Search Results (3,261)

Background: Fungal infections are considered a global health concern, resulting in high morbidity and mortality rates, especially in immunocompromised individuals. Traditional diagnostic techniques, such as microscopy, culture, and polymerase chain reaction (PCR), suffer from low sensitivity, long processing time, and accessibility challenges, especially in resource-limited settings. Artificial intelligence (AI) and machine learning (ML) tools have demonstrated potential to enhance diagnostic accuracy and efficiency. This systematic study assesses the progress, precision, and efficacy of AI-driven diagnostic tools for fungal infections within various clinical contexts in comparison to traditional procedures. Methods: A systematic review was conducted according to PRISMA principles. Literature searches were conducted in PubMed, ScienceDirect, Web of Science, and Ovid, focusing on research employing AI models to diagnose fungal infections. The inclusion criteria were research that compared AI-based tools with conventional diagnostic methods in terms of sensitivity, specificity, and accuracy. Data extraction and quality evaluation were performed utilizing validated instruments, such as the Methodological Index for Non-Randomized Studies (MINORS). Results: Eleven research studies met the inclusion criteria: six retrospective and five prospective investigations. AI models, such as convolutional neural networks (CNNs), Faster R-CNN, VGG19, and MobileNet, have improved diagnostic accuracy, sensitivity, and specificity compared to traditional methods. However, differences in dataset quality, model validation, and real-world applicability remain as limitations. Conclusions: AI-driven diagnostic technologies provide significant benefits in identifying fungal infections, improving the speed and accuracy of diagnoses. However, additional extensive investigations and clinical validation are required to improve model generalizability and facilitate smooth incorporation into healthcare systems. Full article

Fuel scarcity remains a recurrent challenge in Nigeria, with significant socioeconomic consequences despite the country’s status as a major crude oil producer. This study develops a novel deterministic mathematical model to examine the dynamics of petroleum product distribution in Nigeria’s downstream sector, with particular emphasis on Premium Motor Spirit (PMS). The model explicitly incorporates key disruption and behavioral mechanisms: pipeline vandalism, industrial actions, product diversion, and hoarding that collectively drive persistent fuel shortages. The model’s feasibility, positivity of solutions, and existence and uniqueness were established, ensuring consistency with real-world operational conditions. Five equilibrium points were identified, reflecting distinct operational regimes within the distribution network. A critical distribution threshold was analytically derived and numerically validated, revealing that a minimum supply of approximately 42 million liters of PMS per day is required to satisfy demand and eliminate fuel queues. Local and global stability analyses, conducted using Lyapunov functions and the Routh–Hurwitz criteria, demonstrate that stable fuel distribution is achievable under effective policy coordination and stakeholder compliance. Numerical simulations show that hoarding by private retail marketers substantially intensifies scarcity, while industrial actions by transporters exert a more severe disruption than pipeline vandalism. The results further highlight the stabilizing role of alternative transportation routes, such as rail systems, in mitigating infrastructure failures and road-based logistics risks. Although refinery sources are aggregated and rail transport is idealized, the proposed framework offers a robust and adaptable tool for policy analysis, with relevance to both oil-producing and fuel-import-dependent economies. Full article

Background/Objectives: The National Comprehensive Cancer Network (NCCN) classifies prostate cancer with PSA > 20 ng/mL as high risk; however, outcomes within this group are heterogeneous. Emerging data suggest that men with PSA > 20 ng/mL as the sole high-risk feature may have more favorable disease biology. We evaluated outcomes of men with prostate cancer treated with definitive radiation therapy (RT), focusing on the prognostic significance of individual high-risk factors. Methods: We analyzed 742 men with prostatic adenocarcinoma treated with curative-intent RT between 2005 and 2021, including 282 meeting traditional NCCN high-risk criteria. Treatment consisted of dose-escalated RT (median 78 Gy), with androgen deprivation therapy (ADT) administered to 94% (median duration 28 months). Primary endpoints were freedom from biochemical failure (FFBF) and distant metastasis (FFDM). Outcomes were assessed using Kaplan–Meier methods and Cox proportional hazards modeling. Results: At 5 years, high-risk patients demonstrated FFBF of 83% and FFDM of 89%, with significantly worse outcomes among very high-risk subgroups. Men with PSA > 20 ng/mL as their only high-risk feature (n = 49) achieved superior outcomes compared with other high-risk patients (5-year FFBF 94% vs. 74%; FFDM 97% vs. 82%; both p = 0.05), comparable to intermediate-risk disease. On multivariable analysis, Gleason score and clinical T-stage independently predicted poorer outcomes, whereas PSA >20 alone did not. Conclusions: PSA > 20 ng/mL as an isolated high-risk feature is associated with favorable outcomes following definitive RT and appears to be the weakest NCCN high-risk criterion. These findings support refined risk stratification and raise the possibility of treatment de-escalation in select patients. Full article

From adaptation to building effective resilience to climate change is critical for transforming West and Central Africa (WCA) agricultural system. Climate-Smart Agriculture (CSA) is an approach initiated by leading international organizations to ensure food security, increased adaptation to climate change and mitigation. Its application spans from innovative policies, practices, technologies, innovations and financing. However, CSA initiatives lack scientific-based assessment prior to implementation to ensure their effectiveness. To fill this gap, future interventions should not only be assessed using rigorous methodology but should also be built on lessons learned from previous initiatives. Although there are a lot of climate related agricultural initiatives in WCA, most of them have not been analyzed through a CSA lens and criteria to capitalize on their experiences to improve future interventions. In this study we mapped previous climate-related initiatives in WCA, highlighted their gaps and lessons learned to accelerate the implementation of CSA in the region. The study covered 20 countries in WCA: Benin, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Chad, Côte d’Ivoire, Congo, Gabon, Gambia, Ghana, Guinea, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo. CSA initiatives were reviewed using a three-steps methodology: (i) national data collection, (ii) regional validation of the national database, (iii) data analysis including spatial mapping. Data was collected from the websites of international, regional and national organizations working in the field of agricultural development in the region. Each initiative was analyzed using a multicriteria analysis based on CSA principles. A total of 1629 CSA related initiatives were identified in WCA. Over 75% of them were in the form of projects/programs with more of a focus on the first CSA pillar (productivity and food security), followed by adaptation. The mitigation pillar is less covered by the initiatives. Animal production, fisheries, access to markets, and energy are poorly included. More than half of these initiatives have already been completed, calling for more new initiatives in the region. Women benefit very little from the implementation of the identified CSA initiatives, despite the substantial role they play in agriculture. CSA initiatives mainly received funding from technical and financial partners and development partners (45%), banks (22%), and international climate financing mechanisms (20%). Most of them were implemented by government institutions (48%) and development partners (23%). In total, more than 600 billion EUR have been disbursed to implement 83 of the 1629 initiatives identified. These initiatives contributed to reclaiming and/or rehabilitating almost 2 million ha of agricultural land in all countries between 2015 and 2025. Future initiatives should ensure the consideration of the three CSA pillars right from their formulation to the implementation. These initiatives should consider investing in mixed production systems like crop-animal-fisheries. Activities should be built around CSA innovation platforms to encourage networking among actors for more sustainability. Full article

Background/Objectives: The segmentation of three-dimensional radiological images constitutes a fundamental task in medical image processing for isolating tumors from complex datasets in computed tomography or magnetic resonance imaging. Precise visualization, volumetry, and treatment monitoring are enabled, which are critical for oncology diagnostics and planning. Volumetric analysis surpasses standard criteria by detecting subtle tumor changes, thereby aiding adaptive therapies. The objective of this study was to develop an enhanced, interactive Graphcut algorithm for 3D DICOM segmentation, specifically designed to improve boundary accuracy and 3D modeling of breast and brain tumors in datasets with heterogeneous tissue intensities. Methods: The standard Graphcut algorithm was augmented with a clustering mechanism (utilizing k = 2–5 clusters) to refine boundary detection in tissues with varying intensities. DICOM datasets were processed into 3D volumes using pixel spacing and slice thickness metadata. User-defined seeds were utilized for tumor and background initialization, constrained by bounding boxes. The method was implemented in Python 3.13 using the PyMaxflow library for graph optimization and pydicom for data transformation. Results: The proposed segmentation method outperformed standard thresholding and region growing techniques, demonstrating reduced noise sensitivity and improved boundary definition. An average Dice Similarity Coefficient (DSC) of 0.92 ± 0.07 was achieved for brain tumors and 0.90 ± 0.05 for breast tumors. These results were found to be comparable to state-of-the-art deep learning benchmarks (typically ranging from 0.84 to 0.95), achieved without the need for extensive pre-training. Boundary edge errors were reduced by a mean of 7.5% through the integration of clustering. Therapeutic changes were quantified accurately (e.g., a reduction from 22,106 mm³ to 14,270 mm³ post-treatment) with an average processing time of 12–15 s per stack. Conclusions: An efficient, precise 3D tumor segmentation tool suitable for diagnostics and planning is presented. This approach is demonstrated to be a robust, data-efficient alternative to deep learning, particularly advantageous in clinical settings where the large annotated datasets required for training neural networks are unavailable. Full article

The widespread integration of inverter-based distributed generators (IIDGs) severely limits the adaptability of conventional three-step overcurrent protection in distribution networks (DNs). To address weak rural infrastructure and incomplete post-fault data, this paper proposes a dynamic adaptive current protection strategy for active distribution networks (ADNs) against two-phase short-circuit faults (TPSCFs), using local sequence components. First, we derive analytical expressions for positive/negative-sequence current/voltage at feeder outlet protection devices during TPSCFs, analyzing how the IIDG fault output affects these components. Based on this, an adaptive scheme is developed using only local measurements, with feeder head voltage/current sequence components as criteria. Leveraging line impedance and topology, the scheme ensures selective, accurate fault section identification under incomplete measurements, requiring only feeder head sequence data. A high-IIDG-penetration DN model is built in PSCAD/EMTDC, and TPSCFs under various conditions are simulated. Results show the scheme provides rapid, reliable full-line protection for TPSCFs in IIDG-penetrated ADNs, enhancing protection effectiveness. Full article

Background: Barnacles are important marine fouling organisms, and their complex life cycle involves key metamorphic nodes from nauplius to cyprid larvae and then to sessile adults. However, the molecular mechanisms underlying their larval development remain poorly understood. Metabolomics and transcriptomics are powerful tools for exploring biological development pathways and regulatory networks. Methods: We employed non-targeted metabolomics and transcriptomics to analyze three key developmental stages of embryonic stage, nauplius stage, and cyprid stage. Differential metabolites were screened using fold change (FC), p-value, and variable importance in projection (VIP) values, while DEGs were identified with adjusted p-value and |log₂(fold change)| criteria. WGCNA was used to construct gene co-expression networks, and qRT-PCR validated RNA-seq results. Results: A total of 3683 metabolites were identified, with the bile secretion pathway serving as a core regulatory pathway throughout early development. Transcriptomic analysis identified 7234 DEGs, which were clustered into four modules corresponding to different developmental stages. Key pathways such as chitin metabolism, and linoleic acid metabolism were significantly enriched, and qRT-PCR confirmed the reliability of RNA-seq data. Conclusions: This study reveals the metabolic and molecular regulatory mechanisms underlying the early development of M. volcano, highlighting stage-specific metabolic characteristics and core gene modules. The findings provide a theoretical basis for understanding barnacle developmental adaptation strategies and offer potential targets for the development of novel antifouling agents. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published between January 2010 and January 2025 were taken from four major journals using a structured set of nine search queries. After removing duplicates, applying inclusion and exclusion criteria, and screening titles and abstracts, 62 studies were examined for analysis on the basis of a synthesis framework. The studies were classified along three methodological dimensions, qualitative vs. quantitative, model-based vs. data-driven, and informal vs. formal, giving us a unified taxonomy of cyber risk analysis approaches. Among the included studies, 45% appeared to be qualitative or semi-quantitative frameworks such as STRIDE, DREAD, or MITRE ATT&CK; 35% were classified as quantitative or model-based techniques such as Bayesian networks, Markov decision processes, and Petri nets; and 20% adopted data-driven or hybrid AI/ML methods. Only 28% implemented formal verification, and fewer than 10% explicitly linked cyber vulnerabilities to safety consequences. Key research gaps include limited integration of safety–security interdependencies, scarce operational datasets, and inadequate modelling of environmental factors in WPPs. This systematic review highlights a predominance of qualitative approaches and a shortage of data-driven and formally verified frameworks for WPP cybersecurity. Future research should prioritise hybrid methods that integrate formal modelling, synthetic data generation, and machine learning-based risk prioritisation to enhance resilience and operational safety of renewable-energy infrastructures. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article

Background/Objectives: Despite immune checkpoint inhibitors (ICPIs)’s transformation of lung cancer treatment, pneumonitis remains a potentially serious immune-related adverse event. However, reliable data on the comparative risks of individual ICPIs remain unknown. We conducted this network meta-analysis (NMA) to, therefore, quantify and compare the exact pooled burden of pneumonitis risk across multiple ICPI analogs. Methods: We searched the following databases, PubMed, Embase, Scopus MEDLINE and Cochrane Database of Systematic Reviews, as well as gray literature on Google Scholar for eligible studies reporting on the prevalence of pneumonitis following immune check point inhibitor exposures. Pairwise and network meta-analyses were performed to estimate pooled odds ratios (ORs) for pneumonitis, using placebo as the common comparator. Sensitivity analyses assessed the impact of study quality and combination therapies. Results: A total of 29 studies enrolling 15,271 patients with non-small cell lung cancer (NSCLC) or small-cell lung cancer (SCLC) satisfied the inclusion criteria and are included in the meta-analysis. Pembrolizumab was associated with a significantly increased risk of pneumonitis compared to placebo (odds ratio [OR] = 2.67, 95% confidence interval [CI]: 1.70–4.17), with similar elevated risk observed for sugemalimab (odds ratio [OR] = 2.45, 95% confidence interval [CI]: 1.52–3.95). Nivolumab was associated with increased odds of pneumonitis, although with unstable point estimate (odds Ratio [OR] = 2.69, 95% confidence interval [CI]: 0.64–11.35). Statistical heterogeneity was low (H statistics = 1.34). Atezolizumab and ipilimumab demonstrated modest or uncertain risk. Heterogeneity was low (I² = 12%), and results were robust to sensitivity analyses. Higher pneumonitis rates were observed in combination regimens. Conclusions: Our analysis demonstrates that pneumonitis risk varies among ICPIs, with pembrolizumab and sugemalimab showing the highest odds. Although the absolute incidence is low, the potential severity of pneumonitis warrants vigilant monitoring. These results should guide clinicians in risk stratification and treatment planning, and they should support the development of standardized reporting criteria and further comparative research. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article

Ensuring affordable and reliable electricity access to areas with low population density is challenging, as network sparsity and lower connectivity rates can make it nearly impossible for electric utilities to cover the cost of interconnection without raising electricity tariffs. Utility providers that consider extending their networks to remote households must balance multiple and often conflicting objectives, including investment cost, grid resilience, geographical coverage, and environmental impacts. In this paper, a multi-objective decision-making framework is proposed for the electrification of rural households, considering traditional distribution network extension as well as microgrid deployment. In order to condense a wide range of spatial inputs into a tractable problem, a multi-criteria decision-making approach is adopted to identify and rank candidate sites for microgrid deployment that offer superior performance over a variety of technical, environmental, and economic criteria. A novel optimization model is then proposed using multi-objective Chebyshev goal programming, in which project costs, environmental impacts, and energy justice criteria are jointly optimized. The applicability of this framework is demonstrated through a case study of the Shiprock region within the Navajo Nation. The results indicate that the proposed methodology provides a balanced trade-off among conflicting objectives and identifies a priority order of loads to energize first under marginally increasing budgets. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article