Experience of Mental Health Professionals Collaborating with Peer Supporters in a Community Mental Health Service Team

This study explored how mental health professionals collaborate with peer supporters with mental disabilities in a community mental health institution.From January 19 to February 23,2021,three 60 min interviews were conducted with six mental health professionals working at a Korean community center.The results were qualitatively analyzed and divided into four themes and eight categories.The four themes were the perceptions of and challenges in working with peer supporters with mental disabilities,conflict and confusion about working with peer supporters,forming partnerships with peer supporters,and policy support for peer supporters’job security.Participants reported vague anxiety about working with a peer supporter and difficulties with the trial-and-error process of adjusting to the role as challenging.Over time,however,they realized that they needed to make an effort to develop meaningful relationships with peer supporters and mental health professionals.Thus,through this study,we realized that there was a need to improve the system,such as building infrastructure for job stability for peer support workers and capacity building tailored to the mental disorders.Although peer supporters play various roles while working with mental health professionals,this study showed the possibility of mutual growth through communication and cooperation.These findings will help prepare systems necessary for collaboration between the two teams amidst the increasing institutionalization of peer support for mental disorders.

Factors Affecting Treatment Interruption among TB Patients in Lagos Nigeria: Is There Any Need for Treatment Supporters?

Background: This study assessed treatment interruption of tuberculosis (TB) patients managed by treatment supporters and health care workers and other predictors of treatment interruption. Methods: A descriptive cross-sectional study was conducted. Four hundred and seventy new smear positive TB patients above 14 years of age were consecutively recruited between October 1 and December 31 2012 from 34 (23 public and 11 private) directly observed treatment short course (DOTS) facilities that offered TB treatment and microscopy services. They were followed up till treatment was completed. Logistic regression was used to assess the predictors of treatment interruption. Results: A significantly higher proportion of smokers (58.6% vs 38.3%, p = 0.030), patients supervised by treatment supporters (44.4% vs 34.7%, p = 0.032), patients not counselled before initiation of treatment (55.6% vs 38.2%, p = 0.041), patients managed at private DOTS facilities (50% vs 36.3%, p = 0.010) and TB/HIV co-infected patients (54.2% vs 38.6%, p = 0.038) had treatment interruption. Predictors of treatment interruption were supervision by treatment supporters, smoking, lack of pre-treatment counselling and TB/HIV co-infection. Conclusion: A higher proportion of patients supervised by treatment supporters had treatment interruption than those supervised by health care workers. There may be a need to review the concept of treatment supervision by treatment supporters in Lagos state Nigeria.

Listener,Supporter & Savior——On Paul D & the Blacksmith in Toni Morrison's Novels

Beloved and A Mercy are widely regarded as a companion piece to each other.This thesis intends to employ Gerard Genette’s narrow-sense intertextuality to study Two Black male Characters in Beloved&A Mercy:Paul D&the Blacksmith.It is hoped that this thesis will reveal the deep concern of Morrison not only for the future of women of her own race,but also for the black men.And by the creation of Paul D and the blacksmith,Morrison wants to show us her definition of ideal relationships between man and woman:Male participation is indispensable in a female’s growth and redemption.Only in the process of jointly conquering the shadows and tribulations of the past,can they reach real mutual understanding and harmony.

Supporters Family 2004

Since 2004, a total of 23 members have given their financial support to CREIC. Here we wish to express our sincere thanks to them. The names of our sponsors are listed below.

Nanoparticle Exsolution on Perovskite Oxides:Insights into Mechanism,Characteristics and Novel Strategies

Supported nanoparticles have attracted considerable attention as a promising catalyst for achieving unique properties in numerous applications,including fuel cells,chemical conversion,and batteries.Nanocatalysts demonstrate high activity by expanding the number of active sites,but they also intensify deactivation issues,such as agglomeration and poisoning,simultaneously.Exsolution for bottomup synthesis of supported nanoparticles has emerged as a breakthrough technique to overcome limitations associated with conventional nanomaterials.Nanoparticles are uniformly exsolved from perovskite oxide supports and socketed into the oxide support by a one-step reduction process.Their uniformity and stability,resulting from the socketed structure,play a crucial role in the development of novel nanocatalysts.Recently,tremendous research efforts have been dedicated to further controlling exsolution particles.To effectively address exsolution at a more precise level,understanding the underlying mechanism is essential.This review presents a comprehensive overview of the exsolution mechanism,with a focus on its driving force,processes,properties,and synergetic strategies,as well as new pathways for optimizing nanocatalysts in diverse applications.

Is GDNF to Parkinson’s disease what BDNF is to Huntington’s disease?

Neurotrophic factors,or neurotrophins,are a group of molecules supporting the growth,survival,and differentiation of developing and mature neurons.Given their role in the survival of neurons,and often of specific subsets of brain cells,neurotrophins have been implicated in several ways with many neurodegenerative disorders.

Aqueous-phase reforming of hydroxyacetone solution to bio-based H_(2)over supported Pt catalysts

Aqueous-phase reforming(APR)is an attractive process to produce bio-based hydrogen from waste biomass streams,during which the catalyst stability is often challenged due to the harsh reaction conditions.In this work,three Pt-based catalysts supported on C,AlO(OH),and ZrO_(2)were investigated for the APR of hydroxyacetone solution in afixed bed reactor at 225℃and 35 bar.Among them,the Pt/C catalyst showed the highest turnover frequency for H_(2)production(TOF of 8.9 molH_(2)molPt^(-1)min^(-1))and the longest catalyst stability.Over the AlO(OH)and ZrO_(2)supported Pt catalysts,the side reactions consuming H_(2),formation of coke,and Pt sintering result in a low H_(2)production and the fast catalyst deactivation.The proposed reaction pathways suggest that a promising APR catalyst should reform all oxygenates in the aqueous phase,minimize the hydrogenation of the oxygenates,maximize the WGS reaction,and inhibit the condensation and coking reactions for maximizing the hydrogen yield and a stable catalytic performance.

Enhanced stability of nitrogen-doped carbon-supported palladium catalyst for oxidative carbonylation of phenol

Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticles(NPs)on nitrogen-doped carbon;synthesized by using F127 as a stabilizer,as well as chitosan as a carbon and nitrogen source.The Pd/NCF catalyst was efficient and recyclable for oxidative carbonylation of phenol to diphenyl carbonate,exhibiting higher stability than Pd/NC prepared without F127 addition.The hydrogen bond between chitosan(CTS)and F127 was enhanced by F127,which anchored the N in the free amino group,increasing the N content of the carbon material and ensuring that the support could provide sufficient N sites for the deposition of Pd NPs.This process helped to improve metal dispersion.The increased metal-support interaction,which limits the leaching and coarsening of Pd NPs,improves the stability of the Pd/NCF catalyst.Furthermore,density functional theory calculations indicated that pyridine N stabilized the Pd^(2+)species,significantly inhibiting the loss of Pd^(2+)in Pd/NCF during the reaction process.This work provides a promising avenue towards enhancing the stability of nitrogen-doped carbon-supported metal catalysts.

Coalbursts in China: Theory, practice and management

Coalburst is one of the most serious disasters that threaten the safe production of coal mines, and this disaster is particularly serious in China. This paper presents an overview of coalbursts in China since 1980s. From the "stress and energy" and "regional and local" perspectives, the achievements in the theory, practice and management of coalbursts in China are systematically summarized. A theoretical system of coalbursts has been formed to reveal the deformational behavior of coalbursts and explain the mechanism of coalbursts. The occurrence conditions of coalbursts are put forward and the critical stress is obtained. The stress index method for risk evaluation of coalbursts before mining is proposed, and the deformation localization prediction method of coalbursts is put forward. The relationship between energy release and absorption in the process of coalbursts is found, and the prevention and control methods of coalbursts, including the regional method, the local method and support, are presented. The safety evaluation index of coalburst prevention and control is put forward. The integrated prevention and control method for coal and gas outbursts is proposed. The prevention and control technology and equipment of coalbursts have also been developed. Amongst them, the distribution law of the critical stress in China coalburst mines is discovered. The technology and equipment for monitoring, prevention and control of coalbursts, as well as for integrated prevention and control of combined coalbursts and other disasters, have been developed. The energy-absorbing and coalburst-preventing support technology for roadways is invented, and key engineering parameters of coalburst prevention and control are pointed out. In China, coalburst prevention and control laws and standards have been developed. Technical standards for coalbursts are formulated, statute and regulations for coal mines are established, and regulatory documents are promoted.

Research on the flow stability and noise reduction characteristics of quasi-periodic elastic support skin

To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Mechanism of high-preload support based on the NPR anchor cable in layered soft rock tunnels

The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.