The Quality of Five Natural, Historical Italian Cheeses Produced in Different Months: Gross Composition, Fat-Soluble Vitamins, Fatty Acids, Total Phenols, Antioxidant Capacity, and Health Index

Five natural historic cheeses of Southern Italy were investigated—Caciocavallo Palermitano (CP), Casizolu del Montiferru (CdM), Vastedda della Valle del Belìce (VVB), Pecorino Siciliano (PS), and Caprino Nicastrese (CN)—which are produced with raw milk and with traditional techniques and tools, from autochthonous breeds reared under an extensive system. The effects of the month of production on gross composition, MUFA, PUFA, PUFA-ω6, PUFA-ω3, α-tocopherol, retinol, cholesterol, TPC, TEAC, and GHIC were evaluated. In CP, CLA, TPC, and GHIC were higher in April than in February. CdM showed higher values in terms of fat, saturated fatty acids, PUFA-ω3, α-tocopherol, TEAC, and GHIC in May than in February and September, while low values in terms of protein, moisture, and CLA were found. In VVB, MUFA, PUFA-ω6, and α-tocopherol increased in June compared with April; conversely, protein, FRAP, and TEAC were higher in April. In PS, protein, CLA, PUFA, PUFA-ω3, α-tocopherol, and GHIC increased in May compared with January; on the contrary, moisture, NaCl, and TEAC showed high values in January. CN showed higher values in terms of PUFA, PUFA-ω6, PUFA-ω3, TPC, TEAC, and GHIC in April and June compared with January. It is shown that each cheese is unique and closely linked to the production area. Cheeses produced in the spring months showed a high nutritional quality due to the greatest presence of healthy compounds originating from an extensive feeding system.

Influence of Particle Size on Ecotoxicity of Low-Density Polyethylene Microplastics, with and without Adsorbed Benzo-a-Pyrene, in Clam Scrobicularia plana

This study investigated the ecotoxicological effects of differently sized (4–6 µm and 20–25 µm) low-density polyethylene (LDPE) microplastics (MPs), with and without adsorbed benzo-a-pyrene (BaP), in clam Scrobicularia plana. Biomarkers of oxidative stress (superoxide dismutase—SOD; catalase—CAT), biotransformation (glutathione-S-transferases—GST), oxidative damage (lipid peroxidation—LPO) and neurotoxicity (acetylcholinesterase—AChE) were analysed in gills and digestive glands at different time intervals for a total of 14 days of exposure. In order to have a better impact perspective of these contaminants, an integrated biomarker response index (IBR) and Health Index were applied. Biomarker alterations are apparently more related to smaller sized (4–6 µm) MPs in gills and to virgin LDPE MPs in the digestive gland according to IBR results, while the digestive gland was more affected by these MPs according to the health index.

Assessment of Challenges and Opportunities and Identification of Approaches and Innovations in COVID-19 Pandemic Management by Different States in India: A Qualitative Approach

Objective The primary objective of the study was to compare the challenges in implementing various COVID-19-related public health strategies and activities between the selected high health index and low health index states. The secondary objective was to identify the differently managed mechanisms adopted by the health-care delivery system across the states to maintain their functioning during the COVID-19 pandemic. Setting Eight states were divided into two groups; based on their health index and vulnerability index ranking—Kerala, Maharashtra, Gujarat, and Karnataka in top four (Group 1) and Delhi, Tripura, Rajasthan, and Orissa in bottom four states (Group 2). Results There was lack of private sector involvement in both the groups of the states, more so in Group 2. Although transport-related issues were similar in both groups, lack of provision of vehicles for transport for carrying out various COVID and non-COVID activities seemed to be more prominent in Group 2. More obstacles related to infrastructure were observed in Group 1 states. In terms of innovations, commonalities lay in convergence of multiple departments for monitoring, contact tracing, essential supplies, and transportation. Both groups managed routine health services and fund allocation with nearly equal vigour. Major challenges faced were related to human resource, policy management, transportation, routine health services, data management, and infrastructure. HR-related challenges in top four states included confusion due to frequent change in guidelines, unclear micro-containment, and testing guidelines. Discharge guidelines and SOPs related to home isolation of slum dwellers, inter-departmental cooperation and coordination issues faced in greater proportion in top four states; issues with fund allocation for local needs were faced by the Group 2 states. Innovations implemented to meet hurdles faced during the pandemic could be categorized under heads of ‘human resource’, ‘community actions’, ‘policy management’, ‘inter-departmental coordination’, ‘use of technology and media’, and ‘fund allocations’. There was private–public partnership; use of other human resource for health-care delivery; use of technology for health-care delivery was seen in all states but more so in Group 1 states. Conclusion States with higher health index and lower vulnerability index, i.e., Group 1 states faced fewer challenges than those in Group 2. Innovative measures taken at local level to tackle problems posed by the pandemic were unique to the situations presented to them and helped control the disease as effectively as they could.

Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

Sleep Duration as the Main Indicator of Self-Rated Wellness and Health among Healthcare Workers Involved in the COVID-19 Pandemic

Objective: This study was performed during the COVID-19 pandemic to better understand the indicators of self-rated wellness and health among healthcare workers. Methods: Sleep pattern, mood status, nutritional condition, physical activity, habits and the subjective wellness and health index of the healthcare workers of a university affiliated hospital were surveyed. Paired t-tests were performed to compare the participants’ quality of life before and after the outbreak of COVID-19. Multivariable linear regression models with a backward elimination stepwise process determined the parameters that significantly correlated with self-reported wellness and health. Results: Of the 200 healthcare workers who participated in this study, 119 (60%) were female and 81 (40%) were male, with a mean (SD) age of 28.8 (5.9) years. We found that the COVID-19 pandemic significantly changed many lifestyle factors compared to the pre-pandemic states. The scores of sleep quality, mood status, pre-planned physical activity and social activity were reduced by 30%, 40%, 50% and 70%, respectively. The average night sleep duration before the pandemic was 7 h and 22 min, whereas during the pandemic it decreased to 6 h and 44 min, a debt of 38 min in sleep duration every night. As found by multivariable regression modelling, self-reported wellness and health before the pandemic period was associated with wake-up time, mood status, physical activity and diet. During the pandemic period, in addition to these variables, night sleep duration (β = 0.049, p = 0.049) and nap duration (β = 0.009, p = 0.01) were left in the final multivariable model and correlated significantly with the wellness and health index. Conclusion: COVID-19 has detrimentally affected healthcare workers’ well-being and quality of life. Sleep duration was the main factor correlated with subjective wellness and health index during the current COVID-19 pandemic.

Analysis of Higher Education Health Index

To evaluate the health index of education systems in different countries, the is paper establishes a higher education system health index evaluation model through principal component analysis. Five relevant indicators of six countries were selected for principal component analysis. Through annual relevant data of universities in six different countries in the past ten years, we obtained three indicators that mainly affect the health index of higher education, Cumulative contribution rate reached 93.67%, namely, the proportion of education expenditure, the proportion of international students and the rate of higher education enrollment. Then based on the contribution rate of the three main indexes, we constructed a principal component comprehensive evaluation health index model and obtained the health index of the higher education system in six countries. Among them, India and Brazil have the lowest health index scores, respectively 0.4106 and 0.4183. Therefore, the higher education systems of India and Brazil have a lot of room for improvement. Based on the empirical results, we put forward targeted improvement plans and systems.

Lifetime Estimation Based Health Index and Conditional Factor for Underground Cable System

In this paper, a lifetime estimation method for underground cable systems is proposed by combining a health index (HI) and conditional factor (CF). The underground cable system consists of underground cable, joint, termination, manhole, and duct bank. The HI is an indicator to indicate the actual condition of underground cable components and systems whereas the CF is used to indicate different operating stresses of the system under different operating conditions such as percentage loading, electrical stresses, laying structure, environment, etc. The actual technical data as well as historical operating and testing records are applied. The weighting and scoring method with the analytical hierarchy process are used to classify an importance of underground cable components, testing methods, and criteria used in the HI and CF calculation. The annual calculated HIs are plotted to investigate the lifetime trending curve by using a polynomial function. The degradation curve based on calculated CF is estimated by using the Weibull distribution function. Finally, the remaining life of the underground cable system is determined by matching the lifetime trending curve with the degradation curve. Ten practical underground cable systems supplying power in a high voltage power delivery system are evaluated with effective results. The lifetime of the underground cable system can be successfully estimated.

Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning

The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.