Health Indicators Research Articles

Lot quality assurance sampling survey for water, sanitation and hygiene monitoring and evidence-based advocacy in Bentiu IDP camp, South Sudan.

Every year, 60% of deaths from diarrhoeal disease occur in low and middle-income countries due to inadequate water, sanitation, and hygiene. In these countries, diarrhoeal diseases are the second leading cause of death in children under five, excluding neonatal deaths. The approximately 100,000 people residing in the Bentiu Internally Displaced Population (IDP) camp in South Sudan have previously experienced water, sanitation, and hygiene outbreaks, including an ongoing Hepatitis E outbreak in 2021. This study aimed to assess the gaps in Water, Sanitation, and Hygiene (WASH), prioritise areas for intervention, and advocate for the improvement of WASH services based on the findings. A cross-sectional lot quality assurance sampling (LQAS) survey was conducted in ninety-five households to collect data on water, sanitation, and hygiene (WASH) coverage performance across five sectors. Nineteen households were allocated to each sector, referred to as supervision areas in LQAS surveys. Probability proportional to size sampling was used to determine the number of households to sample in each sector block selected using a geographic positioning system. One adult respondent, familiar with the household, was chosen to answer WASH-related questions, and one child under the age of five was selected through a lottery method to assess the prevalence of WASH-related disease morbidities in the previous two weeks. The data were collected using the KoBoCollect mobile application. Data analysis was conducted using R statistical software and a generic LQAS Excel analyser. Crude values, weighted averages, and 95% confidence intervals were calculated for each indicator. Target coverage benchmarks set by program managers and WASH guidelines were used to classify the performance of each indicator. The LQAS survey revealed that five out of 13 clean water supply indicators, eight out of 10 hygiene and sanitation indicators, and two out of four health indicators did not meet the target coverage. Regarding the clean water supply indicators, 68.9% (95% CI 60.8%-77.1%) of households reported having water available six days a week, while 37% (95% CI 27%-46%) had water containers in adequate condition. For the hygiene and sanitation indicators, 17.9% (95% CI 10.9%-24.8%) of households had handwashing points in their living area, 66.8% (95% CI 49%-84.6%) had their own jug for cleansing after defaecation, and 26.4% (95% CI 17.4%-35.3%) of households had one piece of soap. More than 40% of households wash dead bodies at funerals and wash their hands in a shared bowl. Households with sanitary facilities at an acceptable level were 22.8% (95% CI 15.6%-30.1%), while 13.2% (95% CI 6.6%-19.9%) of households had functioning handwashing points at the latrines. Over the previous two weeks, 57.9% (95% CI 49.6-69.7%) of households reported no diarrhoea, and 71.3% (95% CI 62.1%-80.6%) reported no eye infections among children under five. The camp's hygiene and sanitation situation necessitated immediate intervention to halt the hepatitis E outbreak and prevent further WASH-related outbreaks and health issues. The LQAS findings were employed to advocate for interventions addressing the WASH gaps, resulting in WASH and health actors stepping in.

Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary

This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R2 value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results.

Remaining Useful Life Prediction Method Based on Stacked Autoencoder and Generalized Wiener Process for Degrading Bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

The use of multilevel emotion regulation strategies in the context of critical public events: the more the better?

Critical public events, like COVID-19, significantly impact individuals’ emotional and mental health. People tend to use multi-level emotion regulation strategies (intrapersonal, interpersonal and hyper-personal) to cope with these events, resulting in various strategy profiles. However, few studies have examined ER strategies from a multilevel perspective. Therefore, this study examines the use of multi-level strategies during COVID-19, and evaluates the effectiveness of these strategies, with a particular interest in identifying strategy profiles promoting mental health. We conducted a two-wave study (an interval of 1 week) using online questionnaires during COVID-19, with an initial sample of 1,189 participants and 895 samples completing the surveys across the two waves. Cross-lagged analysis indicated that experiential avoidance was reciprocally positively related to negative emotions while perspective-taking and humorous-meme-saving were reciprocally positively related to life satisfaction or positive emotions over time. Cluster analysis suggested that there were 9 different profiles which scored differently on mental health indicators. Specifically, the use of multi-level strategies tended to be associated with greater positive emotions and life satisfaction while with lower negative emotions and loneliness. This study revealed that the use of multi-level strategies plays a protective role in mental health when facing critical public events. These findings expanded our understanding of how multilevel emotion regulation strategies impact mental health during critical public events and identify protective profiles for mental health.

Association of Dry Period Length with Automatic Milking System, Mastitis, and Reproductive Indicators in Cows

The objective of this study was to evaluate the association between dry period (DP) length and various indicators of productivity, reproduction, and udder health in cows managed with an automatic milking system. We analyzed records from 3861 cows, categorizing them into three groups based on their DP duration: (1) <40 days, (2) 40–70 days, and (3) DP > 70 days. Cows with a DP of 40–70 days had an average energy-corrected milk production that was 8.2 kg greater than that of cows with a short DP and 5.0 kg greater than that of cows with a long DP (p < 0.001). Milk from the 40–70-day DP group exhibited the highest lactose concentration (4.64 ± 0.01%). Additionally, cows with the longest DP had the smallest proportion of animals with a milk fat-to-protein ratio of 1.2 to 1.4. Cows with a DP of 40–70 days also showed the lowest milk electrical conductivity across all udder quarters, whereas cows with the shortest DP had the highest conductivity. The highest conception rates were observed in the group with the shortest DP. These results suggest that a DP of 40–70 days is optimal for maximizing milk production and improving both udder health and reproductive performance under AMS. Proper management of DP duration can be an effective strategy for sustainable dairy herd management.

The association between family doctor contract services and the health of middle-aged and older people in China: an instrumental variables analysis

Previous research on the association between Family Doctor Contract Services (FDCS) and health has only considered a single indicator of health and has not considered the endogeneity of independent variables. This study aimed to evaluate the association from a multidimensional perspective of the health of middle-aged and older people using the instrumental variables method and determine the underlying mechanisms. Using data from the 2018 China Health and Retirement Longitudinal Study surveys, a total of 19,438 sample was obtained. Health was measured by health related-quality of life (HR-QoL), subjective well-being, and cognitive function. The instrumental variables method was used to estimate the association. Mediation analysis was employed to analyze the underlying mechanisms. The results of the instrumental variables method showed a correlation between FDCS and health, such as HR-QoL (η = 33.714, p < 0.01), subjective well-being (η = 1.106, p < 0.05), and cognitive function (η = 4.133, p < 0.05). However, we found no evidence that FDCS improved physical health. We also identified reduced utilization of healthcare services and increased social activities as mediators of the effect of FDCS on health. The Chinese government should improve incentive-based initiatives to improve the quality of FDCS. Moreover, more attention needs to be paid to the multidimensional health of middle-aged and older people, especially vulnerable groups, such as older individuals and those in rural areas.

Ecosystem health assessment of East Kolkata Wetlands, India: Implications for environmental sustainability

The East Kolkata Wetlands (EKW) in Kolkata, India, span 12,500 ha and are a vital ecological zone providing several benefits, including water purification, flood control, and biodiversity support. This study investigated land use and land cover (LULC) alterations in the EKW from 1991 to 2023, using a random forest (RF) machine learning model. Significant LULC changes were observed over the 32 years, with wetland areas decreasing from 91.2 km2 in 1991 to 33.4 km2 in 2023, reflecting substantial habitat loss and reduced ecosystem services. Conversely, agricultural land expanded from 27.8 km2 to 58.7 km2, driven by economic and food production needs, and built-up areas increased dramatically from 0.2 km2 to 10.5 km2, indicating rapid urbanization. This study evaluated the health, resilience, and ecosystem functionality of EKW by analysing human-induced land use changes and using ecological indicators and landscape metrics. Landscape and class level metrics such as PLAND, largest patch index (LPI), total edge (TE), edge density (ED), number of patches (NP), and patch density (PD) were used to analyse the spatiotemporal dynamics of the wetlands. This study revealed a significant increase in fragmentation, with the number of patches increasing from 2689 in 1991 to 4532 in 2023, despite a consistent decrease in core wetland areas. Ecosystem health indicators, such as the ecosystem structure index (ESI) and landscape deviation degree (LDD), were used to assess landscape metrics and fragmentation changes. The ESI and other metrics revealed significant temporal fluctuations, providing insights into landscape structure, connectivity, and heterogeneity. The ESI improved from 0.87 in 1991 to 1.03 in 2023, indicating enhanced connectivity and diversity. Conversely, the LDD increased from 20.6% to 56.85%, indicating a shift towards impervious surfaces. The vegetation productivity and ecosystem health index (EHI) decreased, indicating biodiversity loss and reduced carbon sequestration. The EHI also dropped from 0.67 to 0.55, signifying ongoing environmental stress. This study emphasizes the need for conservation efforts to maintain the ecological integrity of the EKW amidst urbanization and land use changes and recommends a balanced approach for sustainable urban development and enhanced wetland resilience.

Analysis of the Ghanaian Health System and Patients Safety within the IHI Triple Aim Framework

Objective: To analyze Ghana’s healthcare system challenges through the Institute for Healthcare Improvement’s Triple Aim framework encompassing population health, patient experience and per capita cost metrics. Method: Dimensional analysis of peer-reviewed papers, government data and case studies assessed population health indicators and equity, literature on patient perspectives regarding quality and responsiveness of care along with evaluation of cost trends and deficiencies driving expenditure growth. Results: Gaps persist in universal health coverage amid rural-urban disparities in access and financial protection. Care experiences vary significantly by socioeconomic status and geography. Rising costs attributed to inefficiencies in areas like procurement and prescriber practices. Scientific Novelty: Unique application of Triple Aim framework for structured health systems analysis in a lower-middle income sub-Saharan country enabling robust diagnosis of coverage, quality, sustainability gaps. Practical Significance: Demonstrates utility of balanced Triple Aim methodology for health policymakers in low-resource settings to systematically evaluate healthcare priorities across access, outcomes, experiences and costs.

Prevalence and Correlates of Psychiatric Symptoms Among Medical Students in Punjab: A Comprehensive Cross-Sectional Study

Background: Pain is evident, pressure, hopelessness and anxiety-is equally well-known to students with each passing academic semester in medical school. To understand the frequency and reasons of mental symptoms in MBBS students, the current study aims at investigating the level of MBBS students at several medical colleges in Punjab, Pakistan. Objective: To compare the results of stress, depressive, and anxious symptoms of several numbers of medical students studying in different medical colleges in Punjab and secondly, to evaluate the role of several demographics on these mental health indicators. Methods: The cross-sectional survey was completed between September 2023 to May 2024 with 350 medical students from multiple private and government medical colleges in Punjab. A cross-sectional survey was carried out including the Depression Anxiety Stress Scale-21 (DASS-21) as the research tool and the data aspires to be statistically analyzed. Results: the results indicated that the percentage of the medical students who reported to be stressed, sad, or anxious was 47.3%, 55.7%, and 61.4%, respectively. Female students also claimed higher levels of depression compared to male students 58.3% vs. 50. 1%, (p ≤0.01) and higher levels of anxiety 65.2% vs. 55.1%, (p ≤0.03). Students in rural locations reported greater levels of stress (52.6% vs. 43.2%, p ≤0.04) than urban ones. Conclusion: The study linked high levels of mental symptoms among medical students in Punjab to characteristics such as being a female student or originating from a rural area. The current study emphasizes the necessity of targeted programs and activities in the areas of mental health promotion and preventive measures among medical college students.

Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division

Remaining life prediction (RUL) is a critical link of maintenance decision-making, the accurate RUL prediction is an important means to monitor the operating status and achieve the safe operation of equipment. However, existing studies rarely considered the multi-stage characteristics of indicator fusion in the degradation process, and directly used the Wiener process to establish degradation model, which results in significant errors in RUL prediction results. Therefore, to solve above issues, a two-stage RUL prediction method of bearing based on the Wiener process model with data fusion and stage division is proposed in the paper. Firstly, the concept of multi-feature fusion is introduced to construct a comprehensive health indicator (CHI) that considers indicator performance. After that, a two-stage RUL prediction model based on the CHI is developed, and a method for detecting change points and dividing stages is proposed. Finally, the effectiveness and predictability of the proposed method and CHI are demonstrated based on the bearing test datasets.

Integrating key ecosystem services to study the spatio-temporal dynamics and determinants of ecosystem health in Wuhan’s central urban area

The central areas of many Chinese cities have experienced large-scale urbanization in recent decades, a trend that is affecting the quality of life of residents and posing challenges to the sustainable development of urban ecosystems. Hence, it is vital to conduct assessments of urban ecosystems’ health conditions to ensure their sustainability. In this study, the spatial and temporal dynamics of ecosystem health in the central city of Wuhan were analyzed using the Vigor-Organization-Resilience-Service (VORS) model. The sensitivity of ecosystem health at different scales was also investigated using hierarchical health assessment delineation and random forest regression methods. The results showed that the composite index of ecosystem health has been declining since 2000, highlighting the negative impacts of shrinking ecological space, declining service capacity and urban expansion. Health zoning divides areas into five categories. Meanwhile, the health zones are located in areas of dense shrubs, cropland and water bodies on the edge of the central city, while the weak zones are mainly in the core of the city, which is 99.33 % impervious surface. The main factors influencing ecosystem health, including impervious surface area, water bodies and topography, vary from region to region. The study proposes targeted ecological management strategies for different health zones in Wuhan, emphasizing ecosystem protection and providing guidance for sustainable urban development.

The Influence of Various Types of Physical Activity on Blood Pressure. A Literature Review

Blood pressure is a key indicator of cardiovascular health, playing a significant role in the diagnosis, prevention, and treatment of heart and vascular diseases. In recent years, interest in the influence of physical activity on blood pressure has increased among researchers and health-conscious individuals. Among the studies presented, researchers examined the effects of various types of physical activity on blood pressure in individuals with hypertension and the level of hypertension control, as well as other components affecting the overall health of patients, such as blood lipid levels, arterial pressure variability over 24 hours, and BMI. The conclusions of the presented studies seemed to be consistent regarding the positive impact of engaging in physical activity on blood pressure and thus the better well-being of patients.

Enhancing kernel oil and tailoring fatty acid composition by genomics-assisted selection for dgat1-2 and fatb genes in multi-nutrient-rich maize: new avenue for food, feed and bioenergy.

Enhancing maize kernel oil is vital for improving the bioavailability of fat-soluble vitamins. Here, we combined favourable alleles of dgat1-2 and fatb into parental lines of four multi-nutrient-rich maize hybrids (APTQH1, APTQH4, APTQH5 and APTQH7) using marker-assisted selection (MAS). Parental lines possessed favourable alleles of crtRB1, lcyE, vte4 and opaque2 genes. Gene-specific markers enabled successful foreground selection in BC1F1, BC2F1 and BC2F2, while background selection using genome-wide microsatellite markers (127-132) achieved 93% recurrent parent genome recovery. Resulting inbreds exhibited significantly higher oil (6.93%) and oleic acid (OA, 40.49%) and lower palmitic acid (PA, 14.23%) compared to original inbreds with elevated provitamin A (11.77 ppm), vitamin E (16.01 ppm), lysine (0.331%) and tryptophan (0.085%). Oil content significantly increased from 4.80% in original hybrids to 6.73% in reconstituted hybrids, making them high-oil maize hybrids. These hybrids displayed 35.70% increment in oil content and 51.56% increase in OA with 36.32% reduction in PA compared to original hybrids, while maintaining higher provitamin A (two-fold), vitamin E (nine-fold), lysine (two-fold) and tryptophan (two-fold) compared to normal hybrids. Lipid health indices showed improved atherogenicity, thrombogenicity, cholesterolaemic, oxidability, peroxidizability and nutritive values in MAS-derived genotypes over original versions. Besides, the MAS-derived inbreds and hybrids exhibited comparable grain yield and phenotypic characteristics to the original versions. The maize hybrids developed in the study possessed high-yielding ability with high kernel oil and OA, low PA, better fatty acid health and nutritional properties, higher multi-vitamins and balanced amino acids, which hold immense significance to address malnutrition and rising demand for oil sustainably in a fast-track manner.

Determining the relationship between oral health and physical function in elderly people.

Poor oral conditions in the elderly may have numerous effects on general health, including physical fitness and performance. This study aimed to determine the relationship between oral health and physical function in elderly people. Physical function and oral health parameters were compared using parametric comparison tests and Pearson correlation analyses. In addition, principal components analysis, hierarchical clustering and multidimensional scaling analysis clustered the patients' physical and oral health scores. The relationship between the groups was also determined using decision tree analysis. A total of 112 elderly patients participated in the study. Grip strength (GS) was higher in patients with high chewing ability, and Timed Up and Go (TUG) scores were lower in the high oral health group (p < .05). GS was correlated with Decay, Missing, and Filled Teeth Index (DMFT) and the number of remaining and functional teeth (p < .05). According to principal component analysis, it was seen that there were three components (oral, functional and quality of life (QoL) parameters), and the features that were related to each other were gathered together. TUG and GS showed the highest relative importance among physical function criteria in the classification based on chewing ability. They were GS and physical activity for oral health-related QoL. In the elderly, higher physical function parameters, especially GS may be an indicator of a better oral health and oral health-related QoL. Preventive physical rehabilitation practices, in addition to oral treatments, may be effective in improving oral health in the elderly.

Unveiling the Shadows: Childhood Traumas and the Dynamics of Seeking Psychological Help and Self-Stigmatization Among Prisoners.

Compared with the general population, prison inmates show a higher prevalence of mental disorders, particularly among those with childhood traumas. Despite childhood traumas being robust indicators of poor mental health, there is limited research on their relationship with prisoners' attitudes toward seeking psychological help. This study investigates the impact of childhood traumas on prisoners' attitudes toward help-seeking and self-stigmatization. This cross-sectional, explanatory study was conducted with 250 persons in prison. Data were collected using a sociodemographic information form, the Childhood Trauma Questionnaire, the Attitudes Toward Seeking Psychological Help Scale, and the Self-Stigma of Seeking Help Scale. Statistical analysis was performed using Spearman correlation test and multiple linear regression analysis with SPSS Version 28. A significant link was found between childhood traumas and self-stigmatization in seeking psychological help. Positive attitudes toward help-seeking were significantly associated with increased self-stigmatization. Childhood traumas were significantly related to single-parent families, conflicted intrafamilial relationships, entering prison at the age of 19 years or older, mental health issues, and behavioral disorders. Moreover, a meaningful association was observed between self-stigmatization in help-seeking and being from a single-parent family with a low socioeconomic status. In conclusion, imprisoned persons with childhood traumas exhibit higher levels of self-stigmatization in seeking psychological help, contributing to negative attitudes. Recommendations for adequate access to mental health services in prisons include fostering a culture of psychological help, enhancing mental health literacy, providing trauma-informed care, and developing comprehensive strategies. In addition, the suggestion is made for the development of societal reintegration programs.

Thermal imaging analysis of ballast fouling: Investigating the effects of parent rock and fouling materials through IRT passive camera

This study explores the use of infrared thermography (IRT) technology for the non-destructive evaluation of ballast fouling in railway tracks, focusing on the influence of parent rock types and fouling materials. Utilizing thermal imaging, the research investigates how variations in ballast conditions affect surface temperature, which serves as an indicator of structural integrity and health. The experimental setup involved ballast samples derived from three different rock types—basalt, limestone, and andesite—fouled with commonly encountered materials like sand and clay at varying percentages. Results demonstrate that fouling level and type significantly influence the thermal signatures captured by IRT passive camera. Notably, ballast derived from darker rocks exhibited higher temperatures, indicating greater emissivity, while fouled ballast showed distinct temperature patterns compared to clean samples, emphasizing the potential of thermal imaging in detecting and quantifying fouling in ballast layers. This research underscores the viability of IRT passive camera in the routine maintenance and monitoring of railway infrastructure, providing a foundation for further development of integrated diagnostic tools for railway management systems.

Main pulmonary artery diameter related to pneumonia severity.

The diameter (mPAD) of the main pulmonary artery (pulmonary artery trunk) is a crucial indicator for cardiovascular health and prognoses in various conditions. Its enlargement is associated with increased mortality and severity in COVID-19-related pneumonia. However, its relevance to non-COVID pneumonia remains uncertain. The aim of this study was to establish an association between mPAD and the severity of non-COVID pneumonia. Eligible participants with qualified Chest Computed Tomography scans from November 2019 to February 2023 were recruited to a cross-sectional retrospective study. They were stratified into pneumonia and non-pneumonia cohorts. Exclusion criteria included pulmonary hypertension, polytrauma, lung neoplasia, or a history of pulmonary stenosis repair. The mPAD was measured in both groups, and medical records were reviewed to identify comorbidities. Pulmonary CT data were classified by pattern and severity, and the mPAD was measured perpendicularly to the long axis of the artery at the point of bifurcation on an axial slice. Analysis of 380 CT scans (52.6% men, 47.4% women; mean age 52.88 ± 17.58) revealed a significant difference in mPAD between pneumonia and non-pneumonia cases (mean difference: 1.19 mm, 95% CI [0.46, 1.92], p = 0.001). Age correlated positively with mPAD (r = 0.231, 95% CI [0.028, 0.069], p < 0.0001), and this correlation persisted after adjusting for confounders (r = 0.220, 95% CI [0.019, 0.073], p = 0.001). Ordinal logistic regression indicated 1.28 times higher odds of severe pneumonia with a larger diameter. The study highlights associations between mPAD, pneumonia, and severity, suggesting clinical relevance. Furthermore, the mPAD should be carefully considered in defining severity criteria for adverse outcomes in pneumonia patients. Further research is needed to refine clinical criteria on the basis of these findings.

Telehealth in geriatric dentistry: A comparative analysis of concordance between virtual and in-person examinations for hospitalized older patients.

This study aims to assess the concordance between in-person clinical and virtual oral examinations among hospitalized older adults. A single examiner performed an in-person clinical examination, recorded systemic health history and oral health indices, clinically documented the Bedside Oral Exam (BOE), and captured photographs for subsequent analysis during a virtual examination. Following a 90-day washout period, a virtual examination was repeated by the same examiner and by a second examiner. Descriptive analysis and a Kappa test were used to compare proportions and evaluate the agreement between results. Intra-examiners presented high percentage of agreement in all domains of BOE (80%-86%), with an exception for gingiva (78%). Kappa's intra-examiners presented moderate scores in saliva, mucous membrane, gingiva and teeth/dentures domains and a strong score in the tongue domain (0.839). Inter examiners presented moderate agreement in lips and gingiva, saliva, mucous membrane, and teeth/dentures domains. Inter examiners Kappa scores were weak for lips (0.395) and gingiva (0.498) domains; moderate for saliva (0.703), mucous membrane (0.769) and teeth/dentures (0.714) domains and strong for the tongue domain (0.872). In this study, a moderate level of agreement was observed between clinical and virtual oral examinations among older hospitalized patients. These findings are encouraging and warrant further investigation about how teledentistry can be used to enhance oral health access to this vulnerable population.

Changes in macroaggregate stability as a result of wetting/drying cycles of soils with different organic matter and clay contents

The wetting–drying (WD) cycles, caused by natural or anthropogenic processes such as rainfall or irrigation, can affect many soil properties. Among these properties, soil aggregate stability has been introduced as a convenient soil health indicator because of its relation to the soil’s primary particles (sand, silt, and clay) and organic matter content (OM). However, previous studies have shown erratic effects depending on soil type and WD cycle setup when measuring aggregate stability. Therefore, this study aimed to characterize the soil primary particles composition and organic matter (OM) content of macroaggregates and measure the effects of WD cycles on aggregate stability. A series of soils with distinctive properties, such as OM and clay contents from five different USDA textural classes (loam, sandy loam, silty clay loam, silty loam, and clay loam) were used. Particle size distribution, OM, and mass fraction were measured in three aggregate size classes (2–1 mm, 1–0.5 mm, and 0.5–0.25 mm), and isolated aggregates were exposed to 3, 6, and 12 wetting and drying cycles. The main results indicate that soils with a high OM content have macroaggregates with finer particles, and the OM in soils is linearly related to the macroaggregate OM content. For 2–1 mm aggregates, a statistically significant reduction (p < 0.05) of water-stable aggregates compared to the control sample (0 cycles) was observed for every cycle, with reduction values between 4.8–7.3 %. An increase was observed only between 6–12 cycles (1.84 %). Additionally, statistically significant reductions were observed after the first three cycles in 1–0.5 mm aggregates and the first six in 0.5–0.25 mm aggregates. Finally, the macroaggregates were more resistant to the WD cycles when their clay and OM contents increased or the soil pH decreased. This study provides high-resolution results of macroaggregate particle size distribution and OM. It relates them to the effects of WD cycles in water-stable aggregates and soils with different land uses.

Multi-Weighted Symbolic Sequence Entropy: A Novel Approach to Fault Diagnosis and Degradation Monitoring of Rotary Machinery

Abstract Structural health monitoring relies heavily on measurements. Entropy theory is emerging as a critical quantitative analysis technique for interpreting measured data for both health monitoring and fault identification of structural systems. This paper introduces the algorithms of weighted symbolic sequence entropy (WSSE) and its multi-weighted derivation, termed multi-weighted symbolic sequence entropy (MWSSE). WSSE optimizes the existing algorithms, including symbolic sequence entropy (SSE) and improved symbolic sequence entropy (ISSE), by introducing a weighting factor in the computation process of the algorithm. WSSE significantly contributes to enlarging the difference between normal and fault sequences of signals in rotary machinery. The multi-weighted derivation of WSSE, namely MWSSE, more effectively depicts the dynamical characteristics of rotary machinery by utilizing entropy values of multiple weighting factors as health indicators. The MWSSE-based degradation monitoring and fault identification approaches developed by the authors are validated through application research and comparative analysis. These novel entropy algorithms offer innovative quantitative analysis techniques for fault diagnosis in rotary machinery and structural health monitoring and hold potential for application in broader research fields.