Good Performance Research Articles

Review of Surface Treatment Technology for Improving Wear Resistance of Magnesium Alloys

Background: As the lightest metal structural material in engineering, magnesium alloy has excellent mechanical properties, such as high specific strength, high specific stiffness, good damping performance, and good machinability. It is widely used in the fields of precision parts, automobiles, aerospace, and military. However, poor friction and wear performance are significant magnesium defects of the alloys, which make its use limited in some areas with high working conditions, so it is essential to improve the wear resistance of the magnesium alloy surface. Objective: The aim of this study was to summarize the technology of improving the wear resistance of magnesium alloy in recent year. The influence of different surface treatment technology for enhancing friction and wear properties was also analyzed, which could provide a reference for related scholars and researchers. Method: In this paper, the literature related to friction and wear properties of magnesium alloys in recent years were reviewed, the principles of various surface treatment technology of magnesium alloys were explained, and the advantages and disadvantages of each technology were analyzed. Results: Based on the literature analyses related to the wear resistance of magnesium alloys, the problems existing in the surface treatment technology for improving the wear resistance of magnesium alloys are summarized, and future development directions are put forward. Conclusion: Among the technologies to improve the wear resistance of magnesium alloys, the combination of various techniques can better meet the working demands. The environmentally friendly and efficient manner has a good prospect for development.

Environmental Sustainability Disclosure and Market Performance of Listed Consumer Goods Firms in Nigeria

This study aimed to examine the effect of environmental sustainability disclosure on the market performance of listed consumer goods firms in Nigeria. This study utilized ex-post facto research design. Data were gathered from the annual reports, sustainability disclosures, and financial databases of Nigerian consumer goods manufacturing firms listed on the Nigerian Exchange Group (NGX) as of December 31, 2023. The study encompassed a population of 21 listed consumer goods manufacturing firms in Nigeria. A census sampling technique was adopted, to investigate the entire population. The research covered a twelve-year period, from 2012 to 2023, to observe trends, patterns, and long-term impacts, enabling a robust analysis. This study employed descriptive statistics (mean, median, variance, standard deviation, skewness, and kurtosis) and inferential statistics (panel regression analysis, correlational analysis etc.) to conduct data analysis. This regression analysis conducted revealed that both environmental protections cost disclosure, and environmental research and development cost disclosure had positive and significant effect on the market performance of listed consumer goods firms in Nigeria. While environmental pollution cost disclosure had a negative but significant effect on market performance. Whereas environmental waste management control cost disclosure had positive and insignificant effect on market performance. The findings indicate that environmental cost disclosures play a significant role in shaping the market performance of consumer goods firms in Nigeria. Specifically, disclosures related to environmental protection and R&D have a positive impact, as they reflect a firm’s commitment to sustainability and innovation. It was recommended that firms should prioritize and continue to disclose their investments in environmental protection and R&D, as these are positively viewed by investors and enhance market performance.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

Application of a Machine Learning Predictive Model for Recurrent Acute Pancreatitis.

Acute pancreatitis is the main cause of hospitalization for pancreatic disease. Some patients tend to have recurrent episodes after experiencing an episode of acute pancreatitis. This study aimed to construct predictive models for recurrent acute pancreatitis (RAP). A total of 531 patients who were hospitalized for the first episode of acute pancreatitis at the Affiliated Hospital of Southwest Medical University from January 2018 to December 2019 were enrolled in the study. We confirmed whether the patients had a second episode until December 31, 2021, through an electronic medical record system and telephone or WeChat follow-up. Clinical and follow-up data of patients were collected and randomly allocated to the training and test sets at a ratio of 7:3. The training set was used to select the best model, and the selected model was tested with the test set. The area under the receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, decision curve, and calibration plots were used to assess the efficacy of the models. Shapley additive explanation values were used to explain the model. Considering multiple indices, XGBoost was the best model. The area under the receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model in the test set were 0.779, 0.763, 0.883, 0.647, 0.341, and 0.922, respectively. According to the Shapley additive explanation values, drinking, smoking, higher levels of triglyceride, and the occurrence of ANC are associated with RAP. The XGBoost model shows good performance in predicting RAP, which may help identify high-risk patients.

Taming Overconfident Prediction on Unlabeled Data From Hindsight.

Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed as the entropy computed by the transformed probabilities in output space. Most existing works distill low-entropy prediction by either accepting the determining class (with the largest probability) as the true label or suppressing subtle predictions (with the smaller probabilities). Unarguably, these distillation strategies are usually heuristic and less informative for model training. From this discernment, this article proposes a dual mechanism, named adaptive sharpening (ADS), which first applies a soft-threshold to adaptively mask out determinate and negligible predictions, and then seamlessly sharpens the informed predictions, distilling certain predictions with the informed ones only. More importantly, we theoretically analyze the traits of ADS by comparing it with various distillation strategies. Numerous experiments verify that ADS significantly improves state-of-the-art SSL methods by making it a plug-in. Our proposed ADS forges a cornerstone for future distillation-based SSL research.

Piecewise modified Prandtl-Ishlinskii model for precise nano-positioning

Piezoelectric ceramic, an indispensable micro-nano actuator, has advantages of small size, high sensitivity and strong controllability, which is widely applied in various fields like micro-nano processing, medicine, chemistry and materials. However, its nonlinear and asymmetric hysteresis features directly affect its positioning accuracy. The classical and generalized Prandtl-Ishlinskii models with linear symmetry operators are commonly used to deal with these issues, but they still face difficulties in asymmetric systems. Therefore, a piecewise modified Prandtl-Ishlinskii (PMPI) model is proposed to compensate nonlinear and asymmetric hysteresis in this paper. The new nonlinear operator and piecewise nonlinear envelope function are introduced into the PMPI model. Independent right and left parts are adopted to describe hysteresis increasing and decreasing stages, while the threshold and weight vectors correspond to them. Weight vectors are extended and a new diagonal matrix is constructed for avoiding excessive parameters and computation. Simulations and experiments are shown that the PMPI model has good compensation performance and effectiveness, which has better fitting, higher accuracy, fewer parameters and computation, and can be well applied to the precise nano-positioning systems with less nonlinear and asymmetric errors.

Development and Validation of a Nomogram for Predicting Breast Malignancy in Male Patients Based on Clinical and Ultrasound Features.

This study aimed to construct a nomogram based on clinical and ultrasound (US) features to predict breast malignancy in males. The medical records between August, 2021 and February, 2023 were retrospectively collected from the database. Patients included in this study were randomly divided into training and validation sets in a 7:3 ratio. The models for predicting the risk of malignancy in male patients with breast lesions were virtualized by the nomograms. Among the 71 enrolled patients, 50 were grouped into the training set, while 21 were grouped into the validation set. After the multivariate analysis was done, pain, BI-RADS category, and elastography score were identified as the predictors for malignancy risk and were selected to generate the nomogram. The C-index was 0.931 for the model. Concordance between predictions and observations was detected by calibration curves and was found to be good in this study. The model achieved a net benefit across all threshold probabilities, which was shown by the decision curve analysis (DCA) curve. We successfully constructed a nomogram to evaluate the risk of breast malignancy in males using clinical and US features, including pain, BI-RADS category, and elastography score, which yielded good predictive performance.

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

PENGARUH KEPEMIMPINAN TERHADAP KINERJA BAGIAN PENGADAAN BARANG DAN JASA DI KABUPATEN ENDE

This study examines the impact of leadership on the purchasing performance of goods and services by the Ende Regency government. The government's provision of goods and services is critical to achieving national development goals as it drives economic growth, creates jobs, and improves community services. Structural Equation Model (SEM) analysis was used in this study with the SmartPLS 3.2.9 application. Respondents comprised 120 actors procuring goods and services, including regional apparatus organizations/apparatus work units and third-party work partner providers in Ende Regency. The descriptive statistical test results show that the leadership and performance variables have comparable average values, around 25.5, with sufficient variation in respondents' assessments. The measurement model test shows that the performance and leadership indicators are valid and highly reliable. The structural model test shows a significant relationship between leadership style and performance outcomes with a p-value of 0.000. Model evaluation shows a high level of fit, with an R square value of 46.3% and Q square of 0.260, and the proposed PLS model has strong predictive ability. These results suggest that effective leadership improves the smooth provision of goods and services in Ende Regency.

Recent Advances in Synthesis and Applications of Organic Ionic Saltsbased Sensor Arrays

Abstract: Sensor arrays contain a group of sensors, improve observations with new dimensions, provide better estimations, and additional parameters in comparison to the individual selective sensor. The array-based sensing technique provides good performance to respond to various gaseous or liquid analytes. Room temperature ionic liquids (RTILs) (melting point <25oC) and Group of uniform materials based on organic salts (GUMBOS) (melting point =25-250oC) are organic ionic salts, composed of an oppositely charged pair of bulky organic cations and bulky organic/inorganic anion and shows interesting tunable physicochemical properties. In this review article, we will discuss the sensing performance of ILs- and GUMBOS-based sensor arrays. ILs-based sensor arrays have been used in electrochemical gas sensing, solvent discrimination, colorimetric gas sensing, sensing of organic compounds, optoelectronic sensing of vapors and solutions, and vapour sensing through IL/QCM systems. GUMBOS-based sensor arrays have been employed in vapour sensing through the GUMBOS/QCM method, detection, and discrimination of proteins.

PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks.

Multiscale features are of great importance in modern convolutional neural networks, showing consistent performance gains on numerous vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multiscale representation ability. However, the design of plug-and-play blocks is getting more and more complex, and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search (NAS). Specifically, we design a new search space PPConv and develop a search algorithm consisting of one-level optimization, zero-one loss, and connection existence loss. PP-NAS minimizes the optimization gap between super-net and subarchitectures and can achieve good performance even without retraining. Extensive experiments on image classification, object detection, and semantic segmentation verify the superiority of PP-NAS over state-of-the-art CNNs (e.g., ResNet, ResNeXt, and Res2Net). Our code is available at https://github.com/ainieli/PP-NAS.

Hybrid CNN-LSTM for Predicting Diabetes: A Review.

Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many machine learning and deep learning techniques have been applied for noninvasive diabetes prediction. The results of some studies have shown that the CNN-LSTM method, a combination of CNN and LSTM, has good performance for predicting diabetes compared to other deep learning methods. This paper reviews CNN-LSTM-based studies for diabetes prediction. In the CNNLSTM model, the CNN includes convolution and max pooling layers and is applied for feature extraction. The output of the max-pooling layer was fed into the LSTM layer for classification. The CNN-LSTM model performed well in extracting hidden features and correlations between physiological variables. Thus, it can be used to predict diabetes. The CNNLSTM model, like other deep neural network architectures, faces challenges such as training on large datasets and biological factors. Using large datasets can further improve the accuracy of detection. The CNN-LSTM model is a promising method for diabetes prediction, and compared with other deep-learning models, it is a reliable method.

Economic Factors and Performance of Selected Quoted Consumer Goods Manufacturing Companies: The Moderating Roles of Firm Agility

The problem of this study is the significant decline in the performance of consumer goods manufacturing companies, which is caused by various economic factors such as import policies, inflation, infrastructure, interest rates, and exchange rates. This study aims to evaluate the impact of economic factors on the performance of publicly traded consumer goods manufacturing companies and test how corporate agility moderates this relationship. The study employed a cross-sectional survey design with a population of 5,625 employees from 12 companies, yielding a final sample of 563. Data was collected through a questionnaire validated by confirmatory factor analysis and measured by a Likert scale. Data analysis was conducted with multiple regression using SPSS version 22.0. The results show that economic factors significantly affect firm performance, and firm agility significantly moderates this relationship. The conclusion of this study suggests that consumer goods manufacturing companies need to improve their agility to overcome the negative impact of economic factors. This study implies the need for more adaptive and flexible management strategies to enhance firm performance in the face of dynamic economic challenges.

One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency.

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge- and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.

SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals.

While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of SmartGD, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

Random Contrastive Interaction for Particle Swarm Optimization in High-Dimensional Environment

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

Prototype-Guided Memory Replay for Continual Learning.

Continual learning (CL) is a machine learning paradigm that accumulates knowledge while learning sequentially. The main challenge in CL is catastrophic forgetting of previously seen tasks, which occurs due to shifts in the probability distribution. To retain knowledge, existing CL models often save some past examples and revisit them while learning new tasks. As a result, the size of saved samples dramatically increases as more samples are seen. To address this issue, we introduce an efficient CL method by storing only a few samples to achieve good performance. Specifically, we propose a dynamic prototype-guided memory replay (PMR) module, where synthetic prototypes serve as knowledge representations and guide the sample selection for memory replay. This module is integrated into an online meta-learning (OML) model for efficient knowledge transfer. We conduct extensive experiments on the CL benchmark text classification datasets and examine the effect of training set order on the performance of CL models. The experimental results demonstrate the superiority our approach in terms of accuracy and efficiency.

The nexus between visitor satisfaction and expenditure behaviour in national parks: The case of Dragon Palace National Park, Guizhou, China

Several studies have explored the relationship between visitor expenditure, experience, satisfaction, intention to recommend, and revisit intention in national parks, but there is a gap in exploring the direct relationship between satisfaction and expenditure of national park visitors. This research identifies relationships between expenditure and behaviour patterns in Dragon Palace National Park, a 5A visitor attraction in Guizhou province, China. A structured survey questionnaire was utilised to collect 412 responses from national park visitors, employing convenience sampling techniques from August to December 2022. A partial least squares-structure equation model 4.0 was used to analyse the data. Findings support the positive relationship between experience, satisfaction, intention to recommend, and revisit intention in the national park. Also, results confirm that rational and emotional dimensions effectively measure the experience level. Further, this research confirms that experience is not a predictor of expenditure and that satisfaction has a negative relationship with expenditure by providing a measurement model to examine the experience in the national park. It frames a new theoretical lens on the relationship between expenditure and satisfaction and contributes significance to national park operations, providing a reference for future research. Management implicationsThe negative relationship between satisfaction and expenditure is interesting. The continuance of fostering national park visitor satisfaction for tourists cannot make sense to improve expenditure. The Dragon Palace National Park should pay more attention to overnight visitors' satisfaction and expenditure since they spend more but are less satisfied. It is vital to enhance the consuming willingness of visitors, improving the expenditure of satisfied visitors. Customising the experience, improving the cost performance of various goods and services, and enriching the experience content may work. The dimensions of the experience have been tested as effective, and the measurement model can be applied in other national parks.

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction.

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module, including this data-driven mechanism, is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.