Characteristics Of Samples Research Articles

Emotion-focused group therapy as a training intervention in a nonclinical sample of graduate students: a feasibility study

ABSTRACT Emotion-focused group therapy (EFT-G) has only been formulated in the last decade and there is little evidence regarding its applicability. Our study aimed to investigate the feasibility of EFT-G in university students and exploring helpful and hindering events of EFT-G from the participants’ point of view. A pre-formed cohort of students (n = 16) within the same class participated in the quasi-experimental study without a control group. They completed 12 sessions of a modified version of the EFT-G. Participants were administered outcome measures at the first and last sessions. After each session, the participants reported their experiences using the HAT form. Pre-post quantitative outcome analysis results revealed a statistically significant but not clinically reliable decrease in emotion regulation difficulties and an improvement in self-compassion and subjective satisfaction with life. The results of the qualitative analysis revealed that helpful and hindering events were organized around a cluster related to the students’ experiences of themselves (e.g. Developing self-understanding, Feelings of empowerment) or a cluster related to other students in the group (e.g. Seeing others’ perspectives, Self-disclosure difficulties). The main limitations of the results are a lower adherence to the original EFT-G model and the characteristics of the group sample (e.g. preexisting class of students).

Longitudinal association of health behaviors and health-related quality of life with military spouse readiness.

Unhealthy behaviors impose costs on health-related quality of life (HRQOL) reducing productivity and readiness among military members (Hoge et al., JAMA 295:1023-32, 2006; Mansfield et al. 362:101-9, 2010). Among married personnel in particular, patterns of spouse health behaviors may play an interdependent role. As a result, the identification of military spouse health factors related to readiness may inform strategies to screen for and identify those in need of greater support and enhance readiness. This study explored behavioral and HRQOL predictors and potential mediators of military spouse readiness utilizing data from the Millennium Cohort Family Study. The analytic sample comprised of 3257 spouses of active-duty, non-separated service members who responded to both waves 1 and 2 of the survey. Sample characteristics are described with respect to demographics (e.g., age, sex, race/ethnicity, etc.), readiness measures (i.e., military satisfaction, lost workdays, health care utilization, military-related stress, and satisfaction), health behaviors (i.e., exercise, sleep, smoking, and alcohol use) and HRQOL (Veterans RAND 12-Item Short Form Survey). We conducted multivariate mediation analyses to evaluate the role of mental and physical HRQOL as mediators between the baseline health behaviors and the health readiness outcomes at follow-up, while adjusting for spouse and service member demographics. HRQOL had direct effects for all five readiness outcomes examined. Multiple health behaviors (insomnia, smoking, binge drinking, and exercise) were further significantly associated with spouse readiness outcomes, although most effects were mediated through HRQOL, suggesting this may be a useful index of military spouse readiness. Insomnia was the specific health behavior most consistently associated with poorer readiness across outcomes, and effects were only partially mediated by physical and mental HRQOL. The results show spouse health behaviors are directly and indirectly (through HRQOL) associated with readiness indicators. This suggests that assessments of modifiable health behaviors (e.g., insomnia symptoms) and mental and physical HRQOL are important indicators of readiness among military spouses and should be used to inform future programs designed to improve population health.

Perceived school bullying and psychotic-like experiences in sexual minority adolescents: the mediating and moderating roles of resilience

AimsThis two-wave, longitudinal study aimed to examine the potential moderating and mediating effects of resilience on the association between perceived school bullying and psychotic-like experiences among Chinese sexual minority adolescents.MethodsA total of 4192 senior high students were included and 984 (23.5%) of them were identified as a sexual minority (mean age = 16.68 years, SD = 0.71). Participants completed two online surveys during April 21 to May 12, 2021 and December 17 to 26, 2021, respectively, as well as completed self-report measures of sample characteristics, perceived school bullying, resilience, and psychotic-like experiences (including two dimensions: delusional experiences and hallucinatory experiences).ResultsPerceived school bullying and resilience were associated with psychotic-like experiences in sexual minority adolescents. Resilience mediated the relationship between perceived school bullying and subsequent psychotic-like experiences (b = 0.03, 95% CI = 0.01 ~ 0.04)/ delusional experiences (b = 0.03, 95% CI = 0.01 ~ 0.04)/ hallucinatory experiences (b = 0.02, 95% CI = 0.01 ~ 0.03). Additionally, resilience only moderated the associations of perceived school bullying with hallucinatory experiences (b = −0.06, 95% CI = −0.12 ~ −0.01).ConclusionsThese findings indicated that resilience plays a crucial role in mediating or moderating the relationship between perceived school bullying and psychotic-like experiences. Assessing and reducing school bullying, as well as promoting resilience, may have important clinical implications for reducing the risk of psychotic-like experiences in sexual minority adolescents.

PIGMENTS AS A MASS MATERIAL: DISCUSSION OF THE METHODOLOGICAL APPROACH IN RESEARCH

PIGMENTS AS A MASS MATERIAL: DISCUSSION OF THE METHODOLOGICAL APPROACH IN RESEARCH

Study on Job Demands, Work Engagement and Growth Mindset among Young Adults

This study examines the interplay among job demands, work engagement, and growth mindset among young adults in the Indian workforce, based on theories like the Job Demands-Resources (JD-R) and mindset theory. Conducted in the Delhi NCR region with 304 participants using purposive sampling, data was collected using Dweck's 3-Item Growth Mindset Scale and the Utrecht Work Engagement Scale. Statistical analyses, including correlation, multiple regression, and moderation regression, were employed. Results indicate significant correlations among job demands, work engagement, and growth mindset, with growth mindset moderating the relationship between job demands and work engagement. These findings offer insights for enhancing employee well-being and productivity, despite limitations such as sample characteristics and self-reported measures.

Obtaining Consent for Research on Risky Behaviours Among Adolescents in Canada: A Scoping Review.

This scoping review explores current practices for obtaining consent in research on risky behaviours among adolescents in Canada. The JBI methodology for scoping reviews was used. The database search was conducted in August 2021 and updated in November 2022. Papers published in 2010 or later were included. Extracted data included study characteristics, sample characteristics, and consent procedures. The review included 83 reports covering 57 studies. Nearly 60% of studies relied on adolescent self-consent for participation. Adolescent self-consent was more common than parental/guardian consent for studies using in-person research methods, older adolescent groups, and particularly vulnerable populations. Parental/guardian consent was more common for studies using younger age groups and general population samples. Adolescent self-consent was more common than parental/guardian consent for most risky behaviours covered by this review. These results provide insight into current consent practices in this area and offer guidance to researchers and institutional review boards in Canada.

Tests Assessing Corrupt Behavior From a Psychological Perspective

Abstract: Corruption is a psychosocial problem that has important implications for different areas of society. In psychology, this problem is usually studied through corrupt behavior. The aim of this study is thus to analyze the tests used to evaluate corrupt behavior from a psychological perspective. A systematic review was conducted following the standards of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 statement of relevant studies published from 2000 to 2023 in Scopus, ScienceDirect and the Web of Science. We have therefore included empirical studies published in Spanish and English, synthesized their information using thematic analysis, and presented these results in a summary table. Specifically, we reviewed 696 articles and 63 relevant full-text articles, and 24 of these studies met the inclusion criteria. Accordingly, we have found that corrupt behavior has been evaluated through three types of tests: surveys, corruption scenarios, and bribery games. The main properties of these tests also vary according to the adopted theoretical framework, domain evaluated, rigor of evaluation, and sample characteristics. These findings can be used to determine the choice of a particular instrument or in the construction of another if the existing instruments are deemed inappropriate.

Mechanical and Microstructural Properties of Mortars: Obsidian Powder Effect

Concrete has been the world's most produced and utilized building material for years due to its economic and easy accessibility. However, it attracts attention due to the CO2 emitted from cement, the raw material of concrete, during the production and consumption stages. Although there are different research studies to reduce this emission, one of the most logical solutions is to use pozzolanic materials with cement and reduce the need for cement. This paper investigated the general material characteristics of mortar samples generated by substituting obsidian pow- der with pozzolanic properties into cement at different ratios by weight. Mortar specimens with varying proportions of obsidian, such as 0%, 10%, 20%, and 30% by weight, were subjected to mechanical tests at 3, 7, 14, and 28 days. Material tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to characterize the material. As a result of the me- chanical tests, 42.52 MPa compressive strength was obtained from the 28-day reference sample, while 44.331 MPa compressive strength was obtained from the mortar sample with 30% obsid- ian substitution. The outcomes of this paper noted that obsidian powder, which has pozzolan- ic properties, increased the mechanical strength of cementitious mortar specimens. This work indicates the suitability of using obsidian as pozzolan material with cement was determined.

DeFRCN-MAM: DeFRCN and multi-scale attention mechanism-based industrial defect detection method

ABSTRACT With the technology development, industrial defect detection based on deep learning has attracted extensive attention in the academic community. Different from general visual objects, industrial defects have the characteristics of small sample, weak visibility and irregular shape, which hinder the application of related studies. According to these problems, a few-shot object detection (FSOD) method based on Decoupled Faster R-CNN (DeFRCN) is proposed in this paper. Firstly, it includes fine-tuning processing, because of the small sample characteristics. To adapt to the invisible characteristics of defects, we introduce the Feature Pyramid Network (FPN) and Residual Attention Module (RAM) into DeFRCN, which can enhance the capture ability of multi-scale features and feature association information. Furthermore, the feature representation ability is strengthened by parallel connecting of two channels, consisting of R-CNN head, box classifier and box regression models. Finally, it is completed that the pre-training, fine-tuning and testing of the proposed network, with DAGM 2007 and NEU-DET public industrial defect datasets as the base class and flange shaft defect data collected in the laboratory as the new class. To verify the effectiveness of the proposed one, we compare them with other classical FSOD methods. The superiority of the proposed method is obvious.

Analysis of the Sedimentary and Geochemical Characteristics of Sedimentary Clastic Rock Samples from the Alpha–Mendeleev Rise

Analysis of the Sedimentary and Geochemical Characteristics of Sedimentary Clastic Rock Samples from the Alpha–Mendeleev Rise

Magnesium-doped zinc oxide film for hydrogen production from wastewater

Recently, developing resources for H2 production has been a supreme priority to maintain the current rate of global warming in waste and drinking water. Metal-doped zinc oixde (ZnO) film-based catalyst materials are better candidates for electrochemical water splitting. Herein, it will be demonstrated that Mg-doped ZnO films can significantly enhance the electrochemical water splitting and H2 production. Therefore, Mg-doped ZnO films were synthesized using the dip coating technique. The undoped and Mg-doped ZnO films' structure, chemical, and morphological properties were characterized using detalied measurements. The electrical and optical characteristics of ZnO and Mg–ZnO samples were investigated. ZnO and Mg–ZnO films coated on the proper reactor were utilized for electrochemical water splitting. The efficiency of the electrochemical water splitting increases from 29 % to 56 % as ZnO is doped with Mg. In addition, the yielding H2 concentration increases from 9.92 × 10−7 mol/L.h to 3.65 × 10−6 mol/L.h when ZnO is doped with Mg. Hence, the work sheds new insight into new ways of fabricating significant photocatalysts for low-cost hydrogen extraction and echo-friendly agents.

Characteristics of Microorganisms and Antibiotic Resistance Genes of the Riparian Soil in the Lanzhou Section of the Yellow River

Riparian soil is a critical area of watersheds. The characteristics of biological contaminants in riparian soil affect the pollution control of the watershed water environment. Thus, the microbial community structure, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) in the riparian soil of the Lanzhou section of the Yellow River were investigated by analyzing the characteristics of soil samples collected from farmland, mountains, and industrial land. The results showed that the Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant phyla in the riparian soil of Lanzhou section of the Yellow River. The microbial structure in the riparian soil was significantly correlated with the land use type (P < 0.05). The α diversity index of bacterial communities in land types was in the order of farmland > mountain > industry. Sulfonamide-typed ARGs were the most dominant genes in the soil of the Lanzhou section of the Yellow River Basin, among which the sul1 gene had the highest abundance, 20-36 000 times that of other detected ARGs. Moreover, the total absolute abundance of ARGs in industrial soil was the highest. Principal coordinate analysis (PCoA) displayed that the ARGs characteristics had a significant correlation with land types (P < 0.05), and intl1 and tnpA-04 drove the diffuseness of sulfonamide and tetracycline ARGs, respectively. Redundancy analysis (RDA) demonstrated that the content of inorganic salt ions and total phosphorus in the soil of the riparian zone of the Yellow River Lanzhou section were the main environmental factors, modifying the distribution of the microbial structure. Halobacterota and Acidobacteriota were the main microflora that drove the structural change in ARGs.

Polyurethane reinforced with micro/nano waste slag as a shielding panel for photons (experimental and theoretical study)

This study not only provides an innovative technique for producing rigid polyurethane foam (RPUF) composites, but it also offers a way to reuse metallurgical solid waste. Rigid polyurethane (RPUF) composite samples have been prepared with different proportions of iron slag as additives, with a range of 0–25% mass by weight. The process of grinding iron slag microparticles into iron slag nanoparticles powder was accomplished with the use of a high-energy ball mill. The synthesized samples have been characterized using Fourier Transform Infrared Spectroscopy, and Scanning Electron Microscope. Then, their radiation shielding properties were measured by using A hyper-pure germanium detector using point sources 241Am, 133 BA, 152 EU, 137Cs, and 60Co, with an energy range of 0.059–1.408 MeV. Then using Fluka simulation code to validate the results in the energy range of photon energies of 0.0001–100 MeV. The linear attenuation coefficient, mass attenuation coefficient, mean free path, half-value layer and tenth-value layer, were calculated to determine the radiation shielding characteristics of the composite samples. The calculated values are in good agreement with the calculated values. The results of this study showed that the gamma-ray and neutron attenuation parameters of the studied polyurethane composite samples have improved. Moreover, the effect of iron slag not only increases the gamma-ray attenuation shielding properties but also enhances compressive strength and the thermal stability. Which encourages us to use polyurethane iron-slag composite foam in sandwich panel manufacturing as walls to provide protection from radiation and also heat insulation.

Research on online anomaly detection methods for bearing degradation

In industrial applications, rolling bearings operate under conditions of high precision and high speed, and their physical and mechanical characteristics change with the increase in operating time. Traditional diagnostic methods struggle to adapt well to the changing characteristics of bearings for online anomaly detection. Therefore, this research proposes an online anomaly detection method for rolling bearings based on time-density-weighted incremental support vector data description (TISVDD). A classification strategy is proposed to prevent samples misclassification in the updating process. The Detection Boundary is established based on SVDD decision boundary to enhance the recognition of abnormal samples in the process of model updating. A dual-screening mechanism update strategy for support vectors is proposed. It involves establishing a preliminary screening mechanism based on the Elimination Boundary. On this basis, an in-depth screening mechanism based on time density weight is introduced by considering spatiotemporal characteristics of samples, enhancing the real-time performance of online anomaly detection for bearings. Building upon the fused dual-boundary SVDD, a TISVDD framework for online anomaly detection is proposed, enabling the detection model to dynamically update in response to data changes over time. To validate the effectiveness of the proposed method, experiments were conducted using the XJTU-SY bearing dataset and real-time datasets collected on an online hardware platform. The results demonstrate the effectiveness and superiority of the method in practical applications.

Environmental phenol exposures in 6- to 12-week-old infants: The Infant Feeding and Early Development (IFED) study

BackgroundExposure to phenols, endocrine-disrupting chemicals used in personal care and consumer products, is widespread. Data on infant exposures are limited despite heightened sensitivity to endocrine disruption during this developmental period. We aimed to describe distributions and predictors of urinary phenol concentrations among U.S. infants ages 6–12 weeks. MethodsThe Infant Feeding and Early Development (IFED) study is a prospective cohort study of healthy term infants enrolled during 2010–2013 in the Philadelphia region. We measured concentrations of seven phenols in 352 urine samples collected during the 6- or 8- and/or 12-week study visits from 199 infants. We used linear mixed models to estimate associations of maternal, sociodemographic, infant, and sample characteristics with natural-log transformed, creatinine-standardized phenol concentrations and present results as mean percent change from the reference level. ResultsMedian concentrations (μg/L) were 311 for methylparaben, 10.3 for propylparaben, 3.6 for benzophenone-3, 2.1 for triclosan, 1.0 for 2,5-dichlorophenol, 0.7 for BPA, and 0.3 for 2,4-dichlorophenol. Geometric mean methylparaben concentrations were approximately 10 times higher than published estimates for U.S. children ages 3–5 and 6–11 years, while propylparaben concentrations were 3–4 times higher. Infants of Black mothers had higher concentrations of BPA (83%), methylparaben (121%), propylparaben (218%), and 2,5-dichorophenol (287%) and lower concentrations of benzophenone-3 (−77%) and triclosan (−53%) than infants of White mothers. Triclosan concentrations were higher in breastfed infants (176%) and lower in infants whose mothers had a high school education or less (−62%). Phenol concentrations were generally higher in summer samples. ConclusionsWidespread exposure to select environmental phenols among this cohort of healthy U.S. infants, including much higher paraben concentrations compared to those reported for U.S. children, supports the importance of expanding population-based biomonitoring programs to infants and toddlers. Future investigation of exposure sources is warranted to identify opportunities to minimize exposures during these sensitive periods of development.

Assessing reliability of naïve respondent-driven sampling samples by using repeated surveys among people who inject drugs (PWID) in New Jersey

IntroductionRespondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings. MethodsWe assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance – People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses. ResultsThe study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples. ConclusionsIn conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

Computational study of the performance of a solar dryer for improvement in the shelf life of the food materials.

In this study, the thermal and drying characteristics of a thin layer food sample were investigated. An indirect type, simple, efficient, and economically feasible solar dryer was fabricated and used for food preservation. However, a dynamic model of a fabricated solar dryer was also presented to gain a better insight into the drying and thermal actions. This model consists of thermal modeling of the drying chamber, solar collector, and solar-dried food sample. The law of conservation of energy was applied to evaluate the temperature at different sections of the solar dryer with respect to drying time. All listed model equations were solved in the MATLAB environment. This study helps to examine the influence of solar radiation on the collector plate temperature, drying chamber temperature, food sample temperature, and performance parameters such as thermal efficiency with respect to drying time. Model data was found in good agreement with experimental data within a 4% error. It is concluded that the drying of food material is affected by air temperature, the collector temperature, mode of heat transfer, and material characteristics such as dimension and mass of the food sample.

A meta-analysis of coping strategies and psychological distress in rheumatoid arthritis.

Theory and research indicate that coping plays a central role in the experience of psychological distress in people with rheumatoid arthritis (RA). This study meta-analysed the associations of adaptive and maladaptive coping strategies with psychological distress in people with RA to quantify and better understand the proposed differential relationships, as well as the factors that might influence these links. Searches of four databases identified eligible studies according to a pre-registered protocol. Two random effects meta-analyses examined the direction and magnitude of the links between adaptive coping (problem-focused and emotional approach coping) and maladaptive coping (emotional avoidance and pre-occupation coping) and psychological distress (stress, anxiety, and depression). Study quality was evaluated using a bespoke tool. Moderator analyses for sample characteristics and distress type were conducted. Searches identified 16 eligible studies with 46 effects. Meta-analysis of maladaptive coping and distress yielded a significant, medium sized association, k = 12, r = .347, 95% CIs [.23, .46]. Moderator analyses were significant only for type of distress, with effects for depression being larger than that for combined distress. Effects did not vary as a function of age, participant sex, or disease duration. Meta-analysis for adaptive coping was not significant, k = 10, r = -.155, 95% CIs [-.31, .01]. Findings from this first meta-analysis of coping and distress in RA indicate that maladaptive but not adaptive coping is associated with greater distress. Further research is needed to grow the evidence base to verify the current findings especially with respect to adaptive coping.

Analysis of nutrient loads, heavy metals and physicochemical properties of wastewater, wetland grass, and papaya samples: Gondar Malt factory, Ethiopia with global implication

Robust attention was brought to researchers due to deterioration of wastewater quality of lakes and reservoirs as major global concerns by industrial release. The uncontrolled releases of effluents impose serious impacts for both aquatic and terrestrial environments. In the current study, many parameters like nutrient loads, heavy metals and physicochemical properties of wastewater, wetland grass, and papaya samples were analysed. The investigated nutrients, alkalinity, and total hardness in fresh water samples were within the allowable limits except for phosphate in fresh wastewater and alkalinity in wastewater. The detected levels of heavy metals (mg/L) in wastewater samples were:- Cd (0.386–0.905), Cr (ND-0.074), Cu (0.064–0.096), Mn (0.184–1.528), Fe (0.167–4.636), Zn (0.175–0.333), and Pb (0.044–0.892) (mg/L). The studied metals in the wastewater sample, except Cd, Fe, and Pb were lower than the allowable limit. The level of heavy metals in the grass and papaya samples ranged from Cd (37.14–147.62), Cr (ND-8.82), Cu (3.14–8.33), Mn (2.89–85.46), Fe(5.0–65.15), Zn (3.44–36.84), and Pb (ND-60.36) (mg/kg). The detected metals were below the permissible limits, except Cd, Cr, and Pb. The findings of the physicochemical characteristics in wastewater samples were computed: pH (6.61–8.54), temperatures (21.63–26.57 °C), TDS (205.9–1896 mg/L), EC (359.9–3226.67 μs/cm), BOD (12.0–732.67 mg/L), COD (3.67–1691.33 mg/L). Except for temperature and pH, all levels in the wastewater were above the recommended limit for wastewater discharge by USEPA.

A domain knowledge-informed design space exploration methodology for mechanical layout design

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective.