Air quality plays a critical role in human well-being, as air pollution significantly contributes to respiratory diseases such as pneumonia, cysts, and asthma. Predicting pollution levels can enable targeted interventions to mitigate associated chronic health risks. According to the World Health Organization, outdoor air pollution is responsible for approximately 4.2 million premature deaths worldwide. This paper proposes a PPDRTL framework to predict roadside air quality based on pollutant deposition on tree leaves. High-resolution images of roadside leaves are captured and analyzed to estimate pollution levels at the street scale. Image features, including contrast, entropy, and standard deviation, are extracted under varying traffic conditions, namely high, medium, and low traffic densities. Segmentation techniques such as PSO, DPSO, and FODPSO are employed to enhance pollutant feature extraction from leaf surfaces. The PPDRTL framework utilizes a Linear Regression model to predict air quality index (AQI) values from leaf image features, while ground truth data are obtained from the Haryana State Pollution Control Board (HSPCB) for validation. For the 10-day pilot dataset, FODPSO-based PPDRTL achieves R2 up to 0.894, with RMSE in the range of 8.76-12.34 μg/m3 across high-, medium-, and low-traffic sites. Furthermore, the framework achieves prediction accuracies of 88.91% for high-traffic areas, 90.70% for medium-traffic areas, and 91.42% for low-traffic areas, demonstrating its effectiveness as a robust approach for fine-grained air quality prediction and environmental monitoring.
Hot pepper (Capsicum annuum L.) is a critical component of Ethiopian cuisine, widely consumed in both its green and red stages. Despite its importance, the productivity of hot pepper in the Amhara region remains suboptimal, primarily due to degraded soil fertility and the depletion of soil organic matter. To address these challenges, a two-year field experiment (2021/22-2022/23) was conducted in Jabithenan District to evaluate the impact of liquid bio-slurry (LBS) and nitrogen (N) fertilizer combinations on soil chemical properties and hot pepper yield. The study employed a randomized complete block design with six treatments: (1) control (no N), (2) recommended nitrogen (RN), (3)75% LBS + 25% RN, (4) 50% LBS + 50% RN, (5) 25% LBS + 75% RN, and (6) 100% LBS. Each treatment was replicated three times. Hot pepper seedlings (variety Mareko Fana) were transplanted into plots measuring 4.2 m × 3 m, with spacing of 0.3 m between plants and 0.7 m between rows. Soil samples were collected pre-planting and post-harvest to analyze particle size distribution, pH, total nitrogen, organic carbon, available phosphorus, and cation exchange capacity (CEC). Yield and yield components were also evaluated. Data were analyzed using ANOVA in SAS software. The results revealed that integrating LBS with RN significantly improved soil chemical properties and hot pepper yield. The 50% LBS + 50% RN and 25% LBS + 75% RN treatments exhibited superior performance, enhancing soil nutrient content and achieving higher yields compared to the control. These treatments also delivered the highest economic benefits, with a marginal rate of return exceeding 100%. Among the tested combinations, 50% LBS + 50% RN is recommended for farmers with access to sufficient LBS resources and livestock.
Microbial degradation of cable insulation materials is a critical issue affecting the reliability of power systems. In this study, metagenomic analysis was employed to reveal the microbial community structure on contaminated substation cables, identifying Curvularia lunata as a dominant fungal species in high-voltage environments. Subsequently, a specific strain, Curvularia lunata B3, was isolated and identified for further investigation. To assess its specific impact on insulation performance, artificial inoculation experiments were conducted on secondary cable samples. Multi-dimensional characterization techniques, including SEM, WCA measurements, halogen moisture analysis, FTIR, XPS, and LCR digital bridge testing, were utilized to evaluate material degradation. The results demonstrated that C. lunata colonization caused significant surface erosion, characterized by the formation of holes and furrows. This physical damage was accompanied by a marked decrease in hydrophobicity, with the water contact angle dropping from 88.30 ± 0.79° to 78.27 ± 1.27°, and a gradual increase in water content to approximately 1.2% over 60 days. Chemical analysis revealed that microbial activity induced oxidation and dechlorination of the PVC insulation, evidenced by the reduction of C-Cl bonds and the emergence of oxygen-containing functional groups. These physicochemical alterations significantly compromised the electrical insulation of the cables, as evidenced by a marked decrease in series resistance (Rs) and an increase in series capacitance (Cs). This study elucidates the mechanisms of fungal erosion on cable insulation and provides a scientific basis for developing targeted protective strategies in power systems.
BACKGROUND:This research investigates blindness prevalence trends in Gulf Cooperation Council (GCC) countries from 1990 to 2021 and provides projections up to 2030. The study aimed to inform public health planning, policy formulation, and healthcare delivery in the region. METHODS:Using data from the Global Burden of Disease Study 2021, we conducted a time-series analysis applying AutoRegressive Integrated Moving Average (ARIMA) models to evaluate age-standardized prevalence rates and Disability-Adjusted Life Years (DALYs) for blindness. Country-specific and gender-specific were analyzed. Forecasts for 2022-2030 were generated and validated against observed data. RESULTS:Between 1990 and 2021, most GCC countries exhibited declining trends in blindness prevalence. Bahrain's rate decreased from 13,498.2 to 12,884.7 per 100,000, Kuwait from 12,873.8 to 12,881.4, Saudi Arabia from 14,207.2 to 14,166.8, and the UAE from 11,905.9 to 11,869.6. Oman and Qatar showed relative stability with minor fluctuations. Gender disparities were consistent, with higher prevalence among females in all countries except the UAE. DALYs also declined across the region, with Saudi Arabia reporting the highest burden. Forecasts for 2022-2030 indicate continued reductions in most countries, though Oman may experience a moderate increase. CONCLUSIONS:The findings underscore the need for targeted public health strategies, improved healthcare infrastructure, and gender-sensitive interventions to address visual impairment in the GCC. Continuous monitoring and international collaboration are essential to sustain progress and mitigate future burdens.
Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.
Background Indigenous weather forecasting plays a critical role in supporting climate-sensitive livelihoods in Eastern Mount Kenya, yet there is limited empirical understanding of the methods, perceived accuracy, and social mechanisms sustaining their use. This study examines these practices and their contribution to community resilience under increasing climate variability. Methods We conducted 81 in-depth interviews with elders aged 65 years and above across the region. Thematic analysis was applied to coded data to identify patterns, indicator types, and perceptions of reliability and resilience, while comparing insights across communities. Results The study makes three original contributions to ethno-meteorological knowledge which include atmospheric, biological and celestial indicators. In addition, it identifies somatic forecasting as a dominant and systematic knowledge domain, conceptualizing the human body as an environmental sensor within indigenous climate knowledge systems. It also demonstrates that declining reliability of indigenous indicators arises from ecological disruptions, highlighting the vulnerability of indigenous knowledge systems to environmental change. Further, the findings reveal a scale mismatch between scientific forecasts and community-level decision needs, showing that hybrid approaches integrating indigenous and scientific knowledge are more trusted and actionable at the local level. Additional insights include the role of elders as informal climate governance actors and the emergence of intergenerational knowledge loss as a key dimension of climate vulnerability. Conclusion By conceptualizing indigenous weather forecasting as a dynamic, socially embedded, and ecologically grounded system, this study provides empirical evidence that can inform the development of a framework for locally responsive hybrid climate approaches. The results indicate the value of systematic documentation and cultural preservation and point to the potential benefits of integrating such knowledge into policy and climate-service design to enhance adaptive capacity and sustain community-based climate resilience.
Safety has always been a concern. Thus, employing and utilizing technology is vital to help society address this concern. This paper looks at the design and implementation of a Smart Safety Protection System (SSPS), which employs the Internet of Things (IoT) to solve the issue of real-time monitoring, geolocation tracking, and emergency alert features. The proposed system is a wearable device that uses sensors and GPS to send continuous data to both an application on smartphones and a database on the cloud. The system’s features include detecting unauthorized removal of the device as well as falls and abnormal temperature or heart rate. If anything happens as mentioned, the system will send alerts to the linked mobile application. A Deep Learning (DL) model is integrated into the SSPS system using multimodal WESAD sensor data. A trained Multi-Layer Perceptron (MLP) neural network achieved 93% accuracy when optimized with Adam and Softmax, enabling reliable identification of Monitored Individuals’ emotional states. Steps of functional and integration testing of the prototype proved the reliability of sensor accuracy, data transmission speed, and real-time emergency response. Ultimately, this work emphasizes the potential of using DL and the IoT to improve Monitored Individuals’ safety.
Top management teams (TMTs) are considerably less diverse than employees at other levels and the general population. Using over 48,000 firm-year observations of executives across 3,807 firms from 1992 to 2017, we find robust evidence that the existing racioethnic composition of TMTs predicts subsequent TMT appointments that preserve the racioethnic status quo. Specifically, having a racioethnic minority group member on a TMT has a negative association with appointing another member of the same group, and the departure of a racioethnic minority group member from a TMT has a strong positive association with an appointment of a member of that group. However, having a CEO from a racioethnic minority group has a positive association with an appointment from the CEO's racioethnic group. Moreover, the associations with departures and CEO racioethnicity appear stronger in more recent years (2005-2017) than in the past (1992-2004). Theories of diversity in the upper echelon of firms that focus on the role of stereotypes cannot fully explain these effects. The results are consistent with a status uncertainty account we derive from theories of intergroup dynamics. The results also show that TMT appointment decisions hinge on finer-grained distinctions among racioethnic categories than typically used by researchers and the US Census.
The study of the production, movement, processing and consumption of copper and bronze objects in the European Bronze Age (c. 2500-800 BC) reveals the underlying mobility and interconnectedness of Bronze Age societies. Long-distance exchange networks spanned the European Continent, and this paper fills an important geographic gap in Bronze Age metal research improving our understanding of the metal flows across Europe. The Netherlands as maritime/riverine east-west and north-south crossroad is a key piece to the Bronze Age metal supply puzzle. More than 200 lead isotope and elemental analyses of copper and bronzes from the Netherlands, featuring the strategic use of portable laser ablation, constitute a key opportunity to test and reshape theories on Bronze Age metal flow. Our analyses indicate that the earliest copper is of Balkan and Slovakian origin with some import of Iberian arsenical alloys followed by an intensification of Slovakian/Únětice metal imports during the Early Bronze Age. In the early phase of the Middle Bronze Age, the Netherlands received fresh copper with nickel and arsenic impurities from both the Great Orme mine in Wales and Mitterberg in Austria, shifting towards Italian Alpine copper in the late Middle Bronze Age. Italian Alpine copper was widely used, probably reaching the British Isles via trade routes through the Netherlands. In the Late Bronze Age, high-impurity antimony-bearing copper sources prevailed in both the Netherlands and in Britain and highly leaded bronze became commonplace, with the added lead being consistent with British sources. The high-impurity element pattern can be linked to recycled Alpine metal with almost no visible contribution of Iberian metal arriving via Atlantic trade routes.
As landscape planning shifts toward experience-oriented design, emotion mapping has become critical to understanding how spatial environments shape human perception. However existing approaches are largely grounded in site-based or area-based spatial units, limiting their ability to represent emotional variation in directional and continuous linear recreational landscapes. To address this gap, this study develops an integrated framework combining panoramic visual capture, VR-based emotion elicitation experiments, and MaxEnt spatial prediction modeling to quantify and map emotional fluctuations along linear landscapes. Using the No. 1 Scenic Road of Wuyi Mountain National Park as a case study, we find that: (1) emotional fluctuations exhibit a hierarchical spatial structure, clustering along the main road and gradually diffusing into surrounding buffer zones; (2) positive and negative emotions significantly co-occur in core scenic segments, challenging the traditional assumption of emotional mutual exclusivity; (3) emotional distributions are jointly influenced by multiple landscape attributes, with key drivers demonstrating pronounced nonlinear threshold effects. This study advances emotional mapping from static area-based settings to continuous linear recreational systems and provides evidence-based support for refined landscape planning and management.
Long-term hospital stays represent a growing challenge for health systems, especially in tertiary hospitals that receive patients with clinically complex pathologies. This phenomenon is often related to social factors, such as abandonment, absence of caregivers and fragility of support networks, and the difficulty of relocation to intermediary institutions. This study aimed to understand the experiences and perceptions of family caregivers and health professionals about prolonged hospital stays in a referral hospital for tropical and infectious diseases in the Brazilian Amazon. This is a qualitative, exploratory study that is based on interviews with ten health professionals and eight family members of patients hospitalized for more than or equal to 180 days. The data were analyzed via thematic analysis. The analyzed cases show an overlap of vulnerabilities, including comorbidities, HIV/AIDS infection and fragility in family ties. The stigma associated with HIV/AIDS and the families’ poor financial conditions hinder care after discharge, making the hospital the only possible space for these patients to stay. The analysis identified five major themes: (1) social hospitalization and institutional dependence; (2) dynamics of family and social ties; (3) routine care and impacts on the lives of caregivers; (4) clinical complexity and obstacles that impede hospital discharge; and (5) the need for public policies and infrastructure. It is concluded that, in the absence of alternatives outside the hospital and effective intersectoral policies, the hospital begins to play a role beyond that of clinical care, and functions as a place to stay and the only space for social bonds for individuals in situations of extreme social vulnerability.
To address the challenges of modality heterogeneity, scale inconsistency, and background interference in dense small object detection under multimodal conditions, this paper proposes a novel detection framework based on cross-modality guidance and hierarchical scale refinement. Built upon the RT-DETR backbone, the framework integrates a Cross-Modality Guided Dynamic Fusion (CMG-DF) module, which performs semantic-level recalibration between infrared and visible features via a learnable modality attention mechanism, and a Hierarchical Scale Refinement Network (HSRN), which enhances semantic consistency and boundary continuity across scales through bidirectional residual flow and graph-based relational modeling. To validate the effectiveness of the proposed method, extensive comparison and ablation studies are conducted on two public multimodal benchmarks, SMOD and LLVIP. Experimental results show that the proposed method achieves 92.7% mAP@50 and 69.5% mAP@50:95 on SMOD, as well as 77.3% mAP@50 and 43.1% mAP@50:95 on LLVIP, consistently outperforming existing state-of-the-art multimodal detection algorithms. Qualitative visualizations further confirm the robustness and enhancement capability of the method for small objects under low illumination, occlusion, and complex background conditions, highlighting its strong structural generalization and practical deployment potential.
The increasing complexity of modern networks, particularly in IoT and distributed cloud environments, poses significant challenges for maintaining configuration integrity and compliance. Existing solutions for network auditing rely heavily on static rules or manual scripting, which fail to scale or adapt to dynamic network conditions. In this work, we propose a novel AI-driven framework, the Dual-Stream Deep Auditing Network (DSDAN), that leverages deep learning to automate configuration auditing and detect policy violations. DSDAN integrates structured network flow features and unstructured device logs through parallel encoder-decoder streams, enabling joint representation learning for robust compliance analysis. For evaluation, we combine IoT Device Network Logs and UNSW-NB15 because they represent two complementary evidence channels used in practical network auditing: device-level operational logs and flow-level behavioral security records. IoT logs support reconstruction-based identification of abnormal device or configuration behavior, while UNSW-NB15 provides labeled network-flow patterns for modeling unauthorized, anomalous, and attack-like activity. Using these complementary sources, DSDAN achieves an overall accuracy of 93.2%, macro F1-score of 0.918, and micro F1-score of 0.927, surpassing baseline models. The model further records an AUC of 0.957, PR-AUC of 0.948, and the lowest IoT log reconstruction error (MSE = 0.031, MAE = 0.020). Despite its dual-stream architecture, DSDAN maintains efficient inference with a latency of 3.1 ms and memory footprint of 110 MB. These results validate the effectiveness of our approach in identifying subtle misconfigurations and unauthorized behaviors often missed by traditional tools.
Metabolic syndrome (MS) is highly prevalent among people living with HIV (PLWH) receiving antiretroviral therapy (ART) and is driven by persistent low-grade inflammation. Hypoxia-inducible factor 1 alpha (HIF-1α) polymorphisms may influence inflammatory pathways underlying MS. This study investigated the association of the rs11549465 HIF-1α polymorphism with inflammatory markers and MS in PLWH receiving ART. We conducted a multicenter case-control study including 116 PLWH treated at two University Clinical Centers in Serbia. Participants were classified according to NCEP ATP III criteria. Genotyping was performed by Real-Time PCR using TaqMan assays. C-reactive protein (CRP) and fibrinogen levels were significantly higher in participants with MS (p < 0.05). ROC analysis demonstrated their potential as biomarkers of MS with cut-off values of 1.85 mg/L for CRP and 2.49 g/L for fibrinogen. Age significantly correlated with CRP and Neutrophil/Lymphocyte ratio (p < 0.05). Elevated CRP and fibrinogen levels were associated with hypertension, hypertriglyceridemia, and other MS-abnormalities (p < 0.05). The HIF-1α rs11549465 CT + TT genotype was associated with higher IL-6 levels among participants with MS (p = 0.026). CRP and fibrinogen may complement existing MS criteria in identifying PLWH at increased risk of cardiovascular disease and diabetes. These findings highlight the role of chronic inflammation and genetic variability in metabolic complications of HIV.
"Late preterm" or "LPT" neonates are generally defined as infants born between 34 0/7 and 36 6/7 weeks gestation and constitute approximately 74% of all preterm births. While much effort has been put into evaluating the nutritional needs of early preterm or low birthweight infants, there is a shortage of research on the nutritional needs of LPT infants. This work examined how adherence to current nutrition guidelines and how the use of fortification of feeds affected the growth of LPT neonates in the first year of life. A retrospective chart review was conducted of 898 neonates born between 34 0/7 and 36 6/7 weeks gestation, as identified from electronic medical records. The study site where the data was obtained was an urban hospital in New Jersey. Head circumference, weight, and length at birth, documented as measurements, percent, and z-scores, at discharge, at two months, at six months, and at twelve months post birth, were collected and evaluated. Data regarding the neonate's feeding regimen, caloric fortification, and type of nutrition at the point of discharge were assessed. Data were analyzed using independent t-tests and Mann-Whittney U tests to assess the relationship between adherence and growth parameters. Of these participants, 50.1% were male, the mean gestational age was 34.91 (±1.19), and the mean birth weight was 2317 (±489) grams. Infants with lower birth weight (BW) were more likely to receive fortification (BW p < 0.001, BW percentile p < 0.001). Participants who followed fortification guidelines had lower birth weight, discharge weight, and weight percentile (p < 0.001), as well as lower z-scores for BW, length, and head circumference (HC) at birth and at discharge. At six and twelve months, weight, weight percentile, length, length percentile, head circumference, and head circumference percentiles were not statistically significant. The results show that adherence to nutritional guidelines recommending fortified feeding is associated with growth within the first year of life of LPT infants. By two, six, and twelve months, there were no differences in z scores in participants with fortification.
Introduction In 2024, only 3,451 individuals accessed free oral HIV pre-exposure prophylaxis (PrEP) at government clinics in Malaysia, despite an estimated 354,000 key population members who could benefit from PrEP. To inform pre-implementation refinement of a pharmacy-led PrEP service delivery model, we explored stakeholder perspectives on implementing the model within private community pharmacies in Malaysia. Materials and methods A one-day stakeholder consultation was conducted in April 2023 in Kuala Lumpur with 28 stakeholders representing community pharmacies, professional societies, telemedicine providers, non-governmental organizations (NGOs), and HIV implementation science researchers. The meeting began with a review of preliminary findings from formative qualitative research involving key populations, community pharmacists, and PrEP prescribers. Through facilitated discussions, stakeholders identified short- and long-term solutions to implementation barriers. Stakeholder inputs from discussions and structured worksheets were synthesized descriptively to identify recurring implementation considerations that informed refinement of the pilot model. Results Key implementation considerations included enhancing awareness and demand generation, ensuring privacy, confidentiality, and affordability, integrating HIV self-testing, using digital checklist-based assessments, simplifying laboratory testing requirements, developing practical workflows supported by a reference guide, strengthening pharmacist training, and fostering cross-sector collaboration. Identified training needs included PrEP fundamentals, eligibility assessment for initiation and continuation, referral procedures, and scenario-based learning. Key competencies included knowledge of HIV and sexually transmitted infections, risk assessment, professional conduct, and person-centered communication. Six study sites were selected, with preparation requirements including HIV self-test kits, PrEP medications, informational materials, trained pharmacists, telemedicine subscriptions, and nearby referral clinics. The consultation informed refinements to the implementation plan. Conclusions This stakeholder consultation informed refinement of a pharmacy-led PrEP service delivery model for pilot implementation in Malaysia and identified practical implementation considerations related to privacy, affordability, HIV self-testing, physician-pharmacist coordination, training, and referral systems. The findings may inform future implementation planning for similar urban, private-sector PrEP delivery models.
In this study, we investigated the prevalence and abundance of the mercury resistance gene merA in human feces, retail chicken meat, and environmental water samples collected from Japan, Vietnam, and Ghana. A real-time PCR assay developed in this study demonstrated high specificity toward merA sequences from more than 12 bacterial species. Using this assay, merA was detected in 6.8% of human fecal samples in Japan (n = 29), in contrast to significantly higher rates observed in Vietnam (70.2%, n = 47) and Ghana (97.4%, n = 39). Similar geographic trends were evident in the chicken meat samples: 18.5% in Japan (n = 27), 66% in Vietnam (n = 91), and 90% in Ghana (n = 10). Environmental water samples showed a consistently high merA detection rate across all countries (75-100%, n = 21), with substantially higher gene copy numbers in Vietnam and Ghana than in Japan. merA was detected in some water samples, even when total mercury concentrations were below the detection limit, indicating that molecular detection may offer greater sensitivity than traditional physicochemical methods. Mercury-resistant bacteria were successfully isolated and cultured, and Citrobacter freundii was identified as the representative strain. Genomic analysis revealed that merA was located on an IncFIB plasmid, flanked by insertion sequences, suggesting its potential for horizontal gene transfer. These findings highlight merA as a promising biomarker for environmental mercury exposure and support the utility of fecal merA analysis as a proxy for assessing mercury-related public health risks.
Iron-oxidizing microbial mats are commonly encountered features at low-temperature (<100˚C) hydrothermal vents found along the seafloor at tectonically active settings. The microbial communities in these mats are uniquely adapted to exploit the specific ecological niches within the physical and chemical gradients of their environment. A vital component for these adaptations is viral infections, although their impacts are poorly constrained. While the microbial communities of iron-oxidizing mats at seafloor hydrothermal vents have been well studied, the role of viruses in affecting these communities with respect to biogeochemical cycling, diversity, and/or population control remains unclear. The goal of this study was to assess the role and impact of viruses within iron-oxidizing mats of the well-studied 9˚N East Pacific Rise (EPR) segment. We found unique viral assemblages at each site, with 41% of the viral operational taxonomic units (vOTUs) shared across all sites, but with differing relative abundances. The virally-encoded auxiliary metabolic genes (AMGs) were also differentially abundant among the sites and included processes related to carbon and sulfur cycling. Virus-host linkages showed that the viral assemblages infect different members of the microbial communities within each mat, as well as the potential to affect ecological shifts in the metabolisms of various hosts due to viral infection, which could affect the rates of microbial processes. In these iron-oxidizing mats, viruses are therefore likely playing a role in the diversity and evolution of the microbial taxa through lysis and the potential to alter host metabolisms. Exploration of virus-host dynamics within iron-oxidizing mats in hydrothermal vent systems like the EPR aid in constraining a vital component of the deep ocean food webs and give critical insight to the ecology of these productive microbial communities.
In January 2018, Winter Storm Grayson deposited a spatially heterogeneous layer of sediment across the Great Marsh on the North Shore of Massachusetts in Essex Bay, Ipswich Bay, and Plum Island. A published study of short-term responses following one growing season showed percent cover of vegetation was significantly lower in plots receiving sediment than controls, while porewater chemistry (salinity, redox potential, and sulfide concentrations) shifted slightly as biogeochemical processes developed in the new sediment. Six growing seasons post-deposition, these sites were re-evaluated to determine whether storm-delivered sediment (i) increases vegetation percent cover and species richness without suppressing plant height, (ii) influences porewater conditions, and (iii) promotes belowground live root density. Results show that vegetation cover and height were not significantly impacted by sediment thickness or year, and no interaction was found between year and sediment thickness. Salinity significantly decreased from 2018 to 2024, while interactive effects appeared for both redox potential and sulfides, largely driven by changes in plots that received 0-2 cm of sediment. Interactive effects of depth and sediment thickness on live root density appear driven by biomass in the control and 4-6 cm plots. Live root density in the top 4 cm was approximately equivalent for all core types with biomass increasing with depth until 10 cm for the plots receiving greater than 2 cm of sediment. Our results suggest that natural sediment inputs can supplement elevation and stimulate belowground biomass, particularly at thicknesses between 2-6 cm, without negatively impacting vegetation cover, species richness, or porewater chemistry at the landscape scale.
The safe and stable operation of pumped storage hydropower (PSH) plants is heavily reliant on the correctness of their Supervisory Control and Data Acquisition (SCADA) systems. Conventional testing methods often fall short in validating the complex, system-wide control logic under comprehensive operational scenarios. To address this, this paper proposes an innovative web-based dynamic simulation test platform featuring a microservices architecture and a zero-client, Simulink-like graphical modeling environment. The platform employs non-linear physics-based models (e.g., rigid water column and dynamic pump-turbine models) to create a high-fidelity virtual replica of a PSH plant. The core contribution lies in enabling engineers to seamlessly translate control logic into executable models without low-level programming. A case study evaluating a governor control algorithm validates the platform’s efficacy. Results demonstrate a highly accurate tracking performance with a maximum error of 0.008 p.u. under normal conditions, and successfully identified a critical overspeed vulnerability (1.42 p.u.) during an injected actuator fault scenario. The platform is further validated through benchmarking against MATLAB/Simulink (trajectory agreement above 99.8%), a quantitative performance characterization (3.1 ms step time, real-time execution scalable to 20 concurrent sessions), a closed-loop hardware-in-the-loop experiment, and a user evaluation yielding a System Usability Scale score of 84.2. This work concludes that the platform provides a robust, risk-free environment for comprehensive pre-commissioning testing, significantly mitigating field deployment risks.
