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The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis.

This paper presents an integrated approach based on physical–mathematical models and numerical simulations to optimize sludge treatment using ultrasound. The main objective is to improve the efficiency of the purification system by reducing the weight and moisture of the purification sludge, therefore ensuring regulatory compliance and environmental sustainability. A coupled temperature–humidity model, formulated by partial differential equations, describes materials’ thermal and water evolution during treatment. The numerical resolution, implemented by the finite element method (FEM), allows the simulation of the system behavior and the optimization of the operating parameters. Experimental results confirm that ultrasonic treatment reduces the moisture content of sludge by up to 20% and improves its stability, making it suitable for agricultural applications or further treatment. Functional controls of sonication and the reduction of water content in the sludge correlate with the obtained results. Ultrasound treatment has been shown to decrease the specific weight of the sludge sample both in pretreatment and treatment, therefore improving stabilization. In various experimental conditions, the weight of the sludge is reduced by a maximum of about 50%. Processed sludge transforms waste into a resource for the agricultural sector. Treatment processes have been optimized with low-energy operating principles. Additionally, besides utilizing energy-harvesting technology, plant operating processes have been optimized, accounting for approximately 55% of the consumption due to the aeration of active sludge. In addition, an extended analysis of ultrasonic wave propagation is proposed.

In this work, a known Eddy Current (EC) model is adapted to characterize subsurface defects in carbon fiber-reinforced polymer (CFRP) plates intended for the civil aerospace industry. The considered defects include delaminations, microcracks, porosity, fiber breakage, and the simultaneous presence of these defects. Each defect is modeled as an additive variation in the material’s electrical conductivity tensor, allowing for a detailed mathematical representation of the defect’s influence on the CFRP’s electromagnetic behavior. The additivity of the variations in the conductivity tensor is justified by the assumption that the defects are not visible to the naked eye, implying that the material does not require non-destructive testing. The adapted EC model admits a unique and stable solution by verifying that all analytical steps are satisfied. To reconstruct 2D maps of the magnetic flux density amplitude, a FEM formulation is adopted, based on the energy functional because it ensures a stable and consistent numerical formulation given its coercivity. Moreover, the numerical approach allows precise and reliable numerical solutions, enhancing the capability to detect and quantify defects. The numerical results show that the obtained 2D maps are entirely superimposable on those highlighting the distribution of mechanical stress states known in the literature, offering a clear advantage in terms of detection costs. This approach provides an effective and economical solution for the non-destructive inspection of CFRP, ensuring accurate and timely defect diagnosis for maintaining structural integrity.

This paper delves into the increasingly recognized yet complex relationship between air pollution and Neurological Diseases. Although the detrimental effects of air pollution on respiratory and cardiovascular health are well-documented, its impact on neurological and cognitive disorders is an emerging area of concern. In this mini review, we explore the intricate mechanisms by which various air pollutants, such as particulate matter, nitrogen oxides, and polycyclic aromatic hydrocarbons, contribute to neurological pathologies. The focus lies on the role of oxidative stress and inflammation in exacerbating conditions like Alzheimer’s disease and Parkinson’s disease. By unraveling these connections, the paper sheds light on the broader implications of environmental factors on neurological health and underscores the urgent need for policy interventions to mitigate air pollution’s impact on the nervous system.

Investigating frontal EEG asymmetry as a possible biomarker of cognitive control abilities is especially important in ecological contexts such as school and work. We used a novel approach combining judgment performance and hemispheric frontal event-related potential (ERP) P300 asymmetry (fP3As) to evaluate aspects of cognitive control (i.e., repetition and switching) in adolescent females over a two-week ordinary school period. While undergoing electroencephalographic recording, students performed a word-colour “Stroop-like” congruence judgment task during morning and afternoon sessions, on Mondays and Wednesdays. Proportion of incongruence and congruence trials was 75% and 25%, respectively. ERP analysis revealed larger “novelty” right hemispheric fP3As amplitude for infrequent congruence but equivalent or significantly smaller than left hemispheric fP3As amplitude for frequent incongruence. RTs increased with extent of right fP3As shift. Behaviorally, repeat trial pairs (i.e., congruent followed by congruent, incongruent followed by incongruent) generally did not differ by time or day and were associated with near-ceiling accuracy. In contrast, switch trial pairs (i.e., congruent followed by incongruent, incongruent followed by congruent) in the afternoon were slower and associated with lower accuracy at the expected 75% criterion rate (i.e., judging incongruence by default), dropping significantly below 75% in the mornings. Crucially, compared to afternoon, morning fP3As patterns did not change adaptively with switch trial pairs. Although retroactive switching during congruence judgment was affected at all testing times, we conclude it was most impaired in the mornings of both early and mid school weeks, supporting misalignment between adolescent circadian cycle and school start time. We discuss some implications for optimal learning of adolescents at school.

Background & aims: Naturogenic aspects of global warming are amplified by the anthropogenic effects of the persistent exponential global population growth. Urbanization without proper planning escalates serious all-time disease pathologies in vulnerable populations, such as children, through pollution of toxic gases. We illustrate these associations with a retrospective study of respiratory heath in children living in downtown Rome when this dense urban center recorded one of the highest air pollution levels in Europe. Methods: Lung and respiratory function parameters were evaluated from 2006 to 2007 in a cohort of 90 children including two age subgroups (>10 years and

IntroductionShorter and/or disrupted sleep during adolescence is associated with cognitive and mental health risks, particularly in females. We explored the relationship between bedtime behavior patterns co-varying with Social Jet Lag (SJL) and School Start Times (SST) and neurocognitive performance in adolescent female students.MethodsTo investigate whether time of day (morning vs. afternoon), early SSTs and days of the school week can be correlated with neurocognitive correlates of sleep insufficiency, we recruited 24 female students aged 16–18 to report sleep logs, and undergo event-related electroencephalographic recordings on Monday, Wednesday, mornings, and afternoons. Using a Stroop task paradigm, we analyzed correlations between reaction times (RTs), accuracy, time of day, day of week, electroencephalographic data, and sleep log data to understand what relationships may exist.ResultsParticipants reported a 2-h sleep phase delay and SJL. Stroop interference influenced accuracy on Monday and Wednesday similarly, with better performance in the afternoon. For RTs, the afternoon advantage was much larger on Monday than Wednesday. Midline Event-Related Potentials (ERPs) yielded higher amplitudes and shorter latencies on Wednesday morning and Monday afternoon, in time windows related to attention or response execution. A notable exception were delayed ERP latencies on Wednesday afternoon. The latter could be explained by the fact that delta EEG waves tended to be the most prominent, suggesting heightened error monitoring due to accumulating mental fatigue.DiscussionThese findings provide insights into the interaction between SJL and SST and suggest evidence-based criteria for planning when female adolescents should engage in cognitive-heavy school activities such as tests or exams.

This review examines the integration of advanced ultrasonic techniques and artificial intelligence (AI) for monitoring and analyzing concrete structures, focusing on detecting and classifying internal defects. Concrete structures are subject to damage over time due to environmental factors and dynamic loads, compromising their integrity. Non-destructive techniques, such as ultrasonics, allow for identifying discontinuities and microcracks without altering structural functionality. This review addresses key scientific challenges, such as the complexity of managing the large volumes of data generated by high-resolution inspections and the importance of non-linear models, such as the Hammerstein model, for interpreting ultrasonic signals. Integrating AI with advanced analytical models enhances early defect diagnosis and enables the creation of detailed maps of internal discontinuities. Results reported in the literature show significant improvements in diagnostic sensitivity (up to 30% compared to traditional linear techniques), accuracy in defect localization (improvements of 25%), and reductions in predictive maintenance costs by 20–40%, thanks to advanced systems based on convolutional neural networks and fuzzy logic. These innovative approaches contribute to the sustainability and safety of infrastructure, with significant implications for monitoring and maintaining the built environment. The scientific significance of this review lies in offering a systematic overview of emerging technologies and their application to concrete structures, providing tools to address challenges related to infrastructure degradation and contributing to advancements in composite sciences. [ABSTRACT FROM AUTHOR]

The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent of the external load that determines a specific induced current map. Starting from an approach known in the literature, according to which the map-external load association is operated through fuzzy similarity computations, in this paper, we generalize this method by reformulating it in terms of intuitionistic fuzzy logic by proposing a classification based on divergence computations. Our approach, acting adaptively on the fuzzification of the maps, results in a better classification percentage, besides significantly reducing the presence of doubtful cases due to the uncertainty of each applied load. Furthermore, a FEM software tool was developed, which turned out to be, to a certain extent, a substitute for the experimental procedure, notoriously more expensive. Even if the procedure was applied on plates subjected to bi-axial loads, it could be used for other types of loads since the classification operator processes the eddy current maps exclusively, regardless of their cause. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach based on physical–mathematical models and numerical simulations to optimize sludge treatment using ultrasound. The main objective is to improve the efficiency of the purification system by reducing the weight and moisture of the purification sludge, therefore ensuring regulatory compliance and environmental sustainability. A coupled temperature–humidity model, formulated by partial differential equations, describes materials' thermal and water evolution during treatment. The numerical resolution, implemented by the finite element method (FEM), allows the simulation of the system behavior and the optimization of the operating parameters. Experimental results confirm that ultrasonic treatment reduces the moisture content of sludge by up to 20% and improves its stability, making it suitable for agricultural applications or further treatment. Functional controls of sonication and the reduction of water content in the sludge correlate with the obtained results. Ultrasound treatment has been shown to decrease the specific weight of the sludge sample both in pretreatment and treatment, therefore improving stabilization. In various experimental conditions, the weight of the sludge is reduced by a maximum of about 50%. Processed sludge transforms waste into a resource for the agricultural sector. Treatment processes have been optimized with low-energy operating principles. Additionally, besides utilizing energy-harvesting technology, plant operating processes have been optimized, accounting for approximately 55% of the consumption due to the aeration of active sludge. In addition, an extended analysis of ultrasonic wave propagation is proposed. [ABSTRACT FROM AUTHOR]

The use of touch for the treatment of psychiatric disorders is increasingly investigated, as it is shown that cognitive symptoms can be improved by various forms of massage. To investigate if the effect of massage is measurable using classical visual event-related potential components (P1, P2, late positive potential (LPP)), we performed a preliminary study on six participants using myofascial induction massage. Participants were shown emotionally valenced or neutral images before and after a 20 min myofascial massage. We found general increases in P2 amplitude following the intervention across all conditions (both neutral and affective), indicating increased attention or salience to visual stimuli. The magnitude of change was visibly larger for unpleasant stimuli, suggesting that visual perception and attention were modulated specifically in response to unpleasant visual images. The LPP showed reductions in amplitude after myofascial massage, suggesting increased emotional modulation following intervention, as a result of possible DMN alterations, consistent with region and function. We conclude that brief myofascial intervention supports other research in the field, finding that physical touch and massage techniques can alter cognition and perception. We posit further research to investigate its future use as an intervention for both physical and cognitive modulation. Importantly, we provide preliminary evidence that the neural processes that resonate with this type of massage involve complex feedforward and backward cortical pathways, of which a significant portion participate in modulating the visual perception of external stimuli.