Effective Monitoring Research Articles

Effective Air Pollution Monitoring System for Eco Balance

Air pollution in urban areas is increasingly driven by vehicle emissions, which release harmful gases include such things as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and particulate matter (PM). These pollutants pose significant risks to both environmental quality and public health, highlighting the urgent need for effective monitoring and control systems. This project presents the development of an automated Effective Air Pollution Monitoring System for Eco Balance, designed to detect exhaust gas levels in real-time as vehicles pass designated points. The system employs advanced sensors to monitor pollutant concentrations and utilizes a camera- based recognition system to capture the number plates of vehicles that exceed permissible emission levels. The collected data, including pollution metrics and vehicle details, is transmitted to the Regional Transport Office (RTO) for necessary enforcement actions. By promoting compliance with emission standards, this automated system aims to significantly reduce vehicle-related air pollution, fostering a cleaner and healthier urban environment

Phylogenetic evaluation and haplotype mapping of phytoplasma disease in sesame: insights into leafhopper-mediated disease transmission through molecular investigation

The predominance of leafhopper species like Orosius albicinctus, Hishimonus phycitis, and Amrasca biguttula biguttula indeed poses a significant threat to sesame production in India and globally. This disease is a major challenge for sesame farming, as it can lead to yield reductions of up to 95%. The current study examined phytoplasma and leafhoppers in sesame and analyzed the phylogenetic differences in the phyllody phytoplasma. The identification of both 16Sr group I and group II phytoplasma isolates, along with the discovery of the 16SrI-B group reported for the first time in Tamil Nadu, represents a significant advancement in our understanding of phytoplasma diversity in the region. The utilization of virtual RFLP analysis in sequencing the sesame phyllody phytoplasma highlights the importance of molecular techniques in characterizing the phytoplasma. Leafhoppers in sesame were a co-occurring species complex of O. albicinctus, H. phycitis, and A. bigutulla bigutulla. The leafhoppers were identified using species-specific taxonomical traits, and a mtCOI gene-based genetic study confirmed the species. In disease transmission efficiency studies in the greenhouse, O. albicinctus transmitted phyllody at a rate ranging from 7.8% to 84%, while H. phycitis transmitted phyllody at a rate of 5.2% to 74%. Both of these species showed the highest transmission efficiency in the combination of three insects per plant with five days of acquisition feeding and one day of inoculation feeding. A comprehensive understanding of leafhopper-phytoplasma interactions through molecular studies supports effective monitoring, early detection, and the development of integrated pest management strategies.

Molecular Diagnostics for Monitoring Insecticide Resistance in Lepidopteran Pests

Chemical control methods to prevent crop damage have long been directly implicated in the selection of lepidoptera insect populations resistant to insecticides. More recently, new products featuring different modes of action (MoA), developed to mitigate the negative effects of control management on both producers and the environment, are rapidly losing efficacy due to the emergence of insect resistance. Among these, certain resistances are associated with molecular changes in the genomes of pest insects that are valuable for developing molecular markers for diagnostic tools, particularly the point mutations. Molecular diagnosis represents an innovative solution for insecticide resistance management (IRM) practices, allowing for the effective monitoring of insecticide resistance. This approach facilitates decision making by enabling the timely alternation between different modes of action (MoAs). In this context, this review focuses on the major lepidopteran pests that affect globally significant crops, discussing the impacts of insecticide resistance. It gathers literature on diagnostic methods; provides a comparative overview of the advantages of different techniques in terms of efficiency, cost, precision, sensitivity, and applicability; and highlights several novel diagnostic tools. Additionally, this review explores the coffee leaf miner, Leucoptera coffeella, as an applied model to illustrate potential approaches for more effective and sustainable control strategies.

Dynamic Monitoring the Friction Transmission of Mine Hoist Using Triboelectric Nanogenerators Self‐Powered Sensor Based on Different Surface Structures Embedded in the Friction Lining

The friction lining is a critical component of the friction hoist, serving as the driving force for lifting through its interaction with the wire rope. Monitoring the friction state between the wire rope and the friction lining is crucial as it directly impacts lifting capacity, work efficiency, and overall safety. By employing finite element simulation and creating surface microstructures on triboelectric nanogenerators (TENG) using ultraviolet laser, this study demonstrats that microstructure can improve voltage output. Compared with TENG without morphology, the voltage is increased by nearly seven times, reaching 2.28 V. Moreover, experiments revealed that embedding TENG at the optimal standard point of the friction lining enables effective monitoring of friction transmission under varied conditions, with the voltage signal showing synchronization with friction force. Notably, the voltage reached 520 mV under increasing specific pressures and stabilized around 670 mV with rising sliding speeds. This research represents a significant step toward real‐time monitoring of intelligent mining systems by dynamically observing friction transmission in mine hoists.

Ensuring safety and monitoring adverse events in clinical trials: Challenges and innovations

Safety and adverse events (AEs) monitoring in clinical trials are critical to ensuring participant safety and the successful development of new therapies. Effective monitoring not only protects patients but also helps identify any risks associated with the investigational product. This paper elaborates on the methodologies used to detect, categorize, and report adverse events during clinical trials, and how these processes are governed by stringent regulatory frameworks. Additionally, the discussion highlights the role of pharmacovigilance systems, the ethical obligations researchers face in AE reporting, and the technological innovations that are transforming safety monitoring. Challenges, including under-reporting, data variability, and interpretation difficulties, are examined in depth. Ultimately, advancements in real-time data collection, adaptive trial designs, and artificial intelligence (AI) are promising to improve the accuracy and timeliness of adverse event monitoring, shaping the future of clinical research and patient safety.

Effectiveness and Risk Assessment Septic Tank Treatment Systems in Rural Cold Regions of China

Rural toilet reform in cold areas is the focus and difficulty of China’s toilet revolution. In this paper, by carrying out the septic tank gradient burial depth test and regular seasonal monitoring of the operation effect of rural septic tanks in cold areas, the frost protection effect and treatment effect of rural toilets in cold areas are clarified, which provide important references for the rural toilet revolution and its practical significance in cold areas of China. Based on controlled tests and correlation analysis methods, different gradient burial depths of septic tanks (40, 60, and 80 cm) and different seasonal operation effects (spring, summer, autumn, and winter) were studied to analyze the influence of factors such as burial depths and temperatures on the effectiveness of anti-freezing and treatment of septic tanks. The results found that the septic tank effluent indexes of the six cold-zone test households could not meet requirements of Class Ⅰ-B in the discharge standard of pollutants for municipal wastewater treatment plants (GB 18918-2002). (The criteria for Class I-B are as follows: COD emission limit of 60 mg/L; SS emission limit of 20 mg/L; TN emission limit of 20 mg/L; TP emission limit of 1 mg/L; NH3-N emission limit of 8 mg/L); The average removal of suspended solids (SSs), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), NH3-N, and NO3-N by septic tanks was 79.7%, 21.8%, 10.5%, 13.5%, 9.76%, and 2.38%, respectively; the water temperature was significantly positively correlated with SS, TN, and TP; the double-layer insulated septic tank with medium burial depth (60 cm) could operate normally in cold areas in winter. The treatment effect of septic tanks on various indicators basically shows that summer > autumn > spring > winter. Temperature is the main limiting factor affecting the treatment effect of septic tanks. Capsule: By carrying out septic tank gradient burial depth tests and regular seasonal monitoring of the operating effect of rural septic tanks in cold areas, we can clarify the anti-freezing effect of rural toilets in cold areas and the effectiveness of treatment.

Spatial distribution, source apportionment, and health risks assessment of trace elements in pre- and post-monsoon soils in the coal-mining region of North Karanpura basin, India.

Coal mining activities in the North Karanpura basin have significantly increased the trace element (TE) concentrations in the soil, resulting in soil pollution and potential health risks. To assess this, 113 soil samples, along with coal, shale, and overburden rocks, were collected from open-cast mining areas during pre-monsoon (Pre-M) and post-monsoon (Post-M) seasons. Seasonal analysis revealed higher TE concentrations in the Post-M period, especially in the SE direction, followed by NE and NW, likely due to surface runoff and deposition, demonstrating temporal variability in TE distribution which corroborated from the spatial distribution maps. Positive matrix factorization (PMF) model identified four factors: mixed sources (F1Pre-M: 37.6 %; F4Post-M: 28.9 %), coal-fired emissions (F2Pre-M: 20.5 %; F3Post-M: 26.0 %), overburden rocks (F3Pre-M: 25.5 %; F2Post-M: 16.7 %), and agricultural and lithogenic origin (F4Pre-M: 16.4 %) during the Pre-M period, attributed to coal mining. Post-M sources were similar, but agricultural and lithogenic origins were replaced by atmospheric deposition (F1Post-M: 28.4 %), enhanced by monsoon effects. Carcinogenic risk assessment revealed that As, Cr, and Ni exceeded acceptable levels for children via ingestion, though adults remained within safe limits. Inhalation and dermal contact were also considered, but ingestion posed the highest risk. The hazard index (HI) via ingestion showed that children had an HI of 1.6 in Pre-M, increasing to 2.66 in Post-M, highlighting their potential vulnerability to non-carcinogenic risks, while adults stayed within safe limits. The expansion of mining areas in the study region led to decrease in vegetative areas which could affect agriculture and local communities, raising a comprehensive environmental and public health issues. These results underline the need for implementing effective biannual soil monitoring and mitigation strategies, such as phytoremediation, bioremediation, rock dust remediation, chemical amendments and improved waste management, to reduce TE contamination.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Management and monitoring of anesthesia during the perioperative period in a Tertiary Hospital in Bangladesh

Background: Anesthesia management is crucial for ensuring patient safety and optimal surgical outcomes during the perioperative period. In Bangladesh, the challenges faced in managing and monitoring anesthesia in tertiary care hospitals necessitate a comprehensive evaluation of current practices. This study aims to assess the anesthetic management protocols and monitoring strategies employed in a tertiary hospital in Bangladesh. Methods: This observational study was conducted in Islami Bank Medical College Hospital, Rajshahi, Bangladesh from July to December 2023. Data were collected from patients undergoing various surgical procedures requiring anesthesia. The anesthetic techniques used, intraoperative monitoring parameters, and any complications encountered were recorded. The study included a review of anesthesia charts, patient records, and interviews with anesthesia providers. Statistical analysis was performed to identify trends and correlations. Results: A total of 200 patients were included in the study. The most commonly used anesthetic technique was general anesthesia (65%), followed by regional anesthesia (30%) and local anesthesia (5%). Intraoperative monitoring parameters included heart rate, blood pressure, oxygen saturation, and end-tidal carbon dioxide levels, which were consistently documented in 95% of cases. Complications occurred in 10% of patients, primarily involving respiratory issues and cardiovascular instability. Proper adherence to monitoring protocols significantly reduced the incidence of complications. Conclusion: Effective management and monitoring of anesthesia during the perioperative period are vital for patient safety in a tertiary hospital setting. The study highlights the importance of standardized protocols and continuous monitoring to minimize complications. Recommendations for improving anesthetic practices include regular training for anesthesia providers and enhancing monitoring infrastructure to ensure optimal patient care.

Real-time measurement of metals in submicron aerosols with particle-into-liquid sampler combined with micro-discharge optical emission spectroscopy.

The paper presents a novel technique for quantifying trace metals in aerosol samples in real time. Airborne metals were continuously collected for one week near the Baltic Sea in Finland using a particle-into-liquid sampler (PILS). The collected liquid samples were analyzed for metals using micro-discharge optical emission spectroscopy (µDOES). The micro-discharge analyzer is designed to perform real-time, on-site measurements of metal concentrations in aqueous solutions. Currently, µDOES can provide online measurements of 30 metals, with typical detection limits from 0.01µg/m3 to 0.06µg/m3 with a long-term repeatability less than 5%. The novelty of this analyzer lies in its compact design, rapid detection capabilities, and ease of operation and maintenance. Several metals, including potassium (K), calcium (Ca), sodium (Na), aluminum (Al), magnesium (Mg), and copper (Cu), were measured in the aerosol samples collected using PILS. The results indicate that this approach has significant potential for future automated online monitoring of airborne metal concentrations, facilitating investigations into their sources and daily variations. The development of real-time technologies for rapid, online, and accurate atmospheric aerosol measurements is essential for advancing climate change research. Such advancements allowing for continuous real-time data enhance our understanding of aerosol dynamics, improve source identification, and inform public health and environmental policies, ultimately contributing to more effective climate change monitoring and mitigation.

Integrating Geospatial and Real Time Technologies for Risk zone monitoring in Periyar Tiger Reserve, India.

There are numerous monitoring technologies available today, owing to the rapid advancements in technology and the increasing demand for safety and security in forests. Real-time monitoring with AI cameras, which are commonly utilized for creating and updating real-time features through surveillance, stands out as one of the most effective monitoring solutions. The objective of this current research is to monitor various risk zones within the Periyar Tiger Reserve by integrating real-time AI camera with geographic data. AI cameras were strategically placed using spatial analysis techniques. Leveraging Geographic Information System (GIS) technology, the system facilitates the spatiotemporal management of multiple cameras and their associated data. The spatial distribution and monitoring range of the AI cameras are depicted on the GIS map, along with the layout densities of the cameras and other pertinent information stored in a geospatial database. Additionally, merging various risk areas identified from past incidents with the camera locations enhances the system's capability to establish accurate topological connections between cameras and other points of interest. The results revealed that only 13% of the risk zone was observable from the nine available Real Time Monitoring towers. However, with the addition of 51 more towers, the visibility of the risk zone would increase to 40%. The remaining 15% of the risk zones were not visible through the existing infrastructure. To address this visibility gap, if permitted by the Wildlife Protection Act, wired communication may need to be implemented instead of wireless for monitoring these areas.

Assessing Nitrate Leaching During Drought and Extreme Precipitation: Exploring Deep Vadose‐Zone Monitoring, Groundwater Observations, and Field Mass Balance

AbstractThe increasing concern over agricultural practices' impact on groundwater quality necessitates comprehensive studies to evaluate and compare monitoring strategies for nitrate leaching. This work addresses this imperative by examining three methodologies: deep vadose‐zone monitoring, shallow groundwater intensive monitoring, and field‐level mass balance. The primary objective of the study was to assess nitrate leaching from an intensively cropped processing tomato rotation field using three different methods. Additionally, this study focuses on contrasting conditions between the growing season (characterized by drought in some years) and the winter/rainy season (characterized by extreme precipitation in some years). Results indicate varying degrees of nitrate leaching across methods, with all approaches detecting leaching events during the growing season and off‐season precipitation. Despite uncertainties inherent in field‐level mass balance estimates, they align reasonably with intensive in‐situ monitoring results using the deep Vadose Monitoring System (VMS). Throughout two growing seasons and corresponding fall‐winter rainy periods, the VMS effectively tracked seasonal nitrogen leaching below the root zone, correlating with observed groundwater nitrate concentrations increases following extreme precipitation events. Nitrate leaching increased during heavy rainfall in the winter following dry summer periods observed across the deep vadose zone using two VMS systems. This underscores the importance of continuous monitoring and assessment in understanding nitrate dynamics and groundwater contamination risks. In conclusion, this study contributes to knowledge and ongoing research by providing insights into effective monitoring strategies for nitrate leaching into groundwater from intensive cropping systems.

Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements

The increasing urgency of addressing climate change has necessitated the adoption of innovative technologies to monitor and mitigate carbon emissions and waste throughout the supply chain. This paper explores the role of digital tools and artificial intelligence (AI) in enabling real-time tracking and analysis of environmental impact at each stage of the supply chain. By integrating IoT devices, big data analytics, and machine learning algorithms, organizations can gain comprehensive insights into their operations, identifying key areas where emissions and waste can be reduced. The use of AI-powered predictive analytics allows companies to model various scenarios, optimizing resource allocation and operational efficiency while minimizing environmental footprints. This research also highlights successful case studies where companies have implemented these technologies, resulting in significant sustainability improvements and cost savings. Furthermore, the paper discusses the challenges associated with data integration, system interoperability, and the need for industry-wide standards to ensure effective monitoring and reporting. The importance of stakeholder collaboration is emphasized, as engaging suppliers, customers, and regulatory bodies is essential for achieving comprehensive sustainability goals. Ultimately, this paper advocates for the strategic implementation of digital tools and AI as pivotal enablers of sustainable supply chain management, providing organizations with the capability to adapt to changing environmental regulations and consumer expectations. By leveraging technology to monitor and reduce carbon emissions and waste, businesses can enhance their competitiveness while contributing to a more sustainable future.

Age-dependent immune profile in healthy individuals: an original study, systematic review and meta-analysis

BackgroundThe circulatory peripheral immune system is the most convenient approach for determining an individual’s immune status. Due to various reasons, while previous studies have addressed the critical impact of age, most individual studies did not analyze immunosenescence in a systemic manner, which complicates the possibility of building a reference range for age-dependent immune profiles for effective immune monitoring. To address this gap, this study analyzed a group of healthy individuals to establish age-specific reference ranges of the healthy circulatory immune profile, and a systematic review and meta-analysis were conducted to validate the findings and create generalizable immune cell reference ranges.ResultsOur study recruited a total of 363 healthy Taiwanese adults (median age 42 years [IQR 30, 62], age range 21 to 87 years, 43.3% male), including 158 under 40 years old, 127 between 40–64 years old, and 78 over 64 years old. Significant age-related alterations were observed in both adaptive and innate immune cell subsets. CD8 + T cells decreased and CD4/CD8 ratio increased, with notable increases in NK cells. CD4 + T cells were less impacted by aging, while CD8 + T cells significantly lost CD28 and increased CD31 expression with age. A clear reverse trend in naïve and memory subsets of CD4 + and CD8 + T cells was observed. Detailed reference ranges for immune cell subsets in healthy Taiwanese adults were established. A systematic review included 7,425 adults and a meta-analysis of 12 eligible studies confirmed our findings in Taiwan, enhancing generalizability.ConclusionsCombined with previous studies and original data through a systematic review and meta-analysis, we highlighted and quantified significant immune profile differences between older and younger individuals. The sex and age-specific reference ranges for peripheral immune cell subsets can serve as a basis for effective immune monitoring of various aging-related illnesses.

Unveiling environmental transmission risks: comparative analysis of azole resistance in Aspergillus fumigatus clinical and environmental isolates from Yunnan, China.

Azole resistance in Aspergillus fumigatus poses a significant clinical challenge globally. Our previous epidemiological analysis revealed a remarkably high frequency (~80%) of azole-resistant A. fumigatus in Yunnan's greenhouse environments, prompting increased local and regional research for targeted control strategies. In this study, we analyzed 94 clinical A. fumigatus isolates from Yunnan, comparing their susceptibility profiles and genotypic characteristics with environmental strains previously isolated. While the overall frequency of azole resistance in clinical isolates was lower than that in environmental samples, a significant prevalence of cross-resistance, with varying resistance patterns based on minimum inhibitory concentration (MIC) levels was observed, which exceeded rates in other regions of China. Specific mutation combinations in the cyp51A gene were linked to elevated MIC values in clinical and/or environmental samples, while some resistant strains with wild-type cyp51A remain unexplained, indicating a need for further investigation into their resistance mechanisms. The differences in unique genetic elements and the distinct genetic differentiation observed between clinical and environmental isolates can be attributed to Yunnan's unique geomorphology and potential genotype importation from other provinces and abroad. Extensive allele exchanges and sharing contributed to the selection of azole-resistant clinical isolates, suggesting a common environmental origin, and the transmission routes of local drug-resistant strains cannot be excluded. These findings emphasize the imperative for regional and targeted surveillance to monitor resistance trends and guide effective antifungal therapy, and management strategies to mitigate invasive aspergillosis risk in this region.IMPORTANCEAzole resistance in Aspergillus fumigatus is a major global health concern, with particularly high rates (~80%) observed in Yunnan's greenhouse environments. This study compares azole resistance in 94 clinical isolates from Yunnan with environmental strains, revealing lower clinical resistance but significant cross-resistance and distinct resistance patterns. Specific mutations in the cyp51A gene were associated with elevated minimum inhibitory concentration values, though some resistant strains had wild-type cyp51A, highlighting the need for further research. The unique genetic profiles and potential external genotype influences in Yunnan emphasize the need for targeted regional surveillance. Effective monitoring and control strategies are essential to manage and mitigate the risk of invasive aspergillosis.

Adherence to infliximab treatment in patients with immune-mediated inflammatory diseases from a referral center in Brazil: a cohort study

BackgroundInfliximab therapy is effective in controlling symptoms and attaining clinical remission of immune-mediated inflammatory diseases. However, treatment adherence is essential to achieve the therapeutic objective. This study aimed to determine the rate of adherence to infliximab treatment in patients treated at a referral center at a university hospital.MethodThis ambispective cohort study included patients treated at the Professor Edgard Santos University Hospital (HUPES) referral center of our university hospital between March 2022 and February 2023. Sociodemographic, clinical, and pharmacotherapeutic data were collected from 101 patients through interviews and medical record reviews using a structured form. The adherence rate was defined as the proportion of days covered in a year. Patients who achieved an adherence rate > 80% were considered adherent.ResultsThe treatment adherence rate was 91.04%. Individuals with inflammatory bowel diseases had a 39.1% higher risk of non-adherence to treatment compared with other patients in our sample (p < 0,05). Most patients achieved remission or control of the underlying disease activity and had good functional capacities. The main reason for absence on the scheduled date was difficulty traveling to the referral center.ConclusionsDespite the reported difficulties, treatment adherence was observed to be high. As the study was conducted in a reference unit with multidisciplinary care and continuous monitoring for treatment effectiveness, safety, and adherence, welcoming and good communication between professionals and patients may have contributed to the high adherence rate.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

Accounting for health equity in policy evaluation: a content analysis of primary research

Abstract Background Public Health researchers increasingly make use of sophisticated study designs and methods to evaluate the effects of policies on health and social outcomes. Natural experiment evaluation (NEE) conceptualises policies as ‘natural’ experiments that occur outside of the control of researchers. Often, NEE employs quasi-experimental methods (QEM) which enable researchers to rigorously evaluate the effect of population-level interventions. As for other health-related research, it is unclear to what extent such public health evaluations can and do consider the (health) equity implications of policies. We sought to investigate how health equity is addressed in NEEs and other policy evaluations that employ QEM. Methods We identified a random sample of primary research as part of a pre-registered scoping review (https://osf.io/3h6cb/). Studies that employed methods labelled as interrupted time series, difference-in-differences, controlled before and after, regression discontinuity, and synthetic control were included. Through content analysis, we examined how health equity was considered in data analysis and discussion sections. Results We analysed 59 studies, of which 25 were stratified for one or more socially-stratifying factors. Results were mainly stratified by sex (n = 15, 25.4%), age (n = 13, 22%), and diverse measures of socioeconomic status (n = 14, 23.7%), i.e. income, employment, education, or other factors. Of these, 21 studies included a discussion of health equity-related results, ranging from a few sentences to a holistic discussion including recommendations for decision-makers. Most commonly, researchers recommended monitoring of policy effects in different sub-populations, and careful policy design that conceptualises complex policy effects, including unintended and unequitable effects. Conclusions Policy evaluations have great potential for informing public health decision-making but health equity aspects were under-examined in these studies. Key messages • Health equity is assessed by evaluating policy effects in relevant sub-populations. Only 25 of 59 studies included results that were stratified for one or more socially stratifying factors. • Studies that were focused on disadvantaged populations commonly included recommendations for policymakers, calling for better policy design and improved monitoring of policy effects.

Re-conceptualising the Theory of Justice and the Needs of Victims of Sexual Violence in Nigeria: an Appraisal of a Victim Centered Approach

Abstract Background In the 21st century, the recognition of the rights and needs of victims of sexual violence has emerged as a fundamental discourse within international, regional, and local policy agendas. This has been influenced by the lasting psychological, emotional, and physical effects of sexual violence on the victims. These commentaries and agitation have spurred the need for alternative ways in which the Nigerian criminal justice system can be strengthened by incorporating criminal justice mechanisms and interventions that would enhance the needs and interests of victims in accessing justice despite its adversarial system of justice. Methods This study aims to appraise the different mechanisms of justice, needs, and interests of victims of sexual violence through a theoretical lens and drawing lessons from South Africa and Australia. To achieve this work, a doctrinal research methodology that utilized existing literature and secondary sources such as textbooks and online websites was adopted. Results The study found that there is a need for accountability for the protection of victims, implementation of legislation, and scaling up of resources for effective support services. In addition, the absence of resources, effective monitoring and compliance institutions, monitoring reports, specialized courts, and special regulations hampers the implementation of a victim-centered approach. Among others, the study recommends an all-inclusive criminal justice mechanism that would further incorporate a victim-centered approach towards advancing the rights and quality of justice for victims of Sexual Violence and the need for a victim impact statement to our criminal justice system for cases of sexual violence. Conclusion The study concludes that there is a need to make provisions for more effective alternative responses to incorporate the justice interests and needs of victims of sexual violence.