Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures

This paper presents a condition monitoring methodology that uses a novel condition indicator (CI) algorithm, allowing for a confident assessment of system health and an advanced warning of failures. The CI is evaluated in the application of a specialized gear rig utilized for the high-cycle fatigue testing of hypoid gears. The CI is shown in this case study to ensure higher confidence in the prediction of failures than other algorithms, with variations in the results. In the comparison, consideration is given to the signal-to-noise ratio, the ability to differentiate between damages and/or damaged components and the sensitivity of the CI to the failure.

Multi-marker metabarcoding resolves subtle variations in freshwater condition: Bioindicators, ecological traits, and trophic interactions

Freshwater systems are experiencing rapid biodiversity losses resulting from high rates of habitat degradation. Ecological condition is typically determined through identifying either macroinvertebrate or diatom bioindicator assemblages and comparing them to their known tolerance to stressors. These comparisons are typically conducted at family or genus levels depending on the availability of taxonomic keys and expertise for focal groups. The objective of this study was to test whether a more taxonomically comprehensive assessment of communities in benthic samples can provide a different perspective of ecological conditions. DNA metabarcoding was used to identify macroinvertebrates and diatoms from kick-net samples collected from sites with different habitat status. Sites with good condition were associated with higher beta diversity as well as slightly higher directed connectance and modularity indicating higher resilience compared with fair condition sites. Indicator value and correlation analyses used DNA metabarcoding data to detect 29 site condition indicator species consistent with known bioindicators and expected relative tolerances. DNA metabarcoding and trophic network analysis also recovered 11 keystone taxa. This study demonstrates the importance of taxonomic breadth across trophic levels for generating biotic data to study ecosystem status, with the potential to scale-up ecological assessments of freshwater condition, trophic stability, and resilience.

Effect of Sleep Quality on Mental Health during COVID- 19 Lockdown

The study was done with the aim of understanding the “Effect of Sleep Quality on Mental Health of Adults during COVID-19 Lockdown”. 136 adults aged between 18 to 32 years were selected through snowball sampling from social media platforms for being a part of the study sample. The Sleep Condition Indicator (Espie, 2014) and General Health Questionnaire (GHQ-12) (Goldberg and Williams, 1988) were used to measure their sleep quality and mental health. It was hypothesized that the sleep quality will have a significant effect on the mental health of adults. It was found out that sleep quality is a significant predictor of mental health. The researcher stated that clinicians and psychologists should investigate about the sleeping patterns and quality of sleep in patients reporting mental health issues, and screen sleeping disorders regularly in population to avoid development of mental health disorders.

Diagnostic Validity of the Sleep Condition Indicator to Screen for Diagnostic and Statistical Manual-5 Insomnia Disorder in Patients with Parkinson’s Disease

<b><i>Background:</i></b> Insomnia is a highly common sleep disorder in patients with Parkinson’s disease (PD). Yet, no screening questionnaires following the Diagnostic and Statistical Manual-5 (DSM-5) criteria have been validated in PD patients. <b><i>Objectives:</i></b> We assessed the validity and reliability of the French version of the sleep condition indicator (SCI), in patients with PD. <b><i>Methods:</i></b> In a sample of 65 patients (46% women, mean age 63.8 ± 7.9 years) with PD, but without dementia, insomnia was assessed with a clinical interview and the SCI. Statistical analyses were performed to determine the reliability, construct validity, and divergent validity of the SCI. In addition, an explanatory factor analysis was performed to assess the underlying structure of the SCI. <b><i>Results:</i></b> Of the 65 patients (mean duration PD 9.7 ± 6.9 years), 51% met the criteria for insomnia disorder when measured with a clinical interview. The mean SCI score was 18.05 ± 8.3. The internal consistency (α = 0.89) of the SCI was high. Using the previously defined cutoff value of ≤16, the area under the receiver operating characteristic curve was 0.86 with a sensitivity of 86% and a specificity of 87%. Exploratory factor analysis showed a 2-factor structure with a focus on sleep and daytime effects. Additionally, good construct and divergent validity were demonstrated. <b><i>Conclusion:</i></b> The SCI can be used as a valid and reliable screener for DSM-5 insomnia disorder in PD patients. Due to its short length, it is useful in both clinical practice and scientific research.

Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation

The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected.

PROFIL KOMPONEN FISIK PREDOMINAN DARI KONDISI FISIK ATLET GULAT JAWA BARAT PADA PON XVIII RIAU 2012

During the National Sports Week XVIII held in September 2012, wrestling athletes from West Java Province could not meet the expected performance; since they got only one gold and two bronze medals. The athlete’s performance usually affected by three factors; physical condition, technique, and mental condition. To achieve the best result, the gold medal, athletes should have good predominant physical condition indicators, such as muscle strength, endurance, power, and flexibility. The purpose of this study is to know the physical condition of West Java’s wrestling athletes in the National Sports Week XVIII. The method used was a cross-sectional descriptive study using secondary data obtained from the Sports Medicine Department of the National Sports Committee of Indonesia in Bandung. The data are chosen from the predominant physical condition indicator explained before, along with cardio-respiratory endurance. After that, we compared the data we obtained with the National Sports Committee of Indonesia’s standards and categorized it specific type; less, enough, good, very good, and perfect. Male and female athletes had different category systems. After analysis, we found that several predominant physical components of wrestling athletes of West Java Province in National Sports Week XVIII had not met the requirement for the good and perfect category andneeded to be improved. In conclusion, athletes should be able to get a gold medal if their predominant physical component achieves the good and perfect category.

Prevalence and associated factors of DSM-5 insomnia disorder in the general population of Qatar

Background Epidemiological studies of insomnia in the Middle East remain scarce. The present study aimed to estimate the prevalence of insomnia and explore its associations in the general population of Qatar. With almost 100 nationalities, Qatar is one of the most culturally diverse, richest, and fastest developing countries in the Arabian Peninsula. Methods A probability sample of community-dwelling adults were surveyed in February of 2019. A total of 1611 respondents completed face-to-face interviews in Arabic or English. Logistic regression modeled associations with insomnia, our dependent variable, as defined by a score of ≤16 on the eight-item Sleep Condition Indicator or according to criteria for insomnia in the Diagnostic & Statistical Manual of Mental Disorders, fifth edition or DSM-5. Results Approximately, 5.5% of the sample screened positive for insomnia and the 30-day prevalence of those who met all the DSM-5 criteria for insomnia disorder was 3.0%. In addition, 2.0% of the sample screened positive for depression and 3.4% for anxiety in the past 2 weeks. Multivariable analysis showed the following were significantly associated with insomnia: Arab ethnicity, young age, unemployment, being married, having less than high school education, fair or poor health, anxiety, and depression. Conclusions Insomnia prevalence was in the lower range of previously reported DSM-defined estimates from developed Western countries. Our findings highlight the need for raising awareness and improving sleep hygiene in potential risk groups such as younger adults and those of Arab ethnicity, in addition to incorporating insomnia screening in the provision of mental health services.

Integrating microbiome, transcriptome and metabolome data to investigate gastric disease pathogenesis: a concise review

Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.