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Hyper crosslinked polymers based hydrogen generation: A combined mechanistic, statistical and machine learning approach
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From agricultural waste to renewable energy: Integration of soil-based microbial fuel cells in biobed systems for Sustainable Treatment of olive oil mill wastewater
No full textOlive mill wastewater (OMW) is a highly concentrated agro-industrial effluent that contains large amounts of organic matter, polyphenols, and salts. If discharged untreated, it poses significant environmental risks. Existing OMWW treatment methods are challenged by high polyphenol toxicity and overlook the potential for bioenergy recovery. A critical gap exists for an integrated, sustainable system that leverages locally-sourced olive industry waste to simultaneously achieve effective OMW detoxification and generate renewable, decentralized bioenergy. This study presents an innovative hybrid treatment approach that integrates soil-based microbial fuel cells (SBMFCs) into biobed systems. This combination allows for simultaneous pollutant removal and bioelectricity generation. The biomixture used in this study consists of soil, compost, biochar, and olive by-products, which were optimized to enhance microbial activity, adsorption capacity, and electrochemical performance. The integrated SBMFC–biobed system achieved a 98.4 % removal rate for chemical oxygen demand (COD) and over 99 % removal of total nitrogen. Additionally, the system-level electrical output increased from 3.01 V to 7.35 V due to microbial adaptation over several operational cycles. Structural and surface analyses (FTIR, XRD, SEM-EDS) confirmed the formation of biofilms and electroactive mineral phases that improved electrode performance. The results underscore the system's capability to convert OMW treatment from an environmental challenge into a renewable energy opportunity. Beyond managing pollution, this dual-function technology aligns with circular economy principles by transforming agricultural wastewater into a resource for sustainable energy and water recovery
Unlocking the potential of peach pulp residue for the simultaneous production of biohydrogen and biobutanol using Clostridium pasteurianum and Clostridium tyrobutyricum
No full textVideoconferencing group parent training program for caregivers of children with ADHD: A preliminary study
No full textPurpose: This study examined the suitability and preliminary efficacy of a newly developed videoconferencing group parent training program for parents of children with Attention Deficit Hyperactivity Disorder (ADHD). This program was designed to address the challenges faced by caregivers by providing structured guidance and support through a telepsychiatric approach. Methods: This quasi-experimental single-group pretest-posttest design was used. The program was developed using Barkley's works, existing literature, expert opinions, and cultural relevance. Implemented in Turkey, the program included nine 60-min weekly group videoconferencing sessions with parents (N = 12; all living in suburban areas) of school-aged children (7–12 years) diagnosed with ADHD. Effectiveness was assessed via the “Descriptive Information Form,” “Zarid Burden Interview (ZBI-22),” “Perceived Stress Scale (PSS-14),” and “Strengths and Difficulties Questionnaire (SDQ).” Program acceptance was evaluated using a Likert scale. Results: Participants reported high satisfaction (95 %) and full treatment retention (100 %). Parents showed significant improvements in burden (r = 0.958), stress (r = 0.734), and emotional and behavioral difficulties (r = 0.264) (p < .05). The program was well-received, and the positive outcomes highlight its potential applicability in broader clinical settings. Additionally, the research power for ZBI-22, PSS-14, and SDQ was determined to be high. Conclusions: This preliminary study supports the feasibility, acceptability, potential efficacy of a videoconferencing parent training program for parents of children with ADHD. Findings suggest a potential for reducing caregiver burden, stress, and emotional and behavioral difficulties, warranting further investigation in larger-scale studies. The development and implementation of this program demonstrate how digital health interventions can enhance access to evidence-based psychosocial support for families, particularly in underserved areas
Knowledge-based training of learning architectures under input sensitivity constraints for improved explainability
No full textThe traditional machine learning (ML) training problem is unconstrained and lacks an explicit formulation of the underlying driving phenomena. Such a formulation, based solely on experimental data, does not ensure the delivery of qualitative knowledge among variables due to many theoretical issues in the optimization task. This study further tightens Artificial Neural Networks (ANNs) training by including input sensitivities as additional constraints and applies to regression and classification tasks based on literature data. In theory, such sensitivity represents the change direction of the target variable per change in measurements from indicators. The resulting nonlinear optimization problem is solved th rough a rigorous solver and includes the sensitivity expressions through algorithmic differentiation. Compared to traditional methods, with an acceptable decrease in the prediction capability, the proposed model delivers more intuitive, explainable, and experimentally verifiable predictions under input variable variations, under robustness to overfitting, while serving robust identification tasks. A classification case study includes a patient-oriented clinical decision support system development based on the impact of cancer-indicating variables. A competitive test prediction accuracy is obtained compared to commonly used algorithms despite 10 % decrease in the training. The regression case is built upon the energy load estimation to account for prominent considerations to obtain desired sensitivity patterns and proposed methodology delivers significant accuracy drop compared to some formulations to address knowledge patterns. The approach delivers a compatible pattern with practitioner expertise and is compared to widely used machine learning algorithms, whose performances are evaluated through common statistics in addition to multi-variable response graphs
CFD-based evaluation of hydrogen–methane blends on temperature uniformity and emission control in ceramic furnaces
No full textThe increasing demand for cleaner and more efficient high-temperature industrial processes has intensified interest in hydrogen-enriched combustion, particularly for ceramic furnaces. This study numerically investigates a multi-burner ceramic furnace operating with methane–hydrogen blends under hydrogen mass fractions of 5–20 % and equivalence ratios ranging from 0.5 to 1.0. The numerical model was validated using experimental temperature data reported in the literature. The results show that hydrogen enrichment enhances combustion efficiency and temperature uniformity while significantly reducing carbon emissions. Compared to the baseline H0 case, the peak flame temperature increased approximately 5.2 %, and temperature uniformity improved by approximately 0.6 %. CO2 emissions decreased by 26.42 % with 20 % hydrogen enrichment, whereas NOX emissions increased by about 37.4 % due to intensified thermal-Zeldovich pathways at higher flame temperatures. The time-dependent ceramic heating model reached 1200 °C at 60,000 s, consistent with experimental observations. Overall, moderate hydrogen blending levels, particularly H10 and H15, provided the best balance between thermal performance and emissions, highlighting the potential of hydrogen-assisted combustion for sustainable ceramic production.</p
Phosphatidylinositol 4-Kinase IIIβ: A Therapeutic Target for Contractile Dysfunction in Hypertrophic Cardiomyocytes
No full textCardiac hypertrophy is an important risk factor for heart failure and is often accompanied by contractile dysfunction. While hypertrophic growth contributes to disease progression, the underlying molecular mechanisms remain incompletely understood. A proposed contributor is a metabolic shift toward glucose uptake, suggesting that kinases regulating this process, such as protein kinase D1 (PKD1) and downstream target phosphatidylinositol 4-kinase IIIβ (PI4KIIIβ), might be effective targets to mitigate cardiac hypertrophy-induced contractile dysfunction. We investigated whether PI4KIIIβ inhibition downregulates enhanced glucose uptake in hypertrophic cardiomyocytes and thereby treats cardiac hypertrophy-induced contractile dysfunction. Hypertrophy was induced in cultured adult rat cardiomyocytes and human stem cell-derived cardiomyocytes using either phenylephrine (PE) or adenoviral PKD1 overexpression. PE-induced hypertrophy was associated with increased mRNA expression of BNP, activation of hypertrophic signaling, morphological alterations, enhanced protein synthesis and glucose uptake, and impaired contractile function. Treatment with the PI4KIIIβ inhibitor MI14 prevented and reversed PE-stimulated glucose uptake and contractile dysfunction, while hypertrophic signaling, cell size, and protein synthesis remained unaffected. Similar effects on glucose uptake were observed in the PKD1 overexpression model. These findings suggest that targeting myocardial substrate metabolism via the PI4KIIIβ pathway, rather than hypertrophic growth itself, could be a promising strategy to treat hypertrophy-induced contractile dysfunction.</p