Search Results (14,351)

By analyzing Food and Drug Administration (FDA) enforcement reports from 2004 to 2025, we can determine the incidence of microbial contamination in non-sterile and sterile drugs in the United States of America and, at the same time, compare the trends and patterns over a period of 21 years to determine the distribution and frequency of microbial contaminants. The most common microorganisms detected from 2019 to 2025 were the mold Aspergillus penicilloides, with 17 citations for sterile products, followed by 16 citations for non-sterile products of Burkholderia cepacia complex (BCC) bacteria. Analysis from the last 21 years revealed the dominant microbial contaminants belong to the BCC, reaching a maximum level between 2012 and 2019. Some of the previous microbial contaminants, such as Salmonella and Clostridium, decline in the 2019–2025 period, with no notifications issued. S. aureus and Pseudomonas contamination persisted through the years but at very low levels. Gram-negative bacteria contaminated non-sterile drugs more frequently than Gram-positive. A worrisome trend continued with unacceptable levels of enforcement reports not providing any information on the identity of the microbial contaminant. New species of Bacillus and Acetobacter nitrogenifigens were responsible for a significant increase in non-sterile drug recalls. The main driver for sterile product recalls over a 21-year period is the lack of assurance of sterility (LAS) where major failures in process design, control, and operational execution were not conducive to the control of microbial proliferation and destruction. Enforcement data analysis identified the problematic trends and patterns regarding microbial contamination of drugs, providing important information to optimize process control and provide a framework for optimizing risk mitigation. Although the 21-year landscape demonstrated that some microbial contaminants have been successfully mitigated, others remain resilient. The emergence of new contaminants highlights the evolving nature of microbial risk. The consistent problem with LAS is not only a major regulatory violation but also a potential catalyst for the next major healthcare-associated outbreak. Full article

Background: Children of mothers exposed to adverse childhood experiences (ACEs) may be at increased risk of sleep disruptions, such as night waking, due to potential suboptimal caregiving or living conditions. Mothers’ ACEs are also associated with maternal depressive symptoms, which in turn are associated with children’s screen time and sleep disruptions, revealing relevant, but unexplored, mediation pathways. This Canadian study investigated if mothers’ ACEs were associated with their 5-year-old children’s sleep disruptions (1) directly and (2) indirectly through independent or serial mediation via maternal depressive symptoms and/or children’s screen time. Methods: Data (n = 622; maternal mean age 32.3 years, 88.4% white) came from the longitudinal APrON Study. ACEs were measured 1 year postpartum. Mother’s depressive symptoms were measured across prenatal and postnatal timepoints. Children’s evening screen time (i.e., number of days in a week children engaged in one hour of screen time before bedtime) and sleep disruptions (number of days in a week their child wakes up multiple times) were measured at 5 years postpartum using adapted scales (52.9% male). PROCESS was used to assess for mediation. Results: Mothers’ ACEs had an indirect effect on their children’s sleep disruptions through mothers’ mean depressive symptoms (effect = 0.018, 95% CI [0.006, 0.034]), but not through children’s screen time. No other effects (i.e., direct, total) were observed. Conclusions: Although replication studies are warranted, this novel study reveals that the effects of maternal ACEs on children’s sleep disruptions may operate indirectly with effects potentiated through maternal depressive symptoms, thus serving as a target for intervention. Full article

The rapid and reliable geometric documentation of historic built heritage is a key requirement for a wide range of conservation, analysis, and risk assessment activities. In recent years, portable and wearable Simultaneous Localization and Mapping (SLAM)-based systems have emerged as efficient tools for fast 3D data acquisition, offering significant advantages in terms of operational speed, accessibility, and flexibility. This paper presents an experimental performance assessment of three portable SLAM-based mobile mapping systems applied to the 3D documentation of historic religious buildings. Two historic parish churches in the Lunigiana region (Italy) are used as case studies to evaluate the systems under real-world conditions. The analysis focuses on key performance indicators relevant to metric documentation, including georeferencing accuracy, 3D model accuracy, point cloud density and resolution, and model completeness. The results highlight the capabilities and limitations of the tested systems, showing that all instruments can efficiently capture the primary geometries of complex historic buildings, while differences emerge in terms of accuracy, data consistency, and readability of architectural details. Although the work is framed within a broader research project addressing seismic vulnerability of historic structures, this contribution specifically focuses on the experimental evaluation of SLAM-based surveying performance. The results demonstrate that portable SLAM systems provide reliable geometric datasets suitable for preliminary documentation tasks and for supporting further multidisciplinary analyses, representing a valuable resource for the rapid 3D documentation of historic built heritage. Full article

The digital transformation of electric power systems into smart grids has significantly expanded the cybersecurity risk landscape of the energy sector. While advanced sensing, communication, automation, and data-driven control improve efficiency, flexibility, and renewable energy integration, they also introduce complex cyber–physical interdependencies and new vulnerabilities across interconnected technical and organisational domains. This study adopts a scoping review methodology in accordance with PRISMA-ScR to systematically analyse smart grid cybersecurity from an architecture-aware and resilience-oriented perspective. Peer-reviewed scientific literature and authoritative institutional sources are synthesised to examine modern smart grid architectures, key security challenges, major cyberthreats, and documented real-world cyber incidents affecting energy infrastructure up to 2025. The review systematically links architectural characteristics such as field devices, communication networks, software platforms, data pipelines, and externally operated services to specific threat mechanisms and observed attack patterns, illustrating how cyber risk propagates across interconnected grid components. The findings show that cybersecurity challenges in smart grids arise not only from technical vulnerabilities but also from architectural dependencies, software supply chains, operational constraints, and cross-sector coupling. Based on the analysis of historical incidents and emerging research, the study identifies key defensive strategies, including zero-trust architectures, advanced monitoring and anomaly detection, secure software lifecycle management, digital twins for cyber–physical testing, and cyber-resilient grid design. The review concludes that cybersecurity in smart grids should be treated as a systemic and persistent condition, requiring resilience-oriented approaches that prioritise detection, containment, recovery, and safe operation under adverse conditions. Full article

Background: Oral food challenges (OFCs) are still the reference standard for confirming food allergy, yet the influence of previous anaphylaxis on challenge outcomes remains uncertain. Patients with a history of anaphylaxis are often considered at higher risk, which may affect the clinical decision-making process. This study aimed to identify predictors of OFC failure stratified by a history of anaphylaxis, given that prior investigations have predominantly considered anaphylaxis as an overall risk factor, without delineating distinct risk factor profiles according to anaphylaxis history. Methods: We conducted a retrospective evaluation of standard-of-care pediatric OFCs to cow’s milk and hen’s egg white. Eligible children had suspected or confirmed IgE-mediated allergy to cow’s milk protein (CMP) or hen’s egg white protein (HEWP) and were stratified by the presence or absence of previous anaphylaxis to the challenged food. Clinical data were compared between groups. Open OFCs were conducted under inpatient supervision with full emergency support. Logistic regression models were used to assess the relationship between comorbidities, specific IgE (sIgE) concentrations and OFC outcomes. Receiver operating characteristic (ROC) analysis evaluated diagnostic accuracy of sIgE concentrations in predicting OFC outcomes. Results: The analysis included 192 pediatric patients undergoing OFCs: 106 to CMP and 86 to HEWP. Six challenges (3.1%) were inconclusive, giving 186 valid results. The overall OFC failure rate was 32.3%. Patients with a past history of anaphylaxis more frequently underwent cow’s milk challenges (p = 0.01). Atopic dermatitis was a more common comorbidity in those without prior anaphylaxis (p = 0.04), regardless of the trigger. In hen’s egg challenges, children with a history of anaphylaxis reacted to significantly lower cumulative doses (p = 0.03) than patients without. Atopic dermatitis was identified as a predictor of OFC failure in children without prior anaphylaxis (p = 0.02), and asthma as a borderline predictor in those with previous systemic reactions (p = 0.05). Specific IgE concentrations correlated with OFC outcomes across allergens, with casein-sIgE showing the highest discriminative performance (AUC = 0.81) in children without previous anaphylaxis. Conclusions: Atopic dermatitis and asthma were identified as potential risk factors influencing OFC outcomes, depending on the patient’s history of anaphylaxis. The predictive accuracy of sIgE was different in groups stratified by presence of prior anaphylaxis, and the relationship between sIgE concentration and clinical reactivity was not identical across the two subpopulations. Casein-sIgE showed the highest diagnostic accuracy in children without previous severe reactions to CMP. Presence of anaphylactic reactions in the past is an important consideration when selecting children for OFCs to CMP and HEWP, since it delineates distinct risk factors for challenge failure in these patient populations. Full article

Construction projects are inherently exposed to high levels of uncertainty due to technical complexity, multiple stakeholders, and dynamic operating environments. However, empirical evidence on the systematic implementation of risk management practices in developing construction contexts remains limited. Unlike studies that assess the effectiveness or outcomes of risk management, this study addresses the gap by examining perception-based evidence of its implementation at the project and organizational levels in Jordanian construction projects. The study focuses on planning, control and monitoring, perceived advantages, and implementation barriers. A quantitative, survey-based research design was employed using purposive sampling. The statistical population consisted of engineers, project managers, and contractors working in the Jordanian construction sector. Out of 280 distributed questionnaires, 232 valid responses were received (response rate: 82.9%). Data were analyzed using descriptive statistics and one-sample t-tests, with the neutral midpoint of the five-point Likert scale (3.00) used as the reference value. The reliability of the instrument was confirmed by Cronbach’s alpha coefficients ranging from 0.814 to 0.868. The findings indicate generally positive perceptions of risk management implementation, with mean values ranging from 3.84 to 4.13. Risk management planning achieved the highest mean score (4.13), whereas control and monitoring practices were comparatively weaker (3.84). Although 82.3% of respondents reported applying risk management techniques, experience levels remain low to moderate. Key barriers include the lack of structured programs, limited knowledge, and insufficient experience. The results highlight the need for institutionalized risk management frameworks and targeted professional training to enhance systematic implementation. Full article

The deployment of large remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) typically requires support vessels, crane systems, and specialized personnel, resulting in increased logistical complexity and operational costs. In this context, lightweight and modular underwater robots have emerged as a cost-effective alternative, capable of reaching significant depths and performing tasks traditionally associated with larger platforms. This article presents a system architecture for recovering a known object using a hybrid-controlled ROV, integrating autonomous perception, high-level interaction, and low-level control. The proposed architecture includes a perception module that estimates the object pose using a Perspective-n-Point (PnP) algorithm, combining object segmentation from a YOLOv11-seg network with 2D keypoints obtained from a YOLOv11-pose model. In addition, a Natural Language ROS Agent is incorporated to enable high-level command interaction between the operator and the robot. These modules interact with low-level controllers that regulate the vehicle degrees of freedom and with autonomous behaviors such as target approach and grasping. The proposed system is evaluated through simulation and experimental tank trials, including object recovery experiments conducted in a 12 × 8 × 5 m test tank at CIRTESU, as well as perception validation in simulated, tank, and harbor scenarios. The results demonstrate successful recovery of a black box using a BlueROV2 platform, showing that architectures of this type can effectively support operators in underwater intervention tasks, reducing operational risk, deployment complexity, and mission costs. Full article

Background: In adult spinal deformity (ASD) surgery, appropriate rod length determination is crucial, as excessive cranial rod length can lead to skin problems, especially in thin elderly patients if proximal junctional kyphosis (PJK) develops. In adolescent idiopathic scoliosis (AIS), correction is primarily performed in the coronal plane, and rod length changes are relatively predictable. Moreover, PJK is uncommon in AIS, making excess rod length rarely a clinical concern. In contrast, ASD correction involves more complex three-dimensional realignment, including restoration of lumbar lordosis (LL), which makes it challenging to predict postoperative changes in rod trace length (RTL). Furthermore, because PJK occurs more frequently in ASD surgery, appropriate rod length selection becomes clinically important. This study aimed to quantitatively evaluate changes in RTL before and after posterior correction. Method: Thirty patients with ASD who underwent staged lateral lumbar interbody fusion (LLIF) followed by posterior corrective fusion from T9 to the pelvis were retrospectively analyzed. RTL before posterior correction (Pre-RTL) was estimated from the planned screw insertional point on axial CT after LLIF, and postoperative RTL (Post-RTL) was measured from screw head centers on post-operative CT. LL and Cobb angle were assessed before and after posterior correction. Correlations between RTL change and alignment change were evaluated. Results: Postoperative RTL was shortened in all patients, with an average reduction of approximately 16–17 mm. RTL shortening demonstrated significant correlations with LL correction (R = 0.51, p = 0.003) and Cobb angle correction (R = 0.70, p = 0.00001). Greater shortening of RTL was observed on the convex side in patients with preoperative Cobb angle ≥ 10° (p = 0.04). Conclusions: Greater coronal deformity, particularly on the convex side, was associated with increased RTL shortening. These findings suggest that routine preparation of excessively long rods may be unnecessary. Consideration of anticipated RTL shortening may help avoid excessive cranial rod length and potentially reduce the risk of skin complications associated with PJK, particularly in thin elderly patients. Full article

Purpose: Revision total hip arthroplasty (RTHA) in the presence of a well-fixed femoral stem is associated with increased risk, as stem removal often results in bone loss, prolonged operative time, and greater blood loss. This problem is particularly relevant for older implants with a 14/16 taper, which is incompatible with most modern femoral heads. The Bioball™ System, a modular head–neck adapter, allows for acetabular or head-only revision while preserving the femoral stem. This study aimed to evaluate long-term clinical and radiological outcomes of RTHA using the Bioball™ System in patients with 14/16 tapers. Methods: A total of 38 patients (23 women, 15 men; mean age 73.5 years) met the inclusion criteria. All procedures were carried out with a well-fixed femoral stem and a 14/16 taper. Revisions were limited to exchange of the acetabular component, liner, or both, avoiding stem removal. The primary indication was acetabular cup loosening (n = 29, 76.3%); liner-only exchange was performed in 9 patients (23.7%). Clinical outcomes were assessed using the modified Merle d’Aubigné and Postel (MAP) score, and radiological evaluation focused on fixation, migration, and loosening. Mean follow-up was 8.44 years. Results: Both the acetabular component and liner were replaced in 76.3% of patients, while 23.7% underwent liner and head exchange only. Longer adapter sizes were most frequently used, and a 7.5° offset adapter was applied in 57.9% of cases. The modified MAP score improved by a mean of 5.7 points (p < 0.05), and VAS pain scores decreased from 7.4 to 2.6 (p < 0.05). No radiological signs of loosening were observed at final follow-up. Conclusions: The Bioball™ System enables effective restoration of hip stability and offset without femoral stem removal, offering favorable long-term clinical and radiological outcomes in revisions involving older 14/16 tapers. Full article

With the expansion of photovoltaic (PV) systems, failures of bypass diodes (BPDs) embedded in PV modules can degrade the power-generation performance and pose safety risks. When a BPD fails, current circulates within the module, leading to overheating and eventual burnout of the failed BPD. The heating characteristics of a BPD depend on its fault resistance, and although many modules are connected in series in actual PV systems, the heating risk at the module-string level has not been sufficiently evaluated to date. In this study, a numerical simulation model is constructed to reproduce the operation of PV modules and module strings containing failed BPDs, and its validity is verified through experiments. The validated numerical simulation results quantitatively illustrate how series-connected PV modules modify the fault-resistance dependence of BPD heating under maximum power-point operation. The results show that, under maximum power-point operation, the fault resistance at which BPD heating becomes critical shifts depending on the number of series-connected modules examined, while the magnitude of the maximum heating decreases as the string length increases. The heat generated in a BPD at the maximum power point decreases as the number of series-connected modules increases for the representative string configurations analyzed. However, under open-circuit conditions due to power-conditioner abnormalities, the power dissipated in the failed BPD increases significantly, posing a very high risk of burnout. Considering that lightning strikes are one of the major causes of BPD failure, adopting diodes with higher voltage and current ratings and improving the thermal design of junction boxes are effective measures to reduce BPD failures. The simulation model constructed in this study, which was experimentally validated for short PV strings, can reproduce the electrical characteristics and heating behaviors of PV modules and strings with BPD failures with accuracy sufficient for comparative and parametric trend analysis, and serves as a practical tool for system-level safety assessment, design considerations, and maintenance planning within the representative configurations analyzed. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

Renewable energy investment evaluation continues to rely predominantly on techno-economic and environmental criteria, while equity-related considerations remain weakly embedded within formal decision-support frameworks. Although recent research increasingly acknowledges social impacts, spatial constraints, policy uncertainty, and financing structures, these dimensions are rarely integrated in a systematic and operational manner into investment appraisal. This paper addresses this gap by advancing an equity-oriented conceptual framework for renewable energy investment evaluation. Using an integrative literature review combined with thematic analysis, the study synthesises insights from techno-economic assessment, multi-criteria decision-making, energy justice scholarship, and equity-focused modelling studies. The analysis demonstrates that existing evaluation approaches inadequately capture distributional impacts, accessibility constraints, differentiated vulnerability, and equity-adjusted risk. In response, the proposed framework systematises these equity dimensions and embeds them directly into the core logic of investment evaluation alongside conventional criteria. By consolidating fragmented research insights into a coherent evaluative structure, the study contributes to the literature by clarifying how equity can be operationalised within renewable energy investment decision-making. The framework provides a foundation for future empirical applications and supports more socially responsive and analytically robust investment evaluation. Full article

Northern Thailand experiences recurrent seasonal haze driven by biomass burning (BB), which results in hazardous PM2.5 exposure and elevated environmental health risks. To address the need for timely and spatially resolved emission information, this study developed and evaluated an operational near-real-time (NRT) biomass-burning PM2.5 emission estimation system using the Fire INventory from NCAR version 2.5 (FINNv2.5). The objectives of this study are threefold: (1) to construct a high-resolution (≤1 km) NRT biomass-burning PM2.5 emission inventory for Northern Thailand; (2) to assess its temporal and spatial consistency with ground-based PM2.5 measurements and satellite fire observations; and (3) to examine its potential utility for informing environmental health risk management. The developed system captured short-lived, high-intensity burning episodes that defined the haze crisis, revealing a distinct peak period from late February to early April. Cumulative emissions from January to April 2024 exceeded 250,000 tons, dominated by Chiang Mai (25.8%) and Mae Hong Son (25.5%), which together contributed 51.3% of regional emissions. Strong correspondence with MODIS/VIIRS FRP (r = 0.79) confirmed the reliability of the NRT emission signal, while regression against observed PM2.5 concentrations indicated a substantial background burden (intercept = 40.41 μg m⁻³) and moderate explanatory power (R² = 0.448), reflecting additional meteorological and transboundary influences. Translating these relationships into operational metrics, an Emission Control Threshold of 1518 tons day⁻¹ was derived to guide targeted burn permitting and reduce population exposure during peak-risk periods. This NRT biomass-burning PM2.5 emission estimation framework offers timely emissions information that may support decision makers in environmental health risk management, including the development of early warnings, adaptive burn-permit strategies, and more coordinated responses across Northern Thailand. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article