Variable-DOF (or kinematotropic) mechanisms are a class of reconfigurable mechanisms. The number of variable-DOF multi-loop planar mechanisms proposed so far is very limited. This paper proposes a novel 8-link variable-DOF planar mechanism with five motion modes. Firstly, a description of the 8-link variable-DOF planar mechanism is presented. Reconfiguration analysis of the 8-link mechanism is then carried out using an elimination and algebraic geometry hybrid approach. The analysis shows that the 8-link mechanism has one 2-DOF motion mode and four 1-DOF motion modes and can switch among three motion modes at four transition configurations and between two motion modes at the remaining four transition configurations. Geometric characteristics of the mechanism in different motion modes are also revealed. Unlike the variable-DOF planar mechanisms found in literature, the proposed 8-link mechanism has two inactive joints in one of its 1-DOF motion modes, and both 4R kinematic sub-chains of the mechanism must appear as parallelograms in a pair or anti-parallelograms in a pair in the same motion mode. As a by-product, a method for factoring trigonometric functions in two angles is proposed.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

The DOF (DNA binding with one finger) family of plant-specific transcription factors (TF) was first identified in maize in 1995. Since then, DOF proteins have been shown to be present in the whole plant kingdom including the unicellular alga Chlamydomonas reinhardtii. The DOF TF family is characterised by a highly conserved DNA binding domain (DOF domain), consisting of a CX2C-X21-CX2C motif which is able to form a zinc finger structure. Early in the study of DOF proteins it became clear their relevance for seed biology. Indeed, the Prolamine Binding Factor (PBF), one of the first DOF proteins characterised, controls the endosperm-specific expression of the zein genes in maize. Subsequently, several DOF proteins from both monocots and dicots have been shown to be primarily involved in seed development, dormancy and germination, as well as in seedling development and other light-mediated processes. In the last two decades the molecular network underlying these processes have been outlined, and the main molecular players and their interactions have been identified. In this review, we will focus on the DOF TFs involved in these molecular networs, and on their interaction with other proteins.

First, a brief overview is provided on humanoid robots, and also models for the dynamic behavior are discussed. As base for these models these two methods Denavit-Hartenberg and Newton-Euler are used. Main aim of this work is to investigate the stability of a biped robot developed from IHRT. There is currently the low base of robot - consisting of feet, legs, hips and upper part of robots body. This structure currently has ten degrees of freedom.

Thermaikos Gulf (TG) is a semi-enclosed, river-influenced, marine system situated in the eastern Mediterranean Sea, sustaining both urban coastal regions and ecologically preserved natural areas. Facing a plethora of environmental and anthropogenic pressures, the TG serves as a critical nexus where human activities intersect with marine ecosystems. The second largest city of Greece, Thessaloniki, is located in the northern part of the gulf, while a large environmentally protected zone with multiple river deltas is lying along its western coasts. The quality and health of the TG’s marine environment are tightly linked to the socioeconomic activities of the coastal communities comprising approximately 1.5 million inhabitants. The main features of TG’s environmental dynamics and ecological status have been scrutinized by dedicated research endeavours during the last 50 years. This review synthesizes the seminal findings of these investigations, offering an evaluation of their contribution to research, their present collective impact, and their trajectory toward the future. A severe deterioration of the TG’s environmental quality was detected in the 1970s and 1980s when treatment of urban wastewater was completely absent. A steady trend of recovery was observed after the 1990s, however without achieving, so far, the goal of a “good environmental state”, mandated by national legislation and European directives. The most important research gaps and uncertainties are also discussed and specific recommendations for the improvement of monitoring and understanding about the physical, biochemical, and ecological state of the gulf are provided. The most significant discrepancy is related to the fragmentary monitoring of the TG in both space (partial coverage) and time (temporal gaps), which has been intensified after 2010 due to the reduced funding for environmental research and protection under the severe Greek economic recession. This review aspires to highlight the importance of bolstering monitoring initiatives that can provide continuous observations and further understanding on the TG's ecological and physicochemical conditions. Targeted recommendations, aimed at ameliorating the fragmented monitoring processes, are also provided, advocating for a holistic approach that can safeguard the ecological integrity of the Thermaikos Gulf and uphold its role as a crucial link to marine biodiversity and sustainability in the Mediterranean basin.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Sensory integration is an essential function of human life, and its decline causes difficulties in the quality of life, especially in older adults. In this study, we proposed an arm-reaching task based on a virtual reality head-mounted display (VR-HMD) system to assess sensory integration in daily life, and examined whether reaching task performance was associated with age and resting-state functional connectivity (rsFC) between the brain regions of interest involved in sensory integration. We hypothesized that performance during a reaching task with multiple cognitive loadings would be affected by aging. The gap angle between the reaching hand and target positions was compared between older and middle-aged adults as a measure of sensory integration function. In addition, rsFC, which was significantly correlated with the gap angles of individual participants in the older adult group, was identified using resting-state functional magnetic resonance imaging data. The mean gap angle was significantly larger in the older adults than in the middle-aged group and was also significantly correlated with connections between the left primary motor area and the left inferior temporal gyrus, temporo-occipital part. Because the connectivity between the areas is known to be related to visuomotor integration, the results suggest the involvement of visuomotor integration in the decline of sensory integration function in old age and the validity of the gap angle during the proposed VR-HMD task as an index for the decline in function.

Background: In recent years, the assessment and guidance of cardiopulmonary resuscitation (CPR) quality using noninvasive and invasive monitoring techniques have been increasingly recommended. In this study, we introduce a new physiological monitoring system that simultaneously measures arterial pressure (AP), venous pressure (VP), and cerebral tissue oxygen saturation (SctO2) during CPR. Methods: This prospective observational study was conducted at a single center (Kagoshima City Hospital). Adult out-of-hospital patients aged ≥18 years with an intravenous femoral arterial line, venous line, and SctO2 monitor were included. We measured femoral arterial pressure (FAP) and femoral venous pressure (FVP) invasively if catheters were immediately inserted into the femoral artery and vein for potential candidates who require interventions such as extracorporeal cardiopulmonary resuscitation but did not receive such interventions as a result. Results: We observed several representative cases that provided the following insights: We presented several cases, including two patients with significant increases in FVP and low SctO2 values, and in both cases, return of spontaneous circulation (ROSC) was not achieved. In contrast, we also presented two cases in which CPR resulted in higher FAP compared to FVP and an upward trend in SctO2 values was observed, and both instances achieved ROSC. Conclusion: We presented a simultaneous physiological monitoring system that can monitor AP, VP, and SctO2 during CPR. Further case accumulations will be necessary to assess the variations in hemodynamic status during CPR and the association between each hemodynamic status and outcomes after cardiac arrest.

This paper presents a novel 3-degrees-of-freedom (3-DOF) haptic master with rubber bands for self-resetting. The mechanical design avoids coupling between three directions mechanically by using three perpendicular axis intersecting at one point. Bevel gear transmission is adopted to increase the compactness of the overall structure. VR-based interactive system is designed and built by incorporating the proposed haptic master. The proposed haptic device can generate force feedback along 3-degree-of-freedom motion using motors and provide command signals to the avatar in the virtual environment. In order to analyze the performance of the developed device in terms of haptic feedback operation, ergonomics assessments are designed and experimentally implemented. Preliminary studies on the influencing factor including the guidance force, the reset force, the speed of the avatar and the arm the length have been conducted. The results of this paper are of great significance for the design of the haptic master and interactive system.

This paper presents the design and analysis of a novel 3-degree-of-freedom (3-DOF) parallel ma-nipulator equipped with self-sensing Ni-Ti (Nitinol) actuators. The manipulator's architecture and mechanical design are elucidated, emphasizing the integration of Nitinol actuators. The self-sensing technique implemented in a previous work was extended to a 20 mm actuator length, and the actuator was used to design the 3 DOF manipulator. Kinematic analyses were conducted to eval-uate the manipulator's performance under various operating conditions. A dynamic model was implemented for the dynamic dimensioning of the actuators, which work synergistically with a bias spring. The manipulator was realized, and a control strategy was implemented. Experimental tests, although documenting some positioning accuracy issues, show the efficacy and potential applica-tions of the proposed manipulator in robotics and automation systems, highlighting the advantages of self-sensing Nitinol actuators in small parallel manipulator designs.

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, marker-based optical motion capture, but at much lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientation in which the sensor is attached to the body segments. This knowledge is commonly obtained via an anatomical calibration procedure based on precisely defined poses or motions, which is time-consuming and error-prone. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

Rollover prevention of partially-filled tank trucks is an industry teaser, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order realize re-liable observation of sloshing liquid, this article firstly proposes a sloshing modeling method based on multi-degree-of-freedom pendulum model, and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under more pervasive working conditions. Secondly, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemical. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified through vehicle-fluid coupled co-simulation, under condition of con-secutive double lane change, observation error of the proposed method is only 25.9% that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover controller.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analysis based on artificial neural networks (ANN) were performed for robot manipulator systems used in the textile industry and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analysis of a 6 degree of freedom (DOF) industrial denim robot manipulator were carried out via 4 different learning algorithms of Delta-Bar-Delta (DBD), Online Back Propagation (OBP), Quick Back Propagation (QBP) and Random Back Propagation (RBP) for proposed neural network predictor. From the results obtained, it was observed that QBP based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modelling of trajectory control. In addition, 3-5-6 type ANN structure was also improved and its Root Mean Square Error (RMSE) and statistical R2 performances were compared to that of 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

A compact 2-DOF (two degrees of freedom) piezoelectric-driven platform for 3D cellular bio-assembly systems has been proposed based on “Z-shaped” flexure hinges. Multiple linear motions with high resolution both in x and y directions are achieved. The “Z-shaped” flexure hinges and the parallel-six-connecting-rods structure are utilized to obtain the lowest working stress while compared with other types of flexure hinges. In order to achieve the optimized structure, matrix-based compliance modeling (MCM) method and finite element method (FEM) are used to evaluate both the static and dynamic performances of the proposed 2-DOF piezoelectric-driven platform. Experimental results indicate that the maximum motion displacements for x stage and y stage are lx=17.65 μm and ly=15.45 μm, respectively. The step response time for x stage and y stage are tx=1.7 ms and ty =1.6 ms, respectively.

Noninvasive brain stimulation technique can be used for modulation of motor behaviors such as reaction time (RT). The main aim of this study was to investigate whether RT during a sequential visual isometric pinch task (SVIPT) can be affected by anodal transcranial direct current stimulation (a-tDCS) over three different stimulation sites such as dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1) or posterior parietal cortex (PPC). We also aimed to assess if stimulation of these cortical sites affect the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: 1) left M1, 2) left DLPFC, 3) left PPC and 4) sham. A-tDCS was applied concurrent with SVIPT in which the participants were precisely controlled their forces to reach seven different target forces from 10 to 40% of maximum voluntary contraction (MVC) presenting on a computer screen with right dominant hand. Four test blocks were randomly performed at baseline and 15 min after the intervention including sequence and random blocks with either hand. Our results showed significant differences between a-tDCS groups on ratio RT in the sequence blocks of both the right trained (P = .041) and left untrained hands (P = .023) for target forces of 15% and 30 % of MVC, respectively. In sequence blocks, M1 showed significant elongation in ratio RT for the target force of 15% and 30 % MVC compared to PPC and Sham groups in the right hand. In sequence blocks with left hand, this negative effect were found between M1-DLPFC and M1-Sham groups. In random blocks, no significant differences were found among four groups at each target forces for the left hand while there were significant differences for 15% (P =. 04) and 40% MVC (P =. 035) in the right hand. Our findings suggest that three different stimulation sites (DLPFC, M1 or PPC) were differentially affected by a single session of a-tDCS at some target forces during SVIPT. The effects of a-tDCS on ratio RT were transferred in the left untrained hand for sequence blocks but not random blocks. Further research is needed to understand fundamental aspects of these main areas of the brain on temporal processing in a precision control task such as SVIPT.

Driving simulators are widely used for understanding human-machine interaction, driver behavior and in driver training. The effectiveness of simulators in these process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

The idea of an extension of life for CubeSats is proposed to reduce space debris in a low-earth orbit. In this work, a gripper is designed for geometry-based grasping in berthing tasks. The grasping operation is outlined for square- and rectangle-profiled CubeSats. Equilibrium conditions are used to choose fingertips shape and parameters for grasping the CubeSat body. A design scheme is proposed to provide the required accuracy. A design concept is developed into a lab prototype by using low-cost 3D printing technique, and a mock-up grasping task that is representative of the berthing operation is evaluated with the lab prototype. Center-mass hanging setup for the prototype and grasped body is used to evaluate the impact of grasping, partially replicating the conditions in space by reducing the effect of gravity on the system.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge-fog-cloud computing, and machine learning (ML)/Artificial Intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), Blockchain, and Digital Twins (DT). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR Metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces Microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike an all-encompassing Metaverses, each Microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each Microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge-fog-cloud computing framework. The Microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the Metaverse.

Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between active MDs and idle MDs in the D2D-MEC (Mobile Edge Computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D-MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently addressed in existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and coupling of actions across time slots, we model the problem as a Markov Decision Process (MDP) and perform the multi-agent DRL through Multi-Agent Proximal Policy Optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison with the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%.

The study sought to determine the impact of the Big Five personality characteristics on aggressiveness among school children aged 12 to 16 in the Jenin governorate. The study employing the Big five-factor scale of Macri and Costa and the scale of aggressive behaviour in three areas [aggressive behaviour against oneself, aggressive behaviour against others, and aggressive behaviour against property]. The sample size in this study was 314 students. The quantitative study tool [the questionnaire] revealed the following correlation between the five major personality factors and aggression: neuroticism [+], openness [-], conscientiousness [-], extraversion [-], agreeableness [-]. The most prevalent main personality variables were conscientiousness and neuroticism. The degree of aggressiveness in the Jenin governorate was relatively low on the overall scale.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

Due to the linear property around an acetylenic carbon, the introduction of such an atom to a small cycle would result in high ring strain. Currently, the smallest isolated rings are five-membered, including metallacycloalkynes and metallapentalynes. Both types contain at least one unusual small bond angle around the acetylenic carbon, thus exhibiting abnormal reactivities. This feature article gives a comprehensive overview on these two kind complexes. The synthesis and reactivities are extensively described, the source of stability is presented, and the future prospect is discussed. The article aims to provide a better development for the chemical diversity of five-membered metallacycloalkynes and metallapentalynes.

We show that the Einstein field equations for a five-dimensional warped spacetime, where only gravity can propagate into the bulk, determine the dynamical evolution of the warp factor of the four-dimensional brane spacetime. This can be explained as a holographic manifestation. The warped 5D model can be reformulated by considering the warp factor as a dilaton field ($\omega$) conformally coupled to gravity and embedded in a smooth $M_4 \otimes R$ manifold. On the brane, where the U(1) scalar-gauge fields live, the dilaton field manifests itself classically as a warp factor and enters the evolution equations for the metric components and matter fields. We write the Lagrangian for the Einstein-scalar-gauge fields in a conformal invariant setting. However, as expected, the conformal invariance is broken (trace-anomaly) by the appearance of a mass term and a quadratic term in the energy-momentum tensor of the scalar-gauge field, arising from the extrinsic curvature terms of the projected Einstein tensor. These terms can be interpreted as a constraint in order to maintain conformal invariance. By considering the dilaton field and Higgs field on equal footing on small scales, there will be no singular behavior, when $\omega\rightarrow 0$ and one can deduce constraints to maintain regularity of the action. Our conjecture is that $\omega$, alias warp factor, has a dual meaning. At very early times, when $\omega \rightarrow 0$, it describes the small-distance limit, while at later times it is a warp (or scale) factor that determines the dynamical evolution of the universe. We also present a numerical solution of the model and calculate the (time-dependent) trace-anomaly. The solution depends on the mass ratio of the scalar and gauge fields, the parameters of the model and the vortex charge $n$.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged >65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

With the rapid development of the Internet of Things, massive amounts of data will be generated at the network edge, resulting in increased latency of the traditional cloud computing model. Cloud service providers deploy edge servers near the users to improve the quality of service. Due to the large scale of networks, the inefficiency of edge server deployment and task scheduling will result in excessive latency and severe workload imbalance among edge servers. In this paper, we propose a Server Deployment and Task Scheduling (SDTS) framework in multi-edge collaborative computing, which can effectively reduce latency and ensure workload balancing. First, a metropolitan area network (MAN) is divided into dense candidate regions (DCRs) based on the density characteristics of mobile end devices, and then servers are deployed independently in each sub-region. Then, a two-step clustering algorithm is designed to deploy servers as close as possible to the mobile devices. Finally, a task scheduling algorithm is presented based on an edge scheduling layer in DCRs, which can leverage collaboration between edge servers to improve the performance of the entire edge computing system. The experimental results based on the Cellular Base Station (CBS) dataset of Shanghai Telecom indicate that the proposed approach outperforms several representative strategies in terms of latency and workload balancing.

The performance of choice-reaction tasks during athletic movement has been demonstrated to evoke unfavorable lower limb biomechanics. However, the mechanism of this observation is unknown. We conducted a systematic review examining the association between 1) the biomechanical and functional safety of unplanned sports-related movements (e.g. jumps/runs with spontaneously indicated landing leg/cutting direction) and 2) markers of perceptual-cognitive function (PGF). A literature search in three databases (Pubmed, ScienceDirect, Google Scholar) identified five relevant articles. Study quality, rated by means of a modified Downs & Black checklist, was moderate to high (average: 13.5/16 points). Four of five papers, in at least one parameter, found either an association of lower PGF and reduced task safety or significantly reduced task safety in low vs. high PGF performers. Yet, as a) the outcomes, populations and statistical methods of the included trials were highly heterogeneous and b) only two out of five studies had an adequate control condition (pre-planned movement task), evidence was classified as conflicting. In sum, PGF may represent a factor increasing injury risk during unplanned sports-related movements but future research strengthening the evidence of this association is warranted.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human's consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user's grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.

Knowledge about pharmacological benefits of different seeds is an important factor for cultivation and application of medicinal herbs and plants. The seeds of medicinal plants are stores of valuable and active secondary metabolites that have been commercially and economically beneficial and helpful for medicine and pharmacy. The major parameter of reproduction and preservation of plants are also seeds which have a functional role in the distribution and establishment of plant in different regions. Five important seeds which have tremendous medicinal and pharmacological benefits are anise, basil, borage, cilantro and chamomile. Anise seed is used as a spice, either whole or ground, and its essential oil and extract are also obtained from the seeds. Basil seeds have a long history of usage in Chinese and Ayurvedic medicine, which are the good source of minerals, high in fiber including pectin, rich is flavonoids and other polyphenols. Borage seed oil is used for skin disorders such as seborrheic dermatitis, atopic dermatitis, and neurodermatitis. Cilantro is an annual herb that is part of the Apiaceae family. The seeds are rich in iron, zinc, copper and essential minerals which can decrease bad cholesterol and improves good cholesterol in the body. Chamomile can be considered for treatment of insomnia, hemorrhoids, anxiety, diarrhea and may help with wound healing and skin irritation. The keyword searches for Anise, Seed, Basil, Borage, Cilantro, Chamomile, Seed biology, Traditional medicinal science and seed anatomy were performed by using Scopus, Web of Science, PubMed and Google scholar. The aim of this article review is to survey the pharmacological and health benefits of seeds of five important medicinal plants.

New proposals for the equation of state of four- and five-dimensional hard-hypersphere mixtures in terms of the equation of state of the corresponding monocomponent hard-hypersphere fluid are introduced. Such proposals (which are constructed in such a way so as to yield the exact third virial coefficient) extend, on the one hand, recent similar formulations for hard-disk and (three-dimensional) hard-sphere mixtures and, on the other hand, two of our previous proposals also linking the mixture equation of state and the one of the monocomponent fluid but unable to reproduce the exact third virial coefficient. The old and new proposals are tested by comparison with published molecular dynamics and Monte Carlo simulation results and their relative merit is evaluated.

Music therapy has served as complementary and alternative medicine for various neurological disorders. Five Phases Music Therapy (FPMT) employs the theory of five phases and five music scales or tones (宫Gong (do), 商Shang (ri), 角Jue (mi), 徵Zhi (so) and 羽Yu (la)) to analyze and treat mind-body illness. In Chinese Medicine (CM), the five music scales are used to connect the human body and the universe, interpret personalities and constitution and analyze the influences of climatic changes on health. FPMT has a self-contained theory and routine of practice application. Large amounts of clinical and fundamental reports have been available and clinical benefits have been obtained. However more systemic clinic research esp. evidence-based and random controlled trials must be performed to validate and optimize its routines and biological and neurological mechanism must be further explored. It’s reasonable to believe that the effective music therapy will attract more attention from the world outside China with the introduction of FPMT.

Age-related changes in behavior and sensory perception have been observed in a wide variety of animal species. In ants and other eusocial insects, workers often progress through an ordered sequence of olfactory-driven behavioral tasks. Notably, these behaviors are plastic, and workers adapt and rapidly switch tasks in response to changing environmental conditions. In the Florida carpenter ant, smaller minors typically perform most of the work needed to maintain the colony while the larger majors are specialized for nest defense and rarely engage in these routine tasks. Here, we investigate the effects of age and task group on olfactory responses to a series of odorant blends in minor and major worker castes. Consistent with their respective roles within the colony, we observed significant age-associated shifts in the olfactory responses of minors as they transitioned between behavioral states, whereas the responses of majors remained consistently low regardless of age. Furthermore, we identified a unitary compound, 3-methylindole, which elicited significantly higher responses and behavioral aversion in minor nurses than in similarly aged foragers suggesting that this compound may play an important role in brood care. Taken together, our results suggest that age- and task-associated shifts in olfactory physiology may play a critical role in the social organization of ant colonies.

Introduction: Child stunting remains a public health concern. It is characterized as poor cognitive and physical development in children due to inadequate nutrition during the first 1,000 days of life. Across South Asia, Pakistan has the second-highest prevalence of stunting. This study has assessed the most recent nationally representative data, the National Nutrition Survey (NNS) 2018, to identify the stunting prevalence and determinants among Pakistani children under five. Methods: The NNS 2018, a cross-sectional household-level survey, was used to conduct a secondary analysis. Data on malnutrition, dietary practices, and food insecurity was used to identify the prevalence of stunting among children under five years by demographic, socioeconomic, and geographic characteristics. The prevalence of stunting was calculated using the World Health Organization (WHO) height for age z-score references. Univariate and multivariable logistic regressions were conducted to identify factors associated with child stunting. Results: Analysis showed that out % of 52,602 children under five, 40.0% were found stunted. Male children living in rural areas were more susceptible to stunting. Furthermore, stunting was more prevalent among children whose mothers had no education, were between 20 and 34, and were employed. In the multivariable logistic regression, male children (AOR=1.08, 95% CI[1.04-1.14], P<0.001) from rural areas (AOR=1.07, 95% CI [1.01-1.14], P=0.014), with the presence of diarrhea in the last two weeks (AOR=1.15, 95% CI [1.06-1.25], P<0.001), with mothers who have no education (AOR=1.57, 95% CI [1.42-1.73], P<0.001) or lower levels of education (Primary: AOR=1.35, 95% CI [1.21-1.51], P<0.001; Middle: AOR=1.29, 95% CI [1.15-1.45], P<0.001) had higher odds of stunting. Younger children aged <6 months (AOR=0.53, 95% CI [0.48-0.58], P<0.001) and 6-23 months (AOR=0.89, 95% CI [0.84-0.94], P<0.001), with mothers aged 35-49 years (AOR=0.78, 95% CI [0.66-0.92], P=0.003) had lower odds of stunting. At the household level, the odds of child stunting were higher in the poorest households (AOR=1.64, 95% CI [1.46-1.83], P<0.001), with ≥7 members (AOR=1.09, 95% CI [1.04-1.15], P<0.001), with no access to improved sanitation facilities (AOR=1.14, 95% CI [1.06-1.22], P<0.001) and experiencing severe food insecurity (AOR=1.07, 95% CI [1.01-1.14], P=0.02). Conclusion: Child stunting in Pakistan is strongly associated with various factors, including gender, age, diarrhea, residence, maternal age and education, household size, food and wealth status, and access to sanitation. To address this, interventions must be introduced to make locally available food and nutritious supplements more affordable, improve access to safe water and sanitation, and promote female education for long-term reduction in stunting rates.

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Leaf mechanical wounding triggers a rapid, within minutes, release of a blend of volatile organic compounds (VOCs). Wounding-induced VOC blend is mainly composed of oxygenated ubiquitous stress volatiles such as methanol and volatile products of lipoxygenase (LOX) pathway (mainly C5 and C6 alcohols and aldehydes and their derivatives), but also includes multiple minor VOCs that collectively act as infochemicals inducing defences in non-damaged plant leaves, neighbouring plants and attracting herbivore enemies. Till present, interspecific variability of the rate of induction and magnitude of wounding-induced emissions, and the extent to which plant structural traits and physiological activity alter these emissions are poorly known. Particularly scarce is the information of the induced emissions in tropical agricultural plant species despite their economic importance and large area of cultivation at regional to global scales. We chose five tropical crops with varying photosynthetic activity and leaf structural characteristics: Abelmoschus esculentus, Amaranthus cruentus, Amaranthus hybridus, Solanum aethiopicum and Telfairia occidentalis to characterize the kinetics and magnitude of wounding-induced emissions, hypothesizing that the induced emission response is greater and faster in physiologically more active species with greater photosynthetic activity than in less active species. Rapid highly repeatable leaf wounds (12-mm cuts) were generated by a within-leaf-chamber cutting knife. Wounding-induced VOC emissions were measured continuously with a proton-transfer reaction time-of-flight mass spectrometer and gas-chromatography mass spectrometry was used to separate isomers. Twenty-three ion VOCs and twelve terpenoid molecule structures were identified, whereas ubiquitous stress volatiles methanol (on average 40% of total emissions), hexenal (24%), and acetaldehyde (11%) were the main compounds across the species. Emissions of low-weight oxygenated compounds (LOC, 70% of total), and LOX products (29%) were positively correlated across species, but minor VOC components, monoterpenoids and benzenoids were negatively correlated with LOC and LOX, indicating a reverse relationship between signal specificity and strength. There was a large interspecific variability in the rate of induction and emission magnitude, but the hypothesis of a stronger emission response in physiologically more active species was only partly supported. In addition, the overall emission levels were somewhat lower with different emission blend compared to the data reported for wild species, as well as different shares for the VOCs in the blend. The study demonstrates that wounding-dependent emissions from tropical agricultural crops can significantly contribute to atmospheric volatiles, and these emissions cannot be predicted based on current evidence of wild plant model systems.

The visual span refers to the number of letters readers can identify in a single fixation without using linguistic skills. Proponents of the visual span hypothesis postulate an influence of early visual processing on reading speed. Given the slowness of reading Arabic texts, the present work aims to study the development of the visual span and its effects on reading speed in the Arabic-speaking context. Thirty-four subjects participated in the study. The trigram task and the rapid serial visual presentation (RSVP) paradigm were used to estimate visual span size and reading speed. In line with our initial assumptions, the results showed a significant effect of grade level on reading speed (F(2,31) = 30.93, p<0.001), visual span size (F(2,31) = 20.57, p<0.001). In good alignment with previous work, our results show that visual span size could explain around 40% of the reading speed variability. Interestingly, our analyses revealed a narrowing of visual span size in our Arabic sample. The results of study 2, suggest that the poor performance in the trigram task is due to poor visual attention capacities in our Arabic readers

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

We asked whether the level of peripheral fatigue would differ when three consecutive exercise trials were completed to task failure, and whether there would be delayed recovery in maximal voluntary contraction (MVC) force, neuromuscular activation and peripheral fatigue following task failure. Ten trained sport students performed three consecutive knee extension isometric trials (T1, T2, T3) to task failure without breaks between trials. T1 and T2 consisted of repeated 5-s contractions followed by 5-s rest. In T1, contractions were performed at a target force at 60% pre-exercise MVC. In T2, all contractions were MVCs, and task failure occurred at 50% MVC. T3 was a sustained MVC performed until force fell below 15% MVC. Evoked force responses to supramaximal electrical femoral nerve stimulation were recorded to assess peripheral fatigue. Electromyography signals were normalized to M-wave amplitude to assess neuromuscular activation. Lower levels of evoked peak forces were observed at T3 compared to T2 and T1. Within 5 s of task failure in T3, MVC force and neuromuscular activation recovered substantially without any recovery in evoked peak force. Neuromuscular activation 5-10 s after T3 was unchanged from pre-exercise values, but evoked peak forces were substantially reduced. These results challenge the existence of a critical peripheral fatigue threshold that reduces neuromuscular activation. Since neuromuscular activation changed independently of any change in evoked peak force, immediate recovery in force production after exercise is due to increased central recruitment and not to peripheral mechanisms.

A systematic study has been conducted on barbiturate complexes of all five alkali metals Li–Cs prepared from the metal carbonates or hydroxide in aqueous solution without other potential ligands present, varying the stoichiometric ratio of metal ion to barbituric acid (BAH). Eight polymeric compounds (two each for Na, K, and Rb and one each for Li and Cs) have been characterised by single-crystal X-ray diffraction. All contain some combination of barbiturate anion BA− (necessarily in a 1:1 ratio with the metal cation M+), barbituric acid, and water. All organic species and water molecules are coordinated to the metal centres via oxygen atoms as either terminal or bridging ligands. Coordination numbers range from 4 (for the Li complex) to 8 (for the Cs complex). Extensive hydrogen bonding plays a significant role in all the crystal structures, almost all of which include pairs of N–H…O hydrogen bonds linking BA− and/or BAH components into ribbons extending in one dimension. Factors influencing the structure adopted by each compound include cation size and reaction stoichiometry as well as hydrogen bonding.

Objectives: Essential thrombocythemia (ET) is a chronic myeloproliferative neoplasm characterized by elevated platelet counts and an increased risk of thrombotic events, including ischemic strokes. Methods: We conducted a retrospective analysis of data from consecutive ischemic stroke patients with ET between March 2014 and February 2023. Results: This case series describes the clinical presentation, radiological features, and management of five patients with ET-associated ischemic strokes, all harboring the JAK2 mutation. The diverse radiological findings suggest that both large and small vessel diseases may be influenced by the prothrombotic state induced by ET. A significant elevation in platelet count was observed to correlate with the emergence of new acute infarctions in some cases. Conclusions: The study highlights combined use of antiplatelet and cytoreductive therapy in preventing secondary stroke events in patients with ET and JAK2 mutations. The heterogeneity of stroke patterns in this population necessitates a comprehensive understanding of the underlying pathophysiological mechanisms and tailored therapeutic approaches.

The quantified residence time distribution (RTD) provides a numerical characterization of mixing in the continue casting tundish, thus allowing the engineer to better understand the metallurgical performance of the reactor. This paper describes a computational fluid dynamic (CFD) modelling study for analyzing the flow pattern and the residence time distribution in a five-strand tundish. Two passive scalar transport equations are applied to separately calculate the E-curve and F-curve in the tundish. The numerical modelling results are compared to the water modelling results for the validation of the mathematical model. The volume fraction of different flow regions (plug, mixed and dead) and the intermixing time during the ladle changeover are calculated to study the effects of the flow control device (FCD) on the tundish performance. The result shows that a combination of the U-baffle with deflector holes and the turbulence inhibitor has three major effects on the flow characteristics in the tundish: i) reduce the extent of the dead volume; ii) evenly distribute the liquid streams to each strand and iii) shorten the intermixing time during the ladle changeover operation.

Word Sense Disambiguation (WSD) stands as a persistent challenge within the Natural Language Processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library, a widely recognized tool, demonstrates suboptimal accuracy. Conversely, the application of deep neural networks offers heightened classification precision, yet their practical utility is constrained by demanding memory requirements. This research paper introduces an innovative method addressing WSD challenges by optimizing memory usage without compromising state-of-the-art accuracy. The presented methodology facilitates the development of WSD system that seamlessly integrates into NLP tasks, resembling the functionality offered by the NLTK library. Furthermore, this paper advocates treating the BERT language model as a gold standard, proposing modifications to manually annotated datasets and semantic dictionaries such as WordNet to enhance WSD accuracy. The empirical validation through a series of experiments establishes the effectiveness of the proposed method, achieving state-of-the-art performance across multiple WSD datasets. This contribution represents advancement in mitigating the challenges associated with WSD, offering a practical solution for integration into NLP applications.

In paired-associate vocabulary learning task, second language learners employ different cognitive strategies, associated with either elaborative or rote rehearsal, resulting in different recall performance. The current study investigated the role of intelligence, measured with Raven’s Advanced Progressive Matrices, in strategy effects on performance and eye movement parameters. The Keyword method, which is regarded as a more effective cognitive strategy in paired-associate task, was induced on a sample of 32 healthy participants with normal or corrected to normal vision. Effective cognitive strategy use, mentioned in a structured post-hoc report, was found to improve performance after strategy induction. Higher intelligence score was associated with higher recall performance in case the Keyword method was reported, but not in the cases when no rote rehearsal was reported. No effect of cognitive strategy use on eye movement measures after explicit cognitive strategy instruction was revealed, which is supposed to be connected with lack of self-sustained strategy use immediately after strategy instruction. The findings emphasize the role of intelligence in cognitive strategy instruction.

The purpose of this study was to compare Electromyographic (EMG) with Mechanomyo- 1 graphic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. 2 Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and 3 submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants 4 then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, 5 at 25%, 50% and 75% of their maximum activity (based on MVC). The low frequency part of the 6 accelerometer signal (< 5 Hz) was used to segment the 6 phases of the motion. EMG and MMG were 7 both recorded during the entire experimental procedure. Root mean square (RMS) and mean power 8 frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG 9 and MMG exhibited similar repeatability scores. They also shared similar RMS vs force relationship, 10 with RMS increasing to 75% MVC and plateaued to 100%. MPF decreased with increasing force to 11 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than 12 RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional 13 activities outside the laboratory. It opens new perspectives for future applications in sports science, 14 ergonomics and human-machine interfaces conception.

If a subject learns about the properties of an alternative under a high state of food deprivation, it will prefer this alternative over a similar one in a later choice test, although the subject is currently under a relative state of satiety. In this experiment, we probe this effect in a delay-discounting task, where the reward of the experienced alternative is progressively delayed. Eight male Wistar rats were trained to respond to an alternative under a high state of deprivation and eight rats under a relative state of satiety. After 20 sessions, their weight was recalibrated to a state of relative satiety. Then, they were exposed to a delay-discounting task where the long-larger alternative was the one used during training. The group that experienced the alternative under a higher level of food deprivation preferred more this alternative, and its preference stayed across time. On the contrary, the group that experienced the alternative under a lower level of food deprivation systematically preferred the small-shorter alternative. Our results support that the reinforcement value may be sensitive to the organism's state at the moment an alternative is experienced for the first time.

Fundamentals of Laparoscopic Surgery (FLS) is a training module designed to provide basic surgical skills. During skill training of the FLS "suturing and intracorporeal knot-tying" task – the most difficult among the five psychomotor FLS tasks, learning from errors is one of the basic principles of motor skill acquisition where appropriate contextual switching of the brain state on error is postulated. This study investigated changes in the brain state following an error event based on the fusion of simultaneously acquired functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. Here, human error processing is postulated to differentiate experts from novices based on the differences in the error-related chain of mental processes. Thirteen right-handed novice medical students and nine expert surgeons participated in this study. Error-related microstate analysis was performed using 32-channel EEG data at a high temporal resolution. Six microstate prototypes were identified from combined EEG data from experts and novices during the FLS task. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10 sec error epoch was significantly affected by the skill level (p<0.01), microstate type (p<0.01), and the interaction between the skill level and the microstate type (p<0.01). Then, the EEG band power (1-40Hz) related to slower oxyhemoglobin (HbO) changes were found using regularized temporally embedded Canonical Correlation Analysis of the fNIRS-EEG signals. The HbO signal from the fNIRS channel overlying ‘Frontal_Inf_Oper_L’, ‘Frontal_Mid_Orb_L’, ‘Postcentral_L’, ‘Temporal_Sup_L’, ‘Frontal_Mid_Orb_R’ cortical areas from Automatic Anatomical Labelling showed significant (p<0.05) difference between experts and novices in the 10-sec error epoch. Here, the frontal/prefrontal cortical areas are postulated to be related to the perception and the activation of the primary somatosensory cortex at the postcentral cortical area is hypothesized to be related to the action underpinning perception-action coupling model for the error-related chain of mental processes. Therefore, our study highlighted the importance of error-related brain states from portable brain imaging when comparing complex surgical skill levels.

The classical approach to non-linear regression in physics, is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then, using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterised by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built-in the models to compensate for the impossibility of modeling things that are, by construction, impossible to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here the MTL architecture allows predicting multiple parameters, the oxygen concentration and the temperature, from a single set of measurements.

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm.

Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

The purpose of the present study was to investigate the effects of psychological pressure on corticospinal excitability, the spinal reflex, lower limb muscular activity, and reaction times during a task involving dominant leg movements. Ten healthy participants performed a simple reaction time task by raising the heel of their dominant foot from a switch. After 20 practice trials, participants performed 20 non-pressure and 20 pressure trials in a counterbalanced order. Stress responses were successfully induced, as indexed by significant increases in state anxiety, mental effort, and heart rates under pressure. Significant increases in motor evoked potential (MEP) amplitude of the tibialis anterior muscle (TA) occurred under pressure. In terms of task-related EMG amplitude, the co-contraction rate between the soleus (SOL) and TA muscles significantly increased along with SOL and TA EMG amplitudes under pressure. Hoffmann reflexes for SOL and reaction times did not change under pressure. These results indicate that corticospinal excitability and leg muscle-related EMG activity increase homogeneously during lower limb movements that are performed under psychological pressure.

Iron is an essential micronutrient for Citrus, playing an important role in photosynthesis and yield. The aim of this paper is to evaluate the tolerance to Fe deficiency of five Citrus rootstocks: sour orange (S), Carrizo citrange (C), Citrus macrophylla (M), Troyer citrange (T) and Volkamer lemon (V). Plants were grown for 5 weeks in nutrient solution that contained the following Fe concentrations (in µM): 0, 5, 10, 15 and 20. At the end of the experiment, biomass (dry weight), leaf area, total leaf chlorophyll (CHL) and the activity of root chelate activity (FCR) were registered. Additionally, the mineral composition of roots (R) and shoots (S) was evaluated. Principal Component Analysis was used to study the relationships between all parameters and, subsequently, the relations between rootstocks. In the first component, N-S, P-S, Ca-S, Cu-S, Zn-S, Mn-S and Zn-R and Mn-R concentrations were related to leaf CHL and FCR. Increases in leaf CHL, Mg-R, DW (shoots and roots) were inversely related to Cu-R, which was shown in the second component. The values obtained were consistent for V10, C15, and C20, but in contrast for S0 and S5. Overall, the Fe/Cu ratio was significantly related to CHL, which may be used to assist rootstock performance.

In May 2022, WHO reported an outbreak of Mpox in several European countries which were previously Mpox free. Mpox (formerly known as monkeypox) is a zoonotic viral disease endemic in Central and West Africa. The sudden emergence of Mpox outside Africa and its subsequent rapid spread lead the WHO to declare the outbreak as Public Health Emergency of International Concern. As of May 15, 2023, a total of 87,704 confirmed cases and 140 deaths had been reported from 111 countries and territories worldwide. Looking back on this outbreak one year later, several important questions have arisen. Here, we address these questions using the classic five Ws: What, When, Where, Who and Why? We discuss these questions and outline what needs to be done to prevent, or at least minimize, outbreaks from emerging and re-emerging viral infection.

Agriculture is the primary source of economic growth in many countries in West Africa. It provides food and livelihoods for the population. The purpose of this study is to review the digital agriculture status in five countries, namely Benin, Burkina Faso, Coˆte d’Ivoire, Ghana, and Nigeria. The study consisted of a bibliometric analysis using a database exported from the web of science through a well-defined search string. Additionally, the cases of digital technology deployment in the countries were assessed through a systematic review approach comparing the technologies used in the target countries. Representative articles from the bibliometric review published in the last five years were selected for the comparative analysis. The bibliometric analysis based on 3,249 publications revealed that the research interests have significantly increased since 2014. The top two countries in terms of authors’ nationality were Nigeria and Ghana, respectively. Climate change, Machine Learning (ML) and adoption were the hot topics of discussion. The digital agriculture survey showed that publications in Nigeria were focused on Internet of Things (IoT), Wireless Sensors Networks, blockchain, and Artificial Intelligence (IA) technologies. Ghana also has a strong interest in blockchain, AI, and big data, while Burkina Faso focused on IoT and AI. Cote d’Ivoire and Benin focused only on AI.

We obtain exact solutions to the field equations for 5 dimensional LRS Bianchi type-I spacetime in $f(R,T)$ theory of gravity where specifically the following three cases are considered: (i) $f(R,T)=\mu(R+T)$, (ii) $f(R,T)=R \mu + R T \mu^2$ and (iii) $f(R,T)=R+\mu R^2+\mu T$ where $R$ and $T$ respectively the Ricci scalar and trace of the energy-momentum tensor. It is found that the equation of state (EOS) parameter $w$ is governed by the parameter $\mu$ involved in the $f(R,T)$ expressions. We fine-tune the parameter $\mu$ to obtain effect of phantom energy in the model, however we also restrict this parameter to obtain a stable model of the universe. It is noted that the model isotropizes at finite cosmic time.

This investigation was conducted to determine the chemical composition and nutritional value of five plant species commonly used as ruminant feeds namely: Artemisia herba-alba, Acer monspessulanum, Amygdalus lycoides, Amygdalus scoparia, and Atriplex leucoclada. After the collecting samples, the chemical compositions of plants included dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE), ash (CA), cell wall (NDF), and non-lignin cell wall (ADF) were determined according to standard methods. For degradation testing, the nylon bag technique was applied using three native Sistani fistula calves. Organic matter digestibility (OMD) and metabolisable energy (ME) were determined through the gas production technique. The CP value ranged from 5.30 (Amygdalus scoparia) to 11.72% (Atriplex leucoclada) while NDF value ranged from 52.62 (Amygdalus lycoides) to 69.05% (Amygdalus scoparia). The range of OMD, DOMD, and ME was from 36.67 to 53.27%, 34.67 to 49.11%, and 5.57 to 8.08 (MJ/kg), respectively. The results showed a positive correlation between cell wall composition and dry matter digestibility in plant species. The nutritional value of Amygdalus lycoides, Atriplex leucoclada, Acer monspessulanum, and Artemisia herba-alba was acceptable composition and digestibility.

Parallel manipulators with multiple end-effectors bring us interesting advantages over conventional parallel manipulators such as improved manipulability, workspace and avoidance of singularities. In this work the kinematics of a five-bar planar parallel manipulator equipped with two end-effectors is approached by means of the theory of screws. As an intermediate step the displacement analysis of the robot is also investigated. The input-output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. In that regard the Klein form of the Lie algebra se(3) of the Euclidean group SE(3) plays a central role. In order to exemplify the method of kinematic analysis, a case study is included. Furthermore, the numerical results obtained by means of the theory of screws are confirmed with the aid of special software like ADAMS.^TM

We explored the trends and the diversity of the mortality and risk factors of age-standardized cardiovascular diseases (CVD) and cerebrovascular disease (CBD) from 2000 to 2020 in five advanced Asian countries, including China, Japan, Singapore, South Korea, and Taiwan. Data were obtained from the Global Burden of Disease (GBD) report. Overall, the CVD mortality rates decreased gradually in all five Asian countries. Singapore had the highest rates before 2011, but Taiwan replaced Singapore as the nation of the highest mortality rates after 2012. Additionally, South Korea had the highest CBD mortality rates before 2016, while Taiwan experienced a peak in 2011 and remained high thereafter. The absolute difference (AD) and relative difference (RD) for CVD mortality rates showed clear declining trends in all five countries, with South Korea and China displaying the highest reductions, respectively. It is noteworthy that overweight and obesity exhibited an increasing trend in all these five Asian countries, with China demonstrating the highest AD and RD. Except for Taiwan, where a negative association of r=-0.732 was observed, CVD mortality was positively associated with CBD, hypertension, and DM mortality in all other four countries, therefore, further investigation into these relationships are needed.

China is a large agricultural country that produces a large amount of crop straw every year. Thus, the development of cost-effective and economic application of invasive plants is warranted. Biochars derived from crop straw has been proven to be promising for adsorbent materials. However, less studies have focused on biochar derived from different types of crop straw as adsorbent under the same conditions to compare their adsorption performance. Here, we characterized the five biochars in the same system (600 ℃). In results, GBC has higher ash content, pH, CEC, specific surface area, mineral composition and oxygen-containing functional groups. The adsorption kinetics can be explained adequately by pseudo-second-order model and Langmuir model, indicating that the adsorption behavior of the biochar is both physical adsorption and chemical adsorption, the adsorption process includes complexation reaction, cationic π bond, ion precipitation and electrostatic adsorption. In conclusion, GBC exhibited higher metal equilibrium adsorption capacities (125 mg·g-1 for Pb2+, 29 mg·g-1 for Cd2+). The solution pH, biochar dosing, pyrolysis temperature and the properties of these heavy metals were responsible for adsorption capacity, thus showing stronger affinity and better adsorption effect. Our results are important for the selection and utilization of plant-based biochar for different heavy metals.

The suitability of an animal model to study human diseases heavily relies on the similarity between the two species at the genetic, epigenetic, gene expression and metabolic levels. However, consistent data from different animal models at each level to evaluate this suitability is lacking. With the availability of genome sequences for many mammalian species it is now possible to compare animal models based on the genomic similarities. Here, we compare the coding sequences (CDS) of five mammalian models that include rhesus macaque, marmoset, pig, mouse and rat with those from human. We identified 10,316 conserved CDS across the five organisms and human based on the sequence similarity. Mapping the human disease-associated single nucleotide polymorphisms (SNPs) from these conserved CDS in each species has identified species-specific association with various human diseases. While associations for a disease such as colon cancer were prevalent in multiple model species, pig showed the highest number (117 diseases) of model-specific human disease associations. Based on the percentage of disease-associated SNP-containing genes, marmoset models are well suited to study many human ailments including behavioral and gastrointestinal diseases. Comparison of gene expression levels of the conserved CDS from colon, heart, kidney, lung, skeletal muscle, and spleen tissues in these models showed correlations with human expression levels in a tissue-specific manner. In the gastrointestinal tissues (colon, spleen), the pig showed the highest correlation while mouse displayed a better correlation in the heart and kidney. This study demonstrated genomic as well as tissue-specific expression-based similarity evaluation of five animal models against human that could help investigators select a suitable animal model to study their targeted disease.

This paper discusses recent developments in creep, over a wide range of temperature, that mqy change our understanding of creep. The five-power law creep exponent (3.5 to 7) has never been explained in fundamental terms. The best the scientific community has done is to develop a natural three power-law creep equation that falls short of rationalizing the higher stress exponents that are typically five. This inability has persisted for many decades. Computational work examining the stress-dependence of the climb rate of edge dislocations we may rationalize the phenomenological creep equations. Harper-Dorn creep, “discovered” over 60 years ago has been immersed in controversy. Some investigators have insisted that a stress exponent of one is reasonable. Others believe that the observation of a stress exponent of one is a consequence of dislocation network frustration. Others believe the stress exponent is artificial due to the inclusion of restoration mechanisms such as dynamic recrystallization or grain growth that is not of any consequence in the five power-law regime. Also, the experiments in the Harper-Dorn regime, which accumulate strain very slowly (sometimes over a year) may not have attained a true steady state. New theories suggest that absence or presence of Harper-Dorn may be a consequence of the initial dislocation density. Novel experimental work suggests that power-law breakdown may be a consequence of a supersaturation of vacancies which increase self-diffusion.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instance when any given region becomes active during the decision-making process. Here we try to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time. Thus, providing both spatial and temporal brain activation information. The activation information can then be further analyzed as per need and requirements. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way.

Imperceptible vibratory noise stimulation has shown to be an effective means of improving stability for both whole body postural control and simple motor control tasks. While the physiological mechanism affording this improvement is uncertain, it is suspected that sensory noise stimulation may elicit a stochastic resonance-like effect within the somatosensory system. A stochastic resonance effect describes the phenomenon in which noise added to a non-linear system improves signal detection rather than degrading it. One hallmark of stochastic resonance is the existence of an optimal noise level which elicits the best system performance. There is disagreement in the literature regarding the presence of an optimal stimulation level for motor stability in humans. The goals of this study were to: 1) determine optimal stimulation level as a function of an individual’s sub-sensory threshold level, and 2) to determine whether performance of a force stability task was significantly better when subjects received stimulation at this identified optimal level compared to other sub-sensory threshold stimulation levels. Eighteen (18) participants completed an isometric finger flexion task with visual feedback while receiving noise stimulation scaled to varying percentages of their individual sub-sensory threshold level. Performance for this force stabilization task was quantified as the root-mean-square (RMS) error between the target force and the actual generated force values. Despite controlling all other signal properties and varying only amplitude, optimal noise stimulation values still varied widely across participants (10-100% sub-sensory threshold level). Statistical modeling revealed a significant improvement in task performance with optimal noise stimulation compared to other sub-sensory stimulation levels (p ≤ 0.019) with estimated marginal mean differences in force errors ranging from 0.13 to 0.23 N. Moderate significant Spearman correlations (r_s = 0.49 and r_s = 0.56, respectively) were found between finger flexion maximal voluntary contraction (MVC) and sub-sensory threshold level and MVC and optimal stimulation level. A strong, significant Spearman correlation (r_s = 0.65) was observed between sub-sensory threshold level and optimal stimulation level. Although these correlations do not provide a means to predict optimal stimulation level as a function of these other measures, optimal stimulation level appears to increase with sub-sensory threshold and MVC.

Nurses experience significant stress and emotional exhaustion, leading to burnout and fatigue. This study assessed how the nurses’ mood and fatigue evolves during their shifts, and the lagged and accumulated factors that influence these phenomena. A two-level design with repeated measures was applied to a sample of 113 nurses, performing an ecological momentary assessment of different parameters and multilevel longitudinal two-level modelling of the data. Accordingly, mood appeared to be explained by effort, by the negative lagged effect of reward and by accumulated effort, each following a quadratic trend, and it was influenced by previously executing a direct care task. By contrast, fatigue was explained by the current and lagged effect of effort, by the lagged effect of reward and by accumulated effort, again following quadratic trends. Fatigue was also associated with direct care, and the prior effect of documentation and communication tasks. Mood was also explained by problem-focused and emotion-focused coping strategies, indicative of negative mood, and by support-seeking and refusal coping strategies. Hence, mood and fatigue do not depend on a single factor like workload but rather, on the evolution and distribution of tasks, as well as on the stress during a shift and how it is handled.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

Aim: The aim of this study was to estimate the number of nurses who independently care for pa-tients with severe respiratory failure receiving mechanical ventilation (MV) or veno-venous ex-tracorporeal membrane oxygenation (VV-ECMO). Additionally, the study analyzed the actual role of nurses in the treatment of patients with MV and VV-ECMO. Methods: We conducted a cross-sectional study using postal surveys. The study included 725 Japanese intensive care units (ICUs). Descriptive statistics were conducted. Results: Among the 725 ICUs, we obtained 302 re-sponses (41.7%) and analyzed 282 responses. The median number of nurses per bed was 3.25. The median proportion of nurses who independently cared for patients with MV was 60% [IQR: 42.3-77.3]. The median proportion of nurses who independently cared for patients with VV-ECMO was 46.9 (35.7-63.3%) in the ICU experiencing VV-ECMO use. Concerning task-sharing, 33.8% of ICUs and nurses did not facilitate weaning from MV. Nurses always ti-trated sedatives in 44.5% of ICUs. Conclusion: Nurse staffing might be inadequate in all ICUs, es-pecially for severe respiratory failure. The proportion of competent nurses for caring for severe respiratory failure in ICUs should be considered when determining the work force of nurses.

The chromatographic conditions were optimized using headspace gas chromatography, and a simple and rapid method was established for the simultaneous determination of five residual solvents in ursodeoxycholic acid raw materials. The corresponding quality standards were revised. The results showed that the concentration of methanol ranged from 0.06mg/ml to 0.3mg/ml, and the concentrations of acetone, tert butanol, ethyl acetate, and triethylamine showed a good linear relationship with the peak area within the range of 0.1mg/ml to 0.5mg/ml (r=0.999); The quantitation limits for methanol, acetone, tert-butanol, ethyl acetate, and triethylamine were 4.2, 0.9, 1.5, 1, and 0.1 μg/ml, respectively, with detection limits of 1.2, 0.25, 0.025, 0.3, and 0.025 μg/ml, respectively. The recovery rates of each solvent ranged from 92.9% to 106.0%, with RSD% (n=9) less than 3.8%; The method exhibited good repeatability, with RSD% (n=6) less than 2.5%. Furthermore, the durability is good. The established method is simple, accurate, specific, and highly sensitive, and can be used for the simultaneous and rapid determination of five residual solvents in ursodeoxycholic acid raw materials.

In this paper, based on the pseudo-totem pole (PTP) circuit, a family of five-level PTP dual boost converters (PDBC) is proposed. Dual boost converter has some unique advantages such as no bridge arm shoot-through risk and no switch body diode reverse recovery problem, so that it has a good application prospect. Compared with conventional three-level PTP converters, PDBCs has higher power density, higher efficiency, and low voltage and current stress. The topological derivation process, working principle, modulation and strategy of the topology are analyzed firstly. Further, the number of power devices, the switches voltage and current stress of the proposed topology is analyzed. Finally, a representative five-level PDBC experimental prototype is designed with AC input 220V/50Hz, DC output 400V/1kW, and peak efficiency of 98.27%. The experimental results show that the five-level PDBC proposed in this paper has higher efficiency and verify the correctness of the topology.

I Introduction: Pneumothorax is a pathological condition characterized by the presence of air between the visceral and parietal pleura. Objectives: To investigate incidence, clinical characteristics, risk factors, management and perinatal outcome among newborns with pneumothorax in a tertiary care center. Materials and Methods: A prospective observational study was conducted in Maternity hospital with tertiary NICU from 2015-2019. We included all neonates with pneumothorax born in our hospital and compared demographic characteristics, perinatal risk factors, anthropometric parameters, concurrent diseases, clinical course and method of chest drainage between full term (≥37 GW) and preterm (<37GW) newborns. Results: Mong 30.378 neonates, 74 developed pneumothorax (2, 4/1000 newborns). The incidence of neonatal pneumothorax (NP) was higher in preterm group (59, 5%), with a mean age 34,62 GW in whole group. The mode of delivery was c. section in 68.9%. NP occurred mostly on the right side (47,3%), on the second day of life , in males (67,6%). Chest drainage was performed in 64,9% of cases. Of 74 NP cases, 64 (85.1%) recovered, 6 (8,1%) died. Conclusion: Pneumothorax is a pathological condition of the newborn, which, if not diagnosed in time and treated adequately, leads to a fatal outcome.ads to a

This article argues for five correctives to the current ethical debate about speciesism, and proposes normative, conceptual, methodological and experimental avenues to move this debate forward. Firstly, it clarifies the Principle of Equal Consideration of Interests and points out limitations of its scope. Secondly, it disambiguates between ‘favouritist’ and ‘species-relative’ views about moral treatment. Thirdly, it argues that not all moral intuitions about speciesism should be given equal weight. Fourthly, it emphasizes the importance of empirical research to corroborate statements about ‘folk speciesism’. Fifthly, it disambiguates between the moral significance of species and the moral status of their individual members. For each of these issues, it is shown that they have either been overlooked, or been given inapt treatment, in recent contributions to the debate. Building on the correctives, new directions are proposed for ethical inquiry into the moral relevance of species and species membership.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

We propose a notion of a ternary skew-symmetric covariant tensor of 3rd order, consider it as a 3-dimensional matrix and study a ten-dimensional complex space of these tensors. We split this space into a direct sum of two five-dimensional subspaces and in each subspace there is an irreducible representation of the rotation group SO(3)↪SO(5). We find two independent SO(3)-invariants of ternary skew-symmetric tensors, where one of them is the Hermitian metric and the other is the quadratic form. We find the stabilizer of this quadratic form and its invariant properties. Making use of these invariant properties we define a SO(3)-irreducible geometric structure on a five-dimensional complex Hermitian manifold. We study a connection on a five-dimensional complex Hermitian manifold with a SO(3)-irreducible geometric structure, find its curvature and torsion. The structures proposed in this paper and their study are motivated by a ternary generalization of the Pauli’s principle proposed by R. Kerner.

Plants are affected by soil environments to the same extent they affect soil functioning through interactions between environmental and genetic factors. Here, five plant species (broad bean, pea, cabbage, fennel, and olive) grown under controlled pot conditions were tested for their ability to differently stimulate the degradation of standard litter. Litter, soil C and N contents and soil microbial abundance were measured. The architecture and morphological traits of roots systems were also evaluated by using specific open-source software (SmartRoot). Soil chemical and microbiological characteristics were significantly influenced by the plant species. Variations in soil C/N dynamics were correlated with the diversity of root traits among species. Early-stage decomposition of the standard litter changed on the basis of the plant species. The results indicated that key soil processes are governed by interactions between plant roots, soil C and N, and the microbial metabolism that stimulate decomposition reactions. This, in turn, can have marked effects on soil chemical and microbiological fertility, both fundamental for sustaining crops, and can promote the development of new approaches for optimizing soil C and N cycling, managing nutrient transport, and sustaining and improving net primary production.

Previous studies reported a reduction of the perceived effort and an improvement of the perfor-mances of healthy subjects and patients when a motor task was combined with artistic images with respect to non-artistic images. This phenomenon, called Michelangelo effect, could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking to 15 healthy subjects and 17 patients with stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demanding of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and number of trials (p = 0.007) needed to complete the task in presence of artistic stimuli, accompanied by a reduction of the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated to visuo-spatial control than to linguistic functions.

Evolutionary algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1 and Part2, the author previously reported experimental evaluations on Genetic Algorithm and Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part3 of the series of comparative research. In this study, Differential Evolution is adopted as the third instance of major evolutionary algorithms. The experimental results show a statistically significant superiority of DE over both GA and ES (p < .01). In addition, DE exhibits its robustness to variations in hyperparameter configurations (the number of offsprings and generations). In the previous experiments, both ES and GA showed significant performance differences depending on the configurations, whereas in the experiment reported in this article, such differences are not detected for DE.

Evolutionary algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the Acrobot control task. In this study, Genetic Algorithm is adopted as another instance of major evolutionary algorithms. Experimental results demonstrate that there was no statistically significant difference between the experimental performances of GA and ES, but the priority of generations and offsprings was different; GA performed better with a greater number of generations while ES performed better with a greater number of offsprings. Eight hidden units were the best among four variations (4, 8, 16 or 32 units), which aligns with previous study using ES.

Evolutionary algorithms find applicability in the reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the pendulum control task. In this study, the Acrobot control task is adopted as another task. Experimental results demonstrate that ES successfully trained a Multi-Layer Perceptron to achieve a remarkable height of 99.85% concerning the maximum height. However, the trained MLP failed to maintain the chain end in an upright position throughout an episode. In this study, it was observed that employing 8 hidden units in the neural network yielded better results with statistical significance compared to using 4, 16, or 32 hidden units. Furthermore, the findings indicate that a larger population size in ES led to a more extensive exploration of potential solutions over a greater number of generations, which aligns with the previous study.

This article contributes to research on mental models and how they underpin decision policies. It proposes a framework for the joint use of mental models of dynamic systems and the theory of mental models initiated by Johnson–Laird and defines two types of errors: (1) misrepresentation of the system’s structure, and (2) failure to deploy relevant mental models of possibilities. We use a dynamic decision task based on Moxnes’ “reindeer experiment” to formulate three intuitive policies, their underlying mental models, and the reasoning, and evaluate the policies under varying initial conditions. Each of the policies generates problematic behaviors like dependance on initial conditions, underperformance because of flawed goal setting and oscillation due to leaving the delay in a feedback loop out of account. We identify errors of both types in the mental models and relate them to the behavioral problems. Limitations and questions for further research conclude the paper.

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic have changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.

The study aimed to assess the prevalence and its correlates of infrequent and frequent bullying victimization (=BV) among school-going adolescents in five “Association of Southeast Asian Nations” (ASEAN) member states. The cross-sectional sample comprised 33,184 school adolescents (14.6 years mean age) from five ASEAN countries of the “Global School-based Student Health Survey” (GSHS) in 2015. Results indicate that 30.6% of participants reported any past-month BV, 33.9% in boys and 27.5 in girls, ranging from 11.8% in Laos to 48.7% in the Philippines. In the adjusted multinomial logistic regression analysis, students from the Philippines and Thailand, experience of hunger, sedentary behaviour, attending physical education classes, being underweight, being overweight or obese, ever amphetamine use, physically assaulted, school truancy, participation in a physical fight, injury, low peer support and psychological distress were associated with BV. Almost one in three adolescents were bullied and several associated variables were identified which can assist in targeting the strategies of intervention.

Widely consumed forest fruits in Gabon, were analyzed for nutrient and bioactive compositions and their potential contributions to meeting the nutrient requirements of consumers. Edible pulps of Panda oleosa, Gambeya lacourtiana and Poga oleosa contained substantial amounts of bioactive compounds; flavonoids (13.5–22.8 mg/100g), proanthocyanins (2.4–7.6 mg/100g), polyphenols (49.6-77.3 mg/100g) and vitamin C (6.7–97.7 mg/100g). The highest content of β-carotene (76.6 µg/100g) was registered in fruits of Pseudospondias longifolia. The fruits of P. oleosa had the highest essential minerals Fe, Zn and Se. If a child aged 1 to 3 years consumed about 200g or if a non-lactating and non-pregnant woman consumed 300g of P. oleosa, A. lepidophyllus, G. lacourtiana, P. longifolia and P. oleosa, they could obtain substantial RDI ranging between 20-100% for energy, vitamins C and E, iron, magnesium, iron and zinc. Forest fruits can considerably contribute towards the human nutrient requirements. Based on the results of this study, forest foods should be considered in formulating policies governing food and nutrition security in Gabon.

Diarrhea and fever are prevalent childhood illnesses with potentially severe consequences, especially when they co-occur. This study investigates the prevalence and determinants of diarrhea, fever, and their coexistence among children under-five in Bangladesh. Data from the 2017-18 Bangladesh Demography and Health Survey (BDHS) were analyzed using multivariable stepwise logistic regression with backward selection. This study found that 5.0% for diarrhea, 34.0% for fever, and 3.0% for the coexistence of both illnesses. Common factors associated with childhood diarrhea and fever included the mother's education, her attitude toward wife-beating, and the child’s age (12-23 months). Diarrhea was associated with households with improved water sources and children in the Barisal division, while fever was linked to underweight children and those from more affluent backgrounds. The coexistence of both was significantly linked to underweight children, higher birth orders, and children from the Rajshahi division. Notably, child illnesses were associated with parental education, higher socio-economic status, and access to improved drinking water sources. Diarrhea affects one in 20 children, fever affects one in three, and the coexistence of both conditions affects one in 35 children in Bangladesh. The findings need further research and policy reviews to develop effective interventions and improve child health in Bangladesh.

This article considers the basic psychological needs theory and tries to determine how much autonomy support from a coach is required for basic psychological needs and how autonomy, competence, relatedness, and the psychological motivational climate can explain task and ego involvement in a group of young elite ice hockey players. Our three hypotheses were as follows: autonomy support from the coach will have an impact on motivational involvement, all three basic needs will have an impact on motivational involvement, and motivational involvement will be explained by the motivational climate. A total of 175 male elite ice hockey players from Norway, ranging in age from 15 to 18 years old, answered questionnaires about autonomy support, perceived motivational climate, achievement goal orientation, and basic psychological needs. A multiple regression analysis was run to predict ego–task involvement using autonomy support from the coach, the need for autonomy, the need for competence, the need for relatedness, the task climate, and the ego climate. The only two variables that statistically significantly predicted ego–task involvement were autonomy support from the coach and the ego climate. The analysis revealed that the athletes had a higher score on task than ego involvement, but when this was transformed into two variables (high and low) for task and ego involvement, we found that most players scored high for both task and ego involvement. We found that autonomy from the coach had a positive relationship with a high score for players on both task and ego involvement. We also found that the three basic psychological needs had no impact on the motivational involvement of the athletes. Lastly, we found that the ego climate had an impact on the motivational involvement. There was a positive relationship between a high score for the ego climate and a high score for both ego and task involvement.

Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters for marginal tool wear and fast cutting speeds and then estimated the corresponding tool life by using the derived parameters.

Purpose: To analyze the five-year results of Trabectome ab interno trabeculectomy of a single glaucoma center. Method: In this retrospective interventional single-center case series, data of 93 patients undergoing ab interno trabeculotomy between September 2010, and December 2012 were included. Kaplan-Meier analysis was performed using success criteria defined as postoperative intraocular pressure (IOP) ≤21 mm Hg, or >20% reduction from preoperative IOP, and no need for further glaucoma surgery. Risk factors for failure were identified using Cox proportional hazards ratio (HR). Results: The retention rate for five years follow-up was 66%. The cumulative probability of success at 1, 2, 3, 4 and 5 years was 82.6%, 76.7%, 73.9%, 72.3%, and 67.5%. Risk factors for failure were lower baseline IOP (HR=0.27, P=0.001), younger age (HR=0.25, P=0.02), and higher central corneal thickness (HR=0.18, P= 0.01). Pseudoexfoliation was associated with a higher success rate (HR= 0.39, P=0.02). IOP was decreased significantly from 20.0±5.6 mmHg at baseline to 15.6±4.6 mmHg at 5-year follow-up (P=0.001). The baseline number of glaucoma medications was 1.8±1.2, which decreased to 1.0±1.2 medications at 5 years. Conclusion: Trabectome surgery was associated with a good long-term efficacy and safety profile in this single-center case series with a high retention rate. A higher baseline IOP, older age, thinner cornea, and pseudoexfoliation glaucoma were associated with a higher success rate.

Children five years or younger in low- and middle-income countries (LMICs) are severely affected by diarrhoea disease, especially in the sub-Saharan region. Hence, the study aimed at determining the prevalence and determinants of diarrhea disease among children under 5 years in Epworth Township, Zimbabwe. A descriptive cross-sectional study was conducted at a local clinic in Epworth Township, Harare. A convenience sampling strategy was used to recruit study participants for participation and 386 children were enrolled in the study. The majority were male children (n=229; 59.3%), while there were more female caregivers (n=370; 95.9%) than male caregivers (n=16; 4.1%). The prevalence of diarrhea disease in the study was 25.1%. The determinants associated with diarrhea were being partially vaccinated (OR 2.38, CI 95% 2.80–8.22), collecting water more than 1 kilometre from a household (OR 4.55; CI 95% 2.10–9.85) and using untreated water (OR 6.22; CI 95% 2.13–18.20). The age of the caregiver and using a clean water container (OR 0.05; p=0.020) were protective factors. Provision of primary health care, especially prevention of disease through immunization and rendering environmental health services could reduce the prevalence of diarrhea in disadvantaged townships.

Evolutionary algorithms and swarm intelligence algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using these algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1, the author previously reported experimental evaluations on Evolution Strategy　for reinforcement learning of binary neural networks, utilizing the Pendulum control task. This article constitutes Part2 of the series of comparative research. In this study, Genetic Algorithm is adopted as another evolutionary algorithm. Wilcoxon signed rank test revealed that there was no statistically significant difference between the fitness scores obtained using GA and those using ES. However, the p-value 0.11 indicated that GA worked better than ES on this training task. As the values of binary weights, {-1, 1} were significantly superior to {0, 1} (p < .01). The motion of the pendulum controlled by the binary MLP after the training showed that the binary MLP successfully swung the pendulum swiftly into an inverted position and maintained its stability after inversion.

Evolutionary algorithms and swarm intelligence algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using these algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1, 2 and 3, the author previously reported experimental evaluations on Evolution Strategy, Genetic Algorithm, and Differential Evolution for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part4 of the series of comparative research. In this study, Particle Swarm Optimization is adopted as an instance of major swarm intelligence algorithms. The experimental result shows that PSO performed worse than all of DE, GA and ES. The difference between PSO and DE was statistically significant (p<.01). In addition, PSO exhibited lower capability in exploring solutions in high-dimensional search spaces than DE, GA, and ES did. A larger swarm size compensated for the weakness of PSO in global exploration, thus making itself more beneficial than a larger number of swarm search iterations.

The author previously reported an experimental result of evolutionary reinforcement learning of neural network controllers for the pendulum task. In the previous work, a conventional multilayer perceptron was employed in which connection weights were real numbers. In this study, the author experimentally applies an evolutionary algorithm to the reinforcement training of binary neural networks. In both studies, the same task and the same evolutionary algorithm are utilized, i.e. the pendulum control problem and Evolution Strategy respectively. The only differences lie in the memory size per connection weight and the model size of the neural network. The findings from this study are (1) the performance of the binary MLP with 32 hidden units was inferior to that of the real-valued MLP with 16 hidden units; however, this difference was not statistically significant (p >.05); (2) the trained binary MLP successfully swung the pendulum swiftly into an inverted position and maintained its stability after inversion, as the real-valued MLP had done; and (3) the memory size required to record the binary MLP with 32 hidden units is 3.1% or 6.2% of the memory size required to record the real-valued MLP with 16 hidden units.

Robot workpiece machining is interesting in industry since it offers some advantages, such as higher flexibility in comparison with the conventional approach based on the CNC technology. However, in recent years we have been facing a strong progressive shift to custom based manufacturing and low volume/high mix production, which require a novel approach to automation by the employment of collaborative robotics. However, collaborative robots feature only limited motion capability, to provide safety in cooperation with human workers. Thus, it is highly necessary to perform more detailed robot task planning to ensure its feasibility and optimal performance. In this paper, we deal with the problem of studying kinematic robot performance in the case of such manufacturing tasks, where the robot tool is constrained to follow the machining path embedded on the workpiece surface at a prescribed orientation. The presented approach is based on the well-known concept of manipulability, although the latter suffers from physical inconsistency due to mixing different units of linear and angular velocity in a general 6 DOF task case. Therefore, we introduce the characteristics of the workpiece surface constraint in the robot kinematic analysis, that enables evaluation of its available velocity capability in a reduced dimension space. Such constrained robot kinematics transform the robot`s task space to a two-dimensional surface tangent plane, and the manipulability analysis may be limited to the space of linear velocity only. Thus, the problem of physical inconsistency is avoided effectively. We show the theoretical derivation of the proposed method, which was verified by numerical experiments.

This study aimed to assess knowledge and practice of caregivers and its relationship to the disease and nutritional status of children under five years of age in rural areas of Sylhet, Bangladesh. A total of 110 households having 6 to 59 months aged children was selected by simple random method from ten rural communities of three Upazila of Sylhet during September 2019 to February 2020. Descriptive statistics were used to assess the WASH knowledge & practice and multivariate chi-square analyses were performed to assess associations among diseases & nutritional status with WASH following a structured questionnaire. The study found a significant association of WASH with childhood disease and nutritional status, and 65% of children were found to be in a diseased state and 35% of children were found to be in a disease-free state within the last six months. The findings sketched that mother with poor wash knowledge and practice was at greater risk for disease outbreaks, disease frequency and duration. The highest incidence of diarrhea was 17% in children aged 12 to 23 months. Significant effect of WASH was also found in children nutrition status, that was reflected in the ratio of stunted, underweight and wasted children. Integrated convergent work focusing on the provision of clean water within the household, stop open defecation, promotion of hand washing, behavior change and poverty alleviation is needed to improve the situation. Health, nutrition and livelihood programs should be uninterrupted, and mothers or caregivers should be encouraged to participate in these programs.

The development in information technology has played an influential role in transforming the restaurant industry services. Therefore, this research’s main agenda is to investigate factors that motivate employees to adopt and continue using information technology services by integrating two famous information system (IS) theories, namely, task technology fit (TTF) and technology continuance theory (TCT). The extant integrative perspective model details the cause-effect relationship between technology adoption and continuance intention. The positivist paradigm forms the basis of this research design, and the approach followed is quantitative research. Using the stratified random sampling technique, the empirical data was collected from 417 restaurant industry employees in the US (United States) on a five-point Likert scale. The PLS-SEM technique was utilized to analyze data while using Smart PLS 3 because of its suitability and wider application currently in the hospitality sector. Results suggest that the recently developed integrated technology continuance research model has considerable influence on predicting pre- and post-adoption behavior with continuance intention for technology usage within the restaurant industry. All hypotheses were found significant except one for the direct association of hedonic motivation and continuance intention of technology adoption. Moreover, the results revealed that factors like perceived security & information privacy and assisting conditions were the most important factors in determining the usage of information technology with continuance intention. Unlike previous research studies that focus majorly only on issues before adoption of informational technology usage, the current focus on investigating continuance intention toward information technology usage by focusing on factors that can also boost post-adoption behavior and pre-adoption usage information technology.