Vegetation Restoration Technique System and Its Ecological Functions of the Degraded Ecosystem in the Arid-Hot Valleys——Taking the Example of Typical Mode in Small Watershed of Yuanmou

This paper concluded the vegetation restoration technique system in the arid-hot valleys and studied the anti-erosion function, environmental function and biological diversity effects of vegetation restoration on the ecosystem in the arid-hot valleys. The results showed that the soil erodibility decreased significantly after the vegetation restoration. The climate environment of the small watershed had a great improvement after the vegetation restoration, of which the temperature decreased, the humidity increased, the harsh environment of dry and hot in this region changed. The studies of the ecosystem biodiversity were mainly on the analysis of the relations between biodiversity and ecological function of the artificial ecological forest pattern and the natural enclosed treatment mode on the severely of degraded land. It could conclude that the natural enclosed treatment mode is helpful to the biodiversity of the ecosystem and the improvement and stability of the ecosystem, and Leucaena artificial forest restoration pattern reduced the species diversity but optimized the ecological function. Therefore, as to the severely and extremely severely degraded ecosystem in the arid-hot valleys, Leucaena pattern of gully control and natural enclosed treatment mode are the relatively optimal choices.

Optimization of tracheoesophageal fistula model established with Tshaped magnet system based on magnetic compression technique

BACKGROUND The magnetic compression technique has been used to establish an animal model of tracheoesophageal fistula(TEF),but the commonly shaped magnets present limitations of poor homogeneity of TEF and poor model control.We designed a Tshaped magnet system to overcome these problems and verified its effectiveness via animal experiments.AIM To investigate the effectiveness of a T-shaped magnet system for establishing a TEF model in beagle dogs.METHODS Twelve beagles were randomly assigned to groups in which magnets of the Tshaped scheme(study group,n=6)or normal magnets(control group,n=6)were implanted into the trachea and esophagus separately under gastroscopy.Operation time,operation success rate,and accidental injury were recorded.After operation,the presence and timing of cough and the time of magnet shedding were observed.Dogs in the control group were euthanized after X-ray and gastroscopy to confirm establishment of TEFs after coughing,and gross specimens of TEFs were obtained.Dogs in the study group were euthanized after X-ray and gastroscopy 2 wk after surgery,and gross specimens were obtained.Fistula size was measured in all animals,and then harvested fistula specimens were examined by hematoxylin and eosin(HE)and Masson trichrome staining.RESULTS The operation success rate was 100%for both groups.Operation time did not differ between the study group(5.25 min±1.29 min)and the control group(4.75 min±1.70 min;P=0.331).No bleeding,perforation,or unplanned magnet attraction occurred in any animal during the operation.In the early postoperative period,all dogs ate freely and were generally in good condition.Dogs in the control group had severe cough after drinking water at 6-9 d after surgery.X-ray indicated that the magnets had entered the stomach,and gastroscopy showed TEF formation.Gross specimens of TEFs from the control group showed the formation of fistulas with a diameter of 4.94 mm±1.29 mm(range,3.52-6.56 mm).HE and Masson trichrome staining showed scar tissue formation and hierarchical structural disorder at the fistulas.Dogs in the study group did not exhibit obvious coughing after surgery.X-ray examination 2 wk after surgery indicated fixed magnet positioning,and gastroscopy showed no change in magnet positioning.The magnets were removed using a snare under endoscopy,and TEF was observed.Gross specimens showed well-formed fistulas with a diameter of 6.11 mm±0.16 mm(range,5.92-6.36 mm),which exceeded that in the control group(P<0.001).Scar formation was observed on the internal surface of fistulas by HE and Masson trichrome staining,and the structure was more regular than that in the control group.CONCLUSION Use of the modified T-shaped magnet scheme is safe and feasible for establishing TEF and can achieve a more stable and uniform fistula size compared with ordinary magnets.Most importantly,this model offers better controllability,which improves the flexibility of follow-up studies.

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

The increasing number of security holes in the Internet of Things(IoT)networks creates a question about the reliability of existing network intrusion detection systems.This problem has led to the developing of a research area focused on improving network-based intrusion detection system(NIDS)technologies.According to the analysis of different businesses,most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques.However,these techniques are not suitable for every type of network.In light of this,whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains.The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets.Six ML models were used in this study,namely,logistic regression(LR),decision trees(DT),Naive Bayes(NB),random forest(RF),K-nearest neighbors(KNN),and linear SVM.The primary detection algorithms used in this study,Principal Component(PCA)and Gini Impurity-Based Weighted Forest(GIWRF)evaluated against three global ToN-IoT datasets,UNSW-NB15,and Bot-IoT datasets.The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm.It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used.Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

Telepresence Robots and Controlling Techniques in Healthcare System

In this era of post-COVID-19,humans are psychologically restricted to interact less with other humans.According to the world health organization(WHO),there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy,and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However,our feature-wise analysis also points to specific technical,appropriate,and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers,building on new perceptions during the COVID-19 pandemic.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Bicuspidization Using the Open-Sleeve Technique for Congenital Aortic Stenosis during Infancy

Congenital aortic stenosis(cAS)frequently requires intervention during the neonatal or infantile period.However,surgical repair is challenging because of the narrow surgical space.We performed bicuspidization using the open-sleeve technique for cAS with a unicuspid aortic valve in two patients.Postoperatively,the patients were doing well without reintervention for the aortic valve for 8 and 6 years,respectively.Their aortic annular diameter increased along with somatic growth.Bicuspidization for neonates or infancy can be performed safely using the open-sleeve technique as its midterm results have been satisfactory.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Exploring battery material failure mechanisms through synchrotron X-ray characterization techniques

Rechargeable battery cycling performance and related safety have been persistent concerns.It is crucial to decipher the capacity fading induced by electrode material failure via a range of techniques.Among these,synchrotron-based X-ray techniques with high flux and brightness play a key role in understanding degradation mechanisms.In this comprehensive review,we summarize recent advancements in degra-dation modes and mechanisms that were revealed by synchrotron X-ray methodologies.Subsequently,an overview of X-ray absorption spectroscopy and X-ray scattering techniques is introduced for charac-terizing failure phenomena at local coordination atomic environment and long-range order crystal struc-ture scale,respectively.At last,we envision the future of exploring material failure mechanism.

Surgical techniques to prevent delayed gastric emptying after pancreaticoduodenectomy

Background: Delayed gastric emptying(DGE) is one of the most common complications after pancreaticoduodenectomy(PD). DGE represents impaired gastric motility without significant mechanical obstruction and is associated with an increased length of hospital stay, increased healthcare costs, and a high readmission rate. We reviewed published studies on various technical modifications to reduce the incidence of DGE. Data sources: Studies were identified by searching Pub Med for relevant articles published up to December 2022. The following search terms were used: “pancreaticoduodenectomy”, “pancreaticojejunostomy”, “pancreaticogastrostomy”, “gastric emptying”, “gastroparesis” and “postoperative complications”. The search was limited to English publications. Additional articles were identified by a manual search of references from key articles. Results: In recent years, various surgical procedures and techniques have been explored to reduce the incidence of DGE. Pyloric resection, Billroth II reconstruction, Braun's enteroenterostomy, and antecolic reconstruction may be associated with a decreased incidence of DGE, but more high-powered studies are needed in the future. Neither laparoscopic nor robotic surgery has demonstrated superiority in preventing DGE, and the use of staplers is controversial regarding whether they can reduce the incidence of DGE. Conclusions: Despite many innovations in surgical techniques, there is no surgical procedure that is superior to others to reduce DGE. Further larger prospective randomized studies are needed.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Advances in the application of auxiliary imaging techniques in parathyroid diseases

Hypoparathyroidism is one of the main complications after total thyroidectomy,severely affecting patients’quality of life.How to effectively protect parathyroid function after surgery and reduce the incidence of hypoparathyroidism has always been a key research area in thyroid surgery.Therefore,precise localization of parathyroid glands during surgery,effective imaging,and accurate surgical resection have become hot topics of concern for thyroid surgeons.In response to this clinical phenomenon,this study compared several different imaging methods for parathyroid surgery,including nanocarbon,indocyanine green,near-infrared imaging techniques,and technetium-99m methoxyisobutylisonitrile combined with gamma probe imaging technology.The advantages and disadvantages of each method were analyzed,providing scientific recommendations for future parathyroid imaging.In recent years,some related basic and clinical research has also been conducted in thyroid surgery.This article reviewed relevant literature and provided an overview of the practical application progress of various imaging techniques in parathyroid surgery.

Spontaneous dislodgment of a peritoneal dialysis catheter inserted using the Seldinger technique:A case report

BACKGROUND Peritoneal dialysis(PD)is an important renal replacement therapy in patients with end-stage renal disease.PD catheters remain the lifeline for patients undergoing PD.The catheter technique survival rate is considered a core PD outcome domain.CASE SUMMARY The PD catheter spontaneously dislodged in a patient undergoing PD during regular fluid exchange without pain.Abdominal computed tomography showed a tunnel infection.A double-cuff straight Tenckhoff catheter had been inserted using the Seldinger technique.Before this incident,the patient had a history of tunnel infections.We speculate that recurrent tunnel infections and catheter insertion using the Seldinger technique may have led to catheter dislodgement.CONCLUSION The present case suggests that clinicians should more rigorously assess the persistence of catheter-related infections concerning the potential complications arising from catheter dislodgement associated with the Seldinger technique.

Myelin histology:a key tool in nervous system research

The myelin sheath is a lipoprotein-rich,multilayered structure capable of increasing conduction velocity in central and peripheral myelinated nerve fibers.Due to the complex structure and composition of myelin,various histological techniques have been developed over the centuries to evaluate myelin under normal,pathological or experimental conditions.Today,methods to assess myelin integrity or content are key tools in both clinical diagnosis and neuroscience research.In this review,we provide an updated summary of the composition and structure of the myelin sheath and discuss some histological procedures,from tissue fixation and processing techniques to the most used and practical myelin histological staining methods.Considering the lipoprotein nature of myelin,the main features and technical details of the different available methods that can be used to evaluate the lipid or protein components of myelin are described,as well as the precise ultrastructural techniques.

Effect of electroacupuncture combined with rehabilitation techniques on shoulder function in patients with rotator cuff injuries

BACKGROUND The rotator cuff is located below the acromion and deltoid muscles and comprises multiple tendons that wrap around the humeral head,maintaining shoulder joint stability.AIM To explore the effect of electroacupuncture combined with rehabilitation techniques on shoulder function in patients with rotator cuff injuries.METHODS We selected 97 patients with rotator cuff injuries treated in the People's Hospital of Yuhuan from February 2020 to May 2023.Patients were grouped using the envelope method.RESULTS After treatment,the study group’s treatment effective rate was 94.90%(46/49 patients),significantly higher than that in the control group(79.17%,38/48 cases;P<0.05).Before treatment,there was no difference in Constant Murley Score(CMS)scores,shoulder mobility,or 36-Item Short Form Health Survey(SF-36)scale scores(P>0.05).Compared with those before treatment,the CMS scores(including pain,daily living ability,shoulder mobility,and muscle strength),all aspects of shoulder mobility(forward flexion,posterior extension,external rotation,internal rotation),and SF-36 scale scores(including physiological,psychological,emotional,physical,vitality,and health status)were higher in both groups after treatment and significantly higher in the study group(P<0.05).There was no difference in the occurrence of complications between the two treatment groups(P>0.05).CONCLUSION Electroacupuncture combined with rehabilitation techniques has a good treatment effect on patients with rotator cuff injuries,helps accelerate the recovery of shoulder function,improves the quality of life,and is highly safe.