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This study examines how different communication modes (two kinds of computer-mediated communication and face-to-face mode) influence EFL learners’ engagement during a collaborative writing task and its impact on subsequent request learning outcomes, measured by sociopragmatic and pragmalinguistic competence. Ninety-six high school English learners were assigned to groups for text-based chat, audio chat, and face-to-face interactions to complete a collaborative writing task in pairs. Two versions of the Written Discourse Completion Test (WDCT) were employed as pre-tests and post-tests to assess and compare the learning outcomes in request-making across the three groups. Cognitive and social engagement of learners were assessed by using multiple measures derived from chat logs and recordings, while a post-task questionnaire was utilized to gauge emotional engagement during the task. The results indicated that learners in the face-to-face and audio-chat conditions demonstrated higher levels of cognitive engagement compared to the text-chat group. The text-chat group reported fewer instances of negative emotions. In terms of request learning outcomes, all groups showed an increase in sociolinguistic appropriateness; however, no significant differences were found in overall pragmalinguistic competence. Social and emotional engagement significantly correlated with sociopragmatic improvement in the audio-chat mode, yet these indicators did not demonstrate significant predictive power.

Ikigai, a Japanese term often referred to as “purpose in life” or “the feeling that life is worthwhile”, is a commonly used indicator of well-being in studies focused on the elderly population in Japan. It can be identified within the different realms like family, work, and friendship. However, as individuals age, their sense of ikigai is prone to diminish. Teachable agents (TAs) have been employed in educational settings for decades to facilitate learning by teaching students. Previous research has indicated that TAs can have a positive impact on self-esteem which is an important aspect of ikigai. TAs may hold the potential to address issues related to declining ikigai that are often associated with aging. By engaging in the act of teaching the agent, elderly individuals may experience a sense of being needed, consequently enhancing their self-esteem and potentially leading to an improvement in their ikigai levels. In this paper, we introduce a TA designed around the concept of ikigai, aiming at assisting the elderly in maintaining a high level of ikigai to support healthy aging. Based on a user study conducted using the phenomenographical approach, we demonstrate the effectiveness of our proposed TA design.

Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected.

Abstract This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a “teacher-student” distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the “parallel-fusion” structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns.

Vehicle tracking detection, recognition and counting is an important part of vehicle analysis. Designing such a model with excellent performance is difficult. The traditional target detection algorithm based on artificial features has poor generalization ability and robustness. In order to take use the deep learning method for vehicle tracking detection, recognition and counting, this paper proposes a vehicle detection method based on yolov5. This method uses the deep learning technology, takes the running vehicles video as the research object, analysis the target detection algorithm, proposes a vehicle detection framework and platform. The relevant detection algorithm of the platform designed in this paper has great adaptability, when displayed under various conditions, such as heavy traffic, night environment, multiple vehicles overlap with each other, partial loss of vehicles, etc. it has good performance. The experimental results show that the algorithm can accurately segment and identify vehicles according to the edge contour of vehicles. It can take use the materials includes pictures, videos, and real-time monitoring, and has a high recognition rate in real-time performance.

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

Abstract Background Proper inhaler device usage is paramount for control of underlying obstructive airway disease. Hence, education to healthcare professionals who will eventually educate patients need to be done effectively. We developed an application for mobile devices for education on six medical inhaler devices, the metered-dose inhaler (MDI), Turbuhaler, Accuhaler, Breezhaler, Ellipta and Respimat, and studied if there were any difference between the application and the manufacturer’s instructions on inhaler technique. The aim of this study is to see if inhaler education via a mobile phone app is comparable to manual instruction for health care professions. Methods Participants, who were nursing students, were randomized to learn the inhaler devices via the manufacturer’s instruction guide or a mobile device app designed specifically for education on inhaler devices. Results There were 45 participants in each group. 78% of them were females with a median age of 21 (IQR 3). 67% used an Apple mobile device and the remainder used an Android device. The mobile device showed better total improvement points for the Turbuhaler device (262 vs 287 points; P = 0.02). Participants learning from the manufacturer’s guide had a significantly higher total improvement points in the Breezhaler (370 vs 327 points; P

BackgroundEffective multicomponent interventions in the community targeted at preventing frailty in at-risk older adults can promote healthy ageing. However, there is a lack of studies exploring the effectiveness of technology-enabled autonomous multi-domain community-based interventions for frailty. We developed a novel end-to-end System for Assessment and Intervention of Frailty (SAIF) with exercise, nutrition, and polypharmacy components. This pilot study aimed to explore SAIF’s effectiveness in improving frailty status, physical performance and strength, and its usability in pre-frail older adults.Materials and methodsThis is a single arm 8-week pilot study in 20 community-dwelling older adults who were pre-frail, defined using the Clinical Frailty Scale (CFS) as CFS 3 + (CFS 3 and FRAIL positive) or CFS 4. For outcomes, we assessed frailty status using the modified Fried Frailty Phenotype (FFP) and CFS; physical performance using Short Physical Performance Battery (SPPB); and Hand Grip Strength (HGS) at baseline and 8-week. User experience was explored using the System Usability Scale (SUS), interest-enjoyment subscale of the Intrinsic Motivation Inventory and open-ended questions. We analyzed effectiveness using repeated-measures tests on pre-post scores, and usability using a convergent mixed-method approach via thematic analysis of open-ended responses and descriptive statistics of usability/interest-enjoyment scales.ResultsSixteen participants (71.8 ± 5.5 years) completed the 8-week study. There was a significant improvement in FFP score (−0.5, p < 0.05, effect size, r = 0.43), but not CFS (−1.0, p = 0.10, r = 0.29). Five (31.3%) improved in frailty status for both FFP and CFS. SPPB (+1.0, p < 0.05, r = 0.42) and HGS (+3.5, p < 0.05, r = 0.45) showed significant improvements. Three themes were identified: “Difficulty in module navigation” (barriers for SAIF interaction); “User engagement by gamification” (facilitators that encourage participation); and “Perceived benefits to physical health” (subjective improvements in physical well-being), which corroborated with SUS (68/100) and interest-enjoyment (3.9/5.0) scores. Taken together, user experience results cohere with the Senior Technology Acceptance and Adoption Model.ConclusionOur pilot study provides preliminary evidence of the effectiveness of SAIF in improving frailty status, physical performance and strength of pre-frail older adults, and offers user experience insights to plan the follow-up large-scale randomized controlled trial.

Band selection is an important preprocessing technique for hyperspectral images to select a band subset with representative information and low correlation. However, most methods focus on removing redundant components without loss of original information, but not distinguishing the noisy and low-discriminating bands which must be manually removed in advance. To find high-discriminating and high-quality bands from the original hyperspectral cube, we propose an importance-assisted column subset band selection (iCSBS) method. First, an active gradient-reference (AGR) index based on iterative reference gradient map is designed to evaluate the importance of each band. Then, the AGR index is incorporated into a column subset selection method to select high-discriminating bands, via simultaneously minimizing the redundancy and maximizing the quality of the selected band subset. Furthermore, as the high dimensionality decreases the contrast between bands, we use Manhattan distance instead of Euclidean distance. The experimental results on three real-world hyperspectral images demonstrate that the proposed method can achieve higher classification accuracy than other state-of-the-art comparison methods, and is especially superior to the geometry-based methods.

In order to reduce the pressure impact of continuous rotary electro-hydraulic servo motor, ensure the smooth pressure transition during the motor commutation, and improve the control accuracy, a kind of pre-compression chamber structure was proposed. The fluid domain models with pre-compressed chamber structure were established. The decompression process of the motor in long radius arc region and boosting process in short radius arc region were simulated and analyzed by FLUENT software. Through comparing and analyzing pressure distribution, outlet flow rate, and cross-section flow rate of fluid models, basing on a drosophila optimization algorithm, the optimum structure size of the pre-compression chamber of the motor is obtained. The results show that the optimal side length of the pre-compression chamber is 10 mm in the compression process, while the optimal side length is 26.273 mm in the decompression process. The pre-compression chamber structure can effectively reduce the pressure impact of the continuous rotary electro-hydraulic servo motor.

Purpose - Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach. Design/methodology/approach - In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014. Findings - During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills. Originality/value - To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills.

Purpose – With the rapid growth of the Internet of Things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Compared with NB-IoT networks, which are mainly built and managed by mobile network operators, LoRa wide area networks (LoRaWAN) are mainly operated by private companies or organizations, which suggests two issues: trust of the private network operators and lack of network coverage. This study aims to propose a conceptual architecture design of a blockchain built-in solution for LoRaWAN network servers to solve these two issues for LoRaWAN IoT solution. Design/methodology/approach – The study proposed modeling, model analysis and architecture design. Findings – The proposed solution uses the blockchain technology to build an open, trusted, decentralized and tamper-proof system, which provides the indisputable mechanism to verify that the data of a transaction has existed at a specific time in the network. Originality/value – To the best of our knowledge, this is the first work that integrates blockchain technology and LoRaWAN IoT technology.

Purpose – This paper aims to adapt and computerize the Trail Making Test (TMT) to support long-term self-assessment of cognitive abilities. Design/methodology/approach – The authors propose a divide-and-combine (DAC) approach for generating different instances of TMT that can be used in repeated assessments with nearly no discernible practice effects. In the DAC approach, partial trails are generated separately in different layers and then combined to form a complete TMT trail. Findings – The proposed approach was implemented in a computerized test application called iTMT. A pilot study was conducted to evaluate iTMT. The results show that the instances of TMT generated by the DAC approach had an adequate level of difficulty. iTMT also achieved a stronger construct validity, higher test–retest reliability and significantly reduced practice effects than existing computerized tests. Originality/value – The preliminary results suggest that iTMT is suitable for long-term monitoring of cognitive abilities. By supporting self-assessment, iTMT also can help to crowdsource the assessment processes, which need to be administered by healthcare professionals conventionally, to the patients themselves.

Aiming at the complicated internal flow state of hydraulic cone valve and the effect of fluid force on the performance, radial offset was seldom considered in the research of cone valve, so the inner flow field model of cone valve was established by UG software, and the mesh model was divided by ICEM CFD software. Then, the simulation calculation was carried out by FLUENT. The distribution of steady-state flow force, pressure field, and velocity field under the different opening of the cone valve was obtained. In order to verify the correctness of radial offset, the hydraulic simulation under the same working condition was carried out by AMESim software. Finally, the radial force on both sides of valve core was calculated on the basis of the pressure field on both sides of the valve core. The results show that the radial force difference on both sides of the spool is small when the valve port is small. With the increase of the valve port opening, the radial force difference between the two sides of the valve core changes larger, and finally fluctuating within a certain range. Then, the radial offset value can be calculated by the radial force formula. According to the obtained offset, the simulation and experimental study on the cavitation of the cone valve are carried out. The simulation results of radial offset are verified according to the distribution position of the cavitation, which lay the foundation for the design and performance research of the hydraulic poppet valve

In open and dynamic multiagent systems (MASs), agents often need to rely on resources or services provided by other agents to accomplish their goals. During this process, agents are exposed to the risk of being exploited by others. These risks, if not mitigated, can cause serious breakdowns in the operation of MASs and threaten their long-term wellbeing. To protect agents from the uncertainty in the behavior of their interaction partners, the age-old mechanism of trust between human beings is re-contexted into MASs. The basic idea is to let agents self-police the MAS by rating each other on the basis of their observed behavior and basing future interaction decisions on such information. Over the past decade, a large number of trust management models were proposed. However, there is a lack of research effort in several key areas, which are critical to the success of trust management in MASs where human beings and agents coexist. The purpose of this paper is to give an overview of existing research in trust management in MASs. We analyze existing trust models from a game theoretic perspective to highlight the special implications of including human beings in an MAS, and propose a possible research agenda to advance the state of the art in this field.

This paper proposes a stripe-PZT sensor-based baseline-free crack diagnosis technique in the heat affected zone (HAZ) of a structure with a welded stiffener. The proposed technique enables one to identify and localize a crack in the HAZ using only current data measured using a stripe-PZT sensor. The use of the stripe-PZT sensor makes it possible to significantly improve the applicability to real structures and minimize man-made errors associated with the installation process by embedding multiple piezoelectric sensors onto a printed circuit board. Moreover, a new frequency-wavenumber analysis-based baseline-free crack diagnosis algorithm minimizes false alarms caused by environmental variations by avoiding simple comparison with the baseline data accumulated from the pristine condition of a target structure. The proposed technique is numerically as well as experimentally validated using a plate-like structure with a welded stiffener, reveling that it successfully identifies and localizes a crack in HAZ.

Cognitive radio (CR) is a promising concept for improving the utilization of scarce radio spectrum resources. Orthogonal frequency division multiplexing (OFDM) is regarded as a technology which is well matched for CR systems. It is shown that channel estimation errors can result in a severe performance degradation in a multiuser OFDM CR system. A simple back-off scheme is proposed, and simulation results are provided which show that the proposed scheme is very effective in mitigating the negative impact of channel estimation errors.