Clinical diagnostic advances in intestinal anastomotic techniques:Hand suturing,stapling,and compression devices

The development of intestinal anastomosis techniques,including hand suturing,stapling,and compression anastomoses,has been a significant advancement in surgical practice.These methods aim to prevent leakage and minimize tissue fibrosis,which can lead to stricture formation.The healing process involves various phases:hemostasis and inflammation,proliferation,and remodeling.Mechanical staplers and sutures can cause inflammation and fibrosis due to the release of profibrotic chemokines.Compression anastomosis devices,including those made of nickel-titanium alloy,offer a minimally invasive option for various surgical challenges and have shown safety and efficacy.However,despite advancements,anastomotic techniques are evaluated based on leakage risk,with complications being a primary concern.Newer devices like Magnamosis use magnetic rings for compression anastomosis,demonstrating greater strength and patency compared to stapling.Magnetic technology is also being explored for other medical treatments.While there are promising results,particularly in animal models,the realworld application in humans is limited,and further research is needed to assess their safety and practicality.

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.

Enhancing lead-free photovoltaic performance:Minimizing buried surface voids in tin perovskite films through weakly polar solvent pre-treatment strategy

Buried interfacial voids have always been a notorious phenomenon observed in the fabrication of lead perovskite films. The existence of interfacial voids at the buried interface will capture the carrier, suppress carrier transport efficiencies, and affect the stability of photovoltaic devices. However, the impact of these buried interfacial voids on tin perovskites, a promising avenue for advancing lead-free photovoltaics, has been largely overlooked. Here, we utilize an innovative weakly polar solvent pretreatment strategy(WPSPS) to mitigate buried interfacial voids of tin perovskites. Our investigation reveals the presence of numerous voids in tin perovskites during annealing, attributed to trapped dimethyl sulfoxide(DMSO) used in film formation. The WPSPS method facilitates accelerated DMSO evaporation, effectively reducing residual DMSO. Interestingly, the WPSPS shifts the energy level of PEDOT:PSS downward, making it more aligned with the perovskite. This alignment enhances the efficiency of charge carrier transport. As the result, tin perovskite film quality is significantly improved,achieving a maximum power conversion efficiency approaching 12% with only an 8.3% efficiency loss after 1700 h of stability tests, which compares well with the state-of-the-art stability of tin-based perovskite solar cells.

The Application of Analytical Techniques to Alpha-Synuclein in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms including cognitive impairment and mood disorders. A hallmark of PD is the accumulation of alpha-synuclein, a presynaptic neuronal protein that aggregates to form Lewy bodies, leading to neuronal dysfunction and cell death. The study of alpha-synuclein and its pathological forms is crucial for understanding the etiology of PD and developing effective diagnostic and therapeutic strategies. Analytical techniques play a pivotal role in elucidating the structure, function, and aggregation mechanisms of alpha-synuclein. Biochemical methods such as Western blotting and enzyme-linked immunosorbent assay (ELISA) are employed to detect and quantify alpha-synuclein in biological samples, offering insights into its expression levels and post-translational modifications. Imaging techniques like immunohistochemistry and positron emission tomography (PET) allow for the visualization of alpha-synuclein aggregates in tissue samples and in vivo, respectively, facilitating the study of its spatial distribution and progression in PD Spectroscopic methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, provide detailed structural information on alpha-synuclein and its isoforms, aiding in the identification of conformational changes associated with aggregation. Emerging techniques such as cryo-electron microscopy (Cryo-EM) and single-molecule fluorescence enable high-resolution structural analysis and real-time monitoring of alpha-synuclein aggregation dynamics, respectively. The application of these analytical techniques has significantly advanced our understanding of the pathophysiological role of alpha-synuclein in PD. They have contributed to the identification of potential biomarkers for early diagnosis and the evaluation of therapeutic interventions targeting alpha-synuclein aggregation. Despite technical limitations and challenges in clinical translation, ongoing advancements in analytical methodologies hold promise for improving the diagnosis, monitoring, and treatment of Parkinson’s disease through a deeper understanding of alpha-synuclein pathology.

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.

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.

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.

Novel fabrication techniques for ultra-thin silicon based flexible electronics

Flexible electronics offer a multitude of advantages,such as flexibility,lightweight property,portability,and high durability.These unique properties allow for seamless applications to curved and soft surfaces,leading to extensive utilization across a wide range of fields in consumer electronics.These applications,for example,span integrated circuits,solar cells,batteries,wearable devices,bio-implants,soft robotics,and biomimetic applications.Recently,flexible electronic devices have been developed using a variety of materials such as organic,carbon-based,and inorganic semiconducting materials.Silicon(Si)owing to its mature fabrication process,excellent electrical,optical,thermal properties,and cost efficiency,remains a compelling material choice for flexible electronics.Consequently,the research on ultra-thin Si in the context of flexible electronics is studied rigorously nowadays.The thinning of Si is crucially important for flexible electronics as it reduces its bending stiffness and the resultant bending strain,thereby enhancing flexibility while preserving its exceptional properties.This review provides a comprehensive overview of the recent efforts in the fabrication techniques for forming ultra-thin Si using top-down and bottom-up approaches and explores their utilization in flexible electronics and their applications.

Electrospinning-hot pressing technique for the fabrication of thermal and electrical storage membranes and its applications

The combination of electrospinning and hot pressing,namely the electrospinning-hot pressing technique(EHPT),is an efficient and convenient method for preparing nanofibrous composite materials with good energy storage performance.The emerging composite membrane prepared by EHPT,which exhibits the advantages of large surface area,controllable morphology,and compact structure,has attracted immense attention.In this paper,the conduction mechanism of composite membranes in thermal and electrical energy storage and the performance enhancement method based on the fabrication process of EHPT are systematically discussed.Moreover,the state-of-the-art applications of composite membranes in these two fields are introduced.In particular,in the field of thermal energy storage,EHPT-prepared membranes have longitudinal and transverse nanofibers,which generate unique thermal conductivity pathways;also,these nanofibers offer enough space for the filling of functional materials.Moreover,EHPT-prepared membranes are beneficial in thermal management systems,building energy conservation,and electrical energy storage,e.g.,improving the electrochemical properties of the separators as well as their mechanical and thermal stability.The application of electrospinning-hot pressing membranes on capacitors,lithium-ion batteries(LIBs),fuel cells,sodium-ion batteries(SIBs),and hydrogen bromine flow batteries(HBFBs)still requires examination.In the future,EHPT is expected to make the field more exciting through its own technological breakthroughs or be combined with other technologies to produce intelligent materials.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors(TKIs)have emerged as the first-line small molecule drugs in many cancer therapies,exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways.However,there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites,which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments,alongside other potential side effects or adverse reactions.Therefore,an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods,clinical pharmacokinetics,and therapeutic drug monitoring of different TKIs.This paper provides a comprehensive overview of the advancements in pretreatment methods,such as protein precipitation(PPT),liquid-liquid extraction(LLE),solid-phase extraction(SPE),micro-SPE(μ-SPE),magnetic SPE(MSPE),and vortex-assisted dispersive SPE(VA-DSPE)achieved since 2017.It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography(HPLC)and high-resolution mass spectrometry(HRMS)methods,capillary electrophoresis(CE),gas chromatography(GC),supercritical fluid chromatography(SFC)procedures,surface plasmon resonance(SPR)assays as well as novel nanoprobes-based biosensing techniques.In addition,a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

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.

A General Analytic Approximation Technique for the Modified Bessel Functions with Fractional Order Iv (x), Applied to I1/6(x) and I1/7(x)

A general technique to obtain simple analytic approximations for the first kind of modified Bessel functions. The general procedure is shown, and the parameter determination is explained through the applications to this particular case I1/6(x)and I1/7(x). In this way, it shows how to apply the technique to any particular orderν, in order to obtain an approximation valid for any positive value of the variable x. In the present method power series and asymptotic expansion are simultaneously used. The technique is an extension of the multipoint quasirational approximation method, MPQA. The main idea is to look for a bridge function between the power and asymptotic expansion of the I1/6(x), and similar procedure for I1/7(x). To perform this, rational functions are combined with hyperbolic ones and fractional powers. The number of parameters to be determined for each case is four. The maximum relative errors are 0.0049 for ν=1/6, and 0.0047 for ν=7. However, these relative errors decrease outside of the small region of the variables, wherein the maximum relative errors are reached. There is a clear advantage of this procedure compared with any other ones.

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.

Randomization Strategies in Image Steganography Techniques:A Review

Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

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.

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.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.