Residential Use Research Articles

Stress Exposure and PTSD in a Cross-Sectional Residential Substance Use Treatment Sample.

Aim 1 of this cross-sectional, observational study with people in residential treatment for substance use disorders (SUDs) was to document stress exposure. Aim 2 was to assess potential sociodemographic and health differences based on probable posttraumatic stress disorder (PTSD) status. Aim 3 was to assess relative contributions of Diagnostic and Statistical Manual (DSM)-congruent versus DSM-incongruent stressors (Criterion A vs non-Criterion A) to mental and physical health. We hypothesized that both types of stressors would significantly contribute to impairment across indicators and that DSM-congruent stressor exposure would be more strongly associated with impairment than DSM-incongruent exposure. We assessed exposure to DSM-congruent traumatic stressors and DSM-incongruent life stressors, PTSD and depressive symptoms, emotion regulation difficulties, substance use recovery capital, and physical/mental health-related quality of life among 136 people in residential SUD treatment who were 64% men, 36% women; 49% white, 41% Black, 11% multiracial/another race; 18% lesbian, gay, or bisexual (LGB+); mean age = 39.82 (standard deviation = 12.24) years. Participants reported experiencing a mean of 9.76 (SD = 6.11) DSM-congruent events. Those with probable PTSD were younger and more likely to be LGB+ than those without probable PTSD (P < .05). Experiencing higher numbers of DSM-congruent events was associated with more severe PTSD and depressive symptoms, emotion regulation difficulties, and lower physical health-related quality of life (P < .05). DSM-incongruent stressor exposure was not independently associated with any indicators. Recovery capital was not associated with either type of stress exposure. Stressful event exposure among people in residential SUD treatment is very high. Those who are younger or LGB+ in residential SUD treatment may be at greater risk of developing PTSD. DSM-congruent stressors are more consistently associated with mental health indicators than are DSM-incongruent stressors. Prioritizing treatment targets and identifying implementable treatment strategies can be challenging with this complex population.

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.

Investigation of the Causes of Soft-Storey and Weak-Storey Formations in Low- and Mid-Rise RC Buildings in Türkiye

This study investigates the causes of soft-storey and weak-storey formations in low- and mid-rise RC (Reinforced Concrete) buildings in Türkiye. In the first phase of the study, 96 model buildings were designated for the examination of soft-storey irregularity when the ground floors are used for commercial purposes and the upper floors for residential use. The ground floor heights that would cause soft-storey irregularity in each of the selected buildings were determined according to the formulas given in the Türkiye Building Earthquake Code (TBEC) and the American Society of Civil Engineers Standard (ASCE). It was found that the ground floor heights obtained according to ASCE are usable in practice, whereas those obtained according to the TBEC, particularly for buildings over three storeys, are excessively high for practical use. This indicates that, even if the buildings in Türkiye are designed with very high ground floor heights, they do not have soft-storey irregularities, according to the TBEC, but soft-storey formation may occur in these buildings due to the high ground floor height as a result of the effects of earthquakes. Instead of the soft-storey irregularity coefficient limit value (nki &gt; 2) found in the TBEC, this study proposes a new limit value to prevent the design of buildings with very high ground floors. In the second phase of the study, for the purpose of examining weak-storey irregularity, 105 model buildings differing in their infill wall layout, number of spans, span length, and number of storeys were selected. The weak-storey irregularity coefficients of each of these models were determined according to the TBEC. The results of the study revealed that buildings with no infill walls in one direction or with infill walls in only one of the exterior axes in one direction have a high risk of having weak storeys.

Wind energy feasibility and wind turbine selection studies for the city Surat, India

Abstract Wind energy represents a clean, abundant and cost-effective power source, fostering job growth and environmental mitigation. Although wind energy harnesses several gigawatts today, its availability hinges on diverse factors, with geographical location standing out. Commercial turbines, with varying capacity ranges, saturate the market. Locating site-specific suitability and matching the appropriate turbine to meet specific requirements are of paramount importance. This study aims to assess the feasibility of wind energy in Surat, Gujarat, India and select an optimal small commercial turbine for residential use. The research involves Rayleigh and Weibull probability distribution functions based on yearlong velocity data. These distributions are fitted with actual data, revealing the most probable velocity (vmf = 3 m/s) and velocity at maximum power (vpmax = 5 m/s). The power availability of the site has been assessed as 42.6 W/m2 using both graphical and analytical methods. Several commercial turbines have been shortlisted based on on-site power criteria and their specifications are evaluated against site power availability. A comparative analysis culminates in identifying the most suitable turbine for the location. The best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential applications.

Individual Trajectories of Specialist Substance Use Disorder and Mental Health Treatment Utilization Among Young Adults With Substance Use Problems: A Cohort Study

Objective The aim of the study was to identify groups of young adults with distinct longitudinal patterns of use of treatment for substance use disorders and mental health (MH) problems and to investigate potential explanatory factors for different patterns of treatment use over time, including sociodemographic factors. Methods The sample consisted of 447 young adults aged 16–29 years who entered long-term residential substance use disorder treatment facilities in Norway from 2011 to 2016. In this study, we obtained data collected by the Norwegian Patient Registry and Statistics Norway. These data were linked with the electronic health record data of the substance use disorder treatment facilities from which the participants were recruited. Growth mixture modeling was performed. The identified groups were further compared using analysis of variance or χ 2 test. Results Four groups of participants for total treatment use, substance use disorder treatment use, and MH treatment use were identified. Most participants from the overall sample were classified as members of a group characterized by a low and stable pattern of treatment use over time. A group with a high and stable pattern of treatment use was identified in total and MH treatment use. The proportion of participants with higher levels of substance use disorder treatment use at the end of the study period than at the beginning was larger (35%) than in the case of MH treatment use (14.2%). Younger age was associated with a decreasing pattern of MH treatment use and with an increasing pattern of substance use disorder treatment use over time. There were larger proportions of female participants in groups with a stable high use of MH treatment and in groups with an initially increasing trend of substance use disorder treatment use. Findings revealed that most participants across the identified groups were recipients of welfare benefits, had low educational attainment, and were not working. Conclusions Results demonstrated significant variation in trajectories of treatment use among young adults with substance use disorder. Differences in treatment use could indicate differences in symptom severity and complexity. In this study, treatment use was associated with socioeconomic factors, sex, and age. Integrative approaches, including interagency and interdisciplinary collaboration, will often be necessary to sufficiently address the multidimensionality of substance use disorder.

Spatial Transformation Analysis in Menganti Subdistrict: A Study of Peri-Urban Area Typologies in the Face of Urban Expansion

The swift urbanization of Indonesian cities, notably Surabaya, has spurred population growth and a heightened demand for land, leading to the expansion of urban areas into peri-urban regions like Menganti District. This district, bordering Surabaya, experiences significant land-use change shaped by the city’s expansion, making it a compelling area for analysis due to its complex dynamics. This study aims to analyze Menganti’s peri-urban spatial transformation and its typology based on the land-use triangle continuum theory. Using indicators like land-use proportions, building density, and educational and healthcare facility distribution, the analysis delineates four zones: Urban fringe, Urban-Rural fringe, Rural-Urban fringe, and Rural-fringe zones. The analysis further involves comparing land-use maps from 2017 and 2022, revealing that Urban fringe and Rural fringe zones primarily witness residential land use growth, while Urban-Rural fringe and Rural-Urban fringe display a mix of residential, industrial, commercial, and warehouse land use growth. Analyzing factors influencing spatial transformation in Menganti’s peri-urban area through Delphi and AHP techniques underscores elements like trend of land-use change, road networks, trade facilities, distance from the district center and Surabaya city, and land prices as pivotal influences. Understanding these dynamics aids in comprehending the evolving landscape and guiding sustainable development strategies in peri-urban regions experiencing urban expansion.

Quality assessment of biomass pellets available on the market; example from Poland

This study evaluates the quality of 30 biomass pellets sold for residential use in Poland. It provides data on their physical, chemical, and petrographic properties and compares them to existing standards and the information provided by the fuel producers. The results reveal considerable variations in the quality of the pellets and show that some of the purchased samples are not within the DINplus and/or ENplus certification thresholds. Among all 30 purchased samples, only one passes the quality thresholds set by the PL-US BIO, a newly established quality certification in Poland that combines quality assessment following DINplus with optical microscopy analysis. The primary issues causing a decrease in pellet quality include elevated ash and fines content, compromised mechanical durability, too low ash melting temperature, and additions of undesired additions like bark, inorganic matter, and petroleum products. Our research highlights the need for improved fuel quality control measures, and transparent and accurate product labeling, as well as the need for a comprehensive and publicly available national database of solid biomass fuel producers and fuels sold. These are essential steps toward increasing customers’ awareness and trust, encouraging them to embrace biomass fuels as reliable and sustainable sources of energy.

Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root mean square error (RMSE). In response to increasing interest in explainable artificial intelligence (XAI), this paper proposes a framework for predicting energy consumption in smart homes. This user-friendly approach helps users understand their energy consumption patterns by employing shapley additive explanations (SHAP) techniques to provide clear explanations. The research uses gradient boosting and long short-term memory neural networks to forecast energy usage. In the context of sustainable urban development, it emphasizes the importance of conserving energy in homes. The paper explores AI and ML methods for predicting residential energy use, aiming to make socially meaningful impacts. It highlights the need to understand the factors affecting predictions to improve the accountability, reliability, and justification of decisions in energy optimization. Explainable AI techniques are used to gain insights into the prediction models and identify factors influencing household energy consumption. This research aids in decision-making processes related to electricity forecasting, advancing discussions on intelligent decision-making in power management, especially in smart grids and sustainable urban development.

Monitoring Urban Heat Island Spatial Variability over Urban Structure Types – A Case Study from a Fast-growing City in the Vietnamese Mekong Delta

This study aims to assess the variation in the temperature patterns for different urban structure types (USTs) in Can Tho City, Vietnam. The study involves extracting thermal infrared data from Landsat imagery to determine the Land Surface Temperature (LST). Surface Urban Heat Island (SUHI) intensity is then calculated by analyzing the temperature differences between urban and non-urban areas. An overlay method was applied to analyze the variations in SUHI intensity within four distinct USTs categories: residential (R), public and special use (P), industrial (I), and green and open spaces (G), as defined based on 53 USTs derived in original data. The results highlight that the highest SUHI intensity was primarily concentrated in the industrial (I) areas, covering over 50% of the region, while residential (R) and public and special use (P) areas exhibited moderate SUHI intensity. Although the green and open space (G) category had a substantial area of open space, it still experienced a low-intensity SUHI, mainly concentrated in heavily sealed areas. This study provides valuable insights into the presence of SUHI in contemporary, rapidly developing cities. It underscores the significance of understanding and addressing SUHI issues, particularly useful for the real-world planning of urban environments.

Influences of sleep, cortisol reactivity, and risk/reward-based decision-making on suicide

Background and objectivesPoor sleep quality is a known contributor to suicidal thoughts and behaviors. This study examines whether sleep quality modulates the effect of an individual's stress response and risk/reward-based decision making on suicide risk. MethodsParticipants were 160 adults at a residential substance use treatment facility with lifetime exposure to trauma who completed a clinician-administered measure of suicide risk, the Iowa Gambling Task (IGT), and a self-report measure of sleep. Cortisol reactivity (i.e., changes in cortisol before and after a personalized trauma script) was used to measure stress response. We used quantile regression to examine the effects of sleep, cortisol, and risk/reward decision-making on suicide risk. ResultsWe found poor sleep quality to be increasingly salient in individuals at greater risk for suicide than those at lower risk for suicide. Furthermore, individuals with moderate to moderate-high levels of suicide risk seem to have greater cortisol reactivity. In the low-moderate quantile, we found suicide risk to be associated with both high stress reactivity and low-risk, high-reward decision-making, as well as low stress reactivity and high-risk/low-reward decision-making. LimitationsThese findings should be interpreted considering several methodological constraints, such as the use of a pre-determined sample and instruments not tailored for our hypotheses, the MINI ‘Suicide’ Module's limited differentiation between suicidal ideation and behavior, and variably timed cortisol sampling. ConclusionsDespite these limitations, the findings from this study support the use of evidence-based interventions focused on improving sleep quality and managing emotional reactivity to decrease suicide risk.

Spatiotemporal Evolution and Transformation Regulation Strategies of Rural Residential Land on the Grand Canal (China)

Spatiotemporal Evolution and Transformation Regulation Strategies of Rural Residential Land on the Grand Canal (China)

Estimating the direct rebound effect for residential electricity use in seventeen European countries: Short and long-run perspectives

The energy saved by improving the building efficiency of European households may not be as much as expected due to a rebound effect. Based on Eurostat and World Bank annual panel data covering the period 1996–2020, this article develops dynamic panel approaches, a generalized method of moments, and first-difference specifications, to investigate the direct rebound effect of residential electricity consumption in selected European countries. Instrumental variable techniques are used to tackle the reverse causality of the electricity price in a demand model. Estimates of the direct rebound effect in residential electricity use are 18% in the short-run and 43% in the long-run. Our findings reject the hypothesis of a backfire effect in residential electricity demand. They have important implications for policy makers, suggesting the need for smart policies that consider energy consumption behaviors and decision-making processes among various households.

DESIGN AND CONSTRUCTION OF A PORTABLE SOLAR WATER HEATER

Solar thermal water heaters are an old technology used a century ago in California. They are now used extensively, in updated form, in many countries. According to government and industry estimates, well-functioning solar water heaters can theoretically displace 50 to 80 percent of the output of a natural gas-fueled household water heater, depending how hot water usage aligns with production and storage capacities. In so doing, they offer tremendous potential for reducing greenhouse gas emissions, fuel consumption, and energy bills. Such performance holds promise for California given its climate change and energy efficiency policy goals, since 40 percent of the natural gas used in California households is used to produce hot water. However, absent programs, only a specialty market for solar water heaters has developed. To encourage wider deployment, the California Solar Initiative—Thermal program offers financial incentives for systems qualifying under a carefully crafted set of specifications. The program has had some limited success since its inception in 2010. Within that context, this research assessed the performance and potential future use of natural gas-displacing solar water heaters in single-family homes in California, attending to a wide range of sociotechnical considerations. This project documented high diversity in user satisfaction and perceived system performance, and a qualified decrease in project costs to below $5,000 per installation. Solar water heating is a technology in progress, not universally suitable but instead appealing to varied niches shaped by household sensibilities, abilities, and hot water use levels. Thus, recent evolution provides a counterpoint to the pessimism, even as serious difficulties remain. The suitability of solar water heating for California households is not purely a matter of cost effectiveness within a typical energy efficiency framework, but also of evolving performance, perceptions, and values in light of ongoing and aspirational energy and social transitions ahead. . Key Words: Solar thermal water heating, renewable energy, domestic hot water, residential natural gas use, sociotechnical energy analysis, energy futures

Using a long‐range autonomous underwater vehicle to find and sample harmful algal blooms in Lake Erie

AbstractCyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

Examining trauma, anxiety, and depression as predictors of dropout from residential treatment for substance use disorders.

Substance use disorders (SUDs) are highly prevalent and have deleterious effects on one's health and well-being. Inpatient treatment for SUDs reduces patient relapse, which subsequently ameliorates these negative effects on the individual and society. Additionally, those who complete treatment are less likely to relapse compared to those who do not complete treatment. Thus, maintaining patient engagement in treatment and reducing the rates of those leaving against medical advice (AMA) is particularly important. Examining the factors and comorbidities that may contribute to treatment dropout has the potential to identify at-risk patients in need of additional individualized intervention. The current study aimed to examine comorbid anxiety, depression, and posttraumatic stress disorder (PTSD) symptoms as predictors of dropout AMA in a residential substance use treatment population. Results showed that patients with social anxiety were more likely to leave treatment AMA, while those with PTSD were more likely to complete treatment. Findings suggest that PTSD-specific treatment, as offered in this facility, may help with patient retention, while group focused therapy may be distressing to those with social anxiety. Clinical implications of this research may include incorporating evidence-based practice for social anxiety early during inpatient treatment to reduce anxiety such that patients may better engage with SUDs treatment.

Optimization of the Water–Energy–Carbon Nexus in the Residential Water Uses of Shanghai, China

Residential water use in urban areas directly affects household energy consumption and associated carbon emissions. Reducing residential water use through water saving can lead to substantial energy saving and lower carbon emissions. Household choices in water appliances and fixtures and water-use behaviors shape water consumption patterns. This paper presents a study in Shanghai, China, where the water consumption structure of households was analyzed through a comprehensive survey. In addition, a season-differentiated water–energy–carbon nexus optimization model with Monte Carlo simulation was built to minimize the costs associated with water and energy usage while maximizing carbon emission reduction. The survey data revealed Shanghai’s water demand structure, thereby highlighting the water-use influence in the city on urban household energy consumption and carbon emissions, which are affected by seasonal temperature variations. The results provide useful insights into the intricate relationships among water use, energy consumption, and carbon emissions at the scale of urban households.

Exploring trade-offs between residential and industrial functions in rural areas and their ecological impacts across transitioning agricultural systems: Evidence from the metropolitan suburbs of China

The rapid transition of agricultural systems substantially affects residential and industrial land use systems in rural areas, often generating spatiotemporal trade-offs between residential and industrial functions and producing considerable ecological impacts, which has thus far not been well understood. We conduct an indicator-based assessment of transitioning agriculture systems, and then links the transitioning agricultural systems to trade-offs between residential and industrial functions from 2005 to 2020 by using a case study—the metropolitan suburbs of Beijing, China. Also, the associated ecological impacts of the trade-offs are characterized based on the calculation of the ecological quality index (EQI) and ecological contribution rate. The results show that trade-offs between residential and industrial functions in the metropolitan suburbs have gradually adapted to the different agricultural systems in transition, which can be characterized by increasing industrial function as well as declining residential function, together with the diversification of land use into a mixed pattern. Additionally, along with the transitioning process comes a U shape of the ecological quality curve, which indicates that relentless industrial sprawl into regions where the agricultural system has a low capacity for technology, as well as decay in rural areas attributed to a rural exodus and industrial decline in semi-subsistence agricultural areas, even cause ecological degradation. In general, trade-offs between residential and industrial functions (especially for the non-agricultural production function) in rural areas could partially and temporally generate unfavorable ecological impacts, but it seems to be a favorable phenomenon to promote ecological quality in the long term. Therefore, to achieve rural sustainable planning, it is necessary for land use management to observe the trade-offs between residential and industrial functions while avoiding negative impacts, such as low-density land use patterns, disordered land use functions, and eco-environmental deterioration. Such effective strategies can contribute to the feasible implementation of policies aiming to achieve the compatible development of liveable residences, highly efficient industrial production, and eco-friendly operations in rural areas.

Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods

Along with the rise in population and the acceleration of economic expansion, there has been a concomitant rise in the urgent requirement for additional property that can serve as a venue for a wide variety of community activities. It is not uncommon for large cities that are the epicenter of urbanization, such as the city of Surabaya, to experience a sharp uptick in the demand for land. One of the regions that has excellent accessibility is the Sidoarjo Regency, which is comparable to the City of Surabaya in this regard. The goal of this research is to use Web-GIS to conduct an analysis of spatial data in order to identify the land functions that are most suitable for use in residential areas. The Analytic Hierarchy Process (AHP) and the Weighted Product Model (WPM) are two of the methodologies that are included in the spatial data modeling method that uses multi-criteria decision making (MCDM). The characteristics parameters that are employed are derived from data such as the distance to the city center, the distance to the market, the distance to the hospital, the distance to public transit, the slope, the kind of soil, and the amount of rainfall. The outcomes of spatial data modeling categorize the suitability of new residential land into categories of land that is not suitable for residential use and land that is acceptable for residential use. A K value of 0.27 is the outcome of the comparison test that was run between the two MCDM approaches using Cohen's Kappa coefficients.

Lead-based paint detection using perovskite fluorescence and X-ray fluorescence

BackgroundIngestion of flakes of Pb-based paint by infants remains a global health hazard with life-long consequences. Pb-based paint was banned for residential use in the US and Western Europe decades ago but is still sold in many countries. This study evaluates the performance of a new kit for detecting exposed Pb-based paint relying on the formation of Pb-halide perovskite that fluoresces bright green under a UV flashlight after spraying a non-toxic reagent. ResultsTests with the Lumetallix kit were conducted in parallel with X-fluorescence and inductively-coupled plasma atomic emission analysis upon acid digestion using paint currently sold in Côte d'Ivoire and samples of older US paint. Comparison of the three different methods indicates a detection limit for the Lumetallix kit of approximately 500 mg kg−1 (ppm) Pb in paint, with a sensitivity of 95 % and selectivity of 94 % relative to that threshold (n = 76). This detection limit is an order of magnitude below the US definition of Pb-based paint of 0.5 % (5,000 ppm) Pb by weight. SignificanceBecause the kit is easy to use, exposed paint posing a risk could reliably be screened at scale by the general public. Any follow-up for confirmation and mitigation based on XRF measurements will need to consider that Pb-based paint covered with low-Pb paint will not respond to the kit but will be detected by XRF through the overpainted layer.

Development of Improved Low-leakage Current H6 Single-Phase Full-Bridge Inverter Topology

Objective: To minimize leakage current in non-isolated photovoltaic grid connected inverters. Methods: The circuit design integrates extra switches and modifies the switch drive signals at the midpoint of the DC bus voltage, altering the common-mode current path. Findings: The proposed topology exhibits significant potential for widespread adoption, finding practical applications in diverse industrial settings as well as in residential use. Novelty: The suggested method implies that the topology presents distinctive or beneficial characteristics in contrast to current solutions, rendering it especially promising for a range of industrial and residential applications. This novelty may arise from enhancements in efficiency, cost-effectiveness, scalability, versatility, or other favorable attributes that distinguish it from traditional approaches. Moreover, Simulation outcomes distinctly demonstrate a noteworthy decrease in leakage current within the refined topology when contrasted with both the H5 topology and the High Efficient and Reliable Inverter Concept (HERIC) topology. This compelling evidence affirms the effectiveness of the proposed configuration in proficiently mitigating leakage current. Keywords: Photovoltaic, Common Mode Voltage, Leakage Current, Grid Connected Inverter