Background: Employee performance evaluation in government institutions often lacks objectivity and consistency, causing unequal treatment and management inefficiency. Specific background: The Lhokseumawe District Prosecutor’s Office required a structured evaluation system for honorary employees to ensure transparent and fair assessments. Knowledge gap: Few studies have applied the Simple Multi Attribute Rating Technique in government-based performance evaluation systems. Aims: This study aimed to design and implement a web-based evaluation system using the Simple Multi Attribute Rating Technique to support accurate and accountable decision-making. Results: The developed system reduced evaluator subjectivity, ensured consistent weighting of criteria, and produced objective evaluation results. Usability testing showed an average System Usability Scale score of 80.92, categorized as “Good.” Novelty: This study presents the first integration of this method into a prosecutorial work environment. Implications: The system contributes to modernization in public human resource management by promoting transparency, fairness, and efficiency in employee evaluation and decision-making. Highlights Structured evaluation system improves fairness and transparency Consistent weighting ensures objective performance assessment Web-based system enhances efficiency and accountability Keywords Employee Performance Evaluation, Simple Multi Attribute Rating Technique, Web-Based System, Public Institution, Accountability

This study aims to describe the role of Management Information Systems (MIS) in improving the quality of education. The research method employed is a literature review with a qualitative analysis approach. The data collected includes scholarly articles, books, and research reports related to MIS and educational quality. The findings indicate that MIS implementation has been effectively executed in various educational institutions. The use of data management applications such as Dapodik and information technology within MIS supports the learning process, enhances efficiency and effectiveness in educational data collection, and facilitates teaching practices utilizing technological infrastructure. The analysis revealed that a universal information system model enables objective evaluation results, allows for the adjustment of evaluation criteria parameters and complexity, and determines the value and importance of parameters. Moreover, the implementation of integrated management information systems assists in decision-making to improve school quality and educational services. The conclusion of this research shows that MIS plays a crucial role in enhancing educational quality through effective data management, facilitating teaching practices, and increasing efficiency in data collection and processing. Thus, MIS can be considered a key strategy for improving overall educational quality

The process of selecting the most outstanding teachers is an important part of efforts to improve the quality of education in schools. At SMAS Sultan Agung Puger, teacher assessments have so far been subjective and have not used a structured system. Thus, this study aims to design and develop a Decision Support System (DSS) to assist the process of selecting the best teachers based on the evaluation criteria for learning administration that have been set by the school. The method used in this study is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), because this method is able to provide objective ranking results oriented towards the level of similarity to the ideal solution. TOPSIS has the advantage of calculating the positive and negative values ​​of each alternative simultaneously. The results of the system implementation show that the teacher with alternative A4 obtained the highest preference value of 1, so he was selected as the best teacher. This system has succeeded in helping to provide more transparent, systematic, and objective evaluation results. Thus, the system built using the TOPSIS method has proven effective in supporting the decision-making process for selecting the best teachers based on learning administration data.

Under various light sources, the color of dyed fabrics could be observed in different ways. In this study, the red (R), green (G) and blue (B) dyed fabrics were evaluated through color difference (ΔE) and illuminance difference (ΔI) under daylight (D65), fluorescence (F, TL84, CWF), and ultraviolet (UV) lights. It was found that when D65, a standard daylight illuminant, is used as the reference, ΔE value under TL84 and CWF light sources was not significantly different. Therefore, D65 can be complemented by TL84 and CWF for color evaluation to enhance accuracy. The study also highlighted that using a 45-degree viewing angle yielded the most objective color evaluation results. This angle provides optimal conditions for observing light reflection, contributing to more reliable color evaluation. Additionally, dye concentration had a significant impact on color evaluation. An incorrect dye concentration can alter the ability of fabric to absorb light, leading to inaccurate evaluations. Furthermore, washing cycles also affect the colorfastness of dyed fabrics, with increased washing leading to a brighter appearance and higher light reflection.

In response to the limitations of current infrared and visible light image fusion algorithms-namely insufficient feature extraction, loss of detailed texture information, underutilization of differential and shared information, and the high number of model parameters-this paper proposes a novel multi-scale infrared and visible image fusion method with two-branch feature interaction. The proposed method introduces a lightweight multi-scale group convolution, based on GS convolution, which enhances multi-scale information interaction while reducing network parameters by incorporating group convolution and stacking multiple small convolutional kernels. Furthermore, the multi-level attention module is improved by integrating edge-enhanced branches and depthwise separable convolutions to preserve detailed texture information. Additionally, a lightweight cross-attention fusion module is introduced, optimizing the use of differential and shared features while minimizing computational complexity. Lastly, the efficiency of local attention is enhanced by adding a multi-dimensional fusion branch, which bolsters the interaction of information across multiple dimensions and facilitates comprehensive spatial information extraction from multimodal images. The proposed algorithm, along with seven others, was tested extensively on public datasets such as TNO and Roadscene. The experimental results demonstrate that the proposed method outperforms other algorithms in both subjective and objective evaluation results. Additionally, it demonstrates good performance in terms of operational efficiency. Moreover, target detection performance experiments conducted on the [Formula: see text] dataset confirm the superior performance of the proposed algorithm.

In order to solve the problem that the expression ability and generalization ability of shallow learning networks to complex functions are limited, and to improve the accuracy of college education quality evaluation, a college education quality evaluation method based on a deep learning network is proposed. Starting from the three aspects of the educational environment, educational quality, and student development, this paper constructs an educational quality evaluation index system including three primary indicators and nine secondary indicators. Take the secondary index in the evaluation index system as the input of the deep learning network, optimize the weights of each layer of the deep learning network by using the unsupervised pre-training model, determine the conditional probability distribution and joint probability distribution of each layer in the restricted Boltzmann machine (RBM) based on the bottom-up unsupervised learning process, and the output layer optimizes the parameters of each layer according to the input differential mean opinion score (DMOS) value and constructs the regression model between the abstract primary index and DMOS value, The objective evaluation results of education quality are obtained according to the prediction of a regression model. The test results show that the linear correlation coefficient and grade correlation coefficient between the evaluation results of this method and the subjective evaluation results are closer to 1.

The UPDRS III scale plays a critical role in diagnosing the progression of Parkinson's disease. Current methods often involve doctors guiding patients through specific actions on the scale, recording their performance, and assigning scores. However, this approach has several drawbacks, including the lengthy time required for doctorpatient communication, the high costs of patients traveling to hospitals for follow-up visits, and the reliance on subjective judgments from doctors, which lack standardized criteria. With advancements in artificial intelligence, many traditional processes have been partially automated. To help patients reduce diagnosis time, lower medical costs, and provide more accurate and objective evaluation results, this paper proposes a Transformer-based pose estimation model for assessing UPDRS III scale actions. By integrating skeleton-based evaluations from the network with a series of post-processing operations, the model enables patients to perform self-assessments of their post-treatment recovery at home, saving doctors significant time. This work introduces a cascaded graph self-attention module, SGAM (Spatial-Graphical Attention Module), to enhance the network's understanding of human topology. Additionally, it proposes a lightweight convolutional block, Chi-block, which employs a novel approach leveraging the attribute invariance of filters to interpret model performance and guide compression. This approach reduces computational costs and model parameters while preserving accuracy. The proposed method demonstrates robust performance on human pose estimation (HPE) datasets and showcases impressive lightweight performance on benchmark datasets such as ImageNet-1K and CIFAR-10. These results demonstrate the potential of artificial intelligence in enabling automated remote diagnosis and treatment for Parkinson's patients.

Abstract In recent years, deep learning algorithms have been gradually applied to the field of art creation, bringing new possibilities for art development. The study uses a generative adversarial network as the underlying logic of the image style migration model, and the improved CycleGAN method is used to assist in the style migration of sketching artworks to assist in their creation. After optimizing the CycleConsistent Generative Adversarial Network model, the loss function was designed to construct an improved GAN-based style migration model for sketch artworks. The CycleGAN model of this paper is compared with other image style migration models and retrograde algorithms in terms of loss, operation efficiency and image quality evaluation, so as to explore the performance of CycleGAN of this paper in sketch artwork style migration. Among all the image style migration algorithms, CycleGAN in this paper has the fastest convergence speed, the smallest number of parameters (20.75M), and the fastest running speed (3.42s, 2.19s, 1.72s). The CycleGAN model in this paper received the best subjective evaluation, with content quality, stylization strength, and favoritism exceeding 60%. The SSIM value and PSNR value of the CycleGAN model in this paper are larger than other models, and the optimal objective evaluation results are achieved.

Abstract The development of artificial intelligence brings new opportunities and challenges to the field of artistic creation. This paper fully analyzes the characteristics and advantages of AIGC-enabled cultural and creative design, and selects CycleGAN algorithm among AI algorithms to carry out cultural and creative design style migration processing. The original CycleGAN algorithm has been improved to construct the AIGC cultural and creative style migration model based on the improved CycleGAN. Take the Forbidden City cultural creations as an example to carry out style migration experiments, and explore the effect of this paper’s improved CycleGAN model on cultural creation style migration and image generation from the objective evaluation and subjective evaluation of the improved CycleGAN-based AIGC cultural creation style migration model. In the experiments of converting original images into cartoon style, oil painting style and new Chinese style, the PSNR value, MS-SSIM value and Per-pixel acc value of this paper’s improved CycleGAN model are all the largest among all the comparison models, and the MSE value is the smallest among all the models, which achieves the optimal objective evaluation results. In subjective evaluation, the improved CycleGAN model in this paper has a comprehensive score of 89.22, which is excellent in style migration and image generation for cultural and creative products.

Pen-based sketching tables are more likely to facilitate users’ communication and creative interaction, avoiding the problems of high learning cost and cumbersome interaction interface that exist in traditional spreadsheet systems, compared to traditional spreadsheets systems based on the WIMP interface paradigm. However, the recognition of hand-drawn sketch tables still faces great challenges due to the lack of open source datasets dedicated to the recognition of hand-drawn sketch tables, as well as the ambiguity and abstraction of the sketches themselves and the arbitrariness of the users in drawing them. In order to solve the specific challenges such as the existence of complex cells, the overlapping of structural frame lines and content, and the redrawing and filling of handwriting, propose an algorithm for freehand sketch table recognition based on table structure understanding. A support vector machines (SVM) is used to classify the stroke information, and then the multi-peak detection algorithm is combined with the real intersection finding and cell feature attribute detection constructed in this paper to achieve the recognition of complex sketch table structure. The objective evaluation results show that the tree-edit-distance-based similarity (TEDS) of this algorithm in the sketch-oriented table structure recognition task is improved by more than 13% compared with the OCR baseline algorithm; the expert evaluation results show that this algorithm outperforms the OCR baseline algorithm in the evaluation dimensions of table structure recognition and matching the content to the corresponding cells.

PurposeOn the basis of demand survey feedback from individuals with disabilities and caregivers, this study designed two sets of functional garments for long-term bedridden patients, with the primary objective of increasing convenience and reducing the physical workload of caregivers.Design/methodology/approachWear trials were conducted by employing 24 subjects to perform 11 different tasks to compare the performance of the two newly developed garments with that of conventional hospital patient apparel. Task operation time, heart rate (HR), electromyography (EMG) signals, and subjective perceptions were evaluated.FindingsThe new functional garments reduced the time required to perform tasks by 29–79%, maintained the average HR of caregivers at approximately the resting threshold and resulted in a 37–74% reduction in the root mean square (RMS) of the EMG at the arm muscles in the private and thigh nursing tasks. All the subjective and objective evaluation results of the caregivers demonstrated varying degrees of correlation.Practical implicationsThis study has practical implications for the design of functional clothing for long-term bedridden patients and provides guidance for evaluating the ergonomics of garments that can be utilized only with caregiver support.Originality/valueIn contrast to previous studies that focused primarily on individuals with disabilities while overlooking the indispensable role of caregivers in the nursing process, this study shifted its emphasis to long-term bedridden patients who relied exclusively on caregivers for daily activities. Additionally, this study attempted to analyze the correlations between the evaluation parameters to explore the relationships between the evaluation methods.

This study evaluated and compared three water quality assessment methods: Canadian Water Quality Index (CWQI), Weighted Euclidean Distance (WED), and Fuzzy Comprehensive Evaluation (FCE) to analyze the water quality of the Fu River in Baoding City, China. Water samples were collected from 19 monitoring sections along the river in 2018 and tested for pH, turbidity, dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus. The CWQI method provided relatively simple water quality rankings. In contrast, the WED and FCE methods incorporated fuzzy mathematical principles to produce more nuanced, multi-dimensional water quality assessments. All three methods indicated serious eutrophication and poor water quality in the Fu River, with ammonia nitrogen and total phosphorus as the primary pollution factors. The FCE method offered the most hierarchical and objective evaluation results. This comparative study demonstrates that employing multiple water quality evaluation techniques can produce more robust and holistic insights into river water quality. The findings provide valuable guidance for selecting site-specific water assessment methods.

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators.

Abstract In this study, the performance of 24 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the dynamic processes of Arctic sea ice concentration (SIC)‐ and El Niño‐Southern Oscillation (ENSO)‐ forced teleconnection during winter is subjectively and objectively evaluated. The Arctic SIC‐forced teleconnection is associated with a warm Arctic‐cold Eurasian pattern of surface temperature (T2m), a low Arctic‐high Eurasian pattern of sea level pressure (SLP), and a southeastward propagating wave‐train originating from Arctic in the upper troposphere. The ENSO‐forced teleconnection is associated with a poleward propagating wave‐train originating from tropical Pacific in the upper troposphere, a low North Pacific‐high Arctic pattern of SLP, and a cold North Pacific‐warm Greenland pattern of T2m. The metrics of Taylor skill scores and Distance between indices of simulation and observation (DISO) are used to objectively and quantitatively evaluate the performance of models. The results of subjective and objective evaluation are essentially consistent. The CanESM5, MPI‐ESM1‐2‐HR, EC‐Earth3, and MRI‐ESM2‐0 models have the best performance in simulating the Arctic SIC‐forced teleconnection. The CESM2, ACCESS‐CM2, NESM3, NorESM2‐MM, CAS‐ESM2‐0, MRI‐ESM2‐0 models have the best performance in simulating the ENSO‐forced teleconnection. The two best‐performing multi‐model ensembles well reproduce the dynamic processes of the Arctic SIC‐ and ENSO‐ forced teleconnection. The diversity of model performance is attributed to the different skills of different models in simulating the interannual variability of Arctic SIC, the anomalous deep warm high over the Barents‐Kara Seas, the interannual variability of tropical Pacific SSTs, and the wave number of poleward propagating Rossby waves.

Object detection models are commonly used in yield estimation processes in intelligent walnut production. The accuracy of these models in capturing walnut features largely depends on the quality of the input images. Without changing the existing image acquisition devices, this study proposes a super-resolution reconstruction module for drone-acquired walnut images, named Walnut-SR, to enhance the detailed features of walnut fruits in images, thereby improving the detection accuracy of the object detection model. In Walnut-SR, a deep feature extraction backbone network called MDAARB (multilevel depth adaptive attention residual block) is designed to capture multiscale information through multilevel channel connections. Additionally, Walnut-SR incorporates an RRDB (residual-in-residual dense block) branch, enabling the module to focus on important feature information and reconstruct images with rich details. Finally, the CBAM (convolutional block attention module) attention mechanism is integrated into the shallow feature extraction residual branch to mitigate noise in shallow features. In 2× and 4× reconstruction experiments, objective evaluation results show that the PSNR and SSIM for 2× and 4× reconstruction reached 24.66 dB and 0.8031, and 19.26 dB and 0.4991, respectively. Subjective evaluation results indicate that Walnut-SR can reconstruct images with richer detail information and clearer texture features. Comparative experimental results of the integrated Walnut-SR module show significant improvements in mAP50 and mAP50:95 for object detection models compared to detection results using the original low-resolution images.

Twenty down jacket fabrics underwent a subjective multisensory evaluation, including tactile, visual, and auditory aspects, and the subjective and objective style evaluation results were compared and analyzed. The subjective style evaluation of down jacket fabrics was divided into comprehensive sensory evaluation and pure sensory evaluation. Pure sensory evaluation included three ways: pure vision, pure touch, and pure hearing. The comprehensive sensory evaluation was to combine the three to evaluate the fabric. The results showed that the pure sensory evaluation results were not much different from the comprehensive sensory evaluation results, and the correlation was strong. In general, the results of the pure tactile test were more consistent with the results of the comprehensive sensory test. The subjective comprehensive sensory evaluation results had a high linear correlation with the calculation results of the objective comprehensive handle evaluation model, and were negatively correlated. The smaller the calculated value of the model, the better the comprehensive handle of the fabric. It also proved the rationality and validity of the objective model, and the feasibility of the CHES-FY textile handle style evaluation instrument to evaluate the handle of down jacket fabrics.

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.

The weighting of indicators in the evaluation of historical building value is typically based on the relative importance of each indicator, often overlooking the influence of subjective scoring discrepancies on these weights. However, such differences in subjective scoring reflect the reliability of scoring data and the degree of emphasis placed on high-value indicators. Considering them as factors influencing indicator weighting can lead to more objective and accurate evaluation results. This study introduced the information content weighting method and the mandatory distribution method, building upon traditional weighting methods. On one hand, the information content weighting method assigned weights based on the consistency of expert scores for the same indicator, thus compensating for the oversight of considering discrepancies in expert scores for the same indicator in weighting. On the other hand, the mandatory distribution method optimized the weighting intervals based on the differences in scores between existing indicators, enhancing the rationality of determining interval boundary parameters. After simulating and validating the feasibility and superiority of these two methods, the study further demonstrated their practical significance using the examples of the Xucun Mansion Ancestral Hall in Shexian County and the Wu Family Branch Ancestral Hall, aiming to provide more reliable and effective guidance for the conservation of historical buildings through value assessment.

A research grant is a fund awarded by an institution allocated to another researcher. The analysis applies an approach from PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluations) to evaluate grantee lecturers and provide recommendations for final weighted scores according to passing grade graduation. The current system for calculations still uses weight percentages that are not processed using one of the decision support system methods, so it takes a long time for reviewers to assess the performance of grant recipient lecturers. Of the existing problems, the closest solution to this problem is the PROMETHEE II method. The PROMETHEE II method is used in complex decision making with many criteria, so that objective and accurate evaluation results are obtained for grant recipient lecturers. The criteria consist of a writing template, research results, mandatory outputs 1 and 2, additional outputs, and the contents of the proposal. The proposals collected are assessed by reviewers and analyzed using the PROMETHEE II method to produce a final score for each grant recipient lecturer. The results of the research show that the PROMETHEE II method provides appropriate final grade recommendations based on the performance and contribution of each lecturer in various aspects of research assessment. The recommendation for the final PROMETHEE II score is where the score above is equal to 0, the results of the lecturer receiving the grant are accepted while the score below 0 is rejected. In this way, recommendations from the final grades obtained can provide an overview of lecturers' contributions in supporting institutional research.

Research on the parameter design and calibration and control of EV in-vehicle active sound generation system