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Cable-driven hyper-redundant manipulator (CDHM) with flexible and compliant configuration has high maneuverability in a tight space owing to its multiple degrees of freedom (DOFs). However, an increase in the DOFs of the manipulator makes it very challenging to solve its inverse kinematics. The present work proposes a novel adaptive piecewise geometry method to solve the inverse kinematics of the CDHM. The corresponding computation efficiency will be much lower for traditional methods, i.e., the generalized inverse of the Jacobian matrix and artificial neural network method. When the end-effector of the manipulator is required to move with a larger range, Joint angle physical limit need to be considered and the proposed method can select the optimal arc configuration to solve the inverse kinematics aiming at reducing joint overrun. An adaptive adjustment coefficient is further introduced to optimize the configuration so that joint motion more reasonable as well as avoid singular configuration. The geometry and joint parameters solved with the proposed novel method are then compared to those of the existing method with the same desired target position to verify the effective of the proposed novel method. Finally, a 12-DOFs hyper-redundant manipulator physical prototype is built, and corresponding experimental results show that with the novel solution method, the manipulator end can precisely reach the expected target position with significantly less computational complexity, which is beneficial to improve real-time control efficiency of the CDHM in practical applications.

Jingyu Lv,1,* Qing Qiu,2,* Baojuan Ye,1,* Qiang Yang1 1Center of Mental Health Education and Research, School of Psychology, School of Education, Jiangxi Normal University, Nanchang, People’s Republic of China; 2School of Intercultural Studies, Post-Doctoral Research Station of Psychology, Jiangxi Normal University, Nanchang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing Qiu, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang, 330022, People’s Republic of China, Tel +86-185-79113027, Email qiuqing26@qq.comPurpose: The COVID-19 pandemic had triggered a serious crisis that had brought stress and challenges to primary school principals, as well as having a dramatic impact on their mental health. This study explored the relationship between cognitive fusion and depression among primary school principals during COVID-19, as well as the mediating role of psychological vulnerability and the moderation role of self-esteem in this process.Patients and Methods: Cognitive Fusion Questionnaire (CFQ), Center for Epidemiological Studies Depression Scale (CES-D), psychological vulnerability scale, and self-esteem scale were used to measure 279 rural primary school principals. The data were analyzed by adopting Pearson’s correlations and moderated mediation analysis.Results: The results revealed that: (1) There were significant relationships among cognitive fusion, depression, psychological vulnerability and self-esteem. (2) The results showed that psychological vulnerability mediated the link between cognitive fusion and depression. (3) Self-esteem moderated the associations between cognitive fusion and depression, and also moderated the associations between cognitive fusion and psychological vulnerability. The relationship between cognitive fusion and depression was weaker for primary school principals with high levels of self-esteem. In contrast, the relationship between cognitive fusion and psychological vulnerability was stronger for primary school principals with low levels of self-esteem.Conclusion: Psychological vulnerability played a mediating role in the relationship between cognitive fusion and depression. Moreover, self-esteem moderated the effect of cognitive fusion on depression, and also the effect of cognitive fusion on psychological vulnerability.Keywords: COVID-19 pandemic, cognitive fusion, psychological vulnerability, depression, self-esteem, primary school principals

Yuhong Deng,1 Pengfei Huang,1 Qiang Yang,2 Baojuan Ye1 1School of Psychology, School of Education, Center of Mental Health Education and Research, Jiangxi Normal University, Nanchang, People’s Republic of China; 2School of Education, Jiangxi Normal University, Nanchang, People’s Republic of ChinaCorrespondence: Baojuan Ye, School of Psychology, School of Education, Center of Mental Health Education and Research, Jiangxi Normal University, Nanchang, People’s Republic of China, Email yebaojuan0806@163.comPurpose: Although the link between psychological flexibility and healthy functioning has been widely analyzed, the employed measurements often lacked accuracy. The current study introduced a person-centered approach that identified subgroups of college students across the dimensions of the Personalized Psychological Flexibility Index (PPFI) and explored how these subgroups relate to a risk factor (perceived stress) and mental health outcomes (depression, anxiety, negative affect, and positive affect) in the context of COVID-19.Methods: A sample of 659 participants (Mage = 19. 99, SD = 1.27; 57.97% females) completed the questionnaires online. Latent profile analysis (LPA) was employed to determine the optimal number of subgroups or profiles. Then, multinomial logistic regression and analyses of variance were used to identify variables associated with profile membership.Results: LPA identified three distinct profiles (active strategy, inconsistent strategy, and passive strategy). Furthermore, multinomial logistic regressions indicated that students with high perceived stress were more likely to be in the passive strategy group than the active strategy group (β = − 0.104, OR = 0.901, p < 0.001) and the inconsistent strategy group (β = − 0.087, OR = 0.917, p < 0.001). Additionally, analyses of variance revealed that the three profiles differed in depression (η2 = 0.062, p < 0.001), anxiety (η2 = 0.059, p < 0.001), negative affect (η2 = 0.047, p < 0.001), and positive affect (η2 = 0.048, p < 0.001).Conclusion: The current study employed LPA based on the PPFI to identify and confirm three profiles of psychological flexibility. We found that perceived stress and mental health outcomes were associated with these three profiles. This study offers a new perspective on understanding psychological flexibility through a person-centered approach. Furthermore, interventions aimed at reducing college students’ perceived stress during the COVID-19 crisis are critical for preventing the deterioration of psychological flexibility.Keywords: SARS-CoV-2, Personalized Psychological Flexibility Index, stress, mental well-being, latent profile analysis

Travelling waves induced by spanwise Lorentz force for skin-friction drag reduction are studied in the present work using direct numerical simulations, with particular focus on the streamwise and spanwise travelling waves. The overall picture of drag reduction in the frequency-wavenumber parameter space, $$\omega -\kappa _x-\kappa _z$$ ω - κ x - κ z is uncovered. It is found that both drag reduction maps for the streamwise and spanwise travelling waves are featured with a drag reduction ($$\mathcal{D}\mathcal{R}$$ D R ) region and a drag increase ($$\mathcal{D}\mathcal{I}$$ D I ) region. For the streamwise travelling wave of spanwise Lorentz force, the $$\mathcal{D}\mathcal{R}$$ D R and $$\mathcal{D}\mathcal{I}$$ D I regions are located in the same parameter regime compared to that of the streamwise travelling wave of spanwise wall velocity, while for the spanwise travelling wave, the $$\mathcal{D}\mathcal{R}$$ D R variation at any fixed $$\kappa _z$$ κ z is similar to that of spanwise oscillating Lorentz force. An exploration of the oblique travelling wave with an angle to the mean flow shows that the optimal drag reduction appears when the wave travels backward relative to the flow direction, and the "ribbon" structure is a general phenomenon appearing in all oblique travelling wave cases. The ensemble averaged positive and negative quasi-streamwise vortices become asymmetric when the travelling wave is imposed, and the near-wall high- and low-speed streaks are significantly tilted in the spanwise direction. Spanwise oscillation, streamwise and spanwise travelling waves share strong similarity in the statistics, energy spectra as well as turbulent structure modulation.

Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The primary concerns in FedOpt can be attributed to the model divergence and communication efficiency, which significantly affect the performance. In this paper, we propose a new method, i.e., LoSAC, to learn from heterogeneous distributed data more efficiently. Its key algorithmic insight is to locally update the estimate for the global full gradient after {each} regular local model update. Thus, LoSAC can keep clients' information refreshed in a more compact way. In particular, we have studied the convergence result for LoSAC. Besides, the bonus of LoSAC is the ability to defend the information leakage from the recent technique Deep Leakage Gradients (DLG). Finally, experiments have verified the superiority of LoSAC comparing with state-of-the-art FedOpt algorithms. Specifically, LoSAC significantly improves communication efficiency by more than $100\%$ on average, mitigates the model divergence problem and equips with the defense ability against DLG., Comment: ACM Transactions on Knowledge Discovery from Datahttps://doi.org/10.1145/3566128

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data. Given an acceptable level of privacy guarantee, the goal of FL is to minimize the time-to-accuracy of model training. Compared with distributed ML in data centers, there are four distinct challenges to achieving short time-to-accuracy in FL training, namely the lack of information for optimization, the tradeoff between statistical and system utility, client heterogeneity, and large configuration space. In this paper, we survey recent works in addressing these challenges and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review system works including measurement studies and benchmarking tools that aim to support FL developers., Comment: This article has been accepted for publication in IEEE Transactions on Big Data. This is the author's version which has not been fully edited and content may change prior to final publication

Based on the as-built load test of a large bridge, this paper introduces the procedure of the prestressed concrete continuous rigid frame bridge load test. Numerical analysis of the bridge was carried out by simulating and establishing a finite element model, and comparative analysis was carried out with the measured values. The results show that the calculated values were basically consistent with the measured values, which showed that the establishment method of the model was reasonable, and the mechanical performance of the bridge met the service requirements of the designed live load.

PurposeThis study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.Design/methodology/approachA research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.FindingsThis study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.Originality/valueThis research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.

Transverse rectangular microgrooves with local machining flaw are investigated using direct numerical simulations from the viewpoint of suppressing the second-mode instability in a Mach 6 hypersonic boundary layer. Two types of machining flaw are modeled for the local deformation of microgrooves, i.e., chamfered and protruded ones. The effect of locally deformed microgrooves on the boundary-layer stability is studied by superposing the second-mode disturbance to the base flow of the boundary layer. The investigated unstable second mode is 400 kHz, and the microgrooves are installed in its synchronous region. The results show that compression and expansion waves are generated on the surface of the deformed microgrooves, and microscale separation bubbles are formed at the rear edge of the deformed microgrooves. With the increase of local deformation height of microgrooves, the suppression effect of the second-mode disturbance gradually increases. As the deformation height reaches 0.09 mm, the amplitude inhibition rate of locally deformed microgrooves on the second-mode disturbance reaches 44.7%, compared to that of the regular one.

Because it is traditionally difficult and time-consuming to identify the foramen ovale (FO) with fluoroscopy, we recently developed the H-figure method to acquire fluoroscopic view of FO with shorter procedure time and less radiation. However, the impact of such an H-figure approach on the clinical outcomes of trigeminal ganglion radiofrequency thermocoagulation (RFT) in treating idiopathic trigeminal neuralgia (ITN) remains unclear. In a 12-month follow-up retrospective cohort study, patients with ITN had fluoroscopy-guided RFT of trigeminal ganglion via either classic approach (n = 100) or H-figure approach (n = 136) to identify FO. Data of continuous variables were analyzed with a Shapiro-Wilk test for normality and subsequently with a Mann-Whitney test, and the binary data were analyzed with a χ2 test. The primary outcome was the facial pain measured by a Visual Analog Scale (VAS) 1 year after the treatment. The secondary outcomes included the quality of the fluoroscopic FO views, the threshold voltage to provoke paresthesia, the procedure time, the number of fluoroscopic images, and the facial numbness VAS. Compared with the classic approach group, the H-figure approach group was associated with better long-term pain relief after the procedure, with significantly fewer patients had pain 3 months (6.6% vs 17.0%, P = .012) and 12 months (21.3% vs 38.0%, P = .005) after the procedure, and among patients who had pain after the procedure, patients in the H-figure group had significantly less pain 6 months after the procedure (VAS median [interquartile range (IQR)]: 3 [2-6] vs 6 [4-7], P < .001). Moreover, compared to the classic approach, the H-figure approach provided better fluoroscopic view of FO, lower threshold voltage to elicit paresthesia (median [IQR]: 0.2 [0.2-0.3] vs 0.4 [0.4-0.5] V, P < .0001), with shorter procedure time (median [IQR]: 7.5 [6.0-9.0] vs 14.0 [10.0-18.0] min, P < .0001), and required fewer fluoroscopic images (median [IQR]: 4.0 [3.0-5.0] vs 8.0 [6.0-10.0], P < .0001). RFT of the trigeminal ganglion using the H-figure approach is associated with superior longer term clinical pain relief than the classic approach in treating ITN.

Yangxiu Hu,1,2 Baojuan Ye,1 Xu-Bin Luo,3 Tingting Ma,2 Zhihua Feng,2 Qiang Yang4 1School of Psychology, Jiangxi Normal University, Nanchang, Jiangxi, People’s Republic of China; 2Center of Mental Health Education, Anhui Health College, Chizhou, Anhui, People’s Republic of China; 3School of Finance, Jiangxi Normal University, Nanchang, Jiangxi, People’s Republic of China; 4School of Education, Jiangxi Normal University, Nanchang, Jiangxi, People’s Republic of ChinaCorrespondence: Xu-Bin Luo, School of Finance, Jiangxi Normal University, Nanchang, Jiangxi, 330022, People’s Republic of China, Tel +86 791-88120173, Email xubinluo@163.comPurpose: The current study examined the effect of stress of COVID-19 on work-family conflict, how perceived work ability may mediate this effect, and lastly how perceived social support may moderate the various indirect pathway during COVID-19 pandemic.Methods: A total of 2558 Chinese adults were recruited from the first author’s university completed the questionnaire including stress of COVID-19 scale, work-family conflict, perceived social support and perceived work ability scale.Results: The present study showed that stress of COVID-19 was positively associated with work-family conflict while negatively associated with perceived work ability, which in turn, was negatively associated with work-family conflict. Perceived social support magnified the effects of perceived work ability on work-family conflict.Conclusion: Findings of this study shed light on a correlation between stress of COVID-19 and work-family conflict. Moreover, this study emphasizes the value of intervening individuals’ perceived work ability and increasing the ability of perceived social support in the context of COVID-19.Keywords: stress of COVID-19, perceived work ability, perceived social support, work-family conflict

In the past decades, artificial intelligence (AI) has achieved unprecedented success, where statistical models become the central entity in AI. However, the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal challenges. To bridge the gap between data privacy and the need for data fusion, an emerging AI paradigm federated learning (FL) has emerged as an approach for solving data silos and data privacy problems. Based on secure distributed AI, federated learning emphasizes data security throughout the lifecycle, which includes the following steps: data preprocessing, training, evaluation, and deployments. FL keeps data security by using methods, such as secure multi-party computation (MPC), differential privacy, and hardware solutions, to build and use distributed multiple-party machine-learning systems and statistical models over different data sources. Besides data privacy concerns, we argue that the concept of “model” matters, when developing and deploying federated models, they are easy to expose to various kinds of risks including plagiarism, illegal copy, and misuse. To address these issues, we introduce FedIPR, a novel ownership verification scheme, by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights (IPR or IP-right for short). While security is at the core of FL, there are still many articles referred to distributed machine learning with no security guarantee as “federated learning”, which are not satisfied with the FL definition supposed to be. To this end, in this paper, we reiterate the concept of federated learning and propose secure federated learning (SFL), where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving. We provide a comprehensive overview of existing works, including threats, attacks, and defenses in each phase of SFL from the lifecycle perspective.

A $ k $-injective-coloring of a graph $ G $ is a mapping $ c: \; V\left(G \right) \to \left\{ {1, 2, \cdots, k} \right\} $ such that $ c\left(u \right) \ne c\left(v \right) $ for any two vertices $ u $ and $ v $ if $ u $ and $ v $ have a common vertex. The injective chromatic number of $ G $, denoted by $ {\chi _i}\left(G \right) $, is the least $ k $ such that $ G $ has an injective k-coloring. In this paper, we prove that for planar graph $ G $ with $ g\left(G \right) \ge 5 $, $ \Delta \left(G \right) \ge 20 $ and without adjacent 5-cycles, $ {\chi _i}\left(G \right) \le \Delta \left(G \right) + 2 $.

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber-physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity.

A natural polymer-based hydrogel with excellent mechanical properties, biodegradability and swelling stability is developed by combining gelatin with hydrazide alginate, which can be loaded with psoralen to effectively promote bone regeneration.

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect users’ data. However, a typical machine learning-based recommendation algorithm requires the data to learn users’ preferences. Some recent works thus turn to develop federated learning-based recommendation algorithms, but most of them either cannot protect the users’ privacy well, or sacrifice the model accuracy. In this article, we propose a lossless and generic federated recommendation framework via fake marks and secret sharing (FMSS). Our FMSS can not only protect the two types of users’ privacy, i.e., rating values and rating behaviors, without sacrificing the recommendation performance, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we extend existing fake items to fake marks, and combine it with secret sharing to perturb the data uploaded by the clients to a server. We then apply our FMSS to six representative recommendation algorithms, i.e., MF-MPC and NeuMF for rating prediction, eALS and VAE-CF for item ranking, and Fossil and GRU4Rec for sequential recommendation. The experimental results demonstrate that our FMSS is a lossless and generic framework, which is able to federate a series of different recommendation algorithms in a lossless and privacy-aware manner.

As a simple and invasive treatment, arthroscopic medial meniscal posterior horn resections (MMPHRs) can relieve the obstructive symptoms of medial meniscus posterior root tears (MMPRTs) but with the risk of aggravating biomechanical changes of the joint. The aim of this study was to analyze dynamic simulation of the knee joint after medial meniscus posterior root tear and posterior horn resection.This study established static and dynamic models of MMPRTs and MMPHRs on the basis of the intact medial meniscus model (IMM). In the finite element analysis, the three models were subjected to 1000 N axial static load and the human walking gait load defined by the ISO14243-1 standard to evaluate the influence of MMPRTs and MMPHRs on knee joint mechanics during static standing and dynamic walking.In the static state, the load ratio of the medial and lateral compartments remained nearly constant (2:1), while in the dynamic state, the load ratio varied with the gait cycle. After MMPHRs, at 30% of the gait cycle, compared with the MMPRTs condition, the maximum von Mises stress of the lateral meniscus (LM) and the lateral tibial cartilage (LTC) were increased by 166.0% and 50.0%, respectively, while they changed by less than 5% during static analysis. The maximum von Mises stress of the medial meniscus (MM) decreased by 55.7%, and that of the medial femoral cartilage (MFC) increased by 53.5%.After MMPHRs, compared with MMPRTs, there was no significant stress increase in articular cartilage in static analysis, but there was a stress increase and concentration in both medial and lateral compartments in dynamic analysis, which may aggravate joint degeneration. Therefore, in clinical treatments, restoring the natural structure of MMPRTs is first recommended, especially for physically active patients.

The association between nonstenotic plaque at the petrous internal carotid artery (ICA) and embolic stroke of undetermined source (ESUS) remains unknown. We aimed to test the hypothesis that the presence of a larger build-up of petrous plaque is more prevalent in the ipsilateral versus the contralateral side among ESUS patients without plaque in the intracranial and proximal ICA.From a total of 243 patients with ESUS and 160 patients with small-vessel disease (SVD) without proximal ICA plaque, we enrolled 88 ESUS and 103 SVD patients without ipsilateral nonstenotic intracranial and proximal ICA plaque in the present study. Targeting the petrous segment of the ICA on two sides, plaque burden including plaque thickness, lumen area, vessel area, wall area, and percentage of luminal stenosis, and composition features (presence/absence of the ruptured fibrous cap, ulcer plaque, thrombus, discontinuity of plaque surface [DPS], intraplaque hemorrhage and complicated plaque) were assessed by high-resolution magnetic resonance imaging.We found a higher prevalence of petrous plaque thickness ≥3.5 mm ipsilateral versus contralateral to the stroke (25/88 [28.4%] vs. 12/88 [13.6%], odds ratio [OR] 3.60, 95% confidence interval [CI] 1.34-9.70), but this imbalance was not seen in SVD. In patients with plaque thickness ≥3.5 mm, the presence of DPS (OR 4.05, 95% CI 1.11-14.78) and complicated plaque (OR 5.00, 95% CI 1.10-22.82) was more closely related to an index ESUS, a finding that was not evident in the subgroup with petrous plaque3.5 mm (p for interaction = 0.027).The present study provided the first evidence supporting a potential etiological role of vulnerable petrous plaque in ESUS.