Comprehensive Performance Assessment Research Articles

Sorption Efficacy of Weathered Basalt Rock for Metal Ions of Nuclear Importance

Background:: A safe and long-term isolation of radionuclides is crucial for efficient waste management in the nuclear fuel cycle. Clay minerals of geological origin are considered candidate barrier materials for the sequestration of nuclear waste for geological disposal applications. Objective:: The present study encompasses a systematic characterization of weathered basalt rock and the influence of its constituent clay minerals (formed mainly due to the weathering process) in the uptake of metal ions of nuclear importance, such as cesium (Cs+) and strontium (Sr2+). Methods: The structural profile of the weathered basalt has been investigated using different analytical techniques, including polarizing microscope, XRD, FTIR and EDXRF. The sorption behaviour of the rock sample for Cs+ and Sr2+ has been investigated in a comprehensive batch mode by varying the experimental conditions. The analytical findings for structure and batch sorption performance of the material have been further correlated to understand the influence of different parameters on the uptake of metal ions and the underlying mechanism. Results:: Structural analyses confirmed the presence of clay minerals viz., kaolinite, illite and montmorillonite in the sample. A comprehensive sorption performance assessment carried out in batch mode at different experimental conditions revealed that the uptake of both the metal ions was rapid and dependent on initial metal ion concentration and solution pH. The uptake of Cs+ ions was found to be higher as compared to the Sr2+ ions. EDXRF analysis confirmed the loading of Cs+ and Sr2+ on the weathered basalt. Experimental batch sorption data presented a better agreement with the theoretical Freundlich isotherm pertaining to the heterogeneous nature of the sorbent. Conclusion:: The studies highlight that the clay minerals formed by structural alteration of basalt rock upon intense weathering could be very useful in fixing the nuclear fission waste components such as Cs+ and Sr2+.

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.

Performance assessment of the effective core potentials under the fermionic neural network: First and second row elements.

The rapid development of deep learning techniques has driven the emergence of a neural network-based variational Monte Carlo (VMC) method (referred to as FermiNet), which has manifested high accuracy and strong predictive power in the electronic structure calculations of atoms, molecules, and some periodic systems. Recently, the implementation of the effective core potential (ECP) scheme has further facilitated more efficient calculations in practice. However, there is still a lack of comprehensive assessments of the ECP's performance under the FermiNet. In this work, we set sail to fill this gap by conducting extensive tests on the first two row elements regarding their atomic, spectral, and molecular properties. Our major finding is that, in general, the qualities of ECPs have been correctly reflected under FermiNet. Two recently built ECP tables, namely, correlation consistent ECP (ccECP) and energy consistent correlated electron pseudopotential (eCEPP), seem to prevail in terms of overall performance. In particular, ccECP performs slightly better on spectral precision and covers more elements, while eCEPP is more systematically built from both shape and energy consistency and better treats the core polarization. On the other hand, the high accuracy of the all-electron calculations is hindered by the absence of relativistic effects as well as the numerical instabilities in some heavier elements. Finally, with further in-depth discussions, we generate possible directions for developing and improving FermiNet in the near future.

Evaluating the influence of marine protected areas on surf zone fish.

Marine protected areas (MPAs) globally serve conservation and fisheries management goals, generating positive effects in some marine ecosystems. Surf zones and sandy beaches, critical ecotones bridging land and sea, play a pivotal role in the life cycles of numerous fish species and serve as prime areas for subsistence and recreational fishing. Despite their significance, these areas remain understudied when evaluating the effects of MPAs. We compared surf zone fish assemblages inside and outside MPAs across 3 bioregions in California (USA). Using seines and baited remote underwater videos (BRUVs), we found differences in surf zone fish inside and outside MPAs in one region. Inside south region MPAs, we observed higher abundance (Tukey's honest significant difference [HSD]=0.83, p=0.0001) and richness (HSD=0.22, p=0.0001) in BRUVs and greater biomass (HSD=0.32, p=0.0002) in seine surveys compared with reference sites. Selected live-bearing, fished taxa were positively affected by MPAs. Elasmobranchs displayed greater abundance in BRUV surveys and higher biomass in seine surveys inside south region MPAs (HSD=0.35, p=0.0003 and HSD=0.23, p=0.008, respectively). Although we observed no overall MPA signal for Embiotocidae, abundances of juvenile and large adult barred surfperch (Amphistichus argenteus), the most abundant fished species, were higher inside MPAs (K-S test D=0.19, p<0.0001). Influence of habitat characteristics on MPA performance indicated surf zone width was positively associated with fish abundance and biomass but negatively associated with richness. The south region had the largest positive effect size on all MPA performance metrics. Our findings underscored the variability in species richness and composition across regions and survey methods that significantly affected differences observed inside and outside MPAs. A comprehensive assessment of MPA performance should consider specific taxa, their distribution, and the effects of habitat factors and geography.

A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion

In the human-machine system of armored vehicles, the cognitive performance state of crews is crucial for the personnel security and combat efficiency. The purpose of this research was to establish a real-time assessment system for cognitive performances of armored vehicle crews, consisting of the data input module, data processing module, data visualization module, and scheduling module. Forty subjects were recruited to cooperate and execute the cross-platform strike task in a virtual simulation platform. The physiological data and operation behavior data was collected during the experiment process. To realize the accurate classification of different cognitive performance states, a multi-source information fusion algorithm was developed based on linear discriminant analysis (LDA) and D-S evidence theory, which included the information collection module, the feature extraction module, and the information fusion module. The results indicated that there existed a significant correlation between the extractive feature indicators (i.e., EOG, ECG, and task performance indicators) and the cognitive performance. The recognition accuracy and the data efficiency of the proposed assessment system were 91.25% and 96.69% respectively by using the complementarity of different evidences, which were higher than the others using partial information sources. This study can provide a reference for the comprehensive assessment of cognitive performance of human operators in military and industrial domains.

Residential long-span timber floor typologies: a comprehensive performance assessment and opportunities for value adding to plantation hardwood

ABSTRACT High-performance engineered wood products (EWPs) and composite mass timber products (CMTPs) are being employed more frequently in residential projects with increasing interest in more sustainable systems that achieve long-spans. The relative performance of these systems is not readily apparent, with individual manufacturers offering proprietary products assembled from specific timber resources. In addition, the suitability of producing these long-span systems using plantation hardwoods is currently unknown. This research investigated the comparative performance of four typical EWPs and CMTPs; 1. solid slab, 2. thin-walled cassette, 3. T-sections, and 4. slab on beam. Key performance metrics of depth, mass, stiffness, vibration response, fire performance and global warming potential were assessed. The mechanical performance of two high-strength plantation hardwood varieties (Eucalyptus nitens and Eucalyptus globulus) were determined experimentally and subsequently used to re-calculate performance criteria for the previously assessed typologies. Substantial improvements over the typical softwood varieties were identified, particularly in structural efficiency, global warming potential and fire performance. This highlights the potential for value adding to plantation hardwoods by using them in high-performance long-span engineered floor products.

Performance Evaluation of VGG_19 Model for Brain Tumor Datasets

This paper presents a comprehensive assessment of the VGG19 model for detecting brain tumors using deep learning methods, particularly convolutional neural networks (CNNs). The study evaluates key performance metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). The investigation delves into the influence of different activation functions, learning rates, and epochs on the VGG19 model's performance. Activation functions are crucial for feature extraction and model convergence, with a comparison made between ReLU and Leaky ReLU to determine their effectiveness in enhancing brain tumor detection accuracy. Additionally, the study examines the impact of various learning rates (0.01, 0.001, 0.0001) on the model's convergence speed and overall performance. Different numbers of training epochs (15, 25, 50) are also considered to strike a balance between computational efficiency and model effectiveness. Evaluation metrics encompass accuracy, precision, recall, F1 score, and AUC, providing a comprehensive assessment of the model's performance across different configurations. The goal is to identify optimal combinations of activation functions, learning rates, and epochs to maximize the VGG19 model's accuracy in detecting brain tumors. This research contributes to advancing efficient and accurate methodologies for brain tumor detection, facilitating early diagnosis and treatment planning. The insights gained from this study can guide the development of more effective deep learning-based systems for medical image analysis, benefiting both patients and healthcare professionals.

The relationship between working memory and anxiety in individuals with early treated phenylketonuria (PKU).

Although early diagnosis and treatment prevent the severe impairments associated with untreated phenylketonuria (PKU), individuals with early treated PKU (ETPKU) nonetheless experience significant neurocognitive and psychological sequelae, including difficulties in working memory (WM) and increased risk of anxiety. The primary objective of the present study was to examine the extent to which anxiety may moderate the relationship between ETPKU and WM performance. A sample of 40 adults with ETPKU and a demographically comparable sample of 40 healthy adults without PKU completed a comprehensive assessment of WM performance and anxiety symptomatology. Data were collected using a variety of remote assessment methods (e.g., web-based neurocognitive tests, semistructured interview, report-based measures). The ETPKU group demonstrated significantly poorer WM performance as compared to the non-PKU group. The groups did not differ significantly in anxiety; however, high anxiety was more common in the ETPKU group (53% of sample) than the non-PKU group (33%). A significant interaction between anxiety, metabolic control (as reflected by Phe levels), and WM performance was observed for the ETPKU group. Individuals with high anxiety and/or high Phe levels (> 360 μmol/L) performed poorer than the non-PKU group. Individuals with low anxiety and relatively low Phe levels (< 360 μmol/L) performed comparably to the non-PKU group. Anxiety was found to moderate the relationship between Phe levels and WM performance in individuals with ETPKU. This finding underscores the importance of accounting for anxiety when evaluating neurocognitive performance in individuals with ETPKU whether for research or clinical purposes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Associations Between Grip Strength and Cerebral Hemodynamics in Young and Middle-Aged Adults

Aerobic fitness and aerobic exercise are widely accepted as having protective effects on cognitive function and brain health, with less understood regarding muscular fitness and resistance exercise. Strength is beneficially associated with cognitive function, but the mechanisms underlying this relationship remain understudied and may relate to cerebral hemodynamics. The purpose of this study was to examine the associations between strength and cerebrovascular hemodynamics in young and middle-aged men and women. It was hypothesized that greater strength would be associated with better cerebral hemodynamics (greater mean blood velocity, lower pulsatility, greater pulsatile damping). Ninety-two generally healthy adults between 18-64 years old (36±16 yrs; BMI 25.9±4.0 kg/m2; n=39 women) underwent a hand grip strength assessment and resting cerebrovascular measures. Grip strength was measured via dynamometer and recorded as the highest value of 3 attempts. Middle cerebral artery (MCA) mean velocity and pulsatility index (PI) were measured via transcranial Doppler. Pulsatile damping was calculated as the ratio between the common carotid artery and MCA PI. Mean arterial pressure was assessed via an oscillometric blood pressure cuff. No significant associations were observed between relative grip strength and MCA mean velocity (r=-0.15), MCA PI (r=0.05), or MAP (r=0.07) after adjusting for age. Greater relative grip strength, however, was associated with greater pulsatile damping after adjusting for age (r=0.21; p&lt;0.05). This relationship appeared to be driven by weak associations within each sex (men, r=0.10; women, r=0.02) that were not statistically significant on their own. Our preliminary results suggest there are no strong relations between grip strength and cerebral hemodynamics among generally healthy young and middle-aged adults after accounting for the effects of age and sex. Future studies should examine sex-specific associations with a larger sample, consider disease states related to cerebrovascular health, and include more comprehensive assessments of muscular strength and performance (e.g., power). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

THE STRUCTURAL BEHAVIOR OF RECYCLED AGGREGATE CONCRETE WALLS UNDER FIRE EXPOSURE: A FINITE ELEMENT ANALYSIS

Climate change is raising the demand for change in the construction industry, particularly in the concrete industry. One of the approaches to increase sustainability in the concrete industry is the use of recycled aggregate concrete (RAC). Many studies examined the type and composition of RAC used in construction applications. However, fewer studies examined the structural engineering applications of RAC. Thus, this study investigates the use of RAC in bearing walls. It presents a comprehensive thermal and structural performance assessment of RAC walls exposed to fire using finite element analysis (FEA). The FE model incorporates experimental material models in the literature that consider the degradation of the mechanical properties of RAC at high temperatures. The investigation encompasses a range of critical response measures, including the out-of-plane response of RAC walls under standard fire exposure and the overall stability of the walls during fire scenarios. The walls' out-of-plane and axial deflection and damage patterns were presented. The upper and lower bounds of performance were presented to examine the effect of material models’ variability on the response. The findings of this study reveal the complex interaction between fire scenarios, material properties, and their effect on the structural behavior of RAC walls. This study contributes to the growing knowledge on using RAC in structural engineering applications and promoting environmentally friendly materials in the construction industry while ensuring structural safety.

Enhancing sports education through big data analytics: Leveraging models for improved teaching strategies

The incorporation of big data analytics into the realm of sports education marks a pioneering shift towards leveraging advanced technological tools to refine teaching methodologies and elevate student performance to unprecedented levels. This article delves into the application of data-driven models that meticulously analyze a multitude of facets within sports education, ranging from student engagement and skill acquisition to comprehensive performance assessments. Utilizing sophisticated quantitative analysis techniques alongside robust mathematical models, this research meticulously evaluates the impact of embedding big data analytics into the core practices of sports education. The essence of this investigation lies in its rigorous analysis of how data-driven insights can be harnessed to inform and enhance coaching strategies, tailor training programs to individual athlete needs, and objectively evaluate performance metrics. By systematically analyzing large datasets that encompass performance statistics, physiological data, and even psychological assessments, educators and coaches can gain a deeper understanding of each student-athlete's unique profile, thereby facilitating more personalized and effective training interventions. The findings from this study highlight the transformative potential of big data analytics in sports education, illustrating how such technological integration can lead to more scientifically informed teaching strategies, optimized training regimens, and significantly improved outcomes for student-athletes. This research not only underscores the benefits of adopting a data-centric approach in sports education but also sets the stage for future innovations that could further revolutionize the field, making it more data-informed, precise, and tailored to the needs and potential of each individual athlete. Through this exploration, it becomes evident that the future of sports education and training lies in the effective integration of big data analytics, promising a new era of enhanced performance and educational excellence.

Effect of Public Fillers on Cement-Stabilized Recycled Mixes of Road Performance: Mechanical Properties, Microstructure, and Durability.

In order to address the challenges of resource utilization posed by construction waste, the substitution of natural aggregate (NA) with public fill (PF) contents was investigated for load reclamation and road grassroots applications. A comprehensive assessment of road performance for the recycled mixture was conducted, focusing on parameters such as unconfined compressive strength, splitting strength, compressive resilience modulus, dry shrinkage, and frost resistance. Additionally, the impact of incorporating PF at various types and replacement ratios on the microstructure of cement-stabilized aggregate (CSA) was analyzed. The results indicated that the unconfined compressive strength of cement-stabilized recycled mixture with varying PF contents meets the base strength requirements for heavy, medium, and light traffic pavement on secondary and sub-secondary roads in China. Notably, the unconfined compressive strength and resilience modulus follow a similar pattern, reaching their peak at a 25% PF content. Microscopic examination reveals that an appropriate PF content leads to the predominant formation of C(N)-A-S-H, hydrotalcite, Ca(OH)2, and CaCO3 as paste reaction products. As the replacement of public fill increases from 0% to 25%, there is a gradual stacking of gel products, which enhances the compactness of the microstructure by cementing together unreacted particles. Consequently, this process reduces dry shrinkage strain and effectively mitigates the formation of reflection cracks. Applying large quantities of public fill to road construction can effectively deal with various waste accumulation problems and produce a novel road material with significant social, economic, and environmental benefits.

EMPLOYEE PERFORMANCE ESTIMATION USING MACHINE LEARNING

In this research, we explore how machine learning is changing the way traditional HR practices handle employee reviews. The need for better evaluation methods for employees is increasing, and machine learning provides a variety of tools for analyzing data to meet this need. These algorithms can offer comprehensive and impartial assessments of performance, covering everything from productivity metrics to project outcomes and qualitative feedback. This study looks at the pros and cons of integrating this technology into HR practices, with the aim of enhancing the accuracy, fairness, and effectiveness of evaluations. The goal is to find a middle ground between technology and human judgment to improve performance reviews in modern workplaces. In today's fast-paced work environment, where excellence is key, traditional subjective performance assessments are becoming outdated. This research delves into the promising role of artificial intelligence in human resource management, particularly in creating detailed and fair performance evaluations. Picture AI algorithms as skilled musicians, harmonizing various data points – from productivity metrics to project achievements to qualitative input – to create a comprehensive picture of each employee's contributions. However, navigating this data-driven process can be complex. The focus of this study is on examining the positives and negatives of utilizing machine learning in HRM. The goal is to improve performance evaluations, ensuring they are both precise and unbiased. By connecting technology with a deeper human insight, organizations can move towards a future where performance appraisals truly maximize the abilities of their employees. Key Words: Performance, Evaluation, Employee Metrics, Human Resource Management (HRM), Examining

Fiber-reinforced concrete in high-temperature environments: In-depth survey on mechanical performance and rheological features

The successful automatic performance of fiber-reinforced concrete (FRC) has led to its widespread application; adding fibers affects its novel properties. It is anticipated that concrete's long-term qualities will be impacted by its workability. A comprehensive assessment of the mechanical performance and rheological properties of FRC is given in this study. It presents more detail on how the FRC behaves in searching temperatures and provides an analysis of several heating as well as cooling methods along with the way they affect test outcomes. The study explores how reinforcing fibers, aggregates and substitute materials affect FRC's fire resistance. In particular, the concept of workability, which is a crucial quality in concrete, is studied and rheological models that make use of several rheometers are developed to clarify the rheological behavior of FRC. Several techniques to improve rheological functionality and performance are revealed in this study. The findings highlight the conflicting characteristics of fibers that can enhance the mechanical aspects of cement while reducing its fresh and rheological qualities. A comparable set of benefits and drawbacks complement FRC's fluidity. The research examines feasible approaches to regulate the rheological characteristics of FRC, thereby simplifying and improving the mechanical performance and workability of FRC.

Evaluating the QileX-RhE skin corrosion test for chemical subcategorization in accordance with OECD TG 431

Skin corrosion testing is integral to evaluating the potential harm posed by chemicals, impacting regulatory decisions on safety, transportation, and labeling. Traditional animal testing methods are giving way to in vitro alternatives, such as reconstructed human epidermis (RhE) models, aligning with evolving ethical standards. This study evaluates the QileX-RhE test system's performance for chemical subcategorization within the OECD TG 431 framework. Results demonstrate its ability to differentiate subcategories, accurately predicting 83% of UN GHS Category 1A and 73% of UN GHS Category 1B/1C chemicals with 100% sensitivity in corrosive prediction. Additionally, this study provides a comprehensive assessment of the test method's performance by employing nuanced parameters such as positive predictive value (PPV), negative predictive value (NPV), post-test odds and likelihood rations, offering valuable insights into the applicability and effectiveness of the QileX-RhE test method.

An investigation on shell-side thermal–hydraulic performance of helically coiled tube heat exchanger for deep underground space

Helically coiled tube heat exchanger (HCHE) was extensively used to enhance the thermal environment of deep underground space. The inefficiency of heat transfer in the mine cooling system results in increased operational energy consumption. To enhance heat utilization, this study examines the impact of air temperature, air velocity, coil diameter, pitch, and elliptical tube aspect ratio on the shell-side thermal performance of HCHE through experimental and numerical analysis. The performance evaluation criterion (PEC) and field synergy are introduced to provide a comprehensive assessment of performance. Results suggest that the air velocity can more significantly affect the thermal performance of HCHE than air temperature. Augmenting the coil diameter from 30 − 40 mm and coil pitch from 7.5 − 9.0 mm led to an increase in the Nu number by 16.5 % and 6.7 %, as well as an increase in friction factor f by 575.2 % and 147.1 %, while a reduction in PEC by 38.4 % and 21.1 % when the air velocity is 5 m/s, respectively. For the helically coiled elliptical-tube heat exchanger (HCHE-E), the thermal performance is significantly dependent on the long axis's direction and its aspect ratio. When the air velocity is 3 m/s, the increase in aspect ratio from 1.0 to 1.8 resulted in a decrease and increase in Nu by 14.7 % and 14.6 %, an increase and decrease in PEC by 24.5 % and 28.4 %, as well as a reduction and increase in f by 31.5 % and 48.1 % for the long axle in the horizontal and vertical directions, respectively. The PEC for HCHE is not significantly dependent on the air velocity. The increase in air velocity can result in the augment in PEC of HCHE-E regardless of long axis's direction. The variation of synergy angle is consistent with the change of Nu. These findings offer valuable insights for optimizing the design parameters of HCHE in deep underground spaces.

Bio-Risk Management Systems: Biosafety Assessment in COVID-19 Referral Hospitals in Indonesia

Numerous hospital laboratories in Indonesia need to implement improved bio-risk management (BRM) systems. There are many potential biohazards in laboratory activities that can impact health and the environment, leading to laboratory incidents. To minimize the impact and occurrence of such incidents, it is necessary to evaluate the implementation of BRM in every hospital laboratory that uses biological agents. This study was conducted in eight COVID-19 reference hospitals in Indonesia in the regions of Sumatra, Kalimantan, and Java, which have committed to implementing BRM systems in their laboratory activities. This research employed a descriptive study design and quantitative methods, with the aim of analyzing and evaluating the implementation of BRM systems in laboratories by assessing the achievements and gap analysis obtained from each laboratory. This research utilized primary data in the form of checklist forms referencing ISO 35001:2019 for the laboratory BRM system. Then, the assessments were based on virtual interviews conducted by the researcher with laboratory personnel as the primary data. The evaluation conducted on gap analysis from the seven clauses in ISO 35001:2019 across all hospitals revealed large gaps, particularly in three clauses: leadership, support, and performance. However, the aspects concerning organization, improvement, and performance evaluation were relatively satisfactory. Hence, there is a need for further improvement in leadership, support, and performance evaluation clauses. Additionally, it is essential to highlight the importance of comprehensive performance assessment, including proactive audits and continuous enhancements to achieve optimal bio-risk management.

Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment.

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from 'What do you see in the image?' and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a 'two-way' model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.

Comparative analysis of the growth performance, vitality, body chemical composition and economic efficiency of the main cultivated strains of Pacific white shrimp (Litopenaeus vannamei) in coastal areas of China

The Pacific white shrimp (Litopenaeus vannamei) is known for its fast growth, strong resistance to adversity, and high adaptability to various environments, which results in significant economic benefits. This study aimed to explore a simple and effective method for farmers to choose high-quality L. vannamei strains with fast growth, high survival rate, low transportation mortality rate, and high economic benefits. In this study, the three main cultivated strains (that is, “Haixingnong”, “Hairen”, and “Huanqiu” strains) of shrimp in coastal areas of China were used to compare the growth performance, survival, ability of dry stress tolerance and jumping, nutritional composition, and aquaculture economic benefits under greenhouse cultivation conditions by measuring and calculating the basic parameters of different strains, recording the survival rates of dry stress at different temperatures, statistics of bounce heights, official methods of official analytical chemicals international, and break-even analysis. The results showed that, after 122 days cultivation, there was no difference of growth performance and survival rate among the three strains (P > 0.05). The dry stress tolerance of “Hairen” strain shrimp was more affected by temperature, while “Haixingnong” strain was the least affected by temperature. The jumping ability and muscle burst strength of “Huanqiu” and “Hairen” strains were significantly stronger than “Haixingnong” strain (P < 0.05). Furthermore, for the amino acid score (AAS) and chemical score (CS) in the three strains, there was no significant differences between Phe and Lys (P > 0.05), but the Thr, Val, Met, Ile, and Leu was significantly lower in “Huanqiu” strain than “Haixingnong” and “Hairen” strains (P < 0.05). According to the sensitivity analysis of the three strains, the sensitivity coefficients were ranked as follows: “Hairen” strain > “Huanqiu” strain > “Haixingnong” strain. The smaller the sensitivity coefficient, the stronger the ability to withstand market price fluctuation risk, therefore “Haixingnong” strain had the strongest ability to resist market price risk. It is worth noting that the feed coefficient of “Haixingnong” strain was significantly lower than that of the other two strains, and the net profit of “Haixingnong” strain was the highest among the three L. vannamei strains, indicating that farmers would have lower investment costs if they raised the “Haixingnong” strain. In summary, although “Huanqiu” and “Hairen” strains exhibited advantages in dry stress tolerance and jumping ability, a comprehensive assessment of growth performance and economic benefits in actual aquaculture indicated that the “Haixingnong” strain can bring more economic benefits to farmers.

Comprehensive performance assessment of a novel biomass-based CCHP system integrated with SOFC and HT-PEMFC

The combination of biomass gasification and fuel cell is a promising way to enhance the system energy efficiency and decrease the fossil fuel consumption. In this study, a new biomass-based combined cooling, heating and power (CCHP) system considering high temperature proton exchange membrane fuel cell (HT-PEMFC) and solid oxide fuel cell (SOFC) is proposed. Syngas from biomass gasifier is utilized by SOFC and HT-PEMFC for power generation, waste heat derived from SOFC and HT-PEMFC are recovered by ORC and absorption chiller for electricity and cooling production, respectively. Energetic, exergetic, economic and environmental investigations are performed to illustrate system comprehensive performances. The results demonstrated that power generation ratio of SOFC to HT-PEMFC is 2.32, exergy and energy efficiency of CCHP system are 25.06% and 49.48%, respectively. Levelized cost of production and CO2 emission rate of multi-products are 0.069$/(kW·h) and 0.673kg/(kW·h), respectively. Moreover, steam to biomass ratio and equivalent ratio show different effects on system performances due to the distinction of syngas composition. Operating temperature of SOFC has a great influence on system exergy and energy efficiency, with increasing by 11.18% and 16.97% respectively. In general, this novel system provides a new pathway for integrating biomass with different fuel cell technologies.