics of mRNA from circulating tumor cells (CTCs), mRNA from extracellular vesicles (EVs), and cell-free DNA (cfDNA) were assessed to examine the relevance of a longitudinal multi-parametric liquid biopsy strategy. Eighteen milliliters of blood wa...
Accurate 3D medical image segmentation is crucial for knowledge-driven clinical decision-making and computer-aided diagnosis. However, current deep learning methods often fail to effectively integrate local structural details from Convolutional Neura...
Phenology is considered an indicator of environmental changes, with direct implications in the length of the growing season; therefore, it offers essential information for a better understanding of the tree–environment relationships that could...
Background/Objectives: Alzheimer’s disease (AD), the most prevalent form of dementia, is characterized by an overall decline in cognitive functioning and represents a major public health crisis. It remains critical to be able to accurately and...
This study develops a data-driven predictive framework integrating hybrid feature selection, interpretable machine learning, and uncertainty quantification to forecast Olympic medal performance among elite nations. Focusing on the top ten countries f...
Background and Objectives: The accurate segmentation of pulmonary nodules in computed tomography (CT) remains a critical yet challenging task due to variations in nodule size, shape, and boundary ambiguity. This study proposes CAAF-ResUNet (Context-A...
Artificial intelligence (AI) systems are increasingly embedded in decisions that affect health, employment, housing, justice, and social policy. Rather than neutral tools, they reflect the data on which they are trained, the objectives they optimize,...
As digital innovations rapidly penetrate aging populations, live-streaming e-commerce acts as a profound external disruption to the informal healthcare subsystem, fundamentally reshaping Health Shared Decision-Making in Elderly Households (HSDM-EH)....
As regulatory requirements increasingly shape automated lending decisions, fairness remains a critical challenge in high-stakes domains, particularly credit scoring. Although artificial intelligence models can achieve strong predictive performance, t...
Background/Objectives: Rectal cancer is a significant global health concern, requiring precise staging and response assessment to make treatment decisions. Magnetic resonance imaging (MRI) is the standard imaging modality for evaluating tumor stage a...
Feature prioritization in early-stage software startups is a critical yet poorly structured challenge, as prevailing frameworks rely predominantly on expert intuition and fail to exploit patterns latent in historical delivery data and labor market dy...
How does state-led farmland protection reshape the industrial structure of cities? While farmland protection is widely understood as an ecological policy, its consequences for industrial transformation remain underexplored. This paper argues that far...
Menopause is a natural stage of hormonal aging in women, accompanied by a series of symptoms that reduce the quality of life of a fully active person. As no therapy is entirely satisfactory, the race for a better option is in full swing. Our study ob...
Reinforced concrete and steel are the most commonly used materials in bridge applications in Quebec (Canada). The production of these materials has a significant environmental impact and contributes to the scarcity of non-renewable resources due to t...
The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging a...
Forecasting crop yield at an early stage of the growing season is crucial to reinforcing climate-smart agriculture and enhancing food security in nations with high vulnerability, including Bangladesh. This study creates an interpretable, ML-based fra...
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