Aims
To identify subgroups of people with internet gaming disorder (IGD) based on addiction‐related resting‐state functional connectivity and how these subgroups show different clinical correlates ...and responses to treatment.
Design
Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets.
Setting
Zhejiang province and Beijing, China.
Participants
One hundred and sixty‐nine IGD and 147 control subjects.
Measurements
k‐Means algorithmic and support‐vector machine‐learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue–reactivity and guessing‐related measures.
Findings
Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 (‘craving‐related subgroup’) and subgroup 2 (‘mixed psychological subgroup’). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10‐fold validation (n = 218) and out‐of‐sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue–reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non‐adaptive emotion‐regulation strategies and guessing‐task fMRI measures. Subgroups 1 and 2 showed largely opposite functional–connectivity patterns in overlapping networks.
Conclusions
There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.
•The effect of vibration on S-CO2 heat transfer was studied experimentally.•Heat transfer deterioration can be mitigated and improved by vibration.•Discuss the effect of vibration on heat transfer ...under various conditions.•The correlation is proposed based on the experimental data.
It is inevitable for mechanical motion to generate vibration, which may affect fluid heat transfer. Although some developments on heat transfer in supercritical carbon dioxide (S-CO2) have been established, there is none research on the impact of vibration on S-CO2 heat transfer currently. The effect of transverse vibration on heat transfer characteristics of S-CO2 in 1200 mm tube is investigated experimentally in this study. The results demonstrate that the vibration enhances the heat transfer of S-CO2 clearly. With the increase of the vibration amplitude, frequency, mass flux, or pressure, the heat transfer enhancement efficiency (HTE) tended to rise, and the HTE of the upper vertex is better than the lower vertex. Within the test range, the highest average HTE is 10.2%. Along the tube, the local HTE in the pseudo critical region is much more noteworthy than in other regions. Finally, the heat transfer correlation is proposed in accordance with the experimental findings, and 95.6% of the data error is within ±15%.
Hereditary spherocytosis (HS) is the most common inherited hemolytic anemia characterized by the presence of spherical-shaped erythrocytes on the peripheral blood smear, hemolysis, splenomegaly, ...jaundice, and gallstones. To date, mutations in at least five genes (ANK1, EPB42, SLC4A1, SPTA1, and SPTB) have been found to be associated with different subtypes of HS. Here, we aim to investigate the presence of novel as well as known mutations in 35 Chinese patients with clinically suspected HS. Whole-exome sequencing (WES) has identified 3 patients with SLC4A1, 16 patients with ANK1, and 16 patients with SPTB mutations, including 5 splicing, 12 nonsense, 9 frameshift, 7 missense, and 1 start-loss mutation, indicating that SPTB and ANK1 are the most frequently mutated genes in Chinese HS patients. Among 34 mutations identified, 21 were novel. Most of SPTB and ANK1 mutations were nonsense (8/16) and frameshift (6/16) mutations. By trio analysis of eight families we have confirmed six de novo mutations. In addition, genotype-phenotype analysis was also performed by comparing clinical manifestations among three groups of patients with SPTB, ANK1, and SLC4A1 mutations. It revealed that patients with ANK1 mutations had a significantly higher level of MCV and MCH but lower percentage of spherocytes compared with those carrying SPTB mutations. In conclusion, our results suggested that molecular diagnosis by next-generation sequencing (NGS) is a fast, economic, and accurate way to detect and identify pathogenic alterations of inherited diseases, highlighting the potential usage of NGS in clinical practice.
•A new energy electric vehicle thermal management system using CO2 as the medium has been applied in extreme low temperature weather (−20 ∼ -50℃).•Using a coolant system to complete heat distribution ...and self-enhanced enthalpy.•Switching modes to achieve higher efficiency by recovering residual heat changes.•Using self-enhanced enthalpy and waste heat recovery methods to avoid heat absorption from the outside, thus avoiding the problem of frosting in the heat exchanger.
The automotive industry has begun to shift to new energy electric vehicles due to the challenges of oil and environmental resources. However, heating in low-temperature environments results in poor endurance of electric vehicles in winter, which has always hindered the development of electric vehicles. The main reason is that the commonly used heating method, Positive Temperature Coefficient Heating (PTC), has a lower efficiency, while traditional heat pumps lack heat absorption and heat exchanger frosting, resulting in heat pumps not being able to be used well in low-temperature environments. This article proposes a transcritical CO2 self-enhanced enthalpy heat pump system suitable for low-temperature environmental applications to address this issue. The system can be divided into self-enhanced enthalpy heat pump (SEHP) mode, self-enhanced enthalpy waste heat hybrid heat pump (SEWHHP) mode, and waste heat recovery heat pump (WHHP) mode based on no waste heat, low waste heat to high waste heat. A mathematical analysis was conducted on the operating characteristics of CO2 in three modes using the AMESIM platform. The heat from the condenser in the system can be distributed by the cooling water circuit to the cabin and evaporator for heat absorption, and the waste heat from the battery motor can also be well utilized by the evaporator. This article focuses on analyzing the efficiency of three modes under different ambient temperatures, target temperatures, and residual heat. When the ambient temperature is −20℃, the mode switches from the SEHP to the SEWHHP mode and then to the WHHP mode, with the efficiency increasing from 0.89 to 1.31 and 1.77, respectively. COP increases with increasing ambient temperature in all modes, with the WHHP mode increasing from 1.46 to 1.67 when the ambient temperature increases from 50 °C to 20 °C. The efficiency is much greater than that of PTC, and this study provides new ideas for the thermal management of electric vehicles in low-temperature environments.
Lead‐free halide double perovskites continue to draw increasing attention in view of their nontoxicity and stability compared to lead‐based perovskites. High‐end full‐color displays and white light ...illumination are based on blue‐light excitation. However, the excitation of blue light of lead‐free halide double perovskites Cs2AgInCl6 at room temperature faces a great challenge, which is crucial to its commercial application. In this study, Cs2Ag0.6Na0.4In0.8Bi0.2Cl6/xKBr(KI), which can be excited under blue light, have been synthesized by solid‐phase compression method. Under blue‐light excitation, Cs2Ag0.6Na0.4In0.8Bi0.2Cl6/16KBr and Cs2Ag0.6Na0.4In0.8Bi0.2Cl6/16KI emit 605 nm orange light and 665 nm red light, respectively. Based on that, orange and red light‐emitting devices are fabricated excited by blue light. The performance of blue‐light excitation can not only make the double perovskites widely used in light‐emitting devices, but also as a light‐emitting down‐shift layer in photovoltaic devices to improve the performance of photovoltaic modules. With the Cs2Ag0.6Na0.4In0.8Bi0.2Cl6/16KBr, the power conversion efficiency of the Si solar cell increases 4.80% and 8.14% under the sunlight and white light‐emitting devices, respectively. The blue‐light excitation of lead‐free double perovskites at room temperature offers greater development potential in the field of display devices and optoelectronics.
Cs2Ag0.6Na0.4In0.8Bi0.2Cl6/xKBr are synthesized by solid‐phase compression method. They can be excited by blue light at room temperature. The performance of blue‐light excitation can not only make the double perovskites widely used in light‐emitting devices, but also as a light‐emitting down‐shift layer in photovoltaic devices to improve the performance of photovoltaic modules.
•Effort-based decision-making is best described by a power discounting model.•Multivariate dmPFC patterns represent subjective value across tasks and datasets.•These effects may not be detectable by ...univariate fMRI analyses.•These findings extend the scope of the neural common currency theory.
Decisions that require taking effort costs into account are ubiquitous in real life. The neural common currency theory hypothesizes that a particular neural network integrates different costs (e.g., risk) and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-related value integration, it is still under debate if effort-based decision-making relies on a domain-general valuation network as implicated in the neural common currency theory. Therefore, we comprehensively compared effort-based and risky decision-making using a combination of computational modeling, univariate and multivariate fMRI analyses, and data from two independent studies. We found that effort-based decision-making can be best described by a power discounting model that accounts for both the discounting rate and effort sensitivity. At the neural level, multivariate decoding analyses indicated that the neural patterns of the dorsomedial prefrontal cortex (dmPFC) represented subjective value across different decision-making tasks including either effort or risk costs, although univariate signals were more diverse. These findings suggest that multivariate dmPFC patterns play a critical role in computing subjective value in a task-independent manner and thus extend the scope of the neural common currency theory.
Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting‐state functional ...connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome‐based predictive modeling (CPM)—a recently developed machine‐learning approach—has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting‐state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting‐state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole‐brain and network‐based analyses showed that the default‐mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting‐state activity in the DMN, the identified networks have been mapped into a three‐subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes.
We integrated the connectome‐based predictive modeling approach with the support vector machine to establish the link between brain connectivity profiles and behavior in internet gaming disorder. We found that the default‐mode network is the most informative network in predicting internet gaming disorder.
•This systematic review was designed to relate theories of IA to treatments, describe the studies of psychotherapies for IA, and propose a model of intervention and addiction based on extant ...studies.•The investigated targeted domains and intervention methods employed are not mutually exclusive, and further research is needed to identify the effective components and mechanisms of action for treatments of IA.•Interventions based on different aspects of IA have been shown to have different effects. To intervene the addiction more effectively, a targeted intervention scheme should be designed based on the individual's extant susceptibility factors and combined with the emerging behavioral and cognitive characteristics.
Internet addiction (IA) may constitute a widespread and serious mental problem. Previous reviews have not fully considered potential factors that may contribute to therapeutic outcomes or predict behavioral changes. Such information is relevant to understand the active ingredients of interventions and to develop more efficacious treatments that target features of IA. This systematic review was designed to relate theories of IA to treatments, describe studies of psychotherapies for IA, and propose a model of addiction and interventions based on extant studies. A computer database search of PubMed, PsychINFO, ScienceDirect, China National Knowledge Infrastructure, and Google Scholar was conducted to identify all available research evidence on psychological treatments for IA (N = 31 studies). Among these psychological interventions, the targeted reduction of addiction-related impulsivity and craving, improvement of cognitive maladjustment, and alleviation of family problems have been investigated in IA interventions. The targeted domains and intervention methods are not mutually exclusive, and further research is needed to demonstrate the effective components and mechanisms of action for treatments of IA. Such research will help generate more efficacious evidence-based interventions.
IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by ...cameras and sensors. While cameras view these things as visual “objects”, these things which are equipped with “sensing devices” also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses’ burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device–object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices’ data and demonstrating the ability to recover the missing bounding box.
Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link ...failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes, loops, and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.
PDF
