Variation In Performance Research Articles

Detecting student attention through electroencephalography signals: a comparative analysis of deep learning models

<span lang="EN-MY">In the landscape of educational technology, understanding and optimizing student attention is important to enhance student’s learning experience. This study explores the potential of using electroencephalography (EEG) signals for discerning students' attention levels during educational tasks. With a cohort of 30 participants, EEG data were meticulously collected and subjected to robust preprocessing techniques, including independent component analysis (ICA) and principal component analysis (PCA). The research then employed different deep learning algorithm such as long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), multi-layer perceptron (MLP), and convolutional neural network (CNN) classifiers to predict students' attention. The results reveal notable variations in the classifiers' predictive performance. Our finding revealed that the LSTM model emerged as the top performer and achieved 96% of the accuracy. This study not only contributes to the advancement of attention detection in educational technology but also underscores the importance of preprocessing methodologies, such as ICA and PCA, in optimizing the performance of deep learning models for EEG-based applications.</span>

Compositional Variations in Wheat Bran Influence Growth Performance, Nutrient Retention, and Cecal Microbiome in Broilers

Recent studies have discovered that wheat bran could stimulate digestive tract development, modulate intestinal microbiota, and improve the digestibility of fibrous ingredients in broilers. Wheat bran varies greatly in its chemical constituents. The objective of this study is to investigate how different types of wheat bran influence growth performance, nutrient digestion, and gut health in broiler chickens. Eight types of wheat bran were sampled and analyzed for nine constituents. A principal component analysis demonstrated that the wheat bran could be clustered into three groups based on the first principal component, namely low-fiber (LF), medium-fiber (MF), and high-fiber (HF) wheat bran. Wheat bran from the LF, MF, and HF groups significantly differed in terms of the contents of NDF, acid detergent fiber (ADF), hemicellulose, in vitro digestible energy (IVDE), crude fat, and ash (p < 0.05). A total of 324 seven-day-old broiler chicks were grouped into six body weight blocks. Each block of birds was randomly divided into six treatments, fed either a corn–soybean meal basal diet or five experimental diets, including 3% wheat bran from the LF, MF, and HF groups, until d 21. The feed/gain ratio was increased, and the coefficients of dry matter (DM, p = 0.001), gross energy (GE, p = 0.005) digestion, energy (p = 0.039), and CP (p = 0.051) retention were reduced in the birds that were fed 3% HF wheat bran. In contrast, in the birds fed 3% LF wheat bran, the coefficients of nutrient digestion and retention of energy and CP were not affected, but the CP (p = 0.009) and the NDF (p = 0.059) digestion ability were increased when switched to the same diet. In the cecal digesta, Barnesiella was enriched in the birds from the LF group, while Lachnospiracea was enriched in the birds from the MF group. Adding a moderate amount of wheat bran from the LF group could promote protein and fiber digestion and increase the abundance of Barnesiella in broilers.

A new method to assess the performance of anti-scatter grids in x-ray projection imaging.

This work proposes a new method to assess the performance of radiographic anti-scatter grids (ASGs) without the use of a narrow primary beam, which is difficult to achieve. Three general purpose ASGs were evaluated, two marketed ASGs and a low frequency and high ratio prototype ASG with molybdenum lamellae. A range of high scatter x-ray beams were used in a standardized geometry, with energies ranging from 60 kV to 121 kV, for five beam sizes between 10 x 10 and 30 x 30 cm2. The scatter fraction (SF) of each beam was measured in the image plane with and without ASG using the lead beam stop method with an extrapolation function derived from the scatter point spread function (PSF). The primary, scatter and total transmissions of the three ASGs measured for the different x-ray beams allowed the calculation of the grid factor, contrast improvement factor and detective quantum efficiency (DQE)as functions of the input SF. The results obtained for the three ASGs are consistent with those obtained with the standard narrow-beam method and data published in the literature, confirmed the prime importance of the ASG primary transmission and revealed important variations in ASG performance, especially as a function of the input SF and beam size. The break-even input SFs at which the imaging system efficiency was improved by the ASGranged between 0.18 and 0.52 for the different ASGs and beam characteristics. The methodis proposed as an alternative to current ASG characterization techniques.

A reinvestigation of cognitive styles in sticklebacks: decision success varies with behavioral type

Abstract The ‘cognitive styles’ hypothesis suggests that individual differences in behavior are associated with variation in cognitive performance via underlying speed-accuracy trade-offs. While this is supported, in part, by a growing body of evidence, some studies did not find the expected relationships between behavioral type and cognitive performance. In some cases, this may reflect methodological limitations rather than the absence of a true relationship. The physical design of the testing arena and the number of choices offered in an assay can hinder our ability to detect inter-individual differences in cognitive performance. Here, we re-investigated the cognitive styles hypothesis in threespine stickleback (Gasterosteus aculeatus), adapting the maze design of a previous study which found no cost to decision success by faster (bolder) individuals. We used a similar design but increased the size of the maze and incorporated an additional choice in the form of a third maze arm. We found, in accordance with cognitive style expectations, that individuals that were consistently slower to emerge from the start chamber made fewer errors than fish that emerged faster. Activity in an open field test, however, did not show evidence of a relationship with decision success, possibly due to the low number of repeated observations per fish in this separate assay. Our results provide further empirical support for the cognitive styles hypothesis and highlight important methodological aspects to consider in studies of inter-individual differences in cognition.

An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits.

Heritage Spanish in Montreal: An Analysis of Clitics in Spontaneous Production Data

This study investigates clitic use in adult heritage speakers (HL speakers) of Spanish, with French as their dominant language. We conducted an exploratory case study using spontaneous production data from HL speakers of Spanish and first-generation Spanish immigrants living in Montreal, Canada. Data were collected through two guided production tasks, one oral and one written, to account for task-induced performance variations. Our analysis focused on clitic production, omission, function, optionality, and grammaticality. The findings reveal both similarities and differences compared to monolingual native Spanish speakers, highlighting a broad range of clitic structures produced by all participants and a potential tendency among HL speakers to favour fixed choices in optional structures. This study provides valuable insights into the production patterns of clitics in adult HL speakers of Spanish with French as their dominant language, contributing to our understanding of bilingual grammar.

A NUMA-Aware Version of an Adaptive Self-Scheduling Loop Scheduler

Parallelizing code in a shared-memory environment is commonly done utilizing loop scheduling (LS) in a fork-join manner as in OpenMP. This manner of parallelization is popular due to its ease to code, but the choice of the LS method is important when the workload per iteration is highly variable. Currently, the shared-memory environment is evolving in high-performance computing as larger chiplet-based processors with high core counts and segmented L3 cache are introduced. These processors have a stronger non-uniform memory access (NUMA) effect than the previous generation of x86-64 processors. This work attempts to modify the adaptive self-scheduling loop scheduler known as iCh ( i rregular Ch unk) for these NUMA environments while analyzing the impact of these systems on default OpenMP LS methods. In particular, iCh is as a default LS method for irregular applications (i.e., applications where the workload per iteration is highly variable) that guarantees “good” performance without tuning. The modified version, named NiCh , is demonstrated over multiple irregular applications to show the variation in performance. The work demonstrates that NiCh is able to better handle architectures with stronger NUMA effects, and particularly is better than iCh when the number of threads is greater than the number of cores. However, NiCh also comes with being less universally “good” than iCh and a set of parameters that are hardware dependent.

Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports.

Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented. We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports. We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2. In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P<.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2's 38% (149/392, P<.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P<.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2. The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics.

Genotype‐by‐environment interactions for mean performance and trait variation in house fly larvae reared on two diets

AbstractSome insect species have been proposed as a sustainable alternative to traditional animal‐based food and feed sources. Optimisation of insect production can generally be achieved using two main approaches: optimising environmental conditions and improving traits of interest through selective breeding. These avenues are not inseparable as performance of a genotype might differ between environments due to phenotypic plasticity and because genotypes can respond differently to environmental changes, that is genotype‐by‐environment interactions (G × E). In insect production, diets can be of variable quality and consist of waste‐ and by‐products of low nutritional value, which might result in decreased performance and/or increased trait variability within a population. Therefore, it is of interest to investigate how genotypes perform across various diets. Here, we investigated plasticity and G × E for mean performance and trait variation, which we define as the ability of a genotype to produce consistent phenotypes within and across environments. We did this by rearing 190 full‐sib families of house fly larvae, Musca domestica L. (Diptera: Muscidae), on two diets based on either alfalfa, Medicago sativa L. (Fabales: Fabaceae), or deproteinated grass. Four larval traits were assessed: egg‐to‐larva viability, surface area, dry weight and relative lipid content. Reaction norms were used to investigate the effects of diet on full‐sib family mean and trait variation within and across environments. We found that families reared on the grass‐based diet had higher performance across all investigated traits than families reared on the alfalfa‐based diet. For both mean performance and trait variation, we found G × E for all investigated traits. These findings suggest that there is genetic variation for plasticity (slope of reaction norms) for both trait mean and trait variation, and thus that there is a potential to breed for genotypes with high performance as well as for genotypes with low trait variation within and across diets.

Research on Optimal Design of Ultra-High-Speed Motors Based on Multi-Physical Field Coupling Under Mechanical Boundary Constraints

This study investigates the impact of rotor structure, material selection, and cooling methods on ultra-high-speed motor performance, revealing performance variation laws under multi-physical field coupling. Considering mechanical boundary constraints, we propose an optimization design method based on a multi-physical field coupling model. Using a MaxPro experimental design, initial samples are obtained and fitted using a Kriging surrogate model. The NSGA-2 algorithm is then applied for optimization, with Relative Maximum Absolute Error (RMAE) and Relative Average Absolute Error (RAAE) employed for accuracy evaluation. The Kriging model is iteratively updated based on evaluation results until the optimal design is achieved. This method enhances motor performance, ensures mechanical boundary conditions, and reduces computational load. Experimental results show significant improvements in efficiency and power density. This study provides theoretical support and technical guidance for ultra-high-speed motor design and offers new ideas for related motor research and development. Future work will explore more efficient and intelligent optimization algorithms to continuously advance ultra-high-speed motor technology.

Polarisation Synthesis Applied to 3D Polarimetric Imaging for Enhanced Buried Object Detection and Identification

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems.

Insights into the Acute Stress of Glutaraldehyde Disinfectant on Short-Term Wet Anaerobic Digestion System of Pig Manure: Dose Response, Performance Variation, and Microbial Community Structure

The outbreak of epidemics such as African swine fever has intensified the use of disinfectants in pig farms, resulting in an increasing residual concentration of disinfectants in environmental media; however, the high-frequency excessive use of disinfectants that damage pig farm manure anaerobic fermentation systems and their mechanisms has not attracted enough attention. Especially, the complex effects of residual disinfectants on anaerobic fermentation systems for pig manure remain poorly understood, thus impeding the application of disinfectants in practical anaerobic fermentation systems. Herein, we explored the effects of glutaraldehyde disinfectant on methane production, effluent physicochemical indices, and microbial communities in a fully automated methanogenic potential test system (AMPTSII). The results show that adding glutaraldehyde led to remarkable alterations in methane production, chemical oxygen demand (COD), volatile solids (VS), and polysaccharide and phosphorus concentrations. During the anaerobic process, the production of methane displayed a notable decrease of 5.0–98% in all glutaraldehyde treatments, and the trend was especially apparent for treatments containing high levels of glutaraldehyde. Comparisons of the effluent quality showed that in the presence of 0.002–0.04% glutaraldehyde, the COD and total phosphorus (TP) increased by 12–310% and 15–27%, respectively. Moreover, the addition of 0.01–0.08% glutaraldehyde decreased the ammonium (NH4+-N) concentration and VS degradation rate by 7.7–15% and 4.9–26.2%. Furthermore, microbiological analysis showed that the glutaraldehyde treatments had adverse effects on the microbial community. Notably, certain functional bacteria were restrained, as highlighted by the decreases in relative abundance and microbial diversity by 1.3–17% and 0.06–21%, respectively. This study provides a theoretical basis for the rational use of disinfectants in anaerobic fermentation systems.

A multi-algorithm pathfinding method: Exploiting performance variations for enhanced efficiency

A multi-algorithm pathfinding method: Exploiting performance variations for enhanced efficiency

Influence of Democratic Leadership Style on Teachers’ Performance in 12 Year-Basic Education in Nyaruguru District, Rwanda

Numerous studies have found that democratic leadership style is commonly applied in Rwanda schools. However, less is known about its influence on teachers’ performance in 12 year-basic education. This article intends to investigate the influence of democratic leadership style on teachers’ performance in terms of instructional planning, student assessment, and teacher-parent collaboration. A quantitative method was used for data collection, utilizing a correlational research design with a sample size made of 164 teachers and 12 head teachers for 12 schools. Proportional sampling was used in determining the number of teachers from each school, simple random sampling to select teachers from the total number of teachers at each school, and purposive sampling to select all head teachers. Data were gathered through the questionnaires distributed to the teachers. Regression and correlational analyses were conducted using SPSS (version 27). The findings revealed that the democratic leadership style has a positive influence on teachers' performance in 12-year basic education. Specifically, 94.7% of the variation in teachers' performance is explained by the democratic leadership style, as indicated by an R-square value of 0.947. The results show that as head teachers increase the use of democratic leadership, teachers' performance improves in areas such as instructional planning, student assessment, and teacher-parent collaboration. Based on these findings, government is recommended to establish a policy framework that explicitly explains and encourages the application of democratic leadership style in 12 year-basic education.

Thermal-Gas-Elastic Coupling Modeling and Performance Analysis of Foil Cylindrical gas Film Seal Considering Speed Slip, Temperature Jump and Shaft Misalignment

Abstract The Compliant foil cylindrical gas film seal is an advanced sealing technology designed for high-speed and high-temperature conditions in a jet engine. Its design involves using a compliant foil to create a cylindrical sealing surface, achieving adaptive sealing in environments with high pressure differences and rotational speeds. For high-speed and high-temperature operation, a speed slip flow model was established by considering film slip flow, wall temperature jump, foil deformation, and gas viscosity-temperature thermal effects. The variations in the lubrication performance of the foil cylindrical gas film seal with changes in shaft misalignment directions, misalignment angles, and operating parameters were investigated. The speed slip leads to a decrease in gas film lift and an increase in leakage. The wall temperature jump phenomenon caused by speed slip leads to a 2.56% decrease in film temperature. The shaft misalignment angles on the high-pressure and low-pressure sides have different impacts on the flow field, with film pressure and temperature differing by 66.78% and 11.19%, respectively.

Factors influencing the variability in performance of cattle grazing tropical pasture

Context Whereas it is known that there is variation in liveweight gain (described as performance in this paper) within a group of grazing cattle, the causes of this variation are not well understood. Aim A 2-year grazing study in northern Queensland was conducted to understand the relative influence of diet selection, grazing behaviour and rumen fermentation on variation in performance of growing cattle. Methods Eighty-nine Bos taurus × B. indicus (~6 months old) growing steers were grazed as a group on mixed tropical pasture for 23 months. Pasture was characterised for biomass, species and nutrient composition. Cattle were ranked according to overall liveweight (LW) gain and the top (high performance, HP) 20 and bottom (low performance, LP) 20 head were compared for diet composition, rumen fermentation and grazing behaviours. Results Pasture biomass, and nutritive value of pasture and diet varied between seasons and years of study. The HP cattle achieved overall LW gains 20% greater than those of the LP cattle mainly due to higher rates of gain in the wet season. However, the nutritive value and rumen fermentation characteristics were similar for both LP and HP cattle, although there was evidence that crude protein and digestibility were modestly higher in diets of HP cattle, especially in the first wet season. Activity, measured as distance travelled, declined as pasture biomass declined, and cattle appeared to favour pasture with higher legume content. Conclusion It is concluded that divergence in performance of cattle on tropical pasture could not be fully explained by measurements taken in this study. Diet selection for plant components with improved nutritive value probably played a small role. Differences in initial LW between the LP and HP groups may have been a contributory factor. Implications Despite comprehensive measurements of factors that influence efficiency and performance on pasture, we were unable to identify definitive causes. Genetic variation in feed intake or efficiency of digestion needs to be investigated.

A unified method to generate representative volume elements with tailored random fibre arrangements to estimate the shear and transverse behaviours of unidirectional continuous fibres composite plies

Axial compressible strength is a key design parameter of CFRP laminate structures, especially for Aerospace where maximum flexure on wings is driven by the global margin of safety on compressive loads. One of its main limiting contributors is the non-linear shear behaviour of the unidirectional ply. This shear response can be predicted by using generated (quasi-)random microstructures set within representative volume elements (RVE). Different microstructures with or without clusters of fibres (resulting from manufacturing processes) are necessary to quantify the mechanical variability. The variability of the microstructures is characterised by the variation of the arrangement, the volume fraction and the misalignment of the fibres. This leads to significant variations in the mechanical performance, in particular for the shear behaviour. In this study, we propose a unified algorithm that makes it possible to generate all the different positions of fibres in microstructures with very high fibre volume fractions. The microstructural parameters responsible for variations in mechanical behaviour are identified to establish a correlation between fibre distribution and the mechanical response. Finally, a composite cross-section is analysed to estimate the local non-linear shear behaviour as well as the compressive strength as an example to illustrate the benefit of complex microstructures modelling.

Heat Transfer Enhancement in Pulsating Flows: A Bayesian Approach to Experimental Correlations

Abstract This research focuses on improving the modeling of heat transfer effects in pulsating exhaust flows. We address the challenges of understanding oscillating flow by employing the Metropolis–Hastings Markov Chain Monte Carlo sampling method for parameter estimation, accounting for measurement uncertainties. The knowledge can be applied to waste heat from reciprocating devices, pulsating turbocharger performance, and flow fields with significant cyclic variations. We demonstrate the feasibility of characterizing heat transfer capacity in pulsating flows using Bayesian inference and polynomial regression for experimental data correlation. This methodology is furthermore applied to identify heat transfer patterns in cold gas flow through a heated pipe across a range of mass flowrates and pulsating frequencies. To achieve this, the thermal performance variations across the length of the pipe through temperature and pressure changes are quantified. The model developed exhibits robust performance and high data efficiency (R2∈[0.83,0.89]) and notable extrapolation capacity in predicting mean heat transfer behavior based on boundary measurements. The results address the lack of experimental insights into pulsating flows encountered in heavy-duty transport applications and can be extended for heat recovery in systems such as exhaust manifolds and organic Rankine cycle gas turbines.

A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

Examining the Influence of Transformational Leadership Style on Teachers’ Performance in 12 Year-Basic Education in Nyaruguru District, Rwanda

While numerous studies determined leadership styles in schools, limited research addresses the specific impact of transformational leadership on teachers’ performance in 12-year Basic Education. This study examined influence of transformational leadership on teachers’ performance in 12 Year Basic Education in Nyaruguru district, Rwanda. The study employed quantitative approach with correlational design, utilizing regression analysis and correlational analysis. This study used the sample of 164 teachers from 12 schools in Nyaruguru district. Proportional sampling determined the number of teachers from each school, and simple random sampling selected individual respondents. Data were collected via questionnaires and analyzed using regression and correlation techniques with SPSS (version 27). Results indicate a significant positive relationship between transformational leadership and teacher performance: individualized influence accounts for 71% of performance variation, idealized influence 70.6%, intellectual stimulation 58.8%, and inspirational motivation 45.9%. Increased use of transformational leadership by headteachers enhances teachers' performance in instructional planning, student assessment, and parental collaboration. The study recommends that the government provide guideline documents on transformational leadership for schools and encourages headteachers to adopt this style to enhance teacher performance.