Empowering distribution system operators: A review of distributed energy resource forecasting techniques.

Effective management of Distributed Energy Resources (DERs) and optimization of grid operations are crucial responsibilities of Distribution System Operators (DSOs). Hence, this comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. These goals underscore the significance of clean and accessible energy, advancements in infrastructure, sustainable urban development, climate change mitigation, and collaborative partnerships. The review's core focuses on the DER forecasting techniques employed by DSOs. It explores various aspects, including data collection methods, load forecasting models, DER generation forecasting, aggregation and integration techniques, and the role of advanced technologies like machine learning and artificial intelligence. The review highlights the critical role of accurate DER forecasts in optimizing grid operations, managing energy flows, and facilitating the integration of renewable energy sources. Furthermore, the review examines the implications of DER forecasting for DSOs in achieving the SDGs. It discusses how DER forecasting facilitates the transition to affordable and clean energy, enhances industry innovation and infrastructure, builds sustainable cities and communities, drives climate action efforts, and fosters stakeholder partnerships. However, the review also identifies challenges and limitations in DER forecasting, including data availability, forecasting accuracy, uncertainty management, and regulatory barriers. It emphasizes further research and development in improved forecasting algorithms, advanced data analytics, and enhanced communication and coordination mechanisms. Finally, this comprehensive critical review highlights the importance of DER forecasting for DSOs in achieving the SDGs. Accurate forecasting can promote sustainable and clean energy practices, drive innovation, build resilient communities, mitigate climate change, and foster collaborative partnerships. The review emphasizes the necessity of advancing DER forecasting techniques and addressing associated challenges to fully realize the potential of DERs in contributing to a sustainable and inclusive future. This comprehensive critical review aims to analyze the current state of DER forecasting practices for DSOs and their implications for achieving the SDG goals. As Distributed Energy Resources (DERs) play an increasingly significant role in the transition to sustainable energy systems, accurate forecasting techniques are essential for optimizing grid operations and facilitating the integration of renewable energy sources. By effectively managing DERs, Distribution System Operators (DSOs) contribute to the advancement of several SDGs, including affordable and clean energy (SDG 7), sustainable infrastructure (SDG 9), climate action (SDG 13), and partnerships for the goals (SDG 17). This review explores the intersection of DER forecasting with the SDGs, highlighting how forecasting initiatives can support national and global efforts toward sustainable development by providing insights into energy demand, grid stability, and renewable energy integration. The goals and targets are derived from an analysis of current trends and the identification of potential development scenarios by 2030. Both optimistic and pessimistic projections are utilized for communicating with the general public and national governments concerning DSM network planning. Utilizing data from various nations enables the identification of effective strategies and the prediction of similar trends in other areas. Simultaneously, the magnitude of activities related to Sustainable Development Goals (SDGs) enables the improvement and efficient organization of data gathering on a global basis. This, in turn, provides a foundation for future forecasting endeavours.

A high-efficiency poly-input boost DC-DC converter for energy storage and electric vehicle applications.

This research paper introduces an avant-garde poly-input DC-DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources-solar energy and fuel cells-with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85-90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91-95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC's adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC-DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model's training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Synthesis of Isatin-derived Heterocycles with Promising Anticancer Activities.

Isatin or 1H-indole-2,3-dione skeleton has been playing a significant role in drug de-sign and development. Isatin itself and many of its derivatives are widely distributed in naturally occurring bioactive compounds. Various synthetic isatin derivatives were found to possess a broad range of significant pharmacological efficacies especially anti-cancer activity against a wide variety of cancer cell lines. Interestingly, on a few occasions, some isatin-derived scaffolds were reported as more potent than the tested reputed drug molecules. As a result, isatin-derived compounds have been gaining significant attention in cancer-based drug developments. In this re-view, we have summarized literature reported during the last two decades related to the synthesis of structurally diverse isatin-derived scaffolds with promising anti-cancer activities.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Enhancing Coverage of Second Booster Dose of DPT Vaccine Coverage With Parental Education: A Cluster Randomized Approach.

OBJECTIVES: To assess the effect of a single clinic-based educational intervention session on parents of children aged 4.5 to 5.5 years on improving the coverage of a second booster dose of the DPT vaccine. The secondary objective was to assess the coverage of second booster dose of the DPT vaccine among children aged > 6 years and to learn about the reasons behind such dropouts, if any. METHODS: The study was conducted in two phases. In the first phase, a cross-sectional study was conducted among children aged > 6 years who were attending pediatric OPD or IPD to determine coverage of the second booster dose of DPT vaccine and possible reasons for dropout. This was followed by a clustered randomized trial evaluating the effect of an educational intervention (Clinic-based, single session) among parents of children aged 4.5 to 5.5 years to inform about counselling and audiovisual information for improving second DPT booster coverage. RESULTS: A total of 384 children were enrolled in the first phase, of which 233 (60.68%) were vaccinated. Subgroup analysis showed significant differences in the vaccine coverage between children from tribaldominant and non-tribal-dominant districts (45.10% vs 63.06%, P = 0.01). Educational intervention resulted in higher vaccination coverage (77.24%) compared to 71.43% in the control arm (P = 0.300). CONCLUSION: The current study showed low coverage for second booster of DPT vaccine. With educational intervention, the target immunization coverage could be attained early which had implications for reducing childhood morbidity due to vaccine-preventable diseases.

Fronts divide diazotroph communities in the Southern Indian Ocean.

Dinitrogen (N2) fixation represents a key source of reactive nitrogen in marine ecosystems. While the process has been rather well-explored in low latitudes of the Atlantic and Pacific Oceans, other higher latitude regions and particularly the Indian Ocean have been chronically overlooked. Here, we characterize N2 fixation and diazotroph community composition across nutrient and trace metals gradients spanning the multifrontal system separating the oligotrophic waters of the Indian Ocean subtropical gyre from the high nutrient low chlorophyll waters of the Southern Ocean. We found a sharp contrasting distribution of diazotroph groups across the frontal system. Notably, cyanobacterial diazotrophs dominated north of fronts, driving high N2 fixation rates (up to 13.96 nmol N l-1 d-1) with notable peaks near the South African coast. South of the fronts non-cyanobacterial diazotrophs prevailed without significant N2 fixation activity being detected. Our results provide new crucial insights into high latitude diazotrophy in the Indian Ocean, which should contribute to improved climate model parameterization and enhanced constraints on global net primary productivity projections.

Exploring the potential of Bacillus for crop productivity and sustainable solution for combating rice false smut disease.

Rice false smut, which is caused by the soil-borne fungal pathogen Ustilaginoidea virens (U. virens), is one of the most threatening diseases in most of the rice-growing countries including India that causes 0.5-75% yield loss, low seed germination, and a reduction in seed quality. The assessment of yield loss helps to understand the relevance of disease severity and facilitates the implementation of appropriate management strategies. This study aimed to mitigate biotic stress in rice by employing a rhizobacterial-based bioformulation, which possesses diverse capabilities as both a plant growth promoter and a biocontrol agent against U. virens. Rhizobacteria were isolated from the soil of the rice rhizospheres from the healthy plant of the false smut affected zone. Furthermore, they were identified as Bacillus strains: B. subtilis (BR_4), B. licheniformis (BU_7), B. licheniformis (BU_8), and B. vallismortis (KU_7) via sequencing. Isolates were screened for their biocontrol potential against U. virens under in vitro conditions. The antagonistic study revealed that B. vallismortis (KU_7) inhibited U. virens the most (44.6%), followed by B. subtilis BR_4 (41.4%), B. licheniformis BU_7 (39.8%), and B. licheniformis BU_8 (43.5%). Various biochemical and plant growth promoting attributes, such as phosphate and Zn solubilization, IAA, ammonium, siderophore, and chitinase production, were also investigated for all the selected isolates. Furthermore, the potential of the isolates was tested in both in vitro and field conditions by employing talc-based bioformulation through bio-priming and root treatment. The application of bioformulation revealed a 20% decrease in disease incidence in plants treated with B. vallismortis (KU_7), a 60.5% increase in the biological yield, and a 45% increase in the grain yield. This eco-friendly approach not only controlled the disease but also improved the grain quality and reduced the chaffiness.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon's grid-connected systems.

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

Prevalence of neural tube defect and its identification during antenatal period: a cross-sectional study in eastern Indian state.

OBJECTIVE: To estimate the prevalence of neural tube defects among all birth outcomes in Odisha during 2016-2022. Additionally, to estimate the identification rate of neural tube defects during Pradhan Mantri Surakshit Matritva Abhiyan sessions. DESIGN: A population-based cross-sectional study with a household survey for neural tube defects using pictorial card as well as a hospital-based study for antenatal ultrasonography data. SETTING: The sample population was selected through multistage random sampling. In the first stage, one district from each zone was selected randomly. In the second stage, using simple random sampling, one community health centre and one urban primary health centre were selected from each district. In the third stage, the population from a block and ward were picked from the selected rural and urban settings, respectively. PARTICIPANTS: All married women in the reproductive age group (18-49 years) residing in these cluster villages in the selected districts were enrolled. RESULTS: The study surveyed 49 215 women and recorded 50 196 birth outcomes, including 49 174 live births, 890 stillbirths and 132 medical terminations of pregnancy. A total of 30 neural tube defect cases were detected. The overall prevalence rate of neural tube defect was 0.59 per 1000 birth outcomes. Spina bifida was the most prevalent neural tube defect with the prevalence of, followed by anencephaly and encephalocele. Despite 26 860 mothers receiving antenatal ultrasonography Pradhan Mantri Surakshit Matritva Abhiyan session, data on neural tube defects and other birth defects detected through these scans is unavailable. CONCLUSION: This study found a low prevalence of neural tube defect in Odisha, which is far lower compared with the older studies from India. There is an urgent need to strengthen the quality of antenatal care services provided under Pradhan Mantri Surakshit Matritva Abhiyan through better training regarding anomaly scans and better data keeping at public healthcare facilities. TRIAL REGISTRATION NUMBER: CTRI/2021/06/034487.

Deeper Insights into Mixed Crowding through Enzyme Activity, Dynamics, and Crowder Diffusion.

Given the fact that the cellular interior is crowded by many different kinds of macromolecules, it is important that in vitro studies be carried out in the presence of mixed crowder systems. In this regard, we have used binary crowders formed by the combination of some of the commonly used crowding agents, namely, Ficoll 70, Dextran 70, Dextran 40, and PEG 8000 (PEG 8), to study how these affect enzyme activity, dynamics, and crowder diffusion. The enzyme chosen is AK3L1, an isoform of adenylate kinase. To investigate its dynamics, we have carried out three single point mutations (A74C, A132C, and A209C) with the cysteine residues being labeled with a coumarin-based solvatochromic probe [CPM: (7-diethylamino-3-(4-maleimido-phenyl)-4-methylcoumarin)]. Both enzyme activity and dynamics decreased in the binary mixtures as compared with the sum of the individual crowders, suggesting a reduction in excluded volume (in the mixture). To gain deeper insights into the binary mixtures, fluorescence correlation spectroscopy studies were carried out using fluorescein isothiocyanate-labeled Dextran 70 and tetramethylrhodamine-labeled AK3L1 as the diffusion probes. Diffusion in binary mixtures was observed to be much more constrained (relative to the sum of the individual crowders) for the labeled enzyme as compared to the labeled crowder showing different environments being faced by the two species. This was further confirmed during imaging of the phase-separated droplets formed in the binary mixtures having PEG as one of the crowding agents. The interior of these droplets was found to be rich in crowders and densely packed, as shown by confocal and digital holographic microscopy images, with the enzymes predominantly residing outside these droplets, that is, in the relatively less crowded regions. Taken together, our data provide important insights into various aspects of the simplest form of mixed crowding, that is, composed of just two components, and also hint at the enhanced complexity that the cellular interior presents toward having a detailed and comprehensive understanding of the same.

Knowledge and practice of breastfeeding among lactating mothers in AIIMS, Bhubaneswar - A hospital-based cross-sectional study.

BACKGROUND: 44% of infants under 6 months of age worldwide are only adequately breastfed. It is also found that only 41.6% of women breastfeed their child within one hour of birth and less than 50% of women continue breastfeeding up to 2 years. This study was conducted to assess the prevalence of early initiation of breastfeeding practice, to explore the knowledge and practices about breastfeeding among lactating mothers, and to find the effect of breastfeeding on the nutritional status of the child. MATERIALS AND METHODS: A hospital-based study was conducted among 160 lactating mothers of infants below six months of age presenting to the immunization clinic at AIIMS Bhubaneswar. A semi-structured questionnaire based on the CDC breastfeeding questionnaire was administered to assess the knowledge and practice. The nutritional status of the infant was assessed by plotting the weight for age and length for age of the infant in the WHO growth charts. RESULTS: 102 (75%) of mothers had initiated breastfeeding within the first hour of delivery. Adequate knowledge and practice of breastfeeding was present only in 4 (2.5%) and 3 (1.9%), respectively. It was found that 13.75% of women were using formula feed. The prevalence of underweight and less than normal length was 40% (n = 64) and 34.37% (n = 55) among infants, respectively. CONCLUSION: This study emphasizes the importance of awareness among lactating mothers about the hindmilk, appropriate practice, and to find its effect on the nourishment of the children.

State-of-the-art review on energy sharing and trading of resilient multi microgrids.

Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange with the grid. Combining numerous MGs to form a multi-microgrid (MMG) is a viable approach to enhance smart distribution networks' operational and financial performance. However, the correlation and coordination of intermittent power generation within each MG network pose many techno-economic challenges for energy sharing and trading. This review offers a comprehensive analysis of these challenges within the framework of MMG operations. It examines state-of-the-art methodologies for optimizing multi-energy dispatch and scrutinizes contemporary strategies within energy markets that contribute to the resilience of power systems. The discourse extends to the burgeoning role of blockchain technology in revolutionizing decentralized market frameworks and the intricacies of MMG coordination for reliable and cost-effective energy distribution. Overall, this study provides ample inspiration for theoretical and practical research to the new entrants and experts alike to develop new concepts for energy markets, scheduling and novel operating models for future resilient multi-energy networked systems/MMGs.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience.

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Gastric Neuroendocrine Tumor With Cystic Hepatic Metastases Mimicking Hepatic Echinococcosis: A Case Report.

Metastatic gastrointestinal neuroendocrine tumors classically appear as contrast-enhancing lesions on computed tomography. However, in a small percentage of patients, these lesions can be cystic in nature, leading to false diagnoses of benign or infectious lesions such as echinococcosis. Hence, every cystic lesion of the liver must be carefully investigated before making the treatment plan. We report a patient with hematemesis caused by a large gastric ulcer with multiple cystic lesions in the left lobe of the liver abutting the stomach. The liver lesions were misdiagnosed as hepatic echinococcosis, and the patient was started on medical therapy. However, when medical therapy failed, the patient underwent surgical excision and the histopathology showed cystic metastases of a gastric neuroendocrine tumor.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

Utilization of Untied Fund and Factors Affecting the Utilization at Sub-Centers of Rural Odisha: A Mixed-Method Study.

Introduction: The untied funds at sub-centers provided flexibility for local action at block and down below levels. Effective utilization of untied fund can strengthen the healthcare. So, our study aims at assessing the knowledge of health workers, male/female (HWF/M), and effectiveness of utilization of untied funds at the sub-center (SC) level. Material and Methods: A mixed method (cross-sectional study and qualitative study) was conducted at Tangi Block, Khordha district, Odisha, in the year 2020. Health workers, male and female in sub-centers, and local stakeholders were interviewed using a structured interview schedule. The tool comprises financial records, a semi-structured questionnaire, and an in-depth interview guide. Universal sampling was adopted. For the cross-sectional study, 24 health workers were male/female, and for the qualitative study, all local stakeholders and 24 health workers female/male were interviewed. The study was conducted as a part of an academic program, and ethics approval was approved by AIIMS Bhubaneswar Institute Ethics Committee. Results: Seven out of 24 sub-centers did not spend any money, with a median unspent amount of 4260 INR (Q1-0, Q3-17300). As many as 36.8% of sub-centers had a delay of 9 months to get the untied fund, which significantly affected the utilization of funds. 37% of HWF/M utilized the fund inappropriately. None of the health workers had complete knowledge regarding the appropriate usage of untied fund. Communication gap, multiple engagements, non-cooperation from officials, delay in technical processing, and irregular Garam Sabha meetings were found to be barriers. Conclusion: Our study found out under-utilization of money in untied fund. Communication gap, non-cooperation form village leaders, poor knowledge, overburdened health workers, improper reporting, and lack of felt need were found to be barriers for prompt utilization.

Incidental Appendico-Ileal Fistula in a Patient With Adhesive Intestinal Obstruction: A Case Report.

A fistulous communication between the appendix and any viscus is rare. Such fistula is most often acquired due to recurrent appendicitis, cystic fibrosis, Crohn's disease, tuberculosis, and malignancy. Here in, we report a rare case of an appendico-ileal fistula incidentally detected during laparotomy for adhesive small bowel obstruction. The fistula was divided, the ileal opening was sutured, and appendectomy was performed. Postoperative recovery was uneventful, with no evidence of malignancy, tuberculosis, or inflammatory bowel disease on the histopathological examination of the appendix.

A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape.

This paper proposes an innovative approach for improving the charging efficiency of electric vehicles (EVs) by combining photovoltaic (PV) systems with AC-DC Power Factor Correction (PFC). The proposed approach employs bi-directional power flow management within the PFC system, allowing for enhanced resource utilization and EV battery capacity under a variety of environmental circumstances. A modified Lyapunov-based robust model reference adaptive controller (M-LRMRAC) is developed to provide real-time Maximum Power Point Tracking (MPPT) for the PV array. By quickly recording the MPP, this controller skilfully adjusts to shifting radiation and temperature dynamics. A noteworthy accomplishment is that the M-LRMRAC outperforms traditional Perturb and Observe (P&O) techniques by achieving quick MPP convergence (0.54 s). Additionally, the benefits of this integrated system go beyond effective MPPT. The method achieves operating at unity power factor and reduces total harmonic distortion, which results in improved power quality when charging EV Batteries (EVB). The entire solution provided by this multifaceted architecture improves the quality of electricity delivered to EV batteries while also increasing energy efficiency. This research helps to the evolution of sustainable and dependable EV charging infrastructure by solving difficulties and optimising performance. The combination of PV systems with AC-DC PFC, aided by the M-LRMRAC technology, presents a viable route for attaining efficient, clean, and high-quality EV charging, hence supporting the shift to a greener and more sustainable transportation landscape.

Prevalence of common mental disorders and treatment gap among patients with non-communicable diseases in the rural areas of East India.

BACKGROUND: Mental and physical non-communicable diseases (NCDs) coexist, because they share common environmental and behavioral risk factors. The treatment gap for common mental disorders, such as depression, anxiety, and substance use, is large compared to other NCDs. OBJECTIVE: To determine the prevalence and treatment gap of common mental disorders among patients with non-communicable diseases. METHODOLOGY: The community-based cross-sectional study was conducted in the rural parts of East India, in people aged 30 years and older with NCDs. A simple random sample was chosen to select the villages and participants. Eligible participants administered with screening and diagnostic questionnaire for depression, anxiety, and substance use. Those diagnosed with mental disorders were again assessed for treatment status in the last 12 months. Non-receipt of treatment was considered as treatment gap. RESULTS: A total of 515 participants were included in the analysis. The overall prevalence of common mental disorders among the study population was 46.4% (95% CI 42.0-50.8), and excluding substance use, the prevalence was 11.7% (95% CI 9.0-14.7). The treatment gap for common mental disorders among patients with non-communicable diseases, including and excluding substance use, was 98.3% (95% CI 95.8-99.5) and 93.3% (95% CI 83.8-98.2), respectively. CONCLUSION: The prevalence and treatment gap of common mental disorders among persons with NCDs was high. Public health interventions need to be emphasized for the integration of mental health care into NCD care.