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The purpose of this paper is to design a smart handheld device with force regulating function, which demonstrates the concept of patient-specialized tools.

This handheld device integrates an electrical bioimpedance (EBI) sensor for tissue measurement and a constant force regulation mechanism for ensuring stable tool–tissue contact. Particular focuses in this study are on the design of the constant force regulation mechanism whose design process is through genetic algorithm optimization and finite element simulation. In addition, the output force can be changed to the desired value by adjusting the cross-sectional area of the generated spring.

The following two specific applications based on ex vivo tissues are used for evaluating the designed device. One is in terms of safety of interaction with delicate tissue while the other is for compensating involuntary tissue motion. The results of both examples show that the handheld device is able to provide an output force with a small standard deviation.

In this paper, a handheld device with force regulation mechanism is designed for specific patients based on the genetic algorithm optimization and finite element simulation. The device can maintain a steady and safe interaction force during the EBI measurement on fragile tissues or moving tissues, to improve the sensing accuracy and to avoid tissue damage. Such functions of the proposed device are evaluated through a series of experiments and the device is demonstrated to be effective.

The purpose of this paper is to develop a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation. Hands-on control and teleoperation are two main procedures switched frequently in teleoperated minimally invasive surgery (MIS). The detailed human activity in the procedures can be defined and recognized using the sensor information. In this paper, a novel continuous adaptive shared control method is proposed for manipulators with Cartesian impedance control in the surgical scenario.

A human activity-aware shared control solution by adjusting the weight function is introduced to achieve smooth transition among different human activities, including hands-on control and teleoperation. Instead of introducing various controllers and switching among them during the surgical procedures, the proposed solution integrated all the human activity-based controllers into a single controller and the transition among the procedures is smooth and stable. The effectiveness of the proposed control approach was verified in a lab setup environment. The results prove that the robot behavior is stable and smooth. The algorithm is feasible and can achieve a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation.

Based on the experiment, the results confirm that the proposed human activity-aware adaptive shared control solution can switch the device behavior automatically using the real-time sensor information. The transition between different activities is smooth and stable.

For teleoperated surgical applications, the proposed method integrated different controllers for various human activities into a single controller by recognizing the activities using the real-time sensor information and the transition between different procedures is smooth and stable. It eases the surgical work for the surgeon and enhances the safety during the transition of control modes. The presented scheme provides a general solution to address the switching of working procedures in teleoperated MIS.

To the best of the authors’ knowledge, this paper is the first to propose human activity-aware adaptive shared control solution for human–robot interaction in surgical operations.

Intense interest has been shown in creating new and effective biocide agents as a result of changes in bacterial isolates, bacterial susceptibility to antibiotics, an increase in patients with burns and wounds and the difficulty of treating infections and antimicrobial resistance. Woven, nonwoven and knitted materials are used to make dressings; however, nonwoven dressings are becoming more popular because of their softness and high absorption capacity. Additionally, textiles have excellent geometrical, physical and mechanical features including three-dimensional structure availability, air, vapor and liquid permeability, strength, extensibility, flexibility and diversity of fiber length, fineness and cross-sectional shapes. It is necessary to treat every burn according to international protocol and along with it has to focus on particular problems of patients and the best possible results.

The objective of this paper is to conduct a thorough examination of research pertaining to the utilization of textiles, as well as alternative materials and innovative techniques, in the context of burn wound dressings. Through a critical analysis of the findings, this study intends to provide valuable insights that can inform and guide future research endeavors in this field.

In the past years, there have been several dressings such as xeroform petrolatum gauze, silver-impregnated dressings, biological dressings, hydrocolloid dressings, polyurethane film dressings, silicon-coated nylon dressings, dressings for biosynthetic skin substitutes, hydrogel dressings, newly developed dressings, scaffold bandages, Sorbalgon wound dressing, negative pressure therapy, enzymatic debridement and high-pressure water irrigation developed for the fast healing of burn wounds.

This research conducts a thorough analysis of the role of textiles in modern burn wound dressings.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

The purpose of this paper is to develop a mesh-free algorithm based on the least square support vector machines method for numerical simulation of the modified Helmholtz equations.

The proposed method deals with a Cauchy problem for the modified Helmholtz equations. The algorithm converts the problem into a quadratic programming. It can be divided into three steps. First, some training points are allocated. Then, an approximate function is constructed. Finally, the shape parameters are estimated.

The proposed method's stability is discussed. Numerical experiments are conducted to check the efficiency of the algorithm. The proposed method is found to feasible for the ill-posed problems of the modified Helmholtz equations.

The originality lies in that the proposed method is applied to solve the modified Helmholtz equations for the first time, and the expected results are obtained.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

The purpose of this paper is to provide a critical synthesis of research conducted within the hospitality and tourism industries in response to the impact of the COVID-19 pandemic, identify key perspectives and themes relating to the recovery and resilience of the two sectors and put forward recommendations that help address organizational and consumer behavior changes produced by the pandemic.

This study adopted a critical reflection approach to identify, select and synthesize relevant research based on which recommendations are drawn.

This study offers a contemporary framework discussing three distinct themes that emerged from existing research regarding the impact of COVID-19 on the hospitality and tourism industries: management, marketing and consumer behavior.

This study offers operational, practical and actionable recommendations for organizations about how to adapt and recover from the impact of the COVID-19 pandemic by guiding the industry in sustaining long-term resilience.

This study provides a critical and current synthesis of selected literature and theory that discuss key implications of the COVID-19 pandemic for the recovery and resilience-building of the hospitality and tourism sectors.

Since the multi-component of powder metallurgy was dispersed, and each component sheared flow and tiered under the action of friction force, it was difficult to disclose the evolution characteristics of each component. Meanwhile, third body mixing with particles of each component covered on the friction surface, which further increased the difficulty of understanding evolution of each component and the corresponding third body in the friction process. To solve this problem, this paper aims to propose a mechanical assembled method which compact several component sheets in order.

Pure copper, aluminum and artificial graphite sheets with thickness 0.5, 1 and 2 mm, respectively, were assembled into a jig by mechanical compact method. The relationship between arrangement patterns of the components and its friction coefficient was studied by using fixed speed friction test machine, the speed range from 200 to 2,000 r/min and the pressure range from 0.25 to 0.64 MPa.

The testing results showed that when the distribution of same components was congregated, friction coefficient dropped from 0.6 to 0.4. While the distribution of different components was dispersed, friction coefficient dropped from 0.6 to 0.25. The friction coefficient decline was caused by performances changes of third body fluidity. The sufficiently mixed third body made third body adhesion weaker and increased third body fluidity. That provoked friction coefficient decreasing obviously at high speed. On the contrary, with the high congregation of same components, strong third body adhesion led to a rougher surface which contributed to a higher friction coefficient.

By means of the mechanical-assembled multi-layer components to reveal the influence mechanism of every component on friction properties, will provide a new test approach for tribology.

The thermo-diffusion analysis of an isotropic cylinder under thermal flux and chemical potential impacts has been discussed. Improvements of Green and Naghdi generalized thermoelasticity theory have been proposed.

Some models with and without energy dissipation have been presented as well as the simple forms of Green–Naghdi (G–N) theories. These novel multi- and single-/dual-phase-lag models are presented to investigate the thermo-diffusion of the solid cylinder. The closed-form solution of thermo-diffusion governing equations of solid cylinder has been obtained to deduce all field variables.

A comparison study between the simple G–N II and III models and their improved models has been presented. The validations of outcomes are acceptable and so benchmarks are reported to help other investigators in their future comparisons.

The modified Green and Naghdi theories of types II and III are presented to get novel and accurate models of single- and dual-phase-lag of multiterms. The heat of mass diffusion equation as well as the constitutive equations for the stresses and chemical potential of a solid cylinder is added to the present formulation. The system of three differential coupled equations is solved, and all field variables are obtained for the thermal diffusion of the solid cylinder. Some validation examples and applications are presented to compare the simple and modified Green and Naghdi theories of types II and III. Sample plots are illustrated along the radial direction of the solid cylinder. Some results are tabulated to serve as benchmark results for future comparisons with other investigators. The reported and illustrated results show that the simple G–N II and III models yield the largest values of all field quantities. The single-phase-lag models give the smallest values. However, the dual-phase-lag model yields results that are intermediate between those of the simple and single-phase-lag G–N models.