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Abstract The heterostructure of two‐dimensional transition metal dichalcogenide (TMDC) has garnered extensive attention, for the junction is the building block of a semiconductor device. However, the controllable synthesis of TMDC heterostructures of different transition metals and different chalcogen elements is still challenging because of the etching by atom substitution during the chemical vapor deposition (CVD) process. Here, a Mo─O transition state with lower energy is introduced to the edge of an as‐grown MoSe2 by using ultraviolet ozone treatment, to prevent the fast atom substitution of S for Se, and enable a stable growth of WS2/MoSe2 lateral heterostructure. A polymer‐free transfer method is developed based on capillary interaction, and atomic structure characterization confirms the high‐quality WS2/MoSe2 lateral heterostructure. The WS2/MoSe2 lateral heterostructure photodetector exhibits superior photoresponse compared to WS2 and MoSe2 devices, with a responsivity of 21.87 A W−1 and a detectivity of 4.2 × 1012 Jones at 350 nm. Kelvin probe force microscopy result reveals that the built‐in electric field within the heterojunction facilitates the effective separation of photogenerated electron‐hole pairs. This study carries profound implications for the CVD growth and polymer‐free transfer of TMDC heterostructures in photodetector applications.

In this paper, a photonic skin sensor based on a balloon-shaped optical fiber structure cascading a Fiber Bragg Grating (FBG) is proposed, which can simultaneously sense pressure and temperature. When the photonic skin feels pressure, the built-in polymer micro-cap is squeezed, causing a change in the radius of the balloon-shaped structure. At the same time, the capillary-enclosed FBG can be used for temperature compensation. The pressure sensitivity of the photon skin sensor can reach 6.556 nm/mN in the range of 0-0.5 mN and the temperature sensitivity can reach -0.192 nm/°C in the range of 30-50 °C. The demodulation of the dual parameter matrix shows that the error rates of pressure and temperature measurements are less than 0.5% and 2.01%, respectively. On this basis, the sensor is applied to robotic arm-assisted puncture experiment detection, and the five states of the puncture process are analyzed. Based on the backpropagation (BP) neural network for repeated training, the results show that its accuracy remains at 98.7%, indicating that the sensor has good robustness. The development of this sensor shows application potential in the field of robotic intelligent skin in the future.

The ever-growing prominence and widespread acceptance of organic light-emitting diodes (OLEDs), particularly those employing thermally activated delayed fluorescence (TADF), have firmly established them as formidable contenders in the field of lighting technology. TADF enables achieving a 100% utilization rate and efficient luminescence through reverse intersystem crossing (RISC). However, the effectiveness of TADF-OLEDs is influenced by their high current density and limited device lifetime, which result in a significant reduction in efficiency. This comprehensive review introduces the TADF mechanism and provides a detailed overview of recent advancements in the development of host-free white OLEDs (WOLEDs) utilizing TADF. This review specifically scrutinizes advancements from three distinct perspectives: TADF fluorescence, TADF phosphorescence and all-TADF materials in host-free WOLEDs. By presenting the latest research findings, this review contributes to the understanding of the current state of host-free WOLEDs, employing TADF and underscoring promising avenues for future investigations. It aims to serve as a valuable resource for newcomers seeking an entry point into the field as well as for established members of the WOLEDs community, offering them insightful perspectives on imminent advancements.

Due to the confidentiality of the classified information system, it is isolated from the external network, but it is necessary to exchange data with an external network. This makes data exchange security between classified networks an important problem. Previous research focused on using access control policies to limit data access at different security levels, using some security checks to detect the security of the data itself. The security threat of the access control policy itself will threaten the data exchange between the classified networks. Moreover, the data should be performed security check isolated from user during the physical transmission to prevent the attack behavior which bypass the data security check from the user side. At present, the cross-network isolation and data exchange models are designed based on fixed business scenarios and lack generality. Therefore, this paper proposes a Turing machine-based theoretical model for cross-network isolated data exchange. This model consists of two parts: the proof system module and the physical transmission channel module. The proof system module uses an interactive Turing machine to model the part of the operational security and data security check of the communicating party's authority to provide security and versatility. The physical transmission channel module isolates different networks and provides a detection mechanism independent of the communication parties to ensure the security and efficiency of the transmission. The proof given shows that the theoretical model can exchange data efficiently and safely. Finally, based on the given theoretical model, an intuitive cross-network isolation and data exchange function model is constructed in the form of a functional tree to illustrate the model versatility.

Excessive carbon emissions are causing the problems of global warming and greenhouse effect. It is urgent to control carbon emissions worldwide. As one of the main sources, it is crucial to reduce the carbon emits of construction industry. Carbon is generated at all phases of the building life cycle. Notably, building design has various extent of influence on carbon emissions at each phase, which is urgently to explore a low carbon method. This paper is to summarize the current status of building design by literature review. The challenges of building design are analysed as the lack of 1) a comprehensive standard system by considering different factors; 2) the carbon emission calculation method; 3) real-time optimization model aiming at carbon reduction. Therefore, the path of “standard-calculation-prediction-optimization” (SCPO) for building design is proposed. This paper can provide theoretical guidance for low carbon building design.

This study tested a parallel two-mediator model in which the relationship between dimensions of emotional intelligence and online gaming addiction are mediated by perceived helplessness and perceived self-efficacy, respectively. The sample included 931 male adolescents (mean age = 16.18 years, SD = 0.95) from southern China. Data on emotional intelligence (four dimensions, including self-management of emotion, social skills, empathy and utilization of emotions), perceived stress (two facets, including perceived self-efficacy and perceived helplessness) and online gaming addiction were collected, and bootstrap methods were used to test this parallel two-mediator model. Our findings revealed that perceived self-efficacy mediated the relationship between three dimensions of emotional intelligence (i.e., self-management, social skills, and empathy) and online gaming addiction, and perceived helplessness mediated the relationship between two dimensions of emotional intelligence (i.e., self-management and emotion utilization) and online gaming addiction. These findings underscore the importance of separating the four dimensions of emotional intelligence and two facets of perceived stress to understand the complex relationship between these factors and online gaming addiction.

Heat shock transcription factor (Hsf) is one of the conserved gene families in plants, playing a crucial role in growth and development, as well as in response to diverse stresses. Although it has been systematically studied in many species, little is known about the Hsf gene family in Chenopodium quinoa, especially those involved in the regulatory network of stress processes. In this study, we identified 23 Hsf genes in quinoa (CqHsfs) through a genome-wide search method based on the latest available genome information. Phylogenetic analysis classified them into three groups, and group A was further divided into nine subgroups, which was supported by conserved domain organizations. Gene structure and multiple sequence alignment analysis revealed that all of the CqHsfs possessed a similar structure organization and were highly conserved in BDB domain. Interaction network analysis identified 13 CqHsfs involved in the network pathway to regulate diverse biological processes. Expression profiles of these CqHsfs were further investigated using the RNA-seq data, and tissue-specific and stress-responsive candidates were identified. Finally, four heat-responsive CqHsfs were selected to validate their expression level through semi-quantitative RT-PCR analysis. This study reported the organization, structure, and expression profiles of the Hsf gene family in quinoa, which will contributes to further functional analysis, and helps to better understand the roles and regulatory mechanism of heat shock factors playing in quinoa and beyond.

The 12-item General Health Questionnaire (GHQ-12) is a self-reporting instrument to assess mental health. The present study was aimed to explore the optimal factor model of the Chinese version of the GHQ-12 in patients with psychological disorders, and examine its measurement invariance across gender. A total of 870 outpatients completed the GHQ-12. Among them, 361 patients also completed the Center for Epidemiologic Studies Depression Scale (CES-D) and the Self-Rating Anxiety Scale (SAS). Confirmatory factor analysis (CFA) was adopted to assess the fit of two models of the GHQ-12. Multi-group confirmatory factor analysis (MGCFA) was performed to verify the measurement invariance of the GHQ-12 across gender. The Cronbach's [alpha] coefficients, McDonald's omega coefficients ([omega]), and mean inter-item correlations (M.sub.IC) were calculated to investigate the internal reliability of the GHQ-12. Pearson correlations between GHQ-12 and CES-D/SAS were performed to examine the convergent construct validity. The results showed that the 3-factor model of the GHQ-12 fitted the data better than the 2-factor model, and the 3-factor model of the GHQ-12 was measurement invariant across gender. The Cronbach's [alpha] coefficients of the GHQ-12 and its factors ranged from 0.63 to 0.83, [omega] ranged from 0.80 to 0.90, and M.sub.IC ranged from 0.28 to 0.46. The scores of the GHQ-12 and its factors had significantly positive correlations with scores of CES-D and SAS. In conclusion, the good factorial validity and reliability of the Chinese version of the GHQ-12 supported its application in evaluating the general mental health of patients in clinical context., Author(s): Zhaoxia Liu [sup.1] [sup.2] , Chang Xi [sup.1] [sup.2] , Mingtian Zhong [sup.3] , Wanrong Peng [sup.1] [sup.2] , Qian Liu [sup.3] , Jun Chu [sup.1] [sup.2] , Kaili [...]

Background: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain. Methods: We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set. Results: Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the '2-back versus 0-back' contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%. Limitations: We used a limited n-back paradigm that precluded the examination of higher working-memory loads. Conclusion: Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia., Introduction Working-memory deficit--the failure to represent, maintain and update information in a short period of time--is a key feature of neurocognitive deficit in schizophrenia. (1) It emerges in the prodromal [...]

Background: Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. Methods: Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. Results: Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. Limitations: Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. Conclusion: A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD., Introduction Major depressive disorder (MDD) is one of the most prevalent mental illnesses; it affects more than 264 million people worldwide and has an estimated lifetime prevalence of 16.2%. (1) [...]

Climate change has heightened the frequency and intensity of extreme heat events in cities, greatly impacting human health, the environment, and socio-economic activities, particularly in densely populated areas. Canopy temperature (T2m) is a key indicator of whether urban area extreme heat is occurring, with significant implications for public health, energy consumption, and pollution levels. However, the diverse urban topography, functional layout, and human activities contribute to significant variations in the distribution of T2m. While computational fluid dynamics (CFD) models offer high-resolution T2m simulations, complexities in urban spatial and temporal dynamics make accurately defining boundary conditions challenging, potentially leading to large simulation errors. This study addressed the challenge of determining precise boundary conditions for urban CFD simulations by employing the Weather Research and Forecasting model to integrate meteorological reanalysis data. Different meteorological reanalysis datasets used to simulate T2m were compared, including Final Operational Global Analysis, Global Forecast System, and European Centre for Medium-Range Weather Forecasts Reanalysis v5. When combined with the reanalysis data, the minimum mean relative error of simulated T2m was 4%, which is a threefold improvement in accuracy compared to traditional meteorological boundary conditions. This study provides technical support for refined zoning of urban extreme heat risk and urban management in the context of climate change. [ABSTRACT FROM AUTHOR]