Optimal Panel Research Articles

Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink

This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.

Thermal performance optimization of a novel integrated photovoltaic thermal collector system

The construction industry is actively seeking energy-efficient solutions to reduce reliance on fossil fuels. Building-integrated photovoltaic thermal (PV-T) systems, combining electricity generation with solar heat utilization, have emerged as a promising technology. However, conventional PV-T systems often face limitations in achieving optimal PV panel angles for maximum solar gain while maintaining architectural aesthetics, especially when integrated into building facades. This study investigates the thermal performance of a novel photovoltaic thermal collector (PV-TC) system specifically designed to address these challenges. This system, intended for installation on south-facing building facades, allows for flexible PV panel angling design to optimize solar energy capture without compromising the building's aesthetic appeal. We employ a simulation-based optimization approach using EnergyPlus, Radiance, and a genetic algorithm to determine the optimal design parameters for minimizing annual energy use intensity (EUI) in Nanjing, China's climate. Our results demonstrate that a fan rate of 288 m³/h, a winter fan setpoint of 21 °C, a summer fan setpoint of 25 °C, and a PV panel angle of 41° achieve the lowest EUI of 92.33 kWh/m2. This optimized configuration effectively balances electricity generation with solar heat utilization, minimizing reliance on conventional HVAC systems. We further analyzed individual parameter influences, demonstrating that increasing fan rate consistently improves heat transfer efficiency, while optimal PV angle and fan setpoint temperature are crucial for maximizing solar heat utilization and nighttime cooling, respectively. By comparison it was found that the installation of the PV-TC system reduces the annual EUI by 22.7 %. These findings provide valuable insights for designing and operating high-performance PV-TC systems that address both energy efficiency and architectural integration concerns in buildings.

Bridging the gap: A comparative analysis of indoor and outdoor performance for photovoltaic-thermal-thermoelectric hybrid systems

This research evaluates the performance of a hybrid thermal-thermoelectric photovoltaic air collector system (PV/T-TE) through experimental investigations. By integrating photovoltaic (PV) panels with thermoelectric (TE) modules, the system aims to enhance efficiency and energy output by converting waste heat into additional electricity. The study examines the impact of radiation intensity, ranging from 455.65 to 795.18 W/m2, on the system's performance, utilizing output temperature (To) and plate temperature (Tp) as key metrics. Managing waste heat in PV technology remains a challenge, impacting efficiency. Traditional PV/T systems generate both electricity and thermal energy but suffer efficiency losses due to inadequate heat management. Integrating TE modules into PV/T systems offers a promising solution, but optimal configurations and performance impacts under varying conditions are underexplored. Limited research focuses on PV/T-TE hybrid systems, with most studies addressing PV-TE systems. The objectives of this research are to experimentally assess the impact of integrating TE modules on the thermal and electrical efficiency of PV/T systems under varying radiation intensities and air mass flow rates. The methodology involves setting up a PV/T-TE hybrid system, conducting experiments under controlled conditions, and analyzing key performance metrics. The study explores optimal TE module configurations and installation techniques to maximize heat transfer and electricity generation. Comparative analysis with conventional PV/T systems establishes the superiority of the hybrid system. Findings indicate significant benefits from incorporating TE modules, enhancing energy output and efficiency by maintaining optimal PV panel temperatures.

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Screening and validating the optimal panel of housekeeping genes for 4T1 breast carcinoma and metastasis studies in mice

The 4T1 model is extensively employed in murine studies to elucidate the mechanisms underlying the carcinogenesis of triple-negative breast cancer. Molecular biology serves as a cornerstone in these investigations. However, accurate gene expression analyses necessitate data normalization employing housekeeping genes (HKGs) to avert spurious results. Here, we initially delve into the characteristics of the tumor evolution induced by 4T1 in mice, underscoring the imperative for additional tools for tumor monitoring and assessment methods for tracking the animals, thereby facilitating prospective studies employing this methodology. Subsequently, leveraging various software platforms, we assessed ten distinct HKGs (GAPDH, 18 S, ACTB, HPRT1, B2M, GUSB, PGK1, CCSER2, SYMPK, ANKRD17) not hitherto evaluated in the 4T1 breast cancer model, across tumors and diverse tissues afflicted by metastasis. Our principal findings underscore GAPDH as the optimal HKG for gene expression analyses in tumors, while HPRT1 emerged as the most stable in the liver and CCSER2 in the lung. These genes demonstrated consistent expression and minimal variation among experimental groups. Furthermore, employing these HKGs for normalization, we assessed TNF-α and VEGF expression in tissues and discerned significant disparities among groups. We posit that this constitutes the inaugural delineation of an ideal HKG for experiments utilizing the 4T1 model, particularly in vivo settings.

Optimizing solar panel tilt angles across diverse Algerian terrain

Purpose. To optimize the efficiency and performance of a solar system by maximizing the capture of solar radiation through determining the most optimal solar panel tilt angle. Methodology. Stochastic techniques are presently utilized for estimating, optimizing, and predicting various solar energy systems. The authors have developed an algorithm that simulates the echolocation behavior of bats. Findings. To attain this objective, we evaluated different angles of inclination for the incident energy surface that will maximize the sunlight. Next, we compared the intensity of incident solar energy from a horizontal surface and the same surface tilted at the optimum angle. As a result, we determined the optimal tilt angles for other Algerian cities not covered by this study, based on two factors: geometry and climate, using multiple linear regression analysis. The results obtained reflect average monthly and annual values for solar panel tilt angles. These results depend on the latitude and sunshine levels of the locations studied. Originality. The present study introduces a computational algorithm that utilizes the echolocation behavior of bats to determine the most advantageous tilt angle for a photovoltaic (PV) panel. Practical value. Optimizing solar panel angles across various terrains in Algeria is crucial for maximizing the energy efficiency of photovoltaic installations. The optimization algorithm based on the echolocation behavior of bats allows for the determination of optimal angles by taking into account regional, climatic, and seasonal variations, thereby increasing energy production. This innovative approach offers an effective solution for improving the profitability of solar systems while contributing to sustainable development.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly “Phenylalanine, tyrosine and tryptophan biosynthesis”, “Phenylalanine metabolism”, “Glycerolipid metabolism” and “Arginine and proline metabolism”. We propose a three-metabolite diagnostic panel—comprising isoleucine, TMAO, and glucose—that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

A novel engineered sandwich composite with ceramic-coated graphite fibre-reinforced face sheets and an auxetic core for enhanced broadband electromagnetic absorption: design using multiscale computational analysis

Abstract A novel sandwich composite is designed for enhanced broadband absorption using a multiscale computational analysis. The sandwich panel is configured with BaTiO3 coated graphite fiber reinforced polymer (C-GFRP) composite, while BaTiO3 based auxetic material is used as the core material. A computationally efficient in-house tool based on the variational asymptotic method is used to homogenise the electromagnetic properties and evaluate the reflection loss characteristics of the proposed sandwich structure using the scattering matrices. A detailed parametric study of 300 analyses is conducted to identify the optimal sandwich panel configurations for achieving broadband reflection loss under the normal incidence of transverse electric (TE) and transverse magnetic (TM) polarised waves. Two different configurations have been obtained, satisfying the requirements of broadband reflection loss in the X-band frequency range. Configuration (a) Vf = 15% without any ceramic coating, and configuration (b) Vf = 20% with ceramic coating volume fraction (Cf) of 70%. Configuration (a) and (b) maintained the desired reflection loss of greater than -10 dB up to an incidence angle of 40◦ and 60◦, respectively. With the demonstrated capability of covering the entire broadband frequency range, the proposed configurations can serve as baselines to explore novel ceramic-based engineered composite architectures for broadband absorption applications.

A new strategy for systematically classifying HLA alleles into serological specificities: Update and refinement.

HLA antigens were historically defined according to the unique reactivity pattern of cells expressing HLA molecules with distinctive clusters of allo-antisera and/or monoclonal antibodies. Subsequently, amino acid residues determining epitopes (DEP) in the HLA molecule were correlated with reactivity patterns. In current clinical practice, the presence of allo-antibodies is assessed using Luminex-based solid phase single antigen bead (SAB) assays for transplantation. Recently, novel antigens were proposed for HLA molecules with DEP patterns that do not match any serologically defined antigens recognised by the WHO Nomenclature Committee. To validate the antigens, mean fluorescence intensity values of SABs tested on >13,000 patients' sera were extracted from clinical databases and analysed by scatter plots using a linear regression model. We found that when two proteins were considered as the same antigen in the original study, for example, HLA-A*02:01 and -A*02:06, their correlation ranked among the highest values at each locus. In contrast, discrete asymmetric outliers were observed when there were different antigens, for example, HLA-A*30:01 and -A*30:02, allowing validation and confirmation of 20 novel antigens for HLA-A, -B, -C and -DR. The outliers were confirmed to be true or false by flow cytometric crossmatches. In addition to the previously defined residues for antigen assignments, findings suggest that further distinction should be made for common antigens by including the substitutions at residue 67 of HLA-B, 67 and 74 of -DR. These serologic analyses can be applied systematically to identify and confirm novel antigens. These developments will lead to designing optimal SAB panels and further improving virtual donor-specific antibodies assessment.

Optimization strategies for bifacial floating photovoltaic systems on different water bodies: A 10E performance evaluation

This study investigates the performance and optimization of standalone floating bifacial solar photovoltaic systems through a comprehensive 10E analysis, covering energy, exergy, economic, environmental, energo-economic, exergo-economic, enviro-economic, energo-environmental, exergo-environmental, energy payback time, and embodied energy factors. The research evaluates energy efficiency, economic feasibility, and environmental impact using various water coolants. Fresh water emerged as the best performer, delivering the highest output power (399 W), final yield (371.94 W), and performance ratio (59.082). Capacity utilization factors were comparable, with fresh water (0.517) and black water (0.516) leading. Fresh water also showed the lowest energy loss (−365.64) and highest exergy efficiency (32.10%). It achieved the lowest levelized cost of energy at 3.39 $/MWh and the highest enviro-economic parameter (243.497). The exergo-environmental analysis demonstrated consistent efficiency, with energy payback time ranging from 37.28 to 37.36, and the embodied energy for the panels was 2840.67 units. Optimization techniques such as random forest, gradient boosting, XGBoost gain, neural network, and response surface methodology were used to identify optimal panel heights, water depths, and azimuth angles. Fresh water was identified as the most effective coolant, balancing high energy efficiency, minimal energy loss, and environmental sustainability. Response surface methodology proved to be the most accurate method for predicting power output and bifacial gain. This study provides valuable insights into improving the efficiency and sustainability of floating bifacial photovoltaic systems under various environmental conditions.

The Impact of Policy Quality and Political Stability on Foreign Direct Investment in ASEAN Countries: An Institutional Economics Analysis

This investigation pivots on the impact of policy quality and political stability on foreign direct investment within ASEAN countries. Both facets form essential components of a country's political risk profile; a lower risk profile, characterized by sturdy policy quality and political stability, tempts investors to channel their capital into preferred investment destinations. Our research employs a panel data regression analysis across seven ASEAN nations; Indonesia, Malaysia, Singapore, Thailand, Vietnam, the Philippines, and Brunei Darussalam, from 2012 to 2022. The Common Effects Model emerged as the optimal panel model during our study. We found that policy quality negatively impacts foreign direct investment, signifying that investments are perceived unprofitable due to the policy quality in the ASEAN region. In counterpoint, political stability has a positive effect, confirming that stable political circumstances in the ASEAN region draw investor interest. These significant findings validate that policy quality and political stability significantly sway foreign direct investment within ASEAN countries, making them crucial considerations for prospective investors.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Improving land-use efficiency of solar power in China and policy implications

Improving the power output of solar photovoltaic (PV) farms is critical to maximize the potential of PV power and reduce extensive land use in the context of large-scale deployment of renewable energy. In this paper we developed an integrated solar power potential assessment framework to quantify the gap between technical potential and actual generation of solar PV farms on national, provincial, and plant scales, and identify the key factors that cause the underperformance of PV farms. Specifically, to assess technical potential, hourly meteorological reanalysis data is adopted and the power generation-related parameters are calculated (i.e. optimal panel tilt, array spacing, packing factor etc.) with consideration of their spatial variability. For actual power generation, a detailed plant-level dataset is first established by this study which integrates technical, operational, and geospatial information from 145 solar farms across seven provinces in China. Our results show that the actual PV power generation per square meter is only 1/3 of the estimated technical potential. Technological factor is the primary factor, accounting for 48.43% of the underperformance, followed by engineering and management factors, accounting for 38.55% and 13.02%, respectively. The novelty of our study is revealing the underperformance level of solar farms in reality, identifying the causing factors, and highlighting the importance of integrating land-use efficiency indicators into solar power planning and development.

Novel feature selection based ANN for optimal solar panels tilt angles prediction in micro grid

Predicting PV system electricity output is necessary for daily operational management and annual power system planning when integrating solar collector-based photovoltaic (PV) stations into micro grids. Tilting the panels at the ideal angle to maximize solar energy capture is necessary to maximize PV station production. This optimal tilt angle (OTA) must be predicted as it is a nonlinear function of the total solar radiation, diffuse solar radiation, and direct solar radiation. This research explores the use of feature selection-based artificial neural networks (ANN) with various machine learning algorithms to predict the OTA for PV systems at specific locations, aiming to maximize PV output in micro grids. The study identifies global solar radiation, diffuse solar radiation, clarity index, and global solar radiation on inclined surfaces as the most critical inputs for predicting OTA, while extraterrestrial radiation is deemed the least significant. Implementing the appropriate input variables significantly enhanced prediction accuracy from 38.59 % to 90.72 %. Among the neural networks evaluated, the Elman neural network demonstrated the greatest improvement.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Demonstration Project: 1.86 MWH Battery Energy Storage System and 540 KVA Inverter Integration

This research focused on the implementation of state-of-the-art system integration, involving a three-phase 540 KVA bidirectional inverter and a lithium-ion battery energy storage system with a capacity of 1.86 MWh, at the Florida Solar Energy Center (FSEC). The system was firstly put into use at the FSEC for load shifting. The second step was to add photovoltaic (PV) panels for peak shaving. The third step was to take the FSEC completely off-grid. The goal of this research was to provide a case study by implementing a power management algorithm, estimating the PV panel size, and presenting the test results and an analysis. Based on the power management algorithm, load shifting was achieved by using 180 kWh energy storage. To acquire peak shaving and the uniformity of power consumption, an 80 kW PV farm was sufficient. The optimal PV panel size was 500 kW for the off-grid scenario.

Extracellular Vesicle miRNAs in Diagnostics of Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge because of its high mortality rate attributed to the late-stage diagnosis and lack of early symptoms. Early cancer diagnostics is crucial for improving the survival rates in GC patients, which emphasizes the importance of identifying GC markers for liquid biopsy. The review discusses a potential use of extracellular vesicle microRNAs (EV miRNAs) as biomarkers for the diagnostics and prognostics of GC. Methods. Original articles on the identification of EV miRNA as GC markers published in the Web of Science and Scopus indexed issues were selected from the PubMed and Google Scholar databases. We focused on the methodological aspects of EV analysis, including the choice of body fluid, methods for EV isolation and validation, and approaches for EV miRNA analysis. Conclusions. Out of 33 found articles, themajority of authors investigated blood-derived extracellular vesicles (EVs); only a few utilized EVs from other body fluids, including tissue-specific local biofluids (washing the tumor growth areas), which may be a promising source of EVs in the context of cancer diagnostics. GC-associated miRNAs identified in different studies using different methods of EV isolation and analysis varied considerably. However, three miRNAs (miR-10b, miR-21, and miR-92a) have been found in several independent studies and shown to be associated with GC in experimental models. Further studies are needed to determine the optimal miRNA marker panel. Another essential step necessary to improve the reliability and reproducibility of EV-based diagnostics is standardization of methodologies for EV handling and analysis of EV miRNA.

Energy and exergo-environmental analysis of a refrigerator-Stirling/Photovoltaic system for cold production

The Stirling refrigerator is a device capable of using green energies such as solar power. In this work, a refrigerator-Stirling/photovoltaic system is proposed to harness solar energy for cold production. The system consists of photovoltaic (PV) panels that capture solar energy and convert it into electricity, and a transmission belt linking the motor to the Stirling beta refrigerator to use the electricity for cooling purposes. The system is modeled on the basis of finite-dimensional thermodynamics, taking into account the refrigerator's internal and external irreversibilities. In addition, the wiring loss, dust coverage and shading of the photovoltaic system and the mechanical power loss of the Stirling refrigerator are taken into account in this model. Power equality has been considered as a system constraint. Finally, the effects of operational and design parameters on system performance are investigated. A numerical code is written in Matlab. The effects of PV module efficiency, Stirling regenerator efficiency and PV derating factor on system performance are quantified, also the optimum operating parameters obtained. The refrigerator-Stirling/photovoltaic has a high COP and a low cooling capacity, in contrast to the refrigerator-Stirling/Dish-Stirling. The optimum solar electric COP and maximum cooling rate are 0.7063 and 1413 W respectively at an optimum panel area of 2 m2. Optimum exergy efficiency is 38.5 % at fv = 0.1, ecological coefficient of performance (ECOP) is 20.3 % at N = 48 rot.s−1, optimum exergy destroyed is 33.31 W at K1 = 21.5 W.K−1. This system can be used to preserve vaccines in hospitals.

Estimating the efficacy of solar photovoltaic panels in Lebanon using a digital surface model: A geospatial approach

With the escalating need for alternative energy sources due to economic crises and fossil fuel shortages in Lebanon, solar photovoltaic (PV) panels have emerged as an attractive solution. This study examines the capacity and efficacy of rooftop-installed PV solar panels. Using geospatial technologies, including Digital Surface Models drone-based photogrammetry, the study assesses geometric and solar characteristics, seasonal solar radiation, solar duration, and power for 40 PV units installed in the study area. This research presents specific quantitative values for optimal orientations that result in high solar radiation across various seasons and identifies varying slopes influencing the performance of PV solar panels. Employing the Agglomerative Hierarchical Clustering (AHC) technique, PV units are systematically classified into clusters labeled as Moderate, High, Low, and Very Low solar power, offering quantitative metrics regarding the effectiveness of distinct panels. The high-efficiency Cluster exhibits an average solar power of 1868.114 kWh/m² during the summer season, whereas the Very Low Cluster, comprising panels with minimal solar power output, averages 150.578 kWh/m² in the same season. In conclusion, the most effective PV solar panels within the study area are those oriented between 195 and 225 degrees, with shallow inclination angles and larger surface areas contributing to enhanced performance in capturing solar radiation and generating power. These precise quantitative insights contribute to informed decision-making for optimizing the placement of PV panels to enhance energy generation. The study's recommendations are substantiated by specific numerical data, guiding future solar installations to maximize solar energy generation.

MicroRNA as Sepsis Biomarkers: A Comprehensive Review.

Sepsis, a life-threatening condition caused by the body's dysregulated response to infection, presents a significant challenge in clinical management. Timely and accurate diagnosis is paramount for initiating appropriate interventions and improving patient outcomes. In recent years, there has been growing interest in identifying biomarkers that can aid in the early detection and prognostication of sepsis. MicroRNAs (miRNAs) have emerged as potential biomarkers for sepsis due to their involvement in the regulation of gene expression and their stability in various biological fluids, including blood. MiRNAs are small non-coding RNA molecules that play crucial roles in post-transcriptional gene regulation by binding to target messenger RNAs (mRNAs), leading to mRNA degradation or translational repression. The diagnostic and prognostic potential of miRNAs in sepsis stems from their ability to serve as sensitive and specific biomarkers reflective of the underlying pathophysiological processes. Compared to traditional biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT), miRNAs offer several advantages, including their early and sustained elevation during sepsis, as well as their stability in stored samples, making them attractive candidates for clinical use. However, despite their promise, the clinical translation of miRNAs as sepsis biomarkers faces several challenges. These include the need for standardized sample collection and processing methods, the identification of optimal miRNA panels or signatures for differentiating sepsis from other inflammatory conditions, and the validation of findings across diverse patient populations and clinical settings. In conclusion, miRNAs hold great promise as diagnostic and prognostic biomarkers for sepsis, offering insights into the underlying molecular mechanisms and potential therapeutic targets. However, further research is needed to overcome existing challenges and realize the full clinical utility of miRNAs in improving sepsis outcomes.