Actual Performance Research Articles

Adding sugar to children's beverages: a theory of planned behavior study of Lebanese mothers.

This study, guided by the theory of planned behavior (TPB), aimed to investigate whether attitude, subjective norms, and perceived behavioral control can predict parents' intentions to reduce or stop adding sugar to their child's beverages, such as tea or milk. The research also seeks to determine the predictive power of the TPB on the actual behavior of parents in reducing or halting sugar intake in their children's drinks. A questionnaire was developed to assess sweetening beverage behavior variables by mothers of children aged 3-7 years attending Lebanese public schools, with 184 responses retained with no missing values for analysis. Regression analysis was utilized to examine determinants of self-reported practice and intentions regarding sweetening beverages. Results showed that attitude and perceived behavioral control were significant predictors of intention to reduce or stop adding sugar to a child's beverages. The perception of behavioral control was found to predict the behavior of adding sugar to a child's beverages, even though the intention to reduce or stop did not. The study findings suggest that individuals with low perceived behavioral control and strong habits would be more likely to be actual performers of the adding sugar behavior regardless of their intention level. When adding sugar to a child's beverages is a common parental habit in low-income Lebanese families, our findings offer valuable insights for developing culturally and socially tailored health promotion strategies to reduce or stop adding sugar to children's beverages and raise parental awareness about the health risks associated with excessive sugar consumption in children.

A Transferability-Based Method for Evaluating the Protein Representation Learning.

Self-supervised pre-trained language models have recently risen as a powerful approach in learning protein representations, showing exceptional effectiveness in various biological tasks, such as drug discovery. Amidst the evolving trend in protein language model development, there is an observable shift towards employing large-scale multimodal and multitask models. However, the predominant reliance on empirical assessments using specific benchmark datasets for evaluating these models raises concerns about the comprehensiveness and efficiency of current evaluation methods. Addressing this gap, our study introduces a novel quantitative approach for estimating the performance of transferring multi-task pre-trained protein representations to downstream tasks. This transferability-based method is designed to quantify the similarities in latent space distributions between pre-trained features and those fine-tuned for downstream tasks. It encompasses a broad spectrum, covering multiple domains and a variety of heterogeneous tasks. To validate this method, we constructed a diverse set of protein-specific pre-training tasks. The resulting protein representations were then evaluated across several downstream biological tasks. Our experimental results demonstrate a robust correlation between the transferability scores obtained using our method and the actual transfer performance observed. This significant correlation highlights the potential of our method as a more comprehensive and efficient tool for evaluating protein representation learning.

Design of corporate human capital value measurement model based on binary tree

The measurement process of corporate human capital value ignores the in-depth analysis of the influencing factors of value measurement, which leads to the inaccuracy of the measurement result of corporate human capital value. Therefore, taking an enterprise in Heilongjiang Province as the research object, a measurement model of enterprise human capital value based on binary tree is designed. Establish the corporate human capital value measurement index system, and initially fix the human capital value. Calculate the floating value of human capital under the complete information, and determine the influencing factors of human capital value measurement. The human capital value of enterprise under incomplete information is calculated to complete the construction of the measurement model of human capital value of enterprise based on binary tree. Taking the actual human resource performance score of an enterprise from 2019 to 2022 as an example, the experiment proved that the design model and the traditional measurement model evaluated the enterprise human resource performance, and proved that the measurement model based on binary tree was more accurate than the traditional model, and the final measurement accuracy of the design model reached 93.5%. Compared with the traditional measurement model ahead of 18.5%–10.3%, compared with the current advanced measurement model ahead of 5.2%–5.7%. The evaluation Angle in the research is slightly lacking of practical significance, and further research can carry out further evaluation on income to maximize the practical significance of the research. However, the model is evaluated from the perspective of pricing, but in real life, the value of enterprise human capital is reflected in income distribution. Therefore, if the measurement of human capital value is combined with income distribution in the next step, the research results will be closer to the actual situation and will have more practical significance.

ICT – (Information and Communication Technology) Based Instruction Toward Performance Framework

The primary objective of the research was to assess the fundamental proficiency levels in utilizing technology among teachers from District I-C Elementary Schools within the Division of Antipolo City during the academic year 2022 – 2023. The study encompassed a sample size of one hundred seventy-seven (177) elementary educators. These participants were characterized based on factors such as age, gender, marital status, job title, educational attainment, years of service, and their exposure to ICT-related training. To ascertain the basic knowledge and skills concerning computer utilization, a combination of a questionnaire checklist and direct observations by the researcher were employed. This approach facilitated the evaluation of both the perceived efficacy and the actual performance of the participants. The study’s findings revealed that a predominant portion of the respondents were married and situated within the age range of 31 to 45 years. A notable proportion held the position of Teacher I and had accumulated a working tenure of 16 to 20 years at their respective schools. Within the educator cohort, approximately 59.3% possessed qualifications at the master’s degree level, whereas none had attained a doctoral degree. The elementary teachers’ fundamental knowledge and skills in using computers within District I-C Elementary Schools were evaluated across various dimensions, including basic computer operation, word processing, electronic worksheet utilization, computer-based presentation, communication and research, and instructional technologies. The assessment indicated that the teachers’ proficiency in these areas was classified as “Competent” in both their perceived efficacy and actual performance. The examination of the significant difference in knowledge and skills related to computer usage, as perceived by the respondents and their actual performance across the identified dimensions or areas led to the rejection of the null hypothesis. In light of the obtained findings and conclusions, several recommendations emerge. Firstly, it is suggested that teachers actively participate in a higher number of seminars and training sessions focused on ICT utilization. Such engagements would facilitate the deeper integration of ICT tools into the teaching-learning process along mathematics in teaching elementary grades, enhancing overall instructional effectiveness. Moreover, it is worthwhile to explore further research endeavors that encompass different variables. Conducting parallel studies that incorporate various aspects or factors could provide a more comprehensive understanding of the complex relationship between teachers, ICT utilization, and effective teaching practices. Keywords: ICT-Based Instruction, Performance Framework, fundamental skills, computer

PERFORMANCE INVESTIGATION OF A 5 MW PHOTOVOLTAIC SYSTEM: ALFASHIR SUDAN

This study comprehensively analyzes the operational performance and economic feasibility of a 5MW grid-connected photovoltaic in Sudan over a two-year monitoring period. Leveraging the capabilities of PVSyst software, the actual plant performance was rigorously compared to simulation predictions. Results revealed a significant disparity between observed and simulated energy generation, prompting the exploration of potential areas for enhancement. Seasonal fluctuations in the performance ratio were evident, with peak values during some seasons, albeit not consistently aligning with simulation projections. Despite these challenges, the Plant made a notable impact by reducing CO2 emissions, highlighting the environmental benefits of solar energy. Financial analysis unveiled discrepancies between actual and simulated savings, underscoring the need for optimization and efficiency improvements. The equity payback period, influenced by electricity pricing policies, was estimated at 15 years, emphasizing its role in assessing project economics. The region's high radiation intensity and humidity levels emerged as significant factors affecting plant performance. Addressing these challenges is crucial for maximizing the PV plant's operational efficiency and viability. Our Recommendations include thoroughly examining control system functions during commissioning and improving operational accuracy.

Multiple choice as formative assessment in dental education.

The effectiveness of multiple-choice questions (MCQs) in dental education is pivotal to student performance and knowledge advancement. However, their optimal implementation requires exploration to enhance the benefits. An educational tool incorporating MCQs was administered from the 5th to the 10th semester in a dental curriculum. The students filled out a questionnaire after the MCQ, which was linked to the learning management system. Four cohorts of four semesters generated 2300 data points analysed by Spearmen correlation and mixed model regression analysis. Demonstrated a significant correlation between early exam preparation and improved student performance. Independent study hours and lecture attendance emerged as significant predictors, accounting for approximately 10.27% of the variance in student performance on MCQs. While the number of MCQs taken showed an inverse relationship with study hours, the perceived clarity of these questions positively correlated with academic achievement. MCQs have proven effective in enhancing student learning and knowledge within the discipline. Our analysis underscores the important role of independent study and consistent lecture attendance in positively influencing MCQ scores. The study provides valuable insights into using MCQs as a practical tool for dental student learning. Moreover, the clarity of assessment tools, such as MCQs, remains pivotal in influencing student outcomes. This study underscores the multifaceted nature of learning experiences in dental education and the importance of bridging the gap between student expectations and actual performance.

Pressure Effects and Countermeasures in Solid‐State Batteries: A Comprehensive Review

AbstractSolid‐state batteries (SSBs) have garnered significant attention as promising and safe electrochemical solutions for high‐energy storage. Despite their advantageous characteristics, the widespread adoption of SSBs encounters significant obstacles. Foremost among these challenges is the inadequate solid‐state electrolyte (SSE)‐electrode contact, particularly under typical operating conditions with moderate pressures. Consequently, substantial external pressures are conventionally applied to establish a tightly bonded and low‐impedance interfacial connection. Unfortunately, high pressure concurrently precipitates detrimental effects, such as SSE structural fractures and premature short circuits. Moreover, the pressure parameters that are currently employed in laboratory‐scale research lack consistency and far exceed the current industrial requirement (< 1 MPa), which undermines the objective evaluation of SSBs’ actual performance and hampers the practical utilization. This review aims to construct a comprehensive perspective on the effect of pressure on SSBs, with a specific focus on decoupling the interfacial/bulk electrochemo‐mechanical dynamics. In particular, the adverse consequences and fundamental causes of the highly‐pressure‐reliance behavior in SSBs are scrutinized, followed by a systematic summarization of the current strategies toward low‐pressure SSBs. Based on these insights, it is put forth promising directions for better disentangling the electrochemo‐mechanical interplay within SSBs and inspiring the development of pressure‐independent SSBs.

4 Utilizing SmartFeed Pro and SuperSmart Feeders for applied beef cattle research

Abstract Designing research for applied beef cattle production systems has always presented challenges for Land Grant University and USDA Scientist. Specifically, creating study designs that have enough replication for statistical inference has often prompted scientists to use designs that do not closely replicate actual production systems. Past research has often used multiple replications (pens/paddocks/pastures) that utilize replication (group averages; 4 – 8 animals) means as the experimental unit. While effective in measuring average treatment responses, these types of study designs do little to describe the actual individual animal behavior and performance in a more practical production environments. Approaches to increasing statistical observations in actual production environments will be discussed in this presentation. Embracing new technologies such as GPS, activity monitors, EID tags, and feed intake monitors (C-Lock Inc, GrowSafe) combined with multiple regression modeling tools can aid in designing and publishing beef cattle production research in actual beef production environments. Research at Montana State University has utilized SmartFeed Pro and SuperSmart Feeders for strategic supplementation research on extensive rangelands and dry-land pastures for the past seven years. Results of these studies have evaluated self-fed (salt-limited; loose, pelleted, and baked molasses blocks) as well as hand-fed supplements where daily intakes are limited by the locking gate on the feeder. This presentation will summarize these studies with a focus on research designs that were successful and/or unsuccessful in respect to characterizing beef cattle performance in production settings. Our goal will be to assist the audience in the application of feed intake technology in designing future applied beef cattle research.

Deep learning technique for plant disease detection

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.

A review of the impact of ground test-related facility effects on gridded ion thruster operation and performance

A key consideration in the interpretation of ground test data of electric propulsion devices purposed for spaceflight is understanding how facility-effects influence thruster operation. This understanding is critical to the prediction of actual thruster performance in space. The necessity of science-based predictions gleaned from ground tests are particularly critical at higher thruster power levels. Operation of engines at higher power levels in vacuum chambers leads to considerable elevation in background pressure, background plasma density, and backsputter rates. This review examines the influence of ground test facility effects on gridded ion thruster operation. Ground test operation is compared with flight data, where available, to obtain a clear picture of operational differences. Mitigation strategies to alleviate facility effects are also commented upon.

Data-Driven Modeling With Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this paper proposes a novel data-driven modeling with experimental augmentation (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EA), leveraging both simulation data and experimental data. In D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.

The bright side and dark side of performance expectations: the role of organizational culture and the impact on employee performance and wellbeing

Organizational culture has the potential to contribute to employee performance and wellbeing when it creates reasonable expectations and provides employees with the necessary skills and other resources to succeed. A wide range of stakeholders inside and outside the organization influence performance expectations, which largely revolve around workload (the quantity, quality and timeliness of the tasks they are entrusted with), customer service, innovation, and internal relationships. Given the relative lack of attention paid in prior research to performance expectations as an element of organizational culture, our qualitative study sought to investigate the sources and types of expectations of employees and the impact on their actual performance and wellbeing. We focused on the finance industry, which has received considerable criticism in various countries in terms of the undue pressure exerted on employees and the effects on their stress levels. It was therefore surprising to find that most of our New Zealand participants felt that expected performance levels were reasonable, apart from the extra but temporary demands created by the Covid-19 pandemic, and that their wellbeing was a genuine concern to their employers. We signal new research directions to explore how organizations can shape cultures where performance expectations lead to good results and staff wellbeing.

Ship SINS/CNS Integrated Navigation Aided by LSTM Attitude Forecast

Under the strong interference of sky background noise, the reliability of celestial navigation system (CNS) measurement will drop sharply, which leads to performance deterioration for ships’ strapdown inertial navigation system (SINS)/CNS integrated navigation. To solve this problem, a long short-term memory (LSTM) model is trained to forecast a ship’s attitude to detect the attitude provided by the CNS, and the LSTM forecasted attitude can also be used as a backup in case of CNS failure. First, the SINS/CNS integrated model is derived based on an attitude solution of the CNS, which provides more favorable feature data for LSTM learning. Then, the key techniques of LSTM modeling such as dataset construction, LSTM coding method, hyperparameter optimization and training strategy are described in detail. Finally, an experiment is conducted to evaluate the actual performance of the investigated methods. The results show that the LSTM model can accurately forecast a ship’s attitude: the horizon reference error is less than 0.5′ and the yaw error is less than 0.6′, which can provide reliable reference attitude for the SINS when the CNS is invalid.

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models.

Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r2 scores of 0.8268 and 0.6875, respectively.

Towards an improved thermometric method: Convective and radiative heat transfer for heat flux measurement through an indirect approach

Actual thermal performance of building walls need to be assessed through on-site tests. Several measurement procedures have been proposed during the last decades. Nonetheless, among them the Thermometric (THM) method has been poorly explored in literature, hence opportunities for improvements can be highlighted. The THM technique is a quite a new and non-standardized procedure for wall thermal characterization, based on the well-known Newton’s law of cooling. This law needs the heat transfer coefficient knowledge and there is no agreement about the value to apply when the THM method is used. In this work, a low-cost experimental apparatus characterized by a heated wooden sample was realized. Sensors were installed to measure heat fluxes through a common heat flux plate. The obtained values were compared with the heat flows achieved via the THM method, where dimensionless groups analysis was performed for computing suitable heat transfer coefficients obtained from data processing. The aim of this work is to lay the groundwork for overcoming the disagreement related to the value of the total heat transfer coefficient in the THM method by proposing and consolidating an alternative approach. The results show that the proposed data processing for the THM method can provide satisfying results in terms of indirect heat flux measurements, showing extremely low percentage differences (ranging from + 1.16 % to + 3.56 %) if compared with the HFM method.

Experimental Investigation of the Dynamic Responses of Thin-Walled and Foam-Filled Steel Tubes Subjected to Repeated Impacts.

In this study, a horizontal impact setup was used to measure the dynamic responses of specimens fixed on a reaction wall and subjected to repeated impacts generated by a large-tonnage impactor. The contact force, deformation process, energy absorption, and other properties of two specimens (a thin-walled steel tube and foam-filled steel tube) were thoroughly investigated. The results demonstrated that the thin-walled tube's properties were consistent with the four-phase and six-phase deformation models and that the foam-filled tube's properties were consistent with the two-phase deformation model. In the early stages of the experiment, the foam-filled and thin-walled tubes were similar in terms of the contact force and energy absorption. However, when the polyurethane (PU) strain reached 0.8, the PU significantly increased the support of the tubes, reduced the contact force (by extending the contact time), and increased the energy absorption capacity by 33.6-43.5%. The crush curves of the specimens were in agreement for cases involving multiple impacts, as well as for one impact with the same impact of kinetic energy. The crush curves can be used to assess the actual performance of crashworthy devices. Furthermore, after repeated impacts, the foam-filled tube exhibited a pseudo-shakedown behavior.

Bayesian analysis on a natural conjugate prior for the nonhomogeneous Poisson process with a power-law intensity under time-truncated sampling

The core content in this paper discusses the Bayesian approach, which essentially estimates and predicts the reliability of a repairable system during the actual testing process. As specified in previous literature, the nonhomogeneous Poisson process (NHPP) with a power-law intensity function has often been employed as a model for describing the failure process of repairable systems since it provides a reasonable way of presenting the rate of change for the reliability of a system as a function of time. This research proposed the same time-truncated sampling assumption to mainly ensure that the joint posterior distribution of the power-law failure process has a considerably closed-form representation. This proposed approach facilitates the analysis of the actual performance, with which analysts can provide the prior information about the authentic means and variations of the shape and scale parameters of the power-law intensity. The corresponding posterior means and variations can then be significantly and easily obtained without any complex integral arithmetic. Using Monte Carlo simulation for comparison, the Bayesian estimation with the proposed prior outperforms the maximum likelihood estimation, particularly when the prior aging of the repairable is underestimated.

Streamlining performance prediction: data-driven KPIs in all swimming strokes

ObjectiveThis study aimed to identify Key Performance Indicators (KPIs) for men’s swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men’s individual 100 m races of the 2019 European Short-Course Swimming Championships.ResultsDuration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs − 2.3% to + 2.3%), Breaststroke (0% bias, LOAs − 0.9% to + 0.9%), Butterfly (0% bias, LOAs − 1.2% to + 1.2%), and Freestyle (0% bias, LOAs − 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.

The Impediments to Self-purification: A Review

Allah Ta’ala, the creator of the world, created humans as the best creatures in the best creation and gave them freedom of will and action. Man can, if he wishes, keep the creator's creative intention intact by positioning in the seat of the best creature in creation, but if he wishes, he can lower himself to the lowest level. He completely keeps up freedom of will and freedom of action. In this case, action selection is its main driving force. A general man by properly following the path of his God (Allah) can appoint himself to a higher position than the innocent angels. On the other hand, he turns himself into the worst creature in creation like a cursed devils. It is only possible for a Muslim who strictly follows the principles of Islam with a holy soul to find the path of Allah and achieve success. Therefore, it is an essential duty of every Muslim, who strictly follows the principles of Islam, to free themselves from all self-destructive activities. Undoubtedly actual performance of these duties opens the way to self-purification. As a result, he has a pure soul achieves the satisfaction of the Almighty Allah, and owns the highly expected paradise. Besides the root of this success is complete self-purification from all evil deeds and actions. Therefore, it is absolutely essential to make every effort to overcome the obstacles of self-purification identified in the light of the Quran- Hadith.

“That’s just Future Medicine” - a qualitative study on users’ experiences of symptom checker apps

BackgroundSymptom checker apps (SCAs) are mobile or online applications for lay people that usually have two main functions: symptom analysis and recommendations. SCAs ask users questions about their symptoms via a chatbot, give a list with possible causes, and provide a recommendation, such as seeing a physician. However, it is unclear whether the actual performance of a SCA corresponds to the users’ experiences. This qualitative study investigates the subjective perspectives of SCA users to close the empirical gap identified in the literature and answers the following main research question: How do individuals (healthy users and patients) experience the usage of SCA, including their attitudes, expectations, motivations, and concerns regarding their SCA use?MethodsA qualitative interview study was chosen to clarify the relatively unknown experience of SCA use. Semi-structured qualitative interviews with SCA users were carried out by two researchers in tandem via video call. Qualitative content analysis was selected as methodology for the data analysis.ResultsFifteen interviews with SCA users were conducted and seven main categories identified: (1) Attitudes towards findings and recommendations, (2) Communication, (3) Contact with physicians, (4) Expectations (prior to use), (5) Motivations, (6) Risks, and (7) SCA-use for others.ConclusionsThe aspects identified in the analysis emphasise the specific perspective of SCA users and, at the same time, the immense scope of different experiences. Moreover, the study reveals ethical issues, such as relational aspects, that are often overlooked in debates on mHealth. Both empirical and ethical research is more needed, as the awareness of the subjective experience of those affected is an essential component in the responsible development and implementation of health apps such as SCA.Trial registrationGerman Clinical Trials Register (DRKS): DRKS00022465. 07/08/2020.