Implementation Of Mitigation Strategies Research Articles

Topography‐driven variability in soil greenhouse gas emissions during potato growth season

AbstractTopographical variations strongly influence the spatial variability of soil physicochemical properties by affecting water retention, nutrient distribution and biochemical activity. These topography‐driven differences in soil dynamics can significantly impact greenhouse gas (GHG) emissions. Understanding the variation in GHG emissions over the growing season across topographic changes can facilitate the development of targeted precision agriculture strategies to mitigate GHG emissions. The objectives of this study were to evaluate the influence of topographical variations on soil properties and to assess the spatiotemporal variations of CO2 and N2O emissions throughout the various crop‐growing stages (CGS) of the potato growing season. Moreover, the impact of topography on potato yield was also examined. The experiment was conducted at Victoria Potato Farm, Prince Edward Island, Canada. A substantial N2O flux (80 g ha−1 day−1) was emitted after fertilizer application over the early CGS, and the upper positions had the highest cumulative N2O emissions (993 g ha−1), which aligned with the higher observed soil moisture in this zone. This finding highlights the critical importance of managing fertilizer application, as well as implementing mitigation strategies based on the spatial variability of soil properties to reduce emissions following fertilization. During the mid and late CGS, the depressional positions showed the highest cumulative N2O emissions (90 and 70 g ha−1, respectively). The highest cumulative CO2 emission was observed from the upper positions during the early CGS (1580 kg ha−1); however, the highest emissions were observed in the depressional areas during the mid and late CGS (1415 and 605 kg ha−1, respectively). Overall, the total N2O emission from the three zones accounting for both the differences in each zone's GHG fluxes and the length of each CGS indicated 43% emission in the upper areas, 32% and 25% for the depressional and mid‐slope positions, respectively. These values were 32%, 36% and 32% for CO2 in the upper, depressional and mid‐slope positions. This emission pattern aligns with the elevated soil‐activated carbon (AC), biological nitrogen availability (BNA) values and soil respiration rates in upper and depressional areas. In this study, significantly higher yields were also observed in depressional areas.

Coastal urban flood risk management: Challenges and opportunities − A systematic review

Generational mechanisms and spatio-temporal evolution patterns of Coastal urban flood risk involve complex interactions between climate change, sea level rise and human-induced factors, necessitating integrated adaptive flood management strategies to mitigate evolving vulnerabilities. This systematic review offers a thorough assessment of the challenges and strategic opportunities for sustainable adaptation in managing flood risk in Coastal urban areas. It integrates emerging innovative technologies and financial solutions to identify promising approaches to implement mitigation strategies and improve Coastal urban flood resilience. Enhancing governance and policy frameworks is crucial for the successful implementation of Coastal Urban Flood Risk Management (CUFRM) plans. An innovative participatory planning framework is developed to promote flood management practices which are socially inclusive and equitable. Funding for green infrastructure and nature-based solutions and the strategic use of public–private partnerships are effective methods for advancing sustainable flood risk management (FRM). The advancements in emerging technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), social media and digital twin technologies, provide dynamic and collaborative platforms for simulating flood scenarios and have potential to significantly improve CUFRM practices. In the end, a cross-country comparison of current practices in Australia, China, Netherlands, the UK and the USA reveals a diverse range of approaches and valuable insights derived from regional experiences. The review provides a comprehensive analysis for researchers, policymakers and practitioners aiming to improve flood resilience in coastal metropolitan regions by learning from effective UFRM approaches that enhance governance structures, infrastructure resilience and funding mechanisms.

Airborne polystyrene nanoplastics exposure leads to heart failure via ECM-receptor interaction and PI3K/AKT/BCL-2 pathways

Environmental contamination has been recognized as a significant threat to human well-being, and recent findings of microplastic presence in human cardiac tissues have raised concerns. However, research on the effects of airborne nanoplastics (NPs) on cardiac physiology remains limited. We utilized a comprehensive body exposure apparatus to simulate the impact of airborne polystyrene NPs pollution, focusing on understanding how airborne NPs affect cardiac morphology and function. Following two weeks of NPs exposure, mice exhibited a 23.89 ± 8.30 % reduction in heart mass, a 20.05 ± 2.97 % decrease in heart rate as detected, and a myocardial electrical conduction block. Echocardiography showed significant changes in cardiac contractility, with increases in cardiac ejection fraction and stroke volume of 13.00 ± 3.00 % and 43.00 ± 17.00 %, respectively. In addition, histologic assessments revealed signs of ventricular hypertrophy, ventricular myocardial hypertrophy, and myocardial necrotic fibrosis. Of particular interest, our mechanistic investigations highlighted the harmful effects of NPs on cardiac structure and function, mediated through extracellular matrix (ECM) receptor interactions and the PI3K/AKT/BCL-2 signaling pathway. The insights gained provide a foundation for understanding the risks posed by airborne NPs to human cardiac health, emphasizing the need for increased vigilance and implementation of mitigation strategies in environmental management.

Can heat conditions affect the heart rate responses, perception of effort, and technical performance of young male football players during small-sided games? a comparative study

BackgroundSoccer coaches often employ small-sided games (SSGs) to elicit both physiological and technical responses from players. However, numerous contextual factors can influence the outcomes of these games. This comparative study aimed to investigate how environmental temperature (< 21ºC and > 29ºC) impacts heart rate responses, perception of effort, and technical performance in young male football players during SSGs.MethodsThis study compares temperatures below 21ºC (∼ 20.4 ± 0.4ºC) with temperatures above 29ºC (∼ 29.7 ± 0.6ºC). This repeated measures study design involved 60 male football players at a trained/developmental level, selected from under-16 and under-19 teams. It aimed to assess the effects of the 3v3 format, conducted repeatedly under conditions of 21ºC and above 29ºC. Throughout the games, mean heart rate responses (HRmean), measured via heart rate sensors; rate of perceived exertion (RPE), assessed using the CR-10 Borg scale; and successful passes and lost balls, tracked through an ad hoc observational analysis tool, were monitored.ResultsNo significant interactions were observed (time*age group) in meanHR (F = 0.159; p = 0.691; \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\eta\\:}_{p}^{2}$$\\end{document}=0.003), RPE (F=0.646; p=0.425; \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\eta\\:}_{p}^{2}$$\\end{document}=0.011), number of passes completed (F=0.204; p=0.654; \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\eta\\:}_{p}^{2}$$\\end{document}=0.003), and number of lost balls (F = 0.157; p = 0.694; \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\eta\\:}_{p}^{2}$$\\end{document}=0.003). Overall, significantly higher heart rate responses in mean HR (p<0.001) and RPE (p<0.001) were observed at temperatures above 29ºC, while significantly more passes were completed at temperatures below 21ºC (p<0.001).ConclusionsHeat conditions significantly intensified the psychophysiological responses in players, concurrently leading to a significant impairment in the number of passes. Coaches should contemplate implementing mitigation strategies to avert performance declines during heat conditions when utilizing SSGs.

Managing Risk Mitigation in Urban Expansion Areas of Argentina’s Drylands: The Gap between Perception and Environmental Behaviour

Accessibility to rigorous scientific information to promote risk mitigation measures by citizens is crucial, especially in the context of climate change and extreme weather events. This study focuses on the perception of flood risk and the implementation of mitigation strategies by residents in drylands urban sprawl areas. Risk perception, defined as the subjective assessment of the likelihood and potential consequences of flooding, is a key element of mitigation. While many studies have explored the link between risk perception and behaviour, this research addresses gaps in understanding how public information affects these perceptions and actions. In areas of rapid urban expansion, where regulation often overlooks environmental features, the lack of adequate information poses significant barriers to effective risk mitigation. This research reveals that although residents claim to understand flooding, their descriptions often indicate a lack of understanding of the phenomenon. This ‘passive optimism’ could be mitigated by providing rigorous information and specific technical recommendations. This study highlights the disconnect between residents’ awareness of flood risks and the inadequacy of preventive measures, underlining the importance of targeted information and collaboration between the scientific community, government sectors, and local populations.

Acanthamoebae as a protective reservoir for Pseudomonas aeruginosa in a clinical environment

Pseudomonas aeruginosa is a growing concern in healthcare-associated infections and poses significant risk to those with serious underlying health conditions. The antimicrobial resistance traits of the pathogen and ability to form biofilms make effective mitigation and disinfection strategies difficult. Added to this challenge is the role that free-living amoebae such as Acanthamoeba play in the detection, disinfection and transmission of P.aeruginosa. P. aeruginosa can survive intracellularly within amoebae, which has the potential to limit detectability and permit transmission into high-risk areas. We screened for the presence of Acanthamoeba spp. and P.aeruginosa within a functioning general hospital in Scotland using a culture and molecular approach, noting their presence at several sites over a four-month period, particularly within floor drains connecting patient rooms. In addition, microbiome analysis revealed that amoebae harbour a unique microbial community comprised primarily of Pseudomonas spp. that were not readily detected using microbiome sequencing techniques on environmental swabs. Having demonstrated that both organisms were consistently present in hospital settings, we investigated the relationship between acanthamoeba and P.aeruginosa in the laboratory, showing that (i) acanthamoeba growth rate is increased in the presence of pseudomonas biofilms and viable pseudomonas persist within the amoebae and (ii) hydrogen peroxide-based disinfectants are significantly less effective against an isolate of P.aeruginosa in the presence of acanthamoeba than when the bacteria are incubated alone. These findings suggest that amoebae, and other protists, can influence the detection and persistence of P.aeruginosa in high-risk areas and should be considered when implementing mitigation strategies.

Association of Definitive Radiotherapy for Esophageal Cancer and the Incidence of Secondary Head and Neck Cancers: A SEER Population-Based Study.

Impact of radiotherapy (RT) for esophageal cancer (EC) patients on the development of secondary head and neck cancer (SHNC) remains equivocal. The objective of this study was to investigate the link between definitive RT used for EC treatment and subsequent SHNC. This study was conducted using the Surveillance, Epidemiology, and End Results (SEER) database to collect the data of primary EC patients. Fine-Gray competing risk regression and standardized incidence ratio (SIR) and propensity score matching (PSM) method were used to match SHNC patients with only primary head and neck cancer (HNC) patients. Overall survival (OS) rates were applied by Kaplan-Meier analysis. In total, 14,158 EC patients from the SEER database were included, of which 9,239 patients (65.3%) received RT and 4,919 patients (34.7%) received no radiation therapy (NRT). After a 12-month latency period, 110 patients (1.2%) in the RT group and 36 patients (0.7%) in the NRT group experienced the development of SHNC. In individuals with primary EC, there was an increased incidence of SHNC compared to the general US population (SIR = 5.95, 95% confidence interval (CI): 5.15 - 6.84). Specifically, the SIR for SHNC was 8.04 (95% CI: 6.78 - 9.47) in the RT group and 3.51 (95% CI: 2.64 - 4.58) in the NRT group. Patients who developed SHNC after RT exhibited significantly lower OS compared to those after NRT. Following PSM, the OS of patients who developed SHNC after RT remained significantly lower than that of matched patients with only primary HNC. An association was discovered between RT for EC and increased long-term risk of SHNC. This work enables radiation oncologists to implement mitigation strategies to reduce the long-term risk of SHNC in patients who have received RT following primary EC.

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

Prioritising urban heat island mitigation interventions: Mapping a heat risk index

The global climate is under threat from increasing extreme heat, evidenced by rising temperatures and a surge in hot days. Heat waves are intensifying worldwide, impacting cities and residents, as demonstrated by the record-breaking heat experienced in the UK in 2022, which resulted in over 4500 deaths. Urban heat islands (UHIs) exacerbate these heat waves, making city residents more vulnerable to heat-related deaths. UHIs occur when temperatures in urban areas exceed those in surrounding rural areas due to the heat-absorbing properties of urban structures. Implementing mitigation strategies, such as green infrastructure, is crucial for enhancing urban resilience and reducing vulnerability to UHIs.Effectively addressing UHIs requires a systematic approach, including developing risk maps to prioritise areas for UHI mitigation strategies. Using remote sensing, GIS, and SPSS correlational analysis, the research aims to develop and assess a Heat Risk Index (HRI). This index integrates UHI spatial intensity, current green cover, and population density at the district level to develop the risk index. This study stands out for its novel approach to developing the HRI, focusing on the localised impact of the UHI in Manchester City, identifying high-risk heat-vulnerable districts, and prioritising implementing effective UHI mitigation strategies.The findings highlight the importance of this approach, revealing that approximately 30 % of Manchester City is affected by UHI effects, with areas near the city centre, characterised by higher population density and reduced green cover, being particularly vulnerable. Furthermore, the study suggests that applying HRIs at a more localised level, such as the neighbourhood level rather than the district level, would provide more relevant and targeted insights for mitigating UHI. A more localised index would offer tailored insights into the unique conditions of each neighbourhood within the districts, enabling more effective mitigation strategies. The HRI developed in this paper serves as a test for a more nuanced and comprehensive index, considering additional variables related to population vulnerability and city urban structure.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

A global analysis of the Rubus virome using public transcriptome data

AbstractViruses are the causal agent of some of the most limiting diseases in Rubus crops. In this regard, a detailed knowledge of the Rubus virome is essential for successfully implementing mitigation strategies and assessing the risk of the emergence of novel viral diseases. Fortunately, the widespread use of RNA sequencing to characterize diverse aspects of the biology of Rubus species can also be a valuable source of data on viruses associated with these hosts. Here, we present a meta-analysis of the Rubus virome based on the re-examination of public RNAseq data from nine Rubus species and 23 hybrid cultivars worldwide. As a result, we identified 23 viruses and one viroid associated with the genus Rubus. Detected viruses are part of eleven families and 26 genera. We also provide evidence of a second species in the genus Brambyvirus, and a novel member of the family Virgaviridae. Our data also revealed raspberry bushy dwarf virus (RBDV) to be the most prevalent and generalist Rubus virus. With respect to the number of species, the genus Ilarvirus and the family Closteroviridae were the most diverse taxonomic ranks affecting Rubus. Network analysis revealed distinct Rubus viromes in different countries and analysis of coinfections revealed three main viral communities infecting these plant species across the world. We also provide information on virus variants from new locations worldwide and data on potential emergent viruses from other plant hosts.

A blockchain technology for improving financial flows in humanitarian supply chains: benefits and challenges

Purpose This study aims to determine how the applications of blockchain technology (BT) can play a crucial role in managing financial flows in the humanitarian supply chain (HSC) and what benefits and challenges are associated with BT in a humanitarian setting. Design/methodology/approach The present study used a qualitative research approach, incorporating a systematic literature review and conducting semi-structured interviews with 12 experts in the fields of humanitarian operations, supply chain management, fintech and information technology. Findings The findings show that the humanitarian sector has the potential to reap significant benefits from BT, including secure data exchange, efficient SCM, streamlined donor financing, cost-effective financial transactions, smooth digital cash flow management and the facilitation of cash programs and crowdfunding. Despite the promising prospects, this study also illuminated various challenges associated with the application of BT in the HSC. Key challenges identified include scalability issues, high cost and resource requirements, lack of network reliability, data privacy, supply chain integration, knowledge and training gaps, regulatory frameworks and ethical considerations. Moreover, the study highlighted the importance of implementing mitigation strategies to address the challenges effectively. Research limitations/implications The present study is confined to exploring the benefits, challenges and corresponding mitigation strategies. The research uses a semi-structured interview method as the primary research approach. Originality/value This study adds to the existing body of knowledge concerning BT and HSC by explaining the pivotal role of BT in improving the financial flow within HSC. Moreover, it addresses a notable research gap, as there is a scarcity of studies that holistically cover the expert perspectives on benefits, challenges and strategies related to blockchain applications for effective financial flows within humanitarian settings. Consequently, this study seeks to bridge this knowledge gap and provide valuable insights into this critical area.

SLEM (Shallow Landslide Express Model): A Simplified Geo-Hydrological Model for Powerlines Geo-Hazard Assessment

Powerlines are strategic infrastructures for the Italian electro-energetic network, and natural threats represent a potential risk that may influence their operativity and functionality. Geo-hydrological hazards triggered by heavy rainfall, such as shallow landslides, have historically affected electrical infrastructure networks, causing pylon failures and extensive blackouts. In this work, an application of the reworked version of the model proposed by Borga et al. and Tarolli et al. for rainfall-induced shallow landslide hazard assessment is presented. The revised model is called SLEM (Shallow Landslide Express Model) and is designed to merge in a closed-from equation the infinite slope stability with a simplified hydrogeological model. SLEM was written in Python language to automatise the parameter calculations, and a new strategy for evaluating the Dynamic Contributing Area (DCA) and its dependence on the initial soil moisture condition was included. The model was tested for the case study basin of Trebbia River, in the Emilia-Romagna region (Italy) which in the recent past experienced severe episodes of geo-hydrological hazards. The critical rainfall ratio (rcrit) able to trigger slope instability prediction was validated against the available local rainfall threshold curves, showing good performance skills. The rainfall return time (TR) was calculated from rcrit identifying the most hazardous area across the Trebbia basin with respect to the position of powerlines. TR was interpreted as an index of the magnitude of the geo-hydrological events considering the hypothesis of iso-frequency with precipitation. Thanks to its fast computing, the critical rainfall conditions, the temporal recurrence and the location of the most vulnerable powerlines are identified by the model. SLEM is designed to carry out risk analysis useful for defining infrastructure resilience plans and for implementing mitigation strategies against geo-hazards.

The effects of standing foot-transmitted vibration on self-reported discomfort ratings.

Occupational foot-transmitted vibration (FTV) exposure is common in industries like mining, construction, and agriculture, often leading to acute and chronic injuries. Vibration assessments require technical expertise and equipment which can be costly for employers to perform. Alternatively, researchers have observed that self-reported discomfort can be used as an effective indicator of injury risk. This study aimed to investigate the effect of standing FTV exposure on self-reported ratings of discomfort, and whether these subjective ratings differed by body area and exposure frequency. Participants (n = 30) were randomly exposed to standing FTV at six frequencies (25, 30, 35, 40, 45, and 50 Hz) for 20-45 seconds. Following each exposure, participants rated discomfort on a scale of 0-9 in four body areas: head and neck (HN), upper body (UB), lower body (LB), and total body. Results indicated that participants experienced the most discomfort in the LB at higher frequencies (p < 0.001), consistent with the resonance of foot structures. The HN discomfort tended to decrease as the exposure frequency increased, although not statistically significant (p > 0.0167). The UB discomfort remained relatively low across all frequencies. The study suggests a potential connection between resonant frequencies and discomfort, potentially indicating injury risk. Although self-reported discomfort is insufficient for directly assessing injury risk from FTV, it provides a simple method for monitoring potential musculoskeletal risks related to vibration exposure at resonant frequencies. While professional vibration assessment remains necessary, self-reported discomfort may act as an early indicated of vibration-induced injuries, aiding in implementing mitigation strategies.

Potential Impact of USPS Mail Delivery Delays on Colorectal Cancer Screening Programs.

Colorectal cancer is the second-leading cause of cancer deaths in the United States. Fecal immunochemical tests (FITs) are a primary means of colorectal cancer screening at some health care institutions because of scheduling backlogs for screening, diagnostic, and surveillance endoscopies. However, delays in mail delivery can impact timely analysis of samples, possibly leading to false-negative results and the need for repeat tests. Some patients might be unwilling to submit another test when informed that an earlier sample cannot be reliably analyzed, resulting in a missed opportunity for screening. The Jesse Brown Veterans Affairs Medical Center has experienced some success through contacting the local US Postal Service (USPS) to avoid these delays; however, the problem often unpredictably recurs with USPS staff turnover. Laboratories and health systems experiencing delays should first ensure that prepaid envelopes have the correct postage and that their USPS accounts are properly funded, to confirm that insufficient funds are not contributing to the delayed deliveries. Adding additional language to the preprinted envelopes, such as "time-sensitive," may also be helpful. Asking patients to drop off test kits at the laboratory or using private letter carriers is not feasible in some communities. Other strategies include establishing a drop-off box at clinic offices or considering other screening methods, such as colonoscopies or flexible sigmoidoscopies. Clinicians who work in health care systems that use FIT kits need to be aware of the impact that local USPS delays can have on the reliability of FIT results. Health systems should be prepared to implement mitigation strategies if significant delays with mail delivery are encountered.

THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN STRATEGIC MANAGEMENT

In today's dynamic and complex business environment, organizations are increasingly turning to artificial intelligence (AI) to gain a competitive edge in strategic management. This abstract delves into the various applications of AI in strategic management, highlighting its transformative impact on decision-making processes, resource allocation, risk management, and overall organizational performance. One of the key areas where AI is revolutionizing strategic management is in decision-making. AI-powered algorithms analyze vast amounts of data, identify patterns, and generate actionable insights to support strategic decision-making processes. These insights enable organizations to make data-driven decisions that are aligned with their long-term objectives and market trends, leading to improved performance and competitive advantage. Furthermore, AI plays a crucial role in optimizing resource allocation within organizations. By leveraging predictive analytics and machine learning algorithms, AI systems can forecast demand, optimize inventory levels, and allocate resources efficiently across different departments or projects. This helps organizations minimize costs, maximize utilization, and enhance overall operational efficiency. Another significant application of AI in strategic management is in risk management. AI-powered risk assessment models can analyze historical data, identify potential risks, and predict future threats to the organization. This proactive approach allows companies to implement mitigation strategies, reduce vulnerabilities, and enhance resilience in the face of uncertainties, thereby safeguarding their long-term sustainability. Moreover, AI-driven technologies such as natural language processing (NLP) and sentiment analysis are transforming how organizations gather and analyze market intelligence. By monitoring social media, news articles, and customer feedback, AI systems can extract valuable insights about market trends, competitor activities, and customer preferences. This real-time information empowers organizations to adapt their strategies swiftly, capitalize on emerging opportunities, and stay ahead in competitive markets. Keywords: Artificial Intelligence, management, technological innovation

PreK-12 school and citywide wastewater monitoring of the enteric viruses astrovirus, rotavirus, and sapovirus

Wastewater monitoring is an efficient and effective way to surveil for various pathogens in communities. This is especially beneficial in areas of high transmission, such as preK-12 schools, where infections may otherwise go unreported. In this work, we apply wastewater disease surveillance using school and community wastewater from across Houston, Texas to monitor three major enteric viruses: astrovirus, sapovirus genogroup GI, and group A rotavirus. We present the results of a 10-week study that included the analysis of 164 wastewater samples for astrovirus, rotavirus, and sapovirus in 10 preK-12 schools, 6 wastewater treatment plants, and 2 lift stations using newly designed RT-ddPCR assays. We show that the RT-ddPCR assays were able to detect astrovirus, rotavirus, and sapovirus in school, lift station, and wastewater treatment plant (WWTP) wastewater, and that a positive detection of a virus in a school sample was paired with a positive detection of the same virus at a downstream lift station or wastewater treatment plant over 97 % of the time. Additionally, we show how wastewater detections of rotavirus in schools and WWTPs were significantly associated with citywide viral intestinal infections. School wastewater can play a role in the monitoring of enteric viruses and in the detection of outbreaks, potentially allowing public health officials to quickly implement mitigation strategies to prevent viral spread into surrounding communities.

Soil erosion is a major drive for nano & micro-plastics to enter riverine systems from cultivated land

Nano and micro-plastics (NMPs, particles diameter <5 mm), as emerging contaminants, have become a major concern in the aquatic environment because of their adverse consequences to aquatic life and potentially human health. Implementing mitigation strategies requires quantifying NMPs mass emissions and understanding their sources and transport pathways from land to riverine systems. Herein, to access NMPs mass input from agricultural soil to riverine system via water-driven soil erosion, we have collected soil samples from 120 cultivated land in nine drainage basins across China in 2021 and quantified the residues of six common types of plastic, including polyvinyl chloride (PVC), polymethyl methacrylate (PMMA), polypropylene (PP), polyethylene (PE), polycarbonate (PC), and polystyrene (PS). NMPs (Σ6plastics) were detected in all samples at concentrations between 3.6 and 816.6 μg/g dry weight (median, 63.3 μg/g) by thermal desorption/pyrolysis-gas chromatography-mass spectrometry. Then, based on the Revised Universal Soil Loss Equation model, we estimated that about 22,700 tonnes of NMPs may enter the Chinese riverine system in 2020 due to agricultural water-driven soil erosion, which occurs primarily from May to September. Our result suggested that over 90% of the riverine NMPs related to agricultural soil erosion in China are attributed to 36.5% of the country's total cultivated land, mainly distributed in the Yangtze River Basin, Southwest Basin, and Pearl River Basin. The migration of NMPs due to water-driven soil erosion cannot be ignored, and erosion management strategies may contribute to alleviating plastic pollution issues in aquatic systems.

Leveraging Digital Forensics in the Age of Smart Grids: A Survey of Tools and Techniques for Securing Electrical Power Systems

The increasing reliance on digital technologies in smart grids introduces vulnerabilities to cyberattacks that can disrupt critical infrastructure and impact daily life. For critical infrastructures to be protected against potential cyber-attacks, many forensic approaches have already been developed to collect, analyze, and digitalize the evidence to assist in the in-depth investigation of any incident. The growing complexity of power grids with interconnected digital components necessitates robust security measures. The modern power grids are not isolated they are interconnected networks of controls where if one component or system is affected the whole system faces a blackout. Network forensics plays a crucial role in investigating these attacks, identifying the source, and implementing mitigation strategies. This paper compares and analyzes three key tools: Wireshark, Nmap and NetMiner used in the network forensics of electrical power systems focusing on their performance assessed against various parameters.

Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams.