Performance Assessment Research Articles

Comparative assessment of emissions, performance, and economics parameters for a dual–fuel diesel generator operating with rice bran biodiesel and hydrogen

The first step to achieving an energy transition is partially substituting fossil fuels with other more environmentally friendly alternatives, such as hydrogen gas. The current research aims to evaluate the influence of hydrogen in a diesel generator fueled with rice bran biodiesel. The above encourages the use of hydrogen and biodiesel production from residual raw material. For the development of the research, a diesel engine bench was used, which operated in five load conditions: 20 %, 40 %, 60 %, 80 %, and 100 %, and was fed with three fuels: −100 %, RB-10 %, and RB-10 % + H2(30 %). The results show that the mixture RB-10 % + H2(30 %) causes a 3.14 % reduction in BSFC and a 3.26 % increase in energy conversion efficiency. In addition, it is observed that a 9.90 %, 12.57 %, and 10.99 % decrease in HC, CO, and smoke opacity emissions compared to pure diesel. On the other hand, the mixture RB-10 % + H2(30 %) reduces by 4.44 %, 5.07 %, and 7.06 % the environmental, social, and ecological impact due to CO2, HC, and CO emissions, as well as a 3.93 % reduction in engine operating cost compared to RB-10 % biodiesel. In general, hydrogen injection is a promising alternative to promote the use of rice bran biodiesel due to its increased performance characteristics and reduced pollutant emissions without the need to modify the engine.

Training framework for high-stakes OSCE: Experience from volunteer standardized patients' bank.

Training of standardized patients (SPs) for national high-stakes OSCE helps to ensure a reliable assessment of student performance in various clinical scenarios. However, workflow protocols to train SPs vary. Medical schools adopt specific measures to ensure standardization. We present a development workflow of the SPs' training framework for high-stakes OSCE using a volunteer SPs' bank. Our approach was guided by the social learning theory. Three educators and 17/20 (85%) members of our volunteer SPs' bank worked in a collaborative partnership on the construction of pedagogical content of the training framework comprising three 2-hour sessions. Since SPs have to demonstrate acquired behaviors, intended learning outcomes used the words "apply", "perform" and "participate." A principal part of the evaluation was the achievement of intended learning outcomes by the SPs during 3 formative OSCEs. Seventeen SPs, 356 fourth year medical students and 60 examiners participated. Quantitative and qualitative data were collected by post-session questionnaires and analyzed using descriptive statistics and thematic analysis. Twelve examiners evaluated a mean of 29.7+/-0.14 SD patient-student encounters. In total, 15/16 SPs (93.8%) considered the contact with students as easy and 4 SPs (31%) reported the experience as stressful. Two themes emerged from the free-text comments: "Gaining experience as SP" and "Concerns for evaluated students." The proposed SPs' training framework for high-stakes OSCE may be useful for other medical disciplines and health professions initiating SP-based assessment programs. The strategy of development and evaluation are outlined to guide a successful application of the curriculum standards.

Construction and Application of a Modern Comprehensive Education Management System in Universities Aimed at Fostering Virtue Through Education

The modern educational landscape in colleges and universities demands efficient and dynamic management systems to handle the ever-increasing complexities of student enrollment, course scheduling, and academic performance assessment. In response to these challenges, this research project introduces a novel approach to education management through the application of Artificial Intelligence. By continuously developing through continuous improvement and optimization, this system is expected to provide modern higher education institutions with a path to sustainable development and a path to improve students' moral character and talents. This research provides higher education administrators and policymakers with valuable insights into how to address today's educational challenges.

Tribological Characteristics of P. stocksii Bamboo Fiber Composites with Permomparative Performance Assessment of Pineapple and Kevlar Fibers Based Friction Compositesanganization Enhancement

Tribological Characteristics of P. stocksii Bamboo Fiber Composites with Permomparative Performance Assessment of Pineapple and Kevlar Fibers Based Friction Compositesanganization Enhancement

Design and Performance Assessment of District Heating Systems in Latvian Region

The energy consumed by buildings for air conditioning accounts for a large percentage of global energy consumption. To promote efficiency and sustainability, the scientific community is making great efforts to develop renewable technologies. A well-known but unfortunately underestimated solution is the development of centralized heating and cooling systems that consistently reduce energy consumption.The work proposes a comparison between three heating configurations covering the demand of a settlement in the Latvian region: 1) centralized district heating (DH) system; 2) 5th generation district heating & cooling (5GDHC) system and 3) individual home heating (HH) systems. Thermal and electrical loads are evaluated by transient simulations of a residential area with 80 buildings for the Riga climate and compared with the same settlement in a Mediterranean region (Milan, IT). The energy plants are based on different technologies: combined heat and power (CHP) plants, gas-fired boilers, and domestic heat pumps.The analysis includes the option of power exchange with the national grid. A transient numerical model has been developed for each solution. Every component is modelled according to performance maps provided by the manufacturers, allowing an accurate simulation in both design and off-design operating conditions. The study includes energy, economic and environmental aspects.The result of the simulation highlights the large difference between the two locations, not only in terms of annual load, but also in terms of load distribution. On an annual basis, the Latvian residential complex requires almost twice as much energy as the Italian one. The thermal losses in the district systems are 4.21% in Milan solution and 5.65% in Riga. The district heating system coupled with heat pump represents the best layout in terms of primary energy consumption in both locations, with energy savings of 50% compared to other solutions. The use of 5GDHC is a good compromise that could increase the use of renewable energy. The adoption of cogeneration plant is a good choice in case of centralized district system that allows the installation of high efficiency genset. On the contrary, for small application as residential, the installation of cogeneration system results expensive and the conversion efficiency does not justify the installation.

A solar diffusion-absorption refrigeration system for off-grid cold-chain provision, Part II: System simulation and assessment of performance

The diffusion-absorption refrigerator (DAR) is a cooling technology that can be driven entirely by thermal energy. With solar-thermal collectors as the heat source, this technology can provide a zero-emissions solution for essential cold-chain provision in the world’s poorest regions. In the accompanying paper, a system model of a solar-driven DAR system was developed for simulating operation under variable solar conditions. In Part II of this study, we apply this model to assess the predicted performance of the system in its intended application: for on-farm refrigeration of harvested crops in rural India. The roles of the working-fluid charge pressure and collector array area for maximum cooling are investigated. A charge pressure of 16 bar is found to be optimal for ambient conditions during the October harvest season in Ahmedabad, with a collector-array area of 1.5 m2 found to be most appropriate for the nominal 70 W cooling-capacity system. However, the time taken to reach the required solar collector temperature for bubble pump activation results in a daily operating period of less than 5 h and a predicted overall solar-to-cooling efficiency of less than 1%. To address these limitations, modifications to various components are considered based on simple assumptions for improving performance.

Probabilistic assessment of seismic performance of slopes considering the sensitivity of sliding surface to input motion

Seismic fragility assessments that predicts the exceedance probabilities of predefined limit states of a structure subjected to earthquakes are widely used to reduce economic losses and casualties. This paper presents a probabilistic assessment of slopes based on the permanent displacements calculated from a suite of two-dimensional (2D) nonlinear dynamic analyses. The slopes are categorized into four groups based on static factor of safety (FSstatic). The centrifuge test measurements of a slope composed of granular soil are utilized to validate the 2D finite element numerical model. The Newmark displacements are calculated by integrating the stresses imposed on the sliding surface. The sliding surface is determined from the maximum accumulated strain contours, thereby accounting for the slope and input motion characteristics. The Newmark displacement is correlated with various earthquake intensity measures (IMs). The optimal IMs are selected based on the goodness of fit, efficiency, practicality, and proficiency. The fragility curves are developed using selected optimal IMs for three sliding displacement-based damage levels, which are minor, moderate, and severe. The results show that FSstatic, which contains information on both the slope geometry and the shear strength of soil, is an effective parameter to estimate the Newmark displacement. In addition, a suite of seismic fragility surfaces are developed using dual IMs.

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach. Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) and Euclid data, which will have similar depths to the Y3 DES Deep Fields.

Evaluation of an artificial intelligence model for identification of mass effect and vasogenic edema on computed tomography of the head.

Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) to consensus neuroradiologist interpretations in detecting mass effect and vasogenic edema. A retrospective standalone performance assessment was conducted on datasets of non-contrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients aged 18 years or older from five hospitals in the United States. The positive cases were selected consecutively based on the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up to three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the AI model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The AI model separately received thin (≤1.5mm) and/or thick (>1.5 and ≤5mm) axial series. The two cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The AI model identified mass effect with sensitivity 96.6% (95% CI, 94.9-98.2) and specificity 89.8% (95% CI, 84.7-94.2) for the thin series, and 95.3% (95% CI, 93.5-96.8) and 93.1% (95% CI, 89.1-96.6) for the thick series. It identified vasogenic edema with sensitivity 90.2% (95% CI, 82.0-96.7) and specificity 93.5% (95% CI, 88.9-97.2) for the thin series, and 90.0% (95% CI, 84.0-96.0) and 95.5% (95% CI, 92.5-98.0) for the thick series. The corresponding areas under the curve were at least 0.980. The assessed AI model accurately identified mass effect and vasogenic edema in this CT dataset. It could assist the clinical workflow by prioritizing interpretation of abnormal cases, which could benefit patients through earlier identification and subsequent treatment. AI = artificial intelligence; AUC = area under the curve; CADt = computer assisted triage devices; FDA = Food and Drug Administration; NPV = negative predictive value; PPV = positive predictive value; SD = standard deviation.

Lightweight railroad semantic segmentation network and distance estimation for railroad Unmanned aerial vehicle images

Derailments significantly harm railroads in terms of severity and fatality rates. Manually monitoring railway tracks is a tedious and often insufficient task to prevent derailment accidents. This necessitates the development of an automated monitoring system to oversee the condition of railway tracks. In the foreseeable future, Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision will play a crucial role in periodically assessing the railway environment to ensure passenger safety. This paper proposes a lightweight and efficient railroad semantic segmentation network (Lite-RSNet), which segments real-time aerial railroad images into rails, gauges, and backgrounds. The segmented regions then compute the distance between rails and obstacles, identifying potential derailment hazards in railroad applications. Lite-RSNet incorporates a multi-level feature fusion framework that includes a residual layer, a multi-head attention layer, a Concurrent Spatial and Channel Squeeze Excitation (CSSE) block, and a Convolutional Block Attention Module (CBAM). The designed approach enhances onboard processing efficiency by streamlining the model to use fewer parameters and reduce storage demands. Performance assessment of the model utilized the Rail Segmentation Dataset (RSD), which consists of high-resolution, low-altitude aerial photographs of Indian railroads. The Lite-RSNet model outperforms current leading methods, maintaining a lean structure with only 6 million parameters and achieving notable metrics of 0.971 for the Dice Coefficient (DC) and 0.947 for the Jaccard Index (JI) on the test dataset. Furthermore, distance estimation between an obstacle and rail has been implemented using image processing techniques as an additional work of this proposal to know about the caution of an obstacle.

Employee Performance Assessment Model in Survey Service Companies Using Analytical Hierarchy Process and Naive Bayes

Assessment of the performance of employees of a survey service company produces a final score that is not in accordance with the company's transformation strategy in responding to external challenges and demands. The variable assessment process still uses 180 Degree Feedback which is one-way from superiors to employees. This makes the company's learning and growth activities, especially in the human resources department, not occur. Therefore, companies should improve the assessment model to become a tool for acquiring highly qualified performing employees. This study contributes to obtain a more comprehensive employee performance assessment model proposal and evaluate the model so that it can be implemented. The research limitation lies in the use of data, namely contract employees with the position of Project Manager. This study uses the Analytical Hierarchy Process and Naïve Bayes methods to obtain variable weights. Based on the results of the analysis, it was found that SOE (State Owned Enterprises) AKHLAK was a new variable. The classification of the assessment variables is divided into Performance and Behavioral aspects based on the Minister of SOE Regulation. Changes that occur include formulas, mechanisms, and rating scales. The results of the verification and validation show that the model is in accordance with the design so that it can be implemented. Model trials with 360 DF show that PM's final score has a good chance of being assessed as a full-time employee. This valuation model can change along with company policies in responding to internal and external changes.

Fine particle capture performance assessment in an electrostatic-fabric integrated precipitator

Electrostatic precipitators, which are more commonly referred to as precipitators, play a crucial role in the process of reducing the quantity of airborne contaminants that are present in both atmospheric and flue gas settings. This is because precipitators are able to lower the concentration of contaminants in the air. The fine particles, ash, and oil that make up these pollutants are included. With a special emphasis on a Cottrell-type electro-filter that makes use of the wire-cylinder arrangement, the goals of this study are to evaluate the practicability of various filtration systems for the purpose of air decontamination. Specifically, the study will focus on the Cottrell-type electro-filter. By conducting a thorough examination of the peculiarities of the electrical and physical parameters that regulate the mechanisms of particle aggregation, our major purpose is to achieve a comprehensive understanding of these parameters. Through the use of experimental design strategies, we will make an effort to construct and perfect the filtration process in the subsequent stage. In addition to that, the purpose of this inquiry is to explore the impact that high voltage levels and the diameter of the conductor wire that is incorporated into the prototype have. Especially notable is the fact that our prototype exhibits an incredible filtration efficiency, with rates reaching up to 98%. This performance is really remarkable. Its exceptional performance reveals that it has the potential to be suitable for a wide variety of contexts, including residential, commercial, healthcare, industrial, and workshop applications. This suggests that it has the potential to be suitable for a wide range of applications.

Performance assessment of prefabricated vertical drains in mitigating soil reliquefaction subjected to repeated seismic events using shaking table experiments

Performance assessment of prefabricated vertical drains in mitigating soil reliquefaction subjected to repeated seismic events using shaking table experiments

Use of optical see-through head-mounted devices in dentistry - a scoping review.

The aim of this scoping review was to identify the scientific evidence related to the utilization of Optical See- Through Head-Mounted Display (OST-HMD) in dentistry, and to determine future research needs. The research question was formulated using the "Population" (P), "Concept" (Cpt), and "Context" (Cxt) framework for scoping reviews. Existing literature was designated as P, OST-HMD as Cpt, and Dentistry as Cxt. An electronic search was conducted in PubMed, Embase, Web of Science, and CENTRAL. Two authors independently screened titles and abstracts and performed the full-text analysis. The search identified 286 titles after removing duplicates. Nine studies, involving 138 participants and 1760 performed tests were included in this scoping review. Seven of the articles were preclinical studies, one was a survey, and one was a clinical trial. The included manuscripts covered various dental fields: three studies in orthodontics, two in oral surgery, two in conservative dentistry, one in general dentistry, and the remaining one in prosthodontics. Five articles focused on educational purposes. Two brands of OST-HMD were used: in eight studies HoloLens Microsoft was used, while Google Glass was utilized in one article. The overall number of included studies was low; therefore, the available data from this review cannot yet support an evidence-based recommendation for the clinical use of OST-HMDs. However, the existing preclinical data indicate a significant capacity for clinical and educational implementation. Further adoption of these devices will facilitate more reliable and objective quality and performance assessments, as well as more direct comparisons with conventional workflows. More clinical studies must be conducted to substantiate the potential benefits and reliability for patients and clinicians.

Does internal audit function outsourcing policy matter for environmental, social and governance performance score? Evidence from Bursa Malaysia

Purpose Anecdotal evidence indicates the internal audit function may be crucial in supporting the implementation of ESG practices and reporting. However, thus far, no study has been empirically conducted to check how and why internal audit function might affect ESG performance. This study aims to address this gap by examining whether the internal audit budget is positively related to the ESG performance of Malaysian listed firms. It also explores whether the sourcing arrangement of the internal audit function can moderate the internal audit budget – ESG nexus. Design/methodology/approach The secondary data for the paper is extracted from two main sources, namely, the Thomson Reuters Eikon database and firms’ annual reports that were downloaded from the Bursa Malaysia website. The final sample consists of public firms listed on Bursa Malaysia over the period 2010 to 2019. Multivariate tests are used to examine the linkage between the variables. Findings The results show that the relationship between the annual internal audit budget and ESG performance is contingent on the sourcing arrangement of the internal audit function. Specifically, the results show that the annual internal audit budget has a positive impact on ESG performance for firms with an in-house internal audit function but not for firms outsourcing an internal audit function to external providers. Overall, the results suggest that the annual internal audit budget promotes ESG performance for firms performing their internal audit activities internally. Practical implications The findings of this paper provide a strong motivation for authorities in Malaysia to develop new policies and rules aiming at ensuring that internal audit departments are adequately resourced to function effectively, thereby promoting corporate ESG performance. Moreover, the findings may be useful in informing the board of directors and other policymakers that establishing an in-house internal audit department assists in advancing corporate sustainability performance. Social implications The findings of this paper suggest that investors, creditors and other stakeholders should link ESG reporting with the attributes of the internal audit function and outsourcing arrangement when evaluating firm performance. The total annual costs allocated to the internal audit function coupled with the outsourcing arrangement by an external provider should be considered for the overall assessment of the ESG performance and provide additional warranty towards corporate goals’ achievement and sustainability for the society. Originality/value This study extends previous studies on the determinants of ESG performance by focusing on two crucial aspects of the internal audit function: the annual budget and the outsourcing arrangement, a hitherto largely unexplored mechanism by the existing literature.

Assessment of operational performance and service quality in inland waterway transport: A case study of Warri Southwest, Nigeria

Purpose: This study aims to evaluate the operational performance and service quality of inland waterway transport in Warri Southwest, Nigeria, by examining the factors that influence them. Methodology: The study employs a quantitative research approach, using a structured questionnaire to collect data from 180 respondents. The data is analyzed using regression analysis and a structural equation model (SEM) with the help of PLS Software Version 13.0. Results: The findings indicate that cargo clearance, infrastructure quality, and storage facilities significantly influence the performance of agent operation in inland waterway transport (p=0.000). Staff performance also directly impacts the provision of logistical services, which affects customer service. However, jetty operations do not promote operational performance (p=0.075). Additionally, staff performance does not significantly influence the association between jetty operation and performance and the relationship between cargo clearance and infrastructure level (p=0.136, 0.388). Theoretical contribution: This study contributes to the literature on inland waterway transport by providing empirical evidence on the factors influencing operational performance and service quality. The findings can help inform the development of policies and strategies to improve the efficiency and effectiveness of inland waterway transport in Nigeria and other similar contexts. Practical implications: The study's recommendations include improving the quality of infrastructure, cargo clearance processes, and storage facilities to enhance operational performance. Additionally, investing in staff training and development can improve the provision of logistical services and, in turn, customer service. The study also suggests that the government and relevant stakeholders should prioritize the development of inland waterway transport to unlock its potential for socio-economic development, particularly in rural and hard-to-reach areas.

Use of data mining algorithms to predict body weight of Dorper sheep Breed in two selected villages of Lepelle-Nkumpi local municipality in Limpopo province, South Africa

Sheep farmers in village areas find it difficult to estimate body weight without the weighing tools. The best fitted model for estimation of sheep body weight is not conclusive. Classification and regression tree (CART), Chi-square automatic interaction detection (CHAID), Exhaustive chi-square automatic interaction detection (Ex-CHAID) and Multivariate adaptive regression spline (MARS) were used to predict body weight (BW) of 306 Dorper sheep (female = 244 and male = 62) aged 1 to 2 years from linear body measurement traits such as rump height (RH), rump length (RL), heart girth (HG), withers height (WH), and body length (BL). Goodness of fit criteria's such as Pearson's correlation coefficients (r), coefficient of determination (R2), Akaike's information criterion AIC and adjusted R2 (Adj.R2) were computed for data mining algorithms performance assessment. The results indicated that MARS was the best data mining algorithm with r of 0.979, R2 of 0.958, Adj.R2 of 0.957, RMSE of 2.503 and AIC of 422.25. The MARS model provided the most appropriate predictive capability in the prediction of body weight for Dorper sheep with HG, WH, RH, BL, and sex (male) as the important traits. As a result, MARS data mining algorithm might be used to identify factors that might be vital to improve body weight of Dorper sheep for breeding programs.

Space performance assessment of traditional houses in Akre

ABSTRACT The complex natural and geological structure of cities affects buildings’ spatial structure in terms of the forms of spatial configuration adopted in the design. Spatial structure affects buildings’ space performance in several aspects, such as privacy and accessibility, which are the most significant indicators of residential houses’ functional performance. Akre, a treasured, historical city in Iraqi Kurdistan, is characterised by a complex and distinct geological nature, and its residential part contains numerous traditional houses that deserve analysis. This study employed both a qualitative approach – via case study plan preparations and photographic documentation – and a quantitative approach, utilising space syntax tools. The results were analysed with the statistical program SPSS and show three types of spatial configuration in the residential part (linear, central and cluster); each type has a different and effective impact on the achievement of space performance in terms of privacy and accessibility. The study concludes that the linear type represents the most private typology, followed by the cluster and then the central type. The centralised typology is the most connected and accessible to different spaces within the residential unit, followed by the cluster type and the linear type, with a significant difference.

Fabrication of cobalt-doped ZnS thin films by successive ionic layer adsorption and reaction for UV–visible photodetectors on flexible substrate

Photodetectors are widely employed in research and innovation, necessitating swift response times, precision, and stability. However, their application in commercial and industrial settings is hindered by limited flexibility and a constrained capacity to respond across a broad spectrum. In this study, we adopted a facile, cost-effective, and room temperature Successive Ionic Layer Adsorption and Reaction (SILAR) technique to craft thin films of Cobalt-doped ZnS on a flexible paper substrate. The Cobalt doping, with varying wt.% (0, 0.5, 1, and 1.5) exhibiting excellent responsivity to both UV and visible spectra. Zinc Sulfide (ZnS), renowned for its wide bandgap, serves as a versatile semiconductor, making it an ideal candidate for photodetectors. The introduction of Cobalt broadened the responsivity of ZnS, covering a wide spectrum, including UV and visible regions. Structural confirmation of the material was achieved through XRD and Raman analyses. Scanning Electron Microscopy revealed the aggregated spherical nanostructure of ZnS on the cellulose substrate. Verification of Cobalt presence in the ZnS thin film and assessment of elemental oxidation states were conducted using XPS. Temporal response and I–V characteristics of the device were evaluated under varying light conditions, specifically at wavelengths of 325 nm and 700 nm at different intensities. Responsivity calculations yielded values of 22.5 mA/W for UV illumination (light intensity 0.6 mW/cm2) and 37.6 mA/W (light intensity 0.4 mW/cm2) for visible light, with corresponding response times recorded as 1.45 s and 2.13 s, respectively. In addition to performance assessments, bending cycle studies were conducted, demonstrating the excellent flexibility of the device up to 800 cycles. The Cobalt-doped ZnS device, as fabricated, emerges as a promising material for energy harvesting.

Effects of whole-body vibration training on lower extremity muscle strength and physical function in elderly patients with sarcopenia

ABSTRACT Objectives To investigate the feasibility and benefits of WBV training in enhancing muscle strength and physical performance in older inpatients with sarcopenia. Methods We included 40 patients with sarcopenia (mean age 85 years; age range 80~90 years) and randomly separated into the WBV group or control (CON) group. The WBV group underwent 8 weeks of conventional rehabilitation training combined with WBV, whereas the CON group received 8 weeks of conventional rehabilitation training alone. The primary outcome was maximal isometric muscle strength. The secondary outcomes were the time-up-and-go test (TUGT), the five-times sit-to-stand test (FTSST), the 6-m gait speed test, and the Morse Fall Scale (MFS). Results After an 8-week intervention, only the quadriceps and tibialis anterior muscles in the CON group showed substantial gains in strength. In the WBV group, significant increases were observed in iliopsoas (P<0.05), quadriceps (P<0.01), hamstring(P<0.01), and tibialis anterior muscle (P<0.01) respectively. For all physical performance assessments, improvements were observed in both CON and WBV groups (P<0.05), the improvement in the WBC group was greater than the CON group (P<0.05). However, there was no significant difference in fall risk between the two groups (P>0.05). Conclusion This result highlights a eight weeks of whole-body vibration training can increase the lower-limb muscular strength and physical performance of older inpatients with sarcopenia.