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The emerging perovskite solar cell (PSC) technology has attracted significant attention due to its superior power conversion efficiency (PCE) among the thin-film photovoltaic technologies. However, the toxicity of lead and poor stability of lead halide materials hinder their commercialization. In this case, after a decade of effort, various categories of lead-free perovskites and perovskite-like materials have been developed, including tin halide perovskites, double perovskites, defect-structured perovskites, and rudorffites. However, the performance of the corresponding devices still falls short of expectations, especially their PCE. The limitations mainly originate from either the unstable lattice structure of these materials, which causes the distortion of their octahedra, or their low dimensionality (e.g., structural and electronic dimensionality)-correlated poor carrier transport and self-trapping effect, accelerating nonradiative recombination. Therefore, understanding the relationship between the structures and performance in these emerging candidates and leveraging these insights to design or modify new lead-free perovskites is of great significance. Herein, we review the variety of dimensionalities in different categories of lead-free perovskites and perovskite-like materials and conclude that dimensionality is an important aspect among the crucial indexes that determine the performance of lead-free PSCs. In addition, we summarize the modulation of both structural and electronic dimensionality, and the corresponding enhanced optoelectronic properties in different categories. Finally, perspectives on the future development of lead-free perovskites and perovskite-like materials for photovoltaic applications are provided. We hope that this review will provide researchers with a concise overview of these emerging materials and help them leverage dimensionality to break the bottleneck in photovoltaic applications. [ABSTRACT FROM AUTHOR]

Modeling the human structure is central for human parsing that extracts pixel-wise semantic information from images. We start with analyzing three types of inference processes over the hierarchical structure of human bodies: direct inference (directly predicting human semantic parts using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). We then formulate the problem as a compositional neural information fusion (CNIF) framework, which assembles the information from the three inference processes in a conditional manner, i.e., considering the confidence of the sources. Based on CNIF, we further present a part-relation-aware human parser (PRHP), which precisely describes three kinds of human part relations, i.e., decomposition, composition, and dependency, by three distinct relation networks. Expressive relation information can be captured by imposing the parameters in the relation networks to satisfy specific geometric characteristics of different relations. By assimilating generic message-passing networks with their edge-typed, convolutional counterparts, PRHP performs iterative reasoning over the human body hierarchy. With these efforts, PRHP provides a more general and powerful form of CNIF, and lays the foundation for more sophisticated and flexible human relation patterns of reasoning. Experiments on five datasets demonstrate that our two human parsers outperform the state-of-the-arts in all cases. [ABSTRACT FROM AUTHOR]

This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structural nature of the task, we introduce a cascaded parsing network (CP-HOI) for a multi-stage, structured HOI understanding. At each cascade stage, an instance detection module progressively refines HOI proposals and feeds them into a structured interaction reasoning module. Each of the two modules is also connected to its predecessor in the previous stage, enabling efficient cross-stage information propagation. The structured interaction reasoning module is built upon a graph parsing neural network (GPNN), which efficiently models potential HOI structures as graphs and mines rich context for comprehensive relation understanding. In particular, GPNN infers a parse graph that i) interprets meaningful HOI structures by a learnable adjacency matrix, and ii) predicts action (edge) labels. Within an end-to-end, message-passing framework, GPNN blends learning and inference, iteratively parsing HOI structures and reasoning HOI representations (i.e., instance and relation features). Further beyond relation detection at a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. A preliminary version of our CP-HOI model reached 1st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation. In addition, our CP-HOI shows promising results on two popular HOI recognition benchmarks, i.e., V-COCO and HICO-DET. [ABSTRACT FROM AUTHOR]

AbstractObjectivesMetchodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONMaintenance hemodialysis patients’ psychology and physiology will have negative effects. Adherence to exercise can effectively enhance the physical function of maintenance hemodialysis patients and reduce the negative psychological emotions. In recent years, virtual reality technology has been gradually applied to the hemodialysis group, providing a new way for the rehabilitation training and healthy life of maintenance hemodialysis patients.To understand the real experience of maintenance hemodialysis patients participating in virtual reality rehabilitation training, and to make reference for the future clinical development of a more perfect virtual reality rehabilitation training program.Phenomenological research method was used to conduct semi-structured interviews with 10 maintenance hemodialysis patients, and the interview data were compiled and analyzed using Colaizzi’s seven-step analysis.Four themes were distilled: (1) Maintenance hemodialysis patients’ overall perceptual experience of the virtual reality rehabilitation system; (2) Physical and psychological improvement of maintenance hemodialysis patients with virtual reality rehabilitation system; (3) Individualized needs of maintenance hemodialysis patients during virtual reality rehabilitation training; (4) Future improvements and recommendations for the virtual reality rehabilitation system.The results of the study suggest that virtual reality improves patients’ motivation to recover and shows positive results and potential benefits in improving physical functioning and negative emotions in maintenance hemodialysis patients.Regular and continuous rehabilitation training is a very effective non-pharmacological treatment to improve the physical functioning and mental health of maintenance hemodialysis patients.Virtual reality technology can stimulate the underlying motivation of maintenance hemodialysis patients to undergo rehabilitation, thus, they are guided to adopt a healthy lifestyle.Virtual reality technology can be an effective aid in the rehabilitation of maintenance hemodialysis patients, thus reducing the workload of rehabilitation clinicians.Regular and continuous rehabilitation training is a very effective non-pharmacological treatment to improve the physical functioning and mental health of maintenance hemodialysis patients.Virtual reality technology can stimulate the underlying motivation of maintenance hemodialysis patients to undergo rehabilitation, thus, they are guided to adopt a healthy lifestyle.Virtual reality technology can be an effective aid in the rehabilitation of maintenance hemodialysis patients, thus reducing the workload of rehabilitation clinicians. [ABSTRACT FROM AUTHOR]

Detection, parsing, and future predictions on sequence data (e.g., videos) require the algorithms to capture non-Markovian and compositional properties of high-level semantics. Context-free grammars are natural choices to capture such properties, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs. In this paper, we generalize the Earley parser to parse sequence data which is neither segmented nor labeled. Given the output of an arbitrary probabilistic classifier, this generalized Earley parser finds the optimal segmentation and labels in the language defined by the input grammar. Based on the parsing results, it makes top-down future predictions. The proposed method is generic, principled, and widely applicable. Experiment results clearly show the benefit of our method for both human activity parsing and prediction on three video datasets. [ABSTRACT FROM AUTHOR]

The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines. [ABSTRACT FROM AUTHOR]

This paper examines how humans adapt to novel physical situations with unknown gravitational acceleration in immersive virtual environments. We designed four virtual reality experiments with different tasks for participants to complete: strike a ball to hit a target, trigger a ball to hit a target, predict the landing location of a projectile, and estimate the flight duration of a projectile. The first two experiments compared human behavior in the virtual environment with real-world performance reported in the literature. The last two experiments aimed to test the human ability to adapt to novel gravity fields by measuring their performance in trajectory prediction and time estimation tasks. The experiment results show that: 1) based on brief observation of a projectile's initial trajectory, humans are accurate at predicting the landing location even under novel gravity fields, and 2) humans' time estimation in a familiar earth environment fluctuates around the ground truth flight duration, although the time estimation in unknown gravity fields indicates a bias toward earth's gravity. [ABSTRACT FROM PUBLISHER]

Mean square exponential stability of both exact solutions and the corresponding θ -EM method for stochastic Volterra integro-differential equations are investigated in this paper. For 1 2 < θ ≤ 1 , we prove that both exact solutions and the corresponding θ -EM method for stochastic Volterra integro-differential equations are mean square exponentially stable under the Khasminskii-type conditions. If 0 ≤ θ ≤ 1 2 , θ -EM method is mean square exponentially stable under the Khasminskii-type condition plus linear growth condition on f. By using Chebyshev inequality and Borel-Cantelli lemma, we can also prove that θ -EM method is almost surely exponentially stable. An example is provided to support our conclusions. [ABSTRACT FROM AUTHOR]

A manufacturing process with the capability to integrate electronics into 3D structures is of great importance to the development of next-generation miniaturised devices. In this study, Selective Laser Melting (SLM) was used to process copper/high-density-polyethylene (HDPE) powder mixtures to build conductive tracks in a 3D circuit system. The effects of copper/HDPE volume ratio, laser input power and scanning speed on the resistivity of CO 2 laser processed tracks were investigated. The resistivity of the tracks decreased from 26.6 ± 0.6 × 10 −4 Ωcm to 1.9 ± 0.1 × 10 −4 Ωcm as the copper volume ratio increased from 30% to 60%. However, further increasing the copper ratio to 100% resulted in poor conductivity. The lowest resistivity was achieved with an input power of 20 W and scanning speed of 80 mm/s. Additionally, processing using single-track-scanning and raster-scanning programs was compared; the overall energy distribution on the surface was more uniform using a raster-scanning program, which further reduced the resistivity to 0.35 ± 0.04 × 10 −4 Ωcm. Based on the results, a 3D multi-layered circuit system was manufactured with the HDPE as the substrate/matrix material and copper/HDPE mixture as the conductive-track material. This circuit system was successfully manufactured, demonstrating the possibility of using SLM technology to manufacture dissimilar materials towards 3D electronic applications. [ABSTRACT FROM AUTHOR]

Smoking cessation in patients treated with clozapine might lead to elevated plasma concentrations and severe side effects. This case report investigated the trajectory of clozapine plasma concentrations over time after smoking cessation in a Chinese inpatient with schizophrenia. This case report delineates the temporal response of plasma clozapine concentrations and dose-corrected clozapine plasma concentrations in a 33-year-old inpatient with schizophrenia who had a substantial smoking history and ceased smoking abruptly during dose titration. This case report presents a sudden increase in plasma clozapine concentrations and dose-corrected plasma clozapine concentrations after smoking cessation, followed by a rapid decline in dose-corrected plasma clozapine concentrations during the initial 2 weeks and a return to pre-cessation levels approximately 1 month later. The findings suggest that clinicians and pharmacists should adjust clozapine dosage in accordance with changes in smoking status, taking into consideration the temporal effects. Post-smoking cessation adjustments to clozapine dosage should be coupled with therapeutic drug monitoring, especially for patients with heavy smoking habits. Moreover, the advice of the clinical pharmacist should be considered in complex cases to ensure safe use of clozapine., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Qi, Ma, Fan, Qi, Yang and An.)

Introduction: This study aimed to investigate the effect of smoking cessation on plasma clozapine (CLO) concentrations in long-term hospitalized Chinese male patients with schizophrenia treated with CLO during the COVID-19 pandemic., Methods: Therapeutic drug monitoring (TDM) data for CLO were collected at Beijing HuiLongGuan Hospital between December 1, 2019 (before smoking cessation) and January 31, 2020 (after smoking cessation) in this retrospective study. Fifty-three male smokers and inpatients with schizophrenia who were treated with CLO were included. Plasma concentrations of CLO were measured using high-performance liquid chromatography-tandem mass spectrometry. The fagerstrom test for nicotine dependence (FTND) was used to assess smoking behavior., Results: The plasma CLO concentrations and dose-corrected plasma CLO concentrations were significantly increased by 29.3 and 23.5%, respectively, after smoking cessation., Discussion: The results suggested that clinicians and pharmacists should adjust the CLO dose based on changes in smoking status in patients stabilized with CLO during the COVID-19 pandemic. Careful TDM for CLO should be performed prior to dose adjustment，to reduce the increased risk of smoking cessation induced side effects, especially for older patients receiving multiple medications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ma, Fan, Qi, Yang and An.)

Objectives: The aim of the present study was to investigate a potential relationship between metabolic parameters and steady-state plasma concentrations of olanzapine (OLA) and its metabolite, 4-N'-desmethyl-olanzapine (DMO) in patients with schizophrenia taking therapeutic doses., Methods: A total of 352 inpatients, diagnosed with schizophrenia according to the DSM-V criteria and treated with OLA, were investigated. The plasma concentrations of OLA and DMO were measured by high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS). Fasting blood samples were measured for insulin, glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), C-reactive protein (CRP) and homocysteine, and differences in these parameters were investigated in relation to plasma concentrations of OLA and DMO., Results: Lower plasma DMO concentrations were associated with higher glucose and TG levels and homeostasis model assessment of insulin resistance (HOMA-IR), while higher plasma OLA concentrations were associated with higher CRP and homocysteine levels in the OLA-treated patients with schizophrenia., Conclusion: These results demonstrate that OLA and its metabolite DMO may have different effects on OLA-induced metabolic abnormalities. DMO might have a counteracting effects on glucose-insulin homeostasis and lipid metabolic abnormalities, which suggests that regular measure of various metabolic parameters and drug monitoring on both OLA and DMO are recommended in OLA-treated patients with schizophrenia., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 An, Fan, Yun, Chen, Qi, Ma, Shi, Wang and Yang.)