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This study aims to investigate the surface modification on 20CrMnTi gear steel individually treated by diamond-like carbon films and nitride coatings.

For this purpose, the mechanical properties of a-C:H, ta-C and AlCrSiN coatings are characterized by nano-indentation and scratch tests. The friction and wear behaviors of these three coatings are evaluated by ball-on-disc tribological experiments under dry contact conditions.

The results show that the a-C:H coating has the highest coating-substrate adhesion strength (495 mN) and the smoothest surface (Ra is about 0.045 µm) compared with the other two coatings. The AlCrSiN coating shows the highest mean coefficient of friction (COF), whereas the ta-C coating exhibits the lowest one (steady at about 0.16). The carbon-based coatings possess excellent self-lubricating properties compared with nitride ceramic ones, which effectively reduce the COF by about 64%. The major failure mode of carbon-based coatings in dry contact is slight abrasive wear. The damage of AlCrSiN coating is mainly adhesive wear and abrasive wear.

It is suggested that the carbon-based film can effectively improve the friction-reducing and wear resistance performance of the gear steel surface, which has a promising application prospect in the mechanical transmission field.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0129/

This paper aims to examine the effect of sesame oil (SO) on fatigue and mental health status in women with nonalcoholic fatty liver disease (NAFLD) undergoing a weight-loss diet.

In total, 60 women with NAFLD were randomly assigned to receive 30 g/day of either SO (n = 30) or sunflower oil (n = 30). All the patients received a hypocaloric diet (−500 kcal/day) for 12 weeks in a double-blinded controlled trial. Anthropometric indices, dietary intake, physical activity, fatigue and mental health status were measured at the baseline and the trial cessation.

In total, 53 participants completed the intervention. Significant reductions in anthropometric indices were observed in both groups (p-value = 0.001). Following SO, fatigue (p-value = 0.002), anxiety (p-value = 0.011) and depression (p-value = 0.013) scores were significantly reduced, while no significant changes were observed in stress scores.

In summary, the present study was conducted to assess the efficacy of SO consumption on fatigue and mental health status among patients with NAFLD. The results revealed SO consumption significantly reduced fatigue, anxiety and depression scores in comparison with the control group, but not for stress scores. Further clinical trials, different doses, with a longer duration of intervention, in different groups, are necessary to confirm the veracity of the results.

Coopetition is a well-studied phenomenon in traditional enterprises. However, it lacks deepening in the social sphere, specifically on hybrid organizations (social and commercial goals). This paper analyzes the configuration of coopetition strategies in social enterprises and how these strategies can improve social value devolution.

The authors conducted a multicase study with Brazilian social enterprises and a social incubator. Semistructured interviews with founders of the social enterprises and the president of the incubator were the primary sources of evidence, supported by observations and secondary data.

The authors identified four main findings: (1) the social incubator induces coopetition among social enterprises; (2) coopetition is necessary to improve market performance; (3) coopetition is a natural strategy resulting from the activity of the social enterprise; (4) the behavior and context of social enterprises generate a new framework for coopetition formation. This framework comprises three stages of value: a social cooperation level to co-creation of value; second, a social competition level to the appropriation of value; and the third coopetition-balanced level to social value devolution.

The authors advance knowledge on coopetition in an exciting, underexplored context, social entrepreneurship. The authors highlight that the coopetition nature and outcome in social enterprises have specificities compared to traditional businesses. The authors also improve the understanding of social value devolution based on simultaneous cooperation and competition among small social enterprises, allowing theoretical and practical implications. Thus, they advance the recurring discussion in coopetition literature beyond the generation and appropriation of value.

The purpose of this study is to investigate the errors arising from the numerical treatment of model processes, paying particular attention to the impact of key system features including widely variable dispersion coefficients, spatiotemporal velocities of algal cells, and the aggregation of algae from single cells to large colonies. An advection–dispersion model has been presented to describe the vertical transport of colonial and motile harmful algae in a lake environment.

Model performance is examined for two different numerical treatments of the advective term: first-order upwind and quadratic upwind with a stability-preserving flux limiter (SMART). To determine how these schemes impact predictions, comparisons are made across a sequence of models with increasing complexity.

Using first-order upwinding for advection–dispersion calculations with a time oscillating velocity field leads to oscillatory numerical dispersion. Subjecting an initially uniform distribution of large-sized algal colonies to a spatiotemporal velocity creates a concentration pulse, which reaches a steady-state width at high-grid Peclet numbers when using the SMART scheme; the pulse exhibits contraction–expansion behavior throughout a velocity cycle at all Peclet numbers when using first-order upwinding. When aggregation dynamics are included with advection-dominated spatiotemporal transport, results indicate the SMART scheme predicts larger peak concentration values than those predicted by first-order upwind, but peak location and the time to large colony appearance remain largely unchanged between the two advective schemes.

To the best of the authors’ knowledge, this study is the first numerical investigation of a novel advection–dispersion model of vertical algal transport. In addition, a generalized expression for the effective dispersion coefficient of temporally variable flow fields is presented.

This paper aims to analyse the effects of events image from host communities’ perspective on the city’s overall image and the intention to recommend the events and the city as a tourism destination.

The research used a bivariate data analysis based on Spearman’s correlation and regression analysis to determine useful variables to predict the intention to recommend the city as a tourism destination. Data collection was face-to-face and online with a non-probabilistic sample of Viseu city residents, the second largest city in the central region of Portugal.

The findings had implications for researchers, governments and stakeholders. From the resident’s point of view, there is a high correlation between the overall city image and the intention to recommend it as a tourism destination. Event image and the intention to recommend the event participation affect the overall city image. Results point out the resident as natural promoters of events and their city if the local events have an appeal that generates their participation. Conclusions indicated that cities need to re-thinking tourism from the citizen’s perspective as staycation is a grown option.

Event image by host-city residents’ perceptions is an underdevelopment theme in the literature, although residents’ participation is essential to the success of most events. Local events can promote tourist citizenship and reinforce the positioning of tourism destinations, associating them with an image of desirable places to visit and live.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

This paper aims to tackle the problem for a fully actuated unmanned aerial vehicle (FUAV) to perform physical interaction tasks in the Global Positioning System-denied environments without expensive motion capture system (like VICON) under disturbances.

A tether-based positioning system consisting of a universal joint, a tether-actuated absolute position encoder and an attitude sensor is designed to provide reliable position feedback for the FUAV. To handle the disturbances, including the tension force caused by the taut tether, model uncertainties and other external disturbances such as aerodynamic disturbance, a hybrid disturbance observer (HDO) combining the position-based method and momentum-based technology with force sensor feedback is designed for the system. In addition, an HDO-based impedance controller is built to allow the FUAV interacting with the environment and meanwhile rejecting the disturbances.

Experimental validations of the proposed control algorithm are implemented on a real FUAV with the result of nice disturbance rejection capability and physical interaction performance.

A cheap alternative to indoor positioning system is proposed, with which the FUAV is able to interact with external environment and meanwhile reject the disturbances under the help of proposed hybrid disturbance observer and the impedance controller.

Long time ago, multistructured materials showed great interest being considered as the bridge between bulk and atomic materials. Core-shell particles are kind of composite materials that refer to multilayered structures with a core totally surrounded by shell(s) (onion-like structure). These new structures can offer an advantage of applying new adjustable parameters like shape, stoichiometry and chemical ordering, in addition to the opportunity of tailoring more complexed structures for different applications. Recently it was found that these structures can be tuned and taken for more advanced path with novel structures formed of core surrounded by multishells. The purpose of this study is to study the effect of the new anticorrosive pigments with its mutual shells and how each shell affects the performance of the pigment in protecting the metal and which shell will be more relevant in its effect.

The prepared pigments were characterized using X-ray fluorescence, X-ray diffraction, TEM and SEM/EDX to prove their core-shell structure, and then they were integrated in coating formulations to evaluate their anticorrosive activity using immersion test and electrochemical impedance spectroscopy (EIS).

The results showed that the prepared core-shell pigments possess a lot of unique characteristics and can offer improved anticorrosive performance in the generated coatings.

Core-mutual shells structured pigments were prepared for improving the corrosion resistivity of the organic coatings as a new trend in anticorrosive pigments.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.