Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Teaching Reform of Environmental Engineering Microbiology Based on OBE Concept in the Context of New Engineering Disciplines

In response to meeting the needs of cultivating applied talents in the construction of new engineering disciplines,based on the concept of Outcome-Based Education(OBE),this study analyzes the problems existing in the teaching of the course Environmental Engineering Microbiology,and put forward some corresponding curriculum reform schemes.According to the target points of professional graduation requirements,the scheme proposes revising the syllabus of Environmental Engineering Microbiology,clarifying the curriculum objectives,updating the teaching content,and reforming the teaching methods.Through these measures,it is intended to achieve the unity of teachers way of"teaching"and students way of"learning",construct a new teaching mode,fully stimulate students subjective initiative,and enhance students innovative consciousness and practical ability.Besides,in this study,a"whole process-diversification"evaluation system is established to comprehensively evaluate students performance in theoretical knowledge learning and practical application,comprehensively evaluate students learning situation,and analyze the teaching effect in real time,so as to achieve continuous improvement,and ultimately achieve the goal of improving classroom quality.

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.

A Learning-Based Channel Model for Synergetic Transmission Technology

It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.

Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusionweighted imaging of the pancreas

BACKGROUND It has been reported that deep learning-based reconstruction(DLR)can reduce image noise and artifacts,thereby improving the signal-to-noise ratio and image sharpness.However,no previous studies have evaluated the efficacy of DLR in improving image quality in reduced-field-of-view(reduced-FOV)diffusionweighted imaging(DWI)[field-of-view optimized and constrained undistorted single-shot(FOCUS)]of the pancreas.We hypothesized that a combination of these techniques would improve DWI image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.AIM To evaluate the efficacy of DLR for image quality improvement of FOCUS of the pancreas.METHODS This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021.We evaluated three types of FOCUS examinations:FOCUS with DLR(FOCUS-DLR+),FOCUS without DLR(FOCUS-DLR−),and conventional FOCUS(FOCUS-conv).The three types of FOCUS and their apparent diffusion coefficient(ADC)maps were compared qualitatively and quantitatively.RESULTS FOCUS-DLR+(3.62,average score of two radiologists)showed significantly better qualitative scores for image noise than FOCUS-DLR−(2.62)and FOCUS-conv(2.88)(P<0.05).Furthermore,FOCUS-DLR+showed the highest contrast ratio and 600 s/mm^(2)(0.72±0.08 and 0.68±0.08)and FOCUS-DLR−showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2(0.62±0.21 and 0.62±0.21)(P<0.05),respectively.FOCUS-DLR+provided significantly higher ADCs of the pancreas and lesion(1.44±0.24 and 3.00±0.66)compared to FOCUS-DLR−(1.39±0.22 and 2.86±0.61)and significantly lower ADCs compared to FOCUS-conv(1.84±0.45 and 3.32±0.70)(P<0.05),respectively.CONCLUSION This study evaluated the efficacy of DLR for image quality improvement in reduced-FOV DWI of the pancreas.DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution.The denoising level of DWI can be controlled to make the images appear more natural to the human eye.However,this study revealed that DLR did not ameliorate pancreatic distortion.Additionally,physicians should pay attention to the interpretation of ADCs after DLR application because ADCs are significantly changed by DLR.

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.

DLBT:Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code

Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.

Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.

Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

A concept analysis of vicarious resilience in mental health nursing

Objective:The introduction of the vicarious resilience concept in psychology and mental health nursing literature is a highly promising advancement.By utilizing this novel concept,experts in various domains can enhance their comprehension of how to foster resilience in individuals by observing and learning from the resilience of others.This concept analysis aims to elucidate the concept of vicarious resilience in mental health nursing by defining its related attributes,antecedents,and consequences.Methods:Walker and Avant’s strategy for concept analysis method was used.This review specifically examined mental health nurse providers.A comprehensive literature search was performed in the PubMed,Medline,the Cochrane Library,and CINAHL databases.The inclusion criterion was Englishlanguage documents on vicarious resilience within mental health nursing.Results:A total of 24 articles were included.The concept’s attributes were empathy,hope,resourcefulness,awareness,and spirituality.Antecedents were associated with listening to patients’trauma narratives,self-care,self-awareness,and support from colleagues.Consequences were enhanced well-being,changes in life goals,adaptation,personal growth,and increased personal resilience.Currently,there is only one tool in the empirical reference.Conclusion:Interpreting the concept of vicarious resilience in mental health nursing and determining its characteristics can be utilized to design nursing interventions to develop vicarious resilience and enhance the quality of care in mental health facilities.

Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Reform of Hospitality English Teaching Based on the Educational Concept of Outcome-Based Education(OBE)

The Outcome-Based Education(OBE)educational concept,proposed by renowned American educator Spady,aims to guide students in mastering learning laws and ultimately achieving expected learning outcomes.The application of OBE educational concept in Hospitality English curriculum not only can effectively enhance the learning effect but also promote the reform of the Hospitality English curriculum,thus improving students’learning enthusiasm.Therefore,taking the Hospitality English curriculum as an example,this paper points out the existing problems in the teaching of this course and suggests the reform measures based on the OBE teaching concept,hoping to provide guidelines for related educational work.

Clinical Study of Accelerated Rehabilitation Concept Combined with Tianji Robot-Assisted Surgery in Lumbar Degenerative Diseases

Objective: To compare the effectiveness and safety of two surgical methods for lumbar degenerative diseases;the combination of the concept of accelerated rehabilitation with the assistance of Tianji Robotics and the concept of accelerated rehabilitation combined with manual pedicle screw placement assisted by conventional C-arm fluoroscopy. Methods: A retrospective analysis was performed on 70 patients who received the concept of accelerated rehabilitation combined with spinal surgery for lumbar degenerative diseases in Baise People’s Hospital from January 2022 to January 2024. Among them, 35 patients in the robot group received accelerated rehabilitation concept combined with robot-assisted surgery;In the conventional C-arm group, 35 patients received the accelerated rehabilitation concept combined with manual pedicle screw placement assisted by conventional C-arm fluoroscopy. VAS score (preoperative/postoperative), ODI score (preoperative/postoperative), intraoperative bleeding volume, postoperative hospital stay, postoperative complications and the accuracy rate of screw placement were compared between the two groups. Result: There was no statistically significant difference in preoperative VAS scores between the robot group and the conventional C-arm group (6.45 ± 0.82 VS 6.63 ± 0.81, P = 0.6600). The postoperative VAS score of the robot group was better than that of the conventional C-arm group (1.69 ± 0.80 VS 2.45 ± 0.85, P = 0.0000*). There was no statistically significant difference in preoperative ODI scores between the robot group and the conventional C-arm group (32.11 ± 3.18 VS 31.66 ± 2.25, P = 0.4900). The postoperative ODI score of the robot group was better than that of the conventional C-arm group (22.68 ± 1.94 VS 24.57 ± 2.25, P = 0.0000*). The postoperative complications in the robot group were less than those in the conventional C-arm group (2.7778% VS 28.5724%, P = 0.0030*). The intraoperative bleeding in the robot group was lower than that in the conventional C-arm group (320.85 ± 276.28 VS 490.00 ± 395.34, P = 0.0420*). The postoperative hospital stay of the robot group was shorter than that of the conventional C-arm group (10.00 ± 9.32 VS 14.49 ± 7.55, P = 0.0300*). The screw placement inaccuracy score of the robot group was lower than that of the conventional C-arm group (0.17 ± 0.51 VS 1.45 ± 1.46, P = 0.0000*). Conclusion: The combination of the concept of accelerated rehabilitation and Tianji Orthopedic robot-assisted surgery is more effective and safer in posterior lumbar decompression and internal fixation surgery with a screw rod system, and is worthy of promotion and application.

Learning of Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning,and discusses several issues related to concept learning,including the assistance of machine learning about concept learning.In terms of individual learning,current evidence shows that the brain tends to process concrete concepts through typical features(shared features);and for abstract concepts,semantic processing is the most important cognitive way.In terms of social learning,interpersonal neural synchrony(INS)is considered the main indicator of efficient knowledge transfer(such as teaching activities between teachers and students),but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning.Ultimately,this article argues that the way the human brain processes concepts depends on the concept’s own characteristics,so there are no“better”strategies in teaching,only more“suitable”strategies.

Value and Measures of Integrating Local Red Culture into the Ideological and Political Course of Environmental Disciplines under the OBE Concept

Based on output-oriented education,the OBE(Outcome-Based Education)concept integrates local red culture into the ideological and political course of environmental disciplines,and is an important part of training applied talents of environmental disciplines in the new era.This educational model makes an innovation on the traditional educational and teaching concepts and centers on students.This paper analyzes the value of integrating local red culture into the ideological and political course under the OBE concept,and puts forward an effective implementation path.

Clinical Study of Applying Enhanced Recovery after Surgery Concept in Single-Segment Lumbar Spinal Stenosis Surgery

Objective: With the aging population and changes in lifestyle, lumbar spinal stenosis has become a common spinal disorder. Treatment modalities have been advancing, and the application of Enhanced Recovery After Surgery (ERAS) principles provides a new approach to postoperative recovery in patients. This study aims to investigate the clinical application effects of ERAS principles in single-level lumbar spinal stenosis surgery. Methods: This study included 64 patients who underwent lumbar fusion surgery in the Spinal Surgery Department of Baise People’s Hospital from July 2022 to July 2024. These patients were divided into an experimental group (ERAS group, 33 cases) and a control group (conventional group, 31 cases) based on perioperative care, receiving ERAS principles and traditional treatment, respectively. A comparison was made between the two groups in terms of gender, age, BMI, intraoperative blood loss, postoperative length of hospital stay, postoperative complications, hospital costs, VAS scores (preoperative/postoperative day 3), and ODI scores (preoperative/postoperative day 3). Results: There were no significant differences in gender, age, and BMI between the ERAS group and the conventional group (gender: χ2 = 0.5008, P = 0.4792;age: 54.55 ± 8.51 years vs. 57.39 ± 8.16 years, P = 0.0892;BMI: 25.11 ± 2.70 vs. 24.77 ± 2.75, P = 0.3098). However, during surgery, patients in the ERAS group had significantly less blood loss than those in the conventional group (197.58 ± 195.51ml vs. 438.71 ± 349.22 ml, P = 0.0006), and the postoperative length of hospital stay was significantly shorter (7.00 ± 2.24 days vs. 11.55 ± 5.23 days, P = 0.0000). On postoperative day 3, VAS scores were significantly better in the ERAS group compared to the conventional group (3.70 ± 0.88 vs. 4.32 ± 0.87, P = 0.0031), and the ODI scores showed significant improvement as well (46.00 ± 3.04 vs. 48.00 ± 3.39, P = 0.0078). Although there were no significant differences in postoperative complications and hospital costs (complications: 3 cases vs. 0 cases, P = 0.2154;hospital costs: 63524.29 ± 17891.80 RMB vs. 58733.84 ± 13280.82 RMB, P = 0.1154), ERAS demonstrated better postoperative recovery outcomes in single-level lumbar spinal stenosis surgery. Conclusion: The study results support the implementation of ERAS principles in single-level lumbar spinal stenosis surgery to promote rapid recovery, reduce healthcare resource consumption, and improve overall patient satisfaction.

Conceptual Analysis of Self-Management in Patients with Hypertension

Objective:To clarify the concept of self-management in hypertensive patients by analyzing the definition,attributes,and measurement tools through a literature review.Methods:An Internet-based search of the databases was conducted using Academic Search Complete,Medical Line,CINAHL,Health Source:Nursing/Scholarly Edition,and Google Scholar.In the search process,keywords or free text were combined by using Boolean operators,with the search terms“self of management”or“self-management,”“concept*analysis”or“concept*definition,”and Walker and Avant’s concept analysis method was used.Results:Analysis of relevant literature summarized the conceptual attributes of self-management in hypertensive patients as the active participation of patients in the treatment process;the presence of interaction provided by patients and health care providers;the use of certain health management tools;and the aim of maintaining and improving the health status and living capacity of hypertensive patients.Conclusion:The concept of self-management for hypertensive patients was clarified,which helps to promote the application of hypertensive self-management in clinical work and improve outcomes and quality of life for patients with hypertension.

The Sustainable Development Pathway of the Biomedical Industry Based on Environmental,Social,and Governance(ESG)Concepts

There is a growing global awareness of environmental,social,and governance(ESG)concerns.The biopharmaceutical industry is an important field that affects human health and well-being,and its sustainable development is now the industry’s focus.Based on the current state of the green development of China’s biopharmaceutical industry,the article proposes suggestions and paths for promoting the industry to better fulfill its social responsibilities and protect the environment while pursuing economic benefits.By doing so,the industry can make a greater contribution to global public health and become an important factor in promoting human health and social prosperity.

Self-management in patients with diabetic foot ulcer:a concept analysis

Objective:This paper presents an analysis of the concept of patient outcomes.Methods:The present study conducted searches on various databases,including Wanfang,Sinomed,CNKI,Pub Med,Cochrane Library,Embase,Web of Science,and Ovid.The paper followed the Walker and Avant concept-analysis approach.Results:Initially,899 pieces of literature were identified through the search process,and after screening,41 of them were ultimately included in the analysis.The identified attributes of the concept included(1)capability,(2)decision making,and(3)action.These antecedents were shaped by factors such as illness perception,self-efficacy,and family and social.The consequences included(1)physiological effects,(2)psychological effects,and(3)social influence.Conclusions:The concept analysis of self-management in patients with diabetic foot ulcers(DFUs)not only aids in clinical practice and suppor ts interventions,but also contributes to the development of self-management theory.The common goal of clinical medical staff is to assist DFU patients in improving cognitive ability,making correct self-management decisions,and enhancing self-management behavior.

The use of concept mapping in data analysis:a phenomenology study of family members'experiences in taking care of people with cancer

Objective:To explain the use of concept mapping in a study about family members'experiences in taking care of people with cancer.Methods:This study used a phenomenological study design.In this study,we describe the analytical process of using concept mapping in our phenomenological studies about family members'experiences in taking care of people with cancer.Results:We developed several concept maps that aided us in analyzing our collected data from the interviews.Conclusions:The use of concept mapping is suggested to researchers who intend to analyze their data in any qualitative studies,including those using a phenomenological design,because it is a time-efficient way of dealing with large amounts of qualitative data during the analytical process.