Statistical Evaluation Research Articles

Impact of high-resolution land use data on numerical simulations of Colombo’s thermal environment during heatwaves

Land Use and Land Cover (LULC) play a crucial role in the regional climate models, as they influence various physical processes interconnected with energy, mass, and atmospheric interactions. This study has examined the impact of high-resolution LULC data on the accuracy of the Weather Research and Forecasting (WRF) model with the Urban Canopy Model in capturing the thermal environment of the Colombo Metropolitan Region (CMR) during heatwaves (HWs). In the performed simulations we used four LULC datasets: USGS, MODIS30, MODIS15, and Esri (Sentinel2). Our investigation is the first application of the WRF model to study the thermal environment in a tropical city such as Colombo. This is also the first research investigation of HWs in the Sri Lanka context from the mesoscale viewpoint. Additionally, this is the first time high-resolution Esri land-use data have been used in a numerical model to examine the thermal environment. By evaluating the three HW cases, we compared the simulation outcomes with observational data from three meteorological stations in CMR. The results from numerical simulations revealed that the LU4 dataset provided the closest agreement with observed Land Surface Temperatures (LST). Daytime LSTs in urban areas peaked above 45 °C, while nighttime temperatures reached 30 °C in commercial zones. Coastal regions consistently exhibited lower temperatures, with daytime LSTs ranging from 40 to 45 °C and nighttime LSTs from 25 to 30 °C, compared to the urban cores. LU4 showed the closest alignment with Himawari satellite data, ensuring superior accuracy. Temperature anomaly analysis indicated that LU4 reduced daytime temperature anomalies by up to 2.5 °C and nighttime anomalies by 1.0 °C compared to other datasets. Statistical evaluations (R = 0.94 in Colombo) further highlight the importance of high-resolution LULC data for enhancing the accuracy of the WRF model in representing urban thermal environments.

Investigation of the Effects of Rooting Medium and IBA Concentration on Rooting and Shoot Development of Bougainvillea (Bougainvillea spp.) Cuttings

In this study, the effect of different rooting mediums (peat moss, peat + sand, peat + loam) and IBA (indole-3-butyric acid) concentration was investigated on hardwood cuttings taken from Bougainvillea glabra, Bougainvillea spectabilis, and Bougainvillea x buttiana species. The cuttings were treated with 0, 1000, 2000 and 3000 mg/l IBA. Statistical evaluation was performed according to a randomized block experimental design with three factors and three repetitions. The results showed that Bougainvillea x buttiana cuttings had the highest number of roots, root fresh weight, longest shoot length, and shoot fresh weight. Cuttings grown in 100% peat medium had the highest rooting percentage, root number, root dry weight, leaf number, and shoot fresh weight. Regarding IBA concentrations, the cuttings treated with 3000 ppm IBA had the highest number of roots, root length, and root fresh weight. Furthermore, in all of the Bougainvillea species, the highest rooting percentage and leaf number were observed in cuttings treated with 2000 ppm IBA. Notably, Bougainvillea x buttiana cuttings treated with 1000 ppm IBA dose and planted in peat medium provided the longest shoot length, highest shoot fresh weight, and shoot dry weight. In conclusion, the findings show that the growing medium has a statistically significant effect on the rooting and root quality of cuttings and that it would be useful to investigate various rooting hormones, dasages and rooting media to increase rooting and shoot development. Considering growing medium plays a crucial role in plant growth, it can be stated that further improvements in rooting and growth can be realized via utilizing the enhanced physical and chemical properties of peat-based mixtures.

The Performance of a High-Resolution WRF Modelling System in the Simulation of Severe Tropical Cyclones over the Bay of Bengal Using the IMDAA Regional Reanalysis Dataset

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement.

RSM modelling and yield prediction of base-catalyzed transesterification of pumpkin-seed oil to biodiesel: artificial neural network, kinetics, and thermodynamics

The study examined the extraction of bio-oil from pumpkin seed and compared the optimization of the production of Fatty acid ethyl ester (FAEE) via the transesterification process using Response Surface Methodology (RSM), and Artificial Neural Network (ANN). This research uniquely highlights the utilization of pumpkin seed oil, a non-edible and sustainable feedstock, and combines RSM and ANN methodologies to enhance the precision of biodiesel optimization. The transesterification experiment was conducted at 60 min reaction time under varying temperature ranges, catalyst weight, stirrer speed, and ethanol-oil molar ratio. The RSM optimized conditions for maximum production were determined to be a 1.3% catalyst concentration, 6:1 ethanol-to-oil molar ratio, 50 °C temperature, and 550 rpm stirrer speed, resulting in a 90% biodiesel yield. The statistical evaluation metrics confirmed the neural network predictions were compromised to 80% yield due to limitations such as insufficient data size and the inherent complexity of the ANN model. The biofuel produced satisfied ASTM specifications peculiar to sustainable environmental applications. The mechanistic parameters revealed variations in thermodynamic stability and feasibility across reaction orders (zero, pseudo-first, and pseudo-second), emphasizing the temperature-dependent effects of the transesterification kinetic pathway on yield, design, and efficiency.

Synthesis, crystal structure, hirshfeld study, DFT analysis, molecular docking study, antimicrobial activity of β-enaminonitrile bearing 1H-pyran

3-Amino-1-(3,4-dimethoxyphenyl)-8-methoxy-1H-benzo[f]chromene-2-carbonitrile (3-ABC, 4) was prepared and affirmed through the spectral data and single crystal X-rays diffraction. The molecule spatial attributes were revealed by the three-dimensional Hirshfeld surface, which is validated by comprehensive statistical evaluations. The key interactions that contribute to the crystal's stability and properties, such as π–π stacking and H⋯X contacts, were highlighted. The dominant forces in the crystal were explained by energy framework calculations and density functional theory analyses, which also show the geometric optimization aspects and molecular reactivity indicators. The focus is on the investigation of frontier molecular orbitals, aromaticity, and π–π stacking abilities. The research concluded by identifying electron density distributions, aromatic features, and possible reactivity sites, offering a complete picture of the compound's structural and electronic landscape. Compound 4 was screened for its antimicrobial capability against three Gram-positive, three Gram-negative bacteria, and three fungi, utilizing the reference antibiotic drug Ampicillin, Gentamycin, and Ketoconazole. Compound 4 exhibited favorable antimicrobial activities that were lower/resembled the reference antimicrobial agents with an IZ range of 15–31 mm. In addition, MIC, MBC, and MFC were assessed and screened for the target molecule 4, unveiling its revealing antimicrobial activities. Lastly, the molecular docking evaluation was implemented for the molecules interaction with the DHFR protein. The compounds exhibit significant interactions with the EGFR protein's active site.

Timing of Therapeutic Extractions Can Affect En Masse Anterior Retraction: A Split Mouth Randomized Clinical Trial.

To investigate the effect of deferred timing of therapeutic extraction on the rate of space closure during en masse anterior retraction. Twenty-six patients (aged 16-24 years) with bimaxillary protrusion, crowding <3 mm, requiring bilateral extraction of four first premolars were recruited. Permuted block randomization was done. Allocations were concealed in opaque envelopes which were numbered and sealed. Each patient's right and left quadrant was randomly assigned for premolar extraction. The extraction of the contralateral side was deferred until the commencement of retraction. The primary outcome was the rate of space closure, and the secondary outcomes were anchorage loss and canine rotation. Blinding was applied only during the outcome assessment. The independent t-test and Intraclass correlation tests were used for statistical evaluation. Twenty-four patients completed the study. The mean rate of space closure over a period of 4 months was found to be significantly higher for the recently extracted site (0.818±0.208) when compared with healed site(0.426±0.184)(p<0.001). The tipping of the canine was also significantly higher for the former (6.042°±1.398°) than the latter (5.125°±1.035°) (p<0.05). However, the amounts of anchorage loss and canine rotation were insignificant. No adverse effects were noted. The rate of space closure at the recent extraction site was faster than that at the healed site. There was no significant difference in the mesial movement of anchorage molars or rotation of canines into the extraction site. The tipping of canines was significantly greater in the recent extracted quadrant. The results of this trial indicate a clinical recommendation to initiate orthodontic retraction immediately following therapeutic extractions and offer a practical, non-invasive, safe procedure for increasing the rate of tooth movement.

Immunohistochemistry‐based molecular subtypes of urothelial carcinoma derive different survival benefit from platinum chemotherapy

AbstractDistinct molecular subtypes of muscle‐invasive bladder cancer (MIBC) may show different platinum sensitivities. Currently available data were mostly generated at transcriptome level and have limited comparability to each other. We aimed to determine the platinum sensitivity of molecular subtypes by using the protein expression‐based Lund Taxonomy. In addition, we assessed the tumor heterogeneity within the primary tumor and between the primary and lymph node (LN) metastatic sites. Thirteen immunohistochemical markers were stained in a tissue microarray with an overall number of 1,508 cores. Statistical evaluation was performed in 199 patients divided into three chemo‐naïve MIBC cohorts: (1) pT3/4 and/or LN+ patients who received radical cystectomy without platinum treatment, (2) patients who received adjuvant chemotherapy (AC), and (3) patients who underwent palliative platinum treatment for metastatic disease or postoperative progression. Overall survival (OS) was used as the primary endpoint. Patients with the genomically unstable (GU) subtype had significantly better OS in the AC group compared to the radical cystectomy group (HR: 0.395, 95% CI: 0.205–0.795, p = 0.005). In contrast, no such association was observed for the basal/squamous (Ba/Sq) subtype. Intratumor heterogeneity was present in 19% of cases, with the lowest level in the Ba/Sq and GU tumors (14% each) and the highest level of 43% in small‐cell/neuroendocrine‐like tumors. There was greater subtype heterogeneity between primary tumors and LN metastases. In conclusion, immunohistochemistry‐based Lund Taxonomy subtypes remain stable within the same primary tumor, with the GU subtype deriving the greatest OS benefit from AC. However, high tumor heterogeneity between the primary tumor and metastatic sites can impact the effectiveness of therapies.

Optimization of the green synthesis of gold nanorods using aqueous extract of peeled sour guava as a source of antioxidants.

Obtaining gold nanorods (AuNRs) through biosynthesis is an alternative that replaces the traditional use of ascorbic acid with chemical compounds such as polyphenols, owing to their notable antioxidant properties. Therefore, we developed an AuNR biosynthesis method using an aqueous extract of sour guava (Psidium araca). Initially, a study was conducted to determine the antioxidant capacity of different parts of the fruit (pulp and peel) over 14 days. Four colorimetric techniques were used: total phenol, ABTS (2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid), FRAP (ferric reducing antioxidant power (FRAP), and DPPH (1,1-diphenyl-2-picrylhydrazyl). Subsequently, in stage 2, the selected aqueous extract was used, and two response surface designs were performed. The objective of this study was to find a model equation that would indicate the optimal parameters for obtaining AuNRs with a surface plasmon band at 808 nm, with possible applications in the health field. The results of the antioxidant capacity experiments were analyzed in Minitab® using a multilevel factorial design, and the peel exhibited the highest antioxidant capacity. Subsequently, the biosynthesis of AuNRs proceeded using a 5-factor response surface experimental design as input variables (concentration in mM of gold, silver, extract, NaBH4, and reaction time in hours) and longitudinal plasmon (LSPR) as output variables. The AuNRs were approximately 30 nm in size with an LSPR between 700 and 800 nm. Statistical model evaluation revealed a dependence between gold and time and gold-silver factors. Finally, antioxidant capacity was used to select the part (peel or pulp) of sour guava that could be used as a weak reducing agent. Moreover, the utility of surface-response methodology was explored to optimize the synthesis of AuNRs using green agents.

AI Transforming Data Networking and Cybersecurity through Advanced Innovations

The rapid expansion of data networking infrastructure has necessitated advancements in cybersecurity to mitigate increasingly sophisticated cyber threats. As the digital landscape evolves, networks are handling unprecedented volumes of data, fueled by innovations like the Internet of Things (IoT), 5G technology, and cloud computing. This growth has created not only opportunities for improved connectivity but also significant challenges in safeguarding sensitive information from advanced cyber threats. Simultaneously, artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize both data networking and cybersecurity. AI’s ability to analyze vast datasets, identify patterns, and make real-time decisions offers a promising solution to the growing complexity of securing modern networks. From enhancing network efficiency through dynamic bandwidth allocation to fortifying defenses against cyberattacks, AI is reshaping how organizations approach data security. This research paper provides an empirical analysis of AI’s applications in data networking and cybersecurity, drawing on data collected from network providers, cybersecurity firms, and governmental agencies. Key areas of focus include predictive threat detection, anomaly identification, and response automation. Through the use of statistical models, graphical analyses, and case study evaluations, the study demonstrates AI’s capacity to preempt cyber threats, optimize network performance, and respond to attacks more effectively than traditional methods. The findings highlight measurable improvements in both network efficiency and threat mitigation, showcasing the practical implications of integrating AI-driven technologies. As networks become more intricate and threats more advanced, leveraging AI for proactive and adaptive security measures will be essential. By addressing current challenges and exploring future possibilities, this paper aims to contribute valuable insights into the transformative role of AI in data networking and cybersecurity.

Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete

Foamed concrete (FC) is increasingly used in modern construction due to its lightweight nature, superior thermal insulation, and sustainable properties. However, accurately predicting its compressive strength remains a challenge due to the complex interactions of its components. This study addresses this gap by employing advanced machine learning tools, including decision tree (DT), bagging, and AdaBoost, to develop predictive models for FC strength. The results provide a significant improvement in prediction accuracy, offering a reliable tool for optimizing FC design in construction applications. This research aims to streamline the sample creation process in the laboratory, minimize the waiting time for sample testing, and reduce the project's overall cost for researchers. A total of 149 data points were used from the literature to prepare a proper data set for modelling purposes. The modelling procedure used Python code via the Anaconda Navigator software. The statistical evaluation of the metrics, such as R2, MAE, and RMSE, along with the sensitivity analysis to check the impact of inputs and the 10-fold cross-validation method to validate the performance, were part of the presented research. Compared to the DT and bagging models, the results demonstrate that the AdaBoost model forecasts FC's compressive strength (CS) more accurately. The AdaBoost model gives the R2 value equal to 0.97, while DT and bagging show 0.86 and 0.94, respectively. The lower error result for the AdaBoost model and higher for both DT and bagging indicates the superior precision level of the AdaBoost approach. Finally, the graphical user interface (GUI) was designed utilizing the implemented models, which indicates the additional positive aspect of the presented study.

Effects of intentionally-treated water on cell migration of human glioblastoma cells.

This study investigated if human glioblastoma cancer cells (U87MG cell line) cultured in intentionally treated water could reduce cell migration, a prerequisite for metastasis, as compared to the same cells cultured in untreated (control) water. Three Buddhist monks entered a meditative state and directed their awareness to bottles of ultrapure water while holding the intention that the water would cause beneficial changes in U87MG. The study was conducted double-blind whereby all aspects of the study involving cell growth and migration measures, as well as all subsequent statistical evaluations, were performed without knowledge of the type of water being used. Cell cultures were incubated in growth mediums prepared with treated and untreated water, and a wound healing assay was employed to measure cell migration. U87MG cells incubated with treated water migrated less efficiently than the same cells in untreated water. A repeated measures ANOVA, spanning four time periods (0, 3, 6, and 9 h), determined that the time × water condition interaction was associated with p < 0.005. Intentioned awareness appeared to change as-yet unknown properties of ultrapure water, resulting in a reduction of U87MG cells migration activity. Further research is warranted to replicate these results and to investigate the underlying protein expression mechanisms in influencing cell migration.

A double-Gaussian wake model considering yaw misalignment

A wake steering has been known to effectively increase wind farm production by deflecting the upstream turbines’ wakes via yaw misalignment, thus minimizing their negative impacts on the downstream turbines' performances. This study presents analytical modeling of horizontal-axis wind turbine (HAWT) wake using low-cost analytical modeling as an alternative to expensive numerical and experimental trials. The existing double-Gaussian (DG) analytical wake model was modified to include the yaw misalignment effect, allowing its usability for the yawed HAWT wake modeling. The benchmark dataset produced by high-fidelity large eddy simulation (LES) of wake flowfields behind the turbine with yaw angles of 0º, 10º, 20º, and 30º were used to validate the accuracy of the DG yaw wake model. Overall, the DG yaw wake model predictions showed good agreement with the benchmark dataset under varying HAWT rotor yaw configurations. The analytical results verified by the LES dataset confirm the effectiveness of yaw misalignment in deflecting the wake trajectory, expediting the wake recovery downstream of the HAWT. In addition, a higher rotor yaw angle improves the wake recovery rate in the prevailing wind direction. Notable deviations against the benchmark dataset were found mainly within the near-wake region owing to flow acceleration arising from turbine-induced turbulence. As a result, the model’s predictions were slightly lower than the benchmark dataset, most likely due to neglecting the acceleration term in the analytical model derivation. Otherwise, the analytical model could accurately predict the mean wake velocity within the far-wake region for all evaluated cases, demonstrating its reliability in estimating wind speed potential within a practical distance for micrositing. These results were also proved quantitatively by statistical evaluations utilizing root mean square error (RMSE) and Pearson correlation coefficient R. The present study points out the importance of the upstream HAWTs’ rotor yaw controls to properly deflect their wakes away from their mainstream trajectories, thus effectively maximizing the wind speed potentials extracted by the downstream HAWTs and improving the overall wind farm production.

Comparison of microbial adhesion and biofilm formation on different orthodontic aligners

This study aimed to compare and evaluate time-dependent biofilm formation and microbial adhesion on 6 different clear aligner systems: Invisalign (Align Technology, San Jose, Calif), Clarity (3M ESPE Maplewood, Minn), ClearCorrect (Institut Straumann AG, Basel, Switzerland), Smartee (Smartee Denti-Technology, Shanghai, China), Orthero (Orthero, Istanbul, Turkey) and Graphy (Graphy Inc, Seoul, South Korea). Streptococcus mutans (ATCC 25175) and Lactobacillus acidophilus (ATCC 4356) were used to evaluate the microbial adhesion and biofilm formation on orthodontic clear aligners at the 0, 24, 48, 72, 96, 120, 168, and 240 hours. Two-way repeated measures analysis of variance (Greenhouse-Geisser) test and post-hoc Bonferroni T2 tests were used for statistical evaluation. The statistical significance level was set at P<0.05. It was found that more bacterial formation occurred on ClearCorrect than on Smartee at 120, 168, and 240 hours (P<0.05). It was observed more biofilm formation at 168 hours on Graphy than on Smartee (P<0.05). It was found that S mutans+ L acidophilus formed more biofilm at 120 and 168 hours on Graphy than on Invisalign (P<0.05). Elevated biofilm formation across all materials carries substantial clinical implications. Orthodontists and patients should remain aware of the increased risk of microbial colonization with extended aligner usage.

Molecular insights on the solvent screening for the benzene extraction from fuels using ionic liquids via QSPR method

Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid–liquid extraction process using ionic liquids (I.Ls) is an option for this separation. The selection of the most appropriate I.L. for this application is a challenging task due to the variety of anion and cation structures. In the current study, the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been evaluated using the Quantitative Structure–Property Relationship (QSPR) method. A dataset comprising of 112 ternary systems (namely, I.L., benzene, and aliphatic hydrocarbon) was compiled after an extensive review of literature. The primary dataset consists of 17 anions, 20 cations, and 12 aliphatic hydrocarbons. Therefore, the impact of the structure of anion, cation, or aliphatic hydrocarbon on the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been investigated. The linear QSPR models were constructed using Multiple Linear Regression (MLR). The statistical evaluation of the final linear model showed that the constructed model (R2 = 0.900) has an acceptable capability to predict the mole fraction of benzene in the I.L.-rich phase. Additionally, non-linear QSPR models were developed using Genetic Programming (GP) and Artificial Neural Network (ANN) machine learning methods. The statistical evaluation of the GP model (R2 = 0.927) and ANN model (R2 = 0.939) showed that non-linear models had slightly higher prediction accuracy compared to the linear model. The final QSPR model was developed using the BELe3 cation descriptor which is a 2D Burden eigenvalues descriptor and HTm anion descriptor which is a 3D GETAWAY descriptor. After model construction, the selected molecular descriptors of anion and cation structures has been interpreted. The results showed that the size and the electronegativity of the atoms in the anion and cation structure are probably important parameters that affect the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases. Additionally, the anion shape can be considered as an effective parameter in the benzene extraction process.

Early detection of bacterial pneumonia by characteristic induced odor signatures

IntroductionThe ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.MethodsA real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.ResultsCharacteristic changes in the VOCs emitted from the mice were detected as early as 4–6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.Conclusionthe non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Transformation of Character Assessment through ICT Technology: A Study of the Use of Web-Based Platforms

Purpose of the study: The purpose of this study was to determine the perceptions of teachers, students, and parents towards the use of a web-based character assessment system supported by Information and Communication Technology (ICT), and to determine the results of student character using a web-based character assessment system. Methodology: This study uses a quantitative research approach with a descriptive design. The tools used include a perception questionnaire and a web-based character assessment system. Data were collected using a questionnaire for perception data, and character data were collected using a web-based platform for character assessment. Analysis was performed using SPSS for descriptive statistical evaluation. Main Findings: The study found that the majority of teachers (70%), students (68.67%), and parents (76%) rated the web-based character assessment system as very effective. The results showed that most students achieved good or excellent character outcomes, with an average score of 70.0. This demonstrates the system's ability to accurately assess and support students' character development. Novelty/Originality of this study: This study introduces a comprehensive, ICT-enabled, web-based character assessment model that combines real-time data, transparency, and collaboration between schools and families. This study advances existing knowledge by demonstrating the potential of technology to enhance character education, providing a scalable framework for broader educational applications.

Level of circulating cytokines in children with chronic liver diseases at different stages of fibrosis

Liver diseases cause about 2 million deaths per year worldwide, with cirrhosis accounting for 2.1% of this number. The cytokine and chemokine balance determines outcomes of immune response. Many cytokines are involved in progression and control of various liver diseases via regulation of cellular activity. Our aim was to assess the level of circulating cytokines and chemokines depending on the stage of fibrosis in children with chronic liver diseases. 51 children with chronic liver diseases were examined (32 children with autoimmune hepatitis, 12 children with Wilson’s disease, 5 children with Gaucher disease and 2 children with glycogen storage disease). All children were assessed for the stage of liver fibrosis (AF) using METAVIR scale (FibroScan F502). The contents of circulating serum cytokines, chemokines, growth factors and angiogenesis was determined by the MILLIPLEX® Human Cytokine/Chemokine/Growth Factor Panel A Magnetic Bead Panel, an immunoassay based on Luminex® technology. Statistical evaluation was carried out using the program “Statistica 10.0”. The levels of EGF, Fractalkine, IFNá, IL-10 and MIG increased significantly from F0 stage to F4 stage. A significant decrease from stage F0 to stage F4 was revealed for eotaxin, IL-5, IL-8, IL-17A. Some cytokines were characterized by nonlinear dynamics: the concentrations of IL-4 and MDC increased significantly from the F0 stage to the F2-3 stage, and then decreased to the F4 stage; the level of IL-18 showed a significant decrease by stage F2-3 relative to F0, then being significantly increased by stage F4. The level of TNFáwas increased at all stages of liver fibrosis and reached its maximum values at stage F2-3 of AF. Our data confirm the significant role of cytokines and chemokines in the pathogenesis of chronic liver diseases. The identified changes in circulating cytokines in the blood serum in children with CKD, depending on the stage of fibrosis, are characterized by differently directed disturbances thus presuming involvement of both pro- and anti-inflammatory mechanisms in the immunopathological process in the course of liver fibrosis formation. Further research is required in order to study the participation of cytokines and chemokines in formation of liver fibrosis for development of targeted therapy for liver diseases.

Comparative analysis and statistical performance evaluation of fractional PID controllers in fuel cell choppers optimized with metaheuristic algorithms

Comparative analysis and statistical performance evaluation of fractional PID controllers in fuel cell choppers optimized with metaheuristic algorithms

Long term functional outcome for the cubital tunnel syndrome after endoscopic assisted release of the ulnar nerve.

The endoscopic assisted release for cubital tunnel syndrome (CuTS) gained popularity in recent years with unclear long-term results. This study aims to evaluate long term results regarding functional and subjective outcomes after endoscopic assisted release for the CuTS. Thirty one patients who have been treatedby endoscopic assisted release for CuTS between 2006 and 2013 were followed up both clinically and with a questionnaire with a mean follow up of 152 months (range 120-204 months). Functional and subjective parameters were evaluated and statistically compared to the contralateral elbow. For statistical evaluation one-sample t-test and the McNemar test were defined as appropriate. Pinch grip strength, two-point discrimination, application of Semmes-Weinstein monofilaments, Tinel's sign, grip and three-point pinch strength showed no significant difference. The DASH score was 14,2 with 96,6% of patients rating good/excellent results. This study found the endoscopic assisted release of the ulnar nerve to be an efficacious and safe method for the treatment of CuTS.

Exploration of histone protein γ-H2AX as a prognostic factor in soft tissue sarcomas and its association with biological behavior, immune cell environment and survival in leiomyosarcoma.

This study evaluates the H2AX/γ-H2AX expression in soft tissue sarcomas (STS), its implications for biological behavior and immune environment, and its potential as a prognostic biomarker. RNA-Seq data from 237 STS were obtained from The Cancer Genome Atlas project. Patients were stratified by H2AX mRNA expression using a survival-associated cutoff. Differentially expressed genes and pathways as well as immune signatures between H2AXhigh- and H2AXlow tumors were identified with DESeq2 analysis, gene set enrichment analyses (GSEA), Enrichr pathway analysis and CIBERSORTx. Tissue microarrays of a different cohort of 291 STS were generated for immunohistochemical staining to assess γ-H2AX protein expression, followed by statistical evaluation. High H2AX mRNA expression was associated with shorter overall survival (OS) in STS (p = 0.02), particularly in leiomyosarcomas (LMS) (p < 0.001), and was a negative prognostic factor in LMS (HR 11.15, p < 0.001). H2AXhigh LMS tumors showed upregulation of cell cycle-related pathways, while H2AXlow LMS exhibited increased inflammatory activity, including elevated M1 macrophage signatures and resting mast cell signatures (both p < 0.001). High γ-H2AX protein levels were an independent negative prognostic factor in the total LMS cohort (HR 12.12, p = 0.025) and in the subgroup of non-uterine LMS (HR 153.80, p = 0.013). Consistent with CIBERSORTx analysis, γ-H2AXlow LMS showed higher mast cell infiltration than γ-H2AXhigh LMS (p = 0.038). In conclusion, H2AX mRNA and γ-H2AX protein expression are associated with distinct biological behavior, differences in the immune cell environment, and might serve as useful prognostic biomarkers in LMS.