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Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error.

This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system.

The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested.

In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures.

Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time).

International outbreak of the SARS-CoV-2 infection has fostered the Italian government to impose the FFP2 protective facial masks in closed environments, including bar, restaurants and, more in general, in the food sector. Protective facial masks are rocketing, both in mass and in costs, in the food sector imposing efforts in fostering reuse strategies and in the achievement of sustainable development goals. The scope of the present paper is to depict possible strategies in manufacturing and reuse strategies that can reduce the carbon footprint (CF) of such devices.

To implement circular economy strategies in the protective facial masks supply chain, it was considered significant to move towards a study of the environmental impact of such devices, and therefore a CF study has been performed on an FFP2 facial mask used in the food sector. Different materials besides the mostly used polypropylene (PP) (polyethylene (PE), polycarbonate (PC), poly (lactic acid) (PLA), cotton, polyurethane (PUR), polystyrene (PS) and nylon 6,6) and different sanitisation alternatives as reuse strategies (both laboratory and homemade static oven, ultraviolet germicidal irradiation) readily implemented have been modelled to calculate the CF of a single use of an FFP2 mask.

The production of textiles in PP, followed by disposal was the main contributor to CF of the single-use FFP2 mask, followed by packaging and transportations. PP and PE were the least impacting, PC, cotton and Nylon 6-6 of the same weight results the worst. PLA has an impact greater than PP and PE obtained from crude oil, followed by PUR and PS. Static laboratory oven obtained an 80.4% reduction of CF with respect to single use PP-made FFP2 mask, whereas homemade oven obtained a similar 82.2% reduction; UV cabinet is the best option, showing an 89.9% reduction.

The key strategies to reduce the environmental impacts of the masks (research for new materials and reuse with sanitisation) should ensure both the retention of filtering capacities and the sanitary sterility of the reused ones. Future developments should include evaluations of textile recycling impacts, using new materials and the evaluation of the life cycle costs of the reused masks.

This paper intends to provide to stakeholders (producers, consumers and policy makers) the tools to choose the best option for producing and reuse environmentally friendly protective facial masks to be used in the food sector, by using both different materials and easily implemented reuse strategies.

The reduction of the CF of protective facial masks in the food sector surely will have relevant positive effects on climate change contributing to reach the goals of reducing CO₂ emissions. The food sector may promote sustainable practices and attract a niche piece of clients particularly sensible to such themes.

The paper has two major novelties. The first one is the assessment of the CF of a single use of an FFP2 mask made with different materials of the non-woven filtering layers; as the major contribution to the CF of FFP2 masks is related to the non-woven textiles manufacturing, the authors test some other different materials, including PLA. The second is the assessment of the CF of one single use of a sanitised FFP2 mask, using different sanitation technologies as those allowed in bars or restaurants.

This study aims to examine the factors predicting burden among the male caregivers of older adults with stroke.

This was a descriptive cross-sectional study. A simple random sampling technique was used to recruit 98 male caregivers in the outpatient department’s neurological clinic, at Banphaeo General Hospital. Data was collected using six questionnaires: the demographic questionnaire, the center for epidemiologic studies depression scale, the perceived health status interview form, the caregiver and patient relationship interview form, the Barthel ADL index and the Zarit burden interview. Descriptive statistics and stepwise multiple regression analysis were used for data analysis.

The male caregivers of older adults with stroke had a mild to moderate level of burden. Factors such as depression of caregivers and activities of daily living of older adults predicted the burden among male caregivers, explaining 53.6% of the variance. The findings imply that nurses can plan new approaches and interventions to alleviate the burden of male caregivers by reducing their depression levels and encouraging activities of daily living in the older adults. In addition, effective programs can be developed to provide informational support to caregivers for reducing their burden level.

Male caregivers with depressive symptoms had an increased caregiving burden. Therefore, health-care professionals should support and formulate guidelines to reduce the burden of caregiving among the male caregivers by considering predictive factors.

The purpose of this paper is to analyze the business model of second-hand fashion stores and explore their challenges/opportunities and suggest potential strategies for second-hand fashion retail stores.

A qualitative research method using in-depth interviews of convenience sample of owners/store managers from within the USA was employed.

Contrasting the traditional retail stores, customers are the primary partners and suppliers of second-hand fashion stores. These stores retain minimal profits given a business model that typically involves sharing profits with customers. Cheaper price, thrill of finding great deals, value for brands and variety are the primary reasons mentioned by respondents for shopping at second-hand stores.

Limitations include the use of a convenience sample of store owners/managers as well as the research is limited to women and children’s stores. Respondents of the study were from the same geographical region and the characteristics of the redistribution markets may vary in a different region.

As a means to foster textile waste reduction through second-hand clothing business, these stores could adopt innovative revenue streams, additional partnerships, and improved fashion and store appeal that may be effective in increasing profits and the number of customers.

This study is one of the early attempts to examine the business model of second-hand fashion stores, a form of collaborative consumption in the fashion context. The study contributes in promoting second-hand fashion stores as a sustainable business model in the fashion industry.

This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

In this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.

For the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.

Epilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier stage and monitoring after recoup.

The methodology involves a combination of three subsystems which monitors the human parameters such as temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters measured are communicated to the caregiver and doctor.

Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and LabVIEW simulators.

The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health monitoring, elderly monitoring and so on.

First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer product for use as monitoring device with high accuracy.