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Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The perception of sound greatly impacts users' emotional states, expectations, affective relationships with products, and purchase decisions. Consequently, assessing the perceived quality of sounds through jury testing is crucial in product design. However, the subjective nature of jurors' responses may limit the accuracy and reliability of jury test outcomes. This research explores the utility of facial expression analysis in jury testing to enhance response reliability and mitigate subjectivity. Some quantitative indicators allow the research hypothesis to be validated, such as the correlation between jurors' emotional responses and valence values, the accuracy of jury tests, and the disparities between jurors' questionnaire responses and the emotions measured by FER (facial expression recognition). Specifically, analysis of attention levels during different statuses reveals a discernible decrease in attention levels, with 70 percent of jurors exhibiting reduced attention levels in the 'distracted' state and 62 percent in the 'heavy-eyed' state. On the other hand, regression analysis shows that the correlation between jurors' valence and their choices in the jury test increases when considering the data where the jurors are attentive. The correlation highlights the potential of facial expression analysis as a reliable tool for assessing juror engagement. The findings suggest that integrating facial expression recognition can enhance the accuracy of jury testing in product design by providing a more dependable assessment of user responses and deeper insights into participants' reactions to auditory stimuli.

Polycystic ovary syndrome (PCOS) is the most frequent endocrine-metabolic disorder among women at reproductive age. The diagnosis is based on the presence of at least two out of three criteria of the Rotterdam criteria (2003). In the last decades, the dysmetabolic aspect of insulin resistance and compensatory hyperinsulinemia have been taken into account as the additional key features in the etiopathology of PCOS, and they have been widely studied. Since PCOS is a complex and multifactorial syndrome with different clinical manifestations, it is difficult to find the gold standard treatment. Therefore, a great variety of integrative treatments have been reported to counteract insulin resistance. PCOS patients need a tailored therapeutic strategy, according to the patient's BMI, the presence or absence of familiar predisposition to diabetes, and the patient's desire to achieve pregnancy or not. The present review analyzes and discloses the main clinical insight of such complementary substances.

Objective PCOS is a syndrome is characterized by 2 out of 3 of the criteria established during the Rotterdam Consensus Conference. Recently the issue of insulin resistance (IR) has caught attention. Subjects A group of overweight/obese PCOS patients ( n  = 30) have been evaluated before and after 3 months of daily integrative administration of d-chiro inositol (DCI) (500 mg) and alpha lipoic acid (ALA) (300 mg). Methods Hormonal and metabolic profiles, oral glucose tolerance test (OGTT) for glucose, insulin and C-peptide response were performed in baseline conditions and after DCI plus ALA treatment. Hepatic Insulin Extraction (HIE) index was computed along the OGTT to evaluate the liver ability in degrading insulin. Results The treatment decreased LH, Androstenedione (A), insulin plasma levels, BMI, HOMA index, AST and ALT. Considering patients for the presence ( n  = 17) or absence of familial diabetes ( n  = 13), the greatest improvements occurred in the former patients. Insulin response to OGTT was greatly reduced after the treatment interval in PCOS with familial diabetes. HIE computation disclosed that in presence of familial diabetes liver degradation of insulin is reduced thus leaving a higher amount of circulating insulin. DCI plus ALA administration decreased AST and ALT and restored hepatic insulin clearance since HIE profile was improved. Conclusion Our study demonstrates that in overweight/obese PCOS the predisposition to familial diabetes triggers IR not only through the endogenous impaired DCI and ALA synthesis but also through a reduced hepatic clearance of insulin. DCI plus ALA administration positively improved hormonal, metabolic profiles as well as liver function.

Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit's payback period. The methodology was applied to a real case study-a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21 °C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms.