Well-being has become extensively viewed as apprehension for administrations in the last decades and schools have been progressively realized as locations for encouraging well-being which is a ...considerable development in inquiries on mediations connected to learner well-being. In this way, the function of teachers has got specific consideration regarding students' well-being, given the merits of teacher-student interactions. High-quality educator-learner relationships offer a support base for long-term learners' education. Educator interpersonal behavior that makes learners feel supported and cared for is known as emotional support. These behaviors can help learners' emotional and social needs; meet learners' families, and being available when learners need additional help. This review attempts to consider the eminence of teacher interpersonal behavior and learner-teacher relations in the classroom and indeed illustrate their relationship and influence on students' well-being. As a final point, this review can provide suggestions and recommendations for teaching participants in the scholastic context.
Cortical spreading depression is a pathophysiological event shared in migraines, strokes, traumatic brain injuries, and epilepsy. It is associated with complex hemodynamic responses, which, in turn, ...contribute to neurological problems. In this study, we investigated the role of canonical transient receptor potential channel 3 (TRPC3) in the hemodynamic responses elicited by cortical spreading depression. Cerebral blood flow was monitored using laser speckle contrast imaging, and cortical spreading depression was triggered using three well-established experimental approaches in mice. A comparison of TRPC3 knockout mice to controls revealed that the genetic ablation of TRPC3 expression significantly altered the hemodynamic responses elicited using cortical spreading depression and promoted hyperemia consistently. Our results indicate that TRPC3 contributes to hemodynamic responses associated with cortical spreading depression and could be a novel therapeutic target for a host of neurological disorders.
Companies are increasingly using artificial intelligence (AI) to provide performance feedback to employees, by tracking employee behavior at work, automating performance evaluations, and recommending ...job improvements. However, this application of AI has provoked much debate. On the one hand, powerful AI data analytics increase the quality of feedback, which may enhance employee productivity (“deployment effect”). On the other hand, employees may develop a negative perception of AI feedback once it is disclosed to them, thus harming their productivity (“disclosure effect”). We examine these two effects theoretically and test them empirically using data from a field experiment. We find strong evidence that both effects coexist, and that the adverse disclosure effect is mitigated by employees' tenure in the firm. These findings offer pivotal implications for management theory, practice, and public policies.
Managerial
Artificial intelligence (AI) technologies are bound to transform how companies manage employees. We examine the use of AI to generate performance feedback for employees. We demonstrate that AI significantly increases the accuracy and consistency of the analyses of information collected, and the relevance of feedback to each employee. These advantages of AI help employees achieve greater job performance at scale, and thus create value for companies. However, our study also alerts companies to the negative effect of disclosing using AI to employee that results from employees' negative perceptions about the deployment of AI, which offsets the business value created by AI. To alleviate value‐destroying disclosure effect, we suggest that companies be more proactive in communicating with their employees about the objectives, benefits, and scope of AI applications in order to assuage their concerns. Moreover, the result of the allayed negative AI disclosure effect among employees with a longer tenure in the company suggests that companies may consider deploying AI in a tiered instead of a uniform fashion, that is, using AI to provide performance feedback to veteran employees but using human managers to provide performance feedback to novices.
Potassium‐ion hybrid capacitors have attracted increasing attention due to good energy density, high power density, and low cost. Ti3C2Tx‐MXene is considered as a promising anode material for K ion ...storage. However, undesirable stacking issues decrease its exposed area and breeds sluggish K ion transport. Herein, a facile spray‐lyophilization strategy is proposed to construct stacking‐resistant Ti3C2Tx with 3D structures. As‐prepared Ti3C2Tx hollow spheres/tubes present stack resistance, a large specific surface area, and a short ion diffusion pathway. When serving as an anode material, it shows enhanced capacity and thickness‐independent rate performance compared to 2D Ti3C2Tx. After 10 000 cycles, a specific capacity of 122 mAh g−1 is obtained at 1 A g−1. Systematic kinetics analyses demonstrate the significance of concentration polarization on the electrode's rate ability. Furthermore, a 3D Ti3C2Tx‖hierarchical porous activated carbon (HPAC) K‐ion hybrid capacitor is assembled and displays remarkable energy and power densities with energy retention of 100% after 10 000 cycles at 1 A g−1 . Following this strategy, other 3D structures from nanosheets can also be obtained, such as 3D Ti3C2Tx microtubes and graphene oxide nanoscrolls. This study provides a viable approach to solve the stacking issues of 2D nanosheets to promote the application of 2D materials.
A spray‐lyophilization strategy is proposed to transform 2D nanosheets such as Ti3C2Tx, Ti2CTx, and graphene oxide into 3D architectures. The obtained 3D Ti3C2Tx presents an aggregation‐resistant, large specific surface, and a short ion transport path, leading to enhanced K ion storage ability. A 3D Ti3C2Tx‖hierarchical porous activated carbon (HPAC) K‐ion hybrid capacitor is assembled and displays remarkable energy and power densities with ultrastable cycling performance.
Our study explores whether lifelong learning is associated with the subjective wellbeing among the elderly in Singapore. Through a primary survey of 300 individuals aged 65 and above, we develop a ...novel index to capture three different aspects of subjective wellbeing, which we term "Quality of Life", "Satisfaction with Life" and "Psychological Wellbeing". Utilizing both supervised and unsupervised machine learning techniques, our findings reveal that attitudes towards lifelong learning are positively associated with quality of life, while participation in class activities is positively associated with all three measures of wellbeing. Although the study does not establish causality, it highlights a connection between lifelong learning and the perceived wellbeing of the elderly, offering support for policies that encourage lifelong learning among this population.
Gate oxide in power metal-oxide-semiconductor field effect transistors (MOSFETs) degrades over time. The degradation leads to an accumulation of oxide-trapped charges within the gate oxide and an ...accumulation of interface-trapped charges at the oxide-semiconductor surface of power MOSFETs. Overtime, such charges significantly alter the electrical parameters of power MOSFETs; to observe this, the electrical parameters are utilized as precursors of gate-oxide degradation. The purpose of this paper is threefold: 1) to propose a new online precursor of gate-oxide degradation-the gate plateau time; 2) to demonstrate a simultaneous dip-and-rebound variation pattern of four precursors of gate-oxide degradation: threshold voltage, gate plateau voltage, gate plateau time, and on-resistance; and 3) to compare the shift tendencies of each precursor over the course of gate-oxide degradation. The existing studies of gate-oxide degradation mechanisms and their effects on threshold voltage and mobility reduction were extended to correlate a variation of all four precursors using analytical expressions. The variation patterns were experimentally verified using high-electric field stressing in two different commercial power MOSFETs. The new precursor, the gate plateau time, was found to be a competitive gate-oxide degradation precursor, as it had a higher positive shift than threshold voltage and gate plateau voltage. In addition, the threshold voltage was found to be the most sensitive indicator of the negative shift (dip), while the on-resistance and gate plateau time were found to be the most sensitive indicators of the positive shift (rebound).
Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on ...a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.
The Z-source inverter, utilizing a unique LC network and previously forbidden shoot-through states, provides unique features, such as the ability to buck and boost voltage with a simple single-stage ...structure. The analysis and control methods provided in the literature are based on an assumption that the inductor current is relatively large, continuous, and has small ripple. This assumption becomes invalid when the load power factor is low or the inductance is small in order to minimize the inductor's size and weight for some applications where volume and weight are crucial. Under these conditions, the inductor current has high ripple or even becomes discontinuous. As a result, the Z-source inverter exhibits new operation modes that have not been discussed before. This paper analyzes these new operation modes and the associated circuit characteristics.
A battery-energy-stored quasi-Z-source cascaded multilevel inverter (qZS-CMI)-based photovoltaic (PV) power generation system combines advantages of a qZS inverter, a CMI, and a battery energy ...storage system. However, unbalanced battery state of charge (SOC) between cascaded H-bridge inverter modules will degrade an entire system's performance and shorten battery lifespan. This paper proposes a control method to balance battery SOCs of all modules, no matter the intermittent states of each module's PV power. The method is on the basis of battery SOCs, SOC limits of each module, and the total power injected into the power grid. Simulation and experimental results verify the proposed control method that ensures identical SOCs for the battery-energy-stored qZS-CMI PV system.
3D single object tracking is a key issue for autonomous following robot, where the robot should robustly track and accurately localize the target for efficient following. In this paper, we propose a ...3D tracking method called 3D-SiamRPN Network to track a single target object by using raw 3D point cloud data. The proposed network consists of two subnetworks. The first subnetwork is feature embedding subnetwork which is used for point cloud feature extraction and fusion. In this subnetwork, we first use PointNet++ to extract features of point cloud from template and search branches. Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively. The second subnetwork is region proposal network(RPN), which is used to get the final 3D bounding box of the target object based on the fusion feature from cross correlation modules. In this subnetwork, we utilize the regression and classification branches of a region proposal subnetwork to obtain proposals and scores, thus get the final 3D bounding box of the target object. Experimental results on KITTI dataset show that our method has a competitive performance in both Success and Precision compared to the state-of-the-art methods, and could run in real-time at 20.8 FPS. Additionally, experimental results on H3D dataset demonstrate that our method also has good generalization ability and could achieve good tracking performance in a new scene without re-training.
PDF
