Search Results (614)

Hydrolysis of cellulose to produce ethanol has become an effective way to utilize biological resources, but its large-scale industrial application has been limited. In this study, a one-pot catalytic conversion process for transforming cellulose into ethanol was developed. Meanwhile, multifunctional Mo/Pt/WO_x/Al₂O₃ catalysts were prepared by loading nano-alumina (Nano-Al₂O₃) via a stepwise impregnation method. The influence of catalysts with varying metal ratios on the types of products generated during the cellulose hydrolysis process to ethanol was examined. The catalyst with 0.1% Mo, 2% Pt, and 7.5% W loadings showed the best selectivity. With an ethanol yield of 45.3% after heating at 5 MPa H₂ and 518 K for 2 h. Nano-Al₂O₃ can provide suitable active sites. The addition of W⁵⁺ and Mo⁰ increased the surface oxygen vacancy density and enhanced the hydrodeoxidation and metal anchoring capacity of the catalyst. The solid solution structure facilitates electron transfer from W and Mo atoms to Pt atoms, forming electron-rich Pt^δ- species, promoting the hydrolysis of cellulose and the formation of ethanol. Full article

Background/Objectives: A multivariable prediction model was developed and externally validated to estimate an individual’s current need to visit a dentist among young to middle-aged adults. The objective was to provide an accessible, non-invasive screening tool for independent home self-assessment or integration within routine healthcare workflows. Methods: A cross-sectional study utilised data from two National Health and Nutrition Examination Survey (NHANES) cycles (2011–2014). Logistic regression was applied to develop the prediction model among 1870 dentate adults aged 30–50, a critical onset period for oral diseases. The primary outcome was derived from objective oral examinations conducted by licensed dentists. The resulting questionnaire-based tool provides two recommendations: a visit to a dentist or continuation of routine oral care. Results: External validation using the independent NHANES 2013–2014 cohort (n = 2024) demonstrated robust and clinically relevant predictive capacity, with an AUC of 0.822 (95% CI 0.803–0.842). The model exhibited acceptable calibration (slope 0.85; intercept 0.04) and stable operation. Decision-curve analysis showed net benefit across thresholds; at Pt = 0.33, 14/100 unnecessary screenings were avoided compared with treat-all. Internal validation using a 10% hold-out partition (n = 184) supported these findings, indicating similarly strong discrimination (AUC 0.817, 95% CI 0.751–0.884). Conclusions: Early midlife is a consistent and informative period for oral health intervention. A scalable 14-predictor architecture using accessible indicators established high clinical utility, supporting integration into digital health and primary care. This approach enables efficient population-level prevention. Large-scale, nationally representative NHANES data allowed for the investigation of less-studied factors, including intermittent smoking, metabolic dysregulation, and elevated cholesterol levels. Full article

Direct methanol fuel cells (DMFCs) have attracted considerable attention recently for various applications ranging from portable ones to transportation. The efficiency of DMFCs depends on the kinetics of anodic and cathodic electrocatalysts. Due to sluggish anodic methanol oxidation reaction (MOR), DMFCs require an effective and bifunctional catalyst for promoting efficient MOR. The state-of-the-art MOR catalysts, such as Pt/C and Pt-Ru/C, have been shown to exhibit reasonable MOR activity; however, the insufficient mass activity and poor stability of carbon-supported catalysts have been a major limitation, requiring an alternative, efficient, electrocatalyst that exhibits high mass and specific activities. In addition, electrocatalysts without any carbon support (self-supported electrocatalysts) further mitigate their poor stability and therefore enhance their durability. In this regard, metal aerogel catalysts, which are entirely composed of metallic networks, recently attained special interest due to their specific advantages over conventional carbon supports, such as high catalyst utilization and improved electronic conductivity and stability. In this review, we systematically reviewed various metal aerogel catalysts developed for MOR since their first discovery in 2009. The metal aerogel demonstrated superior MOR performance relative to carbon-supported commercial catalysts, with enhancements ranging from 2-fold to 22-fold of mass activity. We also statistically compared the mass activity of metal aerogels with traditional carbon-supported, non-carbon-supported, and advanced shape-controlled catalysts and found that metal aerogels exhibited high mass activities compared to other catalyst systems. Therefore, we clearly establish that metal aerogel catalysts possess great potential as efficient MOR catalysts in DMFCs. In addition, we have provided several future research directions and strategies for further development of metal aerogel-integrated DMFC devices. Full article

Background/Objectives: Major Depressive Disorder (MDD) is a leading cause of disability, yet physical therapy (PT) is underrepresented in hospital-based psychiatric care. While exercise is a known adjunctive treatment, specific evidence on functional, task-oriented interventions in acute settings remains scarce. This study explored changes in quality of life, depressive symptoms, pain, and self-efficacy in patients with MDD following a specialized hospital-based PT program focused on functional movement and autonomy. Methods: We conducted a prospective pre–post case series in the Short-Stay Psychiatric Unit of the Royo Villanova University Hospital (Zaragoza, Spain). We recruited seven adult patients with MDD via convenience sampling. The intervention consisted of a group-based PT program (two 45 min sessions/week during the hospital stay) utilizing task-oriented functional exercises targeting progressive strength, balance, and motor control designed to enhance self-efficacy through activities of daily living (ADLs), combined with health education. Outcomes included the EQ-5D-3L (quality of life), MADRS (depression), NRS (pain), GSE (self-efficacy), and GCPC-UN-ESU (satisfaction). Results: All seven participants (100%) exhibited a positive upward trend in self-perceived health status via the EQ-VAS (mean increase of 35 points). Six cases (85.7%) showed preliminary positive trends in the anxiety/depression dimension of the EQ-5D-3L, with the mean Single Index Value increasing from 0.310 to 0.683. Reductions in depressive symptom severity were observed in six participants, with several transitioning toward moderate or mild levels. Additionally, four patients reported descriptive reductions in pain intensity and showed favorable shifts in self-efficacy scores. Six participants expressed high satisfaction with the intervention. Conclusions: Integrating a hospital-based functional PT program with standard care may offer preliminary benefits for quality of life and reduce depressive symptoms in MDD patients. These findings suggest that task-oriented PT presents a feasible complementary approach for acute psychiatric admissions, although larger controlled trials are needed to confirm these exploratory results. Full article

As the Italian hop industry undergoes consolidation, assessing the environmental pressure of diverse cultivation and processing models is essential for sustainable growth. This study characterizes the Product Environmental Footprint (PEF) of Italian hop production through a multi-case analysis of eight representative farms. A primary data collection tool was utilized to quantify resource inputs, including water management, nutritional strategies, and phytosanitary defense. Following a rigorous thermodynamic consistency screening of the field data to eliminate unrepresentative parameters, the life cycle inventory focused on two validated regional anchor cases. The findings reveal a high degree of management heterogeneity, with dry cone yields ranging from 400 to 1673 kg of dry matter per hectare. Two functional units were defined: 1 kg of fresh hop cones (FU1) to assess cultivation impacts, and 1 kg of processed products (FU2) at the brewery gate to evaluate the full supply chain. Integrating deterministic life cycle impact outputs with a probabilistic Monte Carlo uncertainty analysis, the results indicate that the environmental impact varies significantly across commercial formats: Cryogenic Powder (2.33 ± 0.34 mPt/kg) represents the most resource-intensive format, while Raw Bales and T90 Pellets from high-yield models exhibit scores as low as 1.36 and 1.55 mPt/kg, respectively. The study identifies the agricultural phase as the primary environmental hotspot, driven predominantly by water deprivation. To address these burdens, a Sustainable Italian Hop (SIH) integrated scenario was developed. By combining precision irrigation, thermal decarbonization via biomass valorization, and a direct-to-pellet processing flow, this model achieved a 70% total reduction in the environmental footprint score (0.465 ± 0.076 mPt/kg) and an 86% reduction in water use impacts. Finally, the socio-technical and financial barriers to implementing the SIH framework are qualitatively evaluated. These results provide actionable benchmarks for aligning the emerging Italian hop supply chain with European Union climate neutrality objectives. Full article

Background/Objectives: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has been associated with potential clinical and economic benefits; however, its adoption among patients remains limited in some healthcare settings. This study examined patients’ intention to use RM services by applying an extended Technology Acceptance Model (TAM) that incorporates perceived effectiveness (PE), perceived barriers (PB), perceived threat (PT), and economic considerations, as well as the influence of socioeconomic factors. Methods: A cross-sectional survey was conducted among 104 patients with CIEDs in Taiwan using validated questionnaires. Structural equation modeling (SEM) was employed to examine the relationships among the proposed constructs. The association between intention to use and actual service utilization was explored. The correlations between sociodemographic factors and the constructs were analyzed using analysis of variance (ANOVA). Results: SEM showed that perceived effectiveness (PE), perceived usefulness (PU) and perceived ease of use (PEOU) were significantly associated with intention to use RM services, with economic considerations also having a significant contribution. Intention to use RM services further predicted actual adoption. However, PB and PT did not moderate these relationships. Sociodemographic factors influenced RM acceptance, with younger, more educated, employed, higher-income, and professionally employed patients reporting stronger perceptions and greater intention to use RM. Conclusions: This study reinforces the TAM framework in the context of health-related technology adoption. Overall, the adoption of RM services is complex and shaped by psychological, economic, and demographic factors, highlighting the need for user-friendly design, targeted education on clinical benefits, and flexible pricing and reimbursement strategies to improve equitable and sustained use. Full article

The main sources of biodegradable waste come from agriculture and municipal waste, with animal manure and food waste (FW) being the most representative respectively. Most of this waste remains still unexploited, while there is skepticism regarding the environmental footprint of various methods of their utilization. This work provides a reliable comparative environmental evaluation using life cycle assessment (LCA). In the present work, LCA applied to compare two alternative scenarios regarding the management of (a) sheep and goat manure and (b) FW. Alternative scenarios for sheep and goat manure include composting for fertilizer and energy production via anaerobic digestion (AD), while FW scenarios include incineration and energy production through AD. In both case studies, the AD scenario generates environmental benefits (expressed as negative damage) across all three damage categories namely resource scarcity, human health and ecosystem quality. Regarding sheep and goat manure, the most significant effect of AD is on human health (−0.016 Pt) while in the scenarios of FW the superior performance of AD is particularly evident in the ecosystem quality (−0.21 Pt). Both case studies reached the same conclusion pointing out that the use of sustainable technologies for managing agricultural and municipal waste mitigates the environmental impacts. Full article

In this review paper, we present a framework for the characterization of optimal decision rules in M-ary hypothesis-testing problems where the performance metric is defined as a function of pairwise error probabilities. This framework is based on the approaches developed in several recent studies in the literature, which are unified and presented in a tutorial fashion in this paper. A pairwise error probability represents the probability of selecting a specific hypothesis when a different hypothesis is true, and can be stacked into a pairwise probability vector for a given problem. In the considered framework, instead of optimizing the performance metric of interest over the infinite-dimensional set of all possible decision rules, the optimization is performed directly over the compact and convex set of all achievable pairwise probability vectors. We demonstrate that any pairwise probability vector within this feasible set can be realized via a randomization of at most two likelihood ratio quantizers (LRQs) with different sets of parameters. While one of these LRQs can always be selected as a deterministic LRQ, the other one is possibly a randomized LRQ, which can be written as a randomization of at most $M(M-1)$ deterministic LRQs, with M denoting the number of hypotheses. The main advantage of this framework is that it allows for the attainment of pairwise probability vectors that do not reside on the boundary of the feasible set and that are fundamentally inaccessible via LRQs, which are optimal for classical performance metrics such as the Bayes risk or the Neyman–Pearson criterion. Furthermore, we show that the characterization of decision rules with the presented framework is particularly advantageous for performance metrics based on prospect theory (PT), such as behavioral utility. Specifically, it is demonstrated that the optimal pairwise probability vector for a PT-based metric is not guaranteed to lie on the boundary of the feasible set of pairwise probability vectors. This results in suboptimal performance achieved by LRQs for such performance metrics. On the other hand, the randomized decision rules characterized in this paper can achieve pairwise probability vectors located in the interior of the feasible set, thereby yielding optimal performance. Numerical results corroborate these findings, demonstrating that the decision rules characterized within our framework yield optimal behavioral utility-based performance scores. Full article

Background: Lumbar facet joints are a significant source of chronic low back pain (CLBP), and medial branch blocks (MBBs) are the widely accepted reference diagnostic approach for diagnosis. However, clinical response varies. This study aims to investigate whether sagittal spinopelvic alignment parameters can predict the clinical efficacy of MBB in patients with facet-mediated CLBP. Methods: In this prospective observational study, 110 patients (aged 40–80) with facet-related CLBP underwent diagnostic MBBs using a double-block protocol. Spinopelvic parameters, including pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), and lumbar lordosis (LL), were measured on standing lateral radiographs. Clinical response was defined as a ≥80% reduction in Visual Analog Scale (VAS) scores. Data were analyzed using multivariate logistic regression and Receiver Operating Characteristic (ROC) curves. Results: Responders (n = 68) were significantly younger and had a lower BMI than non-responders (n = 42) (p < 0.05). Non-responders exhibited significantly higher PI–LL mismatch (18.6° ± 7.4 vs. 3.9° ± 4.2, p < 0.001), higher PT (23.6° ± 5.1 vs. 17.4° ± 4.5, p < 0.001), and lower LL (35.8° ± 7.2 vs. 45.2° ± 6.4, p < 0.001). ROC analysis identified a PI–LL mismatch threshold of >12.5° as the strongly associated with negative short-term diagnostic response (AUC = 0.892). Multivariate analysis confirmed that PI–LL mismatch > 12.5° was a potential associated factor within the investigated model of poor response (OR: 4.25, 95% CI: 2.10–8.60, p < 0.001), while age and BMI were not significant in the adjusted model. Conclusions: Sagittal spinopelvic malalignment, specifically an increased PI–LL mismatch, is strongly associated with reduced diagnostic utility of MBB. Integrating biomechanical assessment into clinical decision-making may improve patient selection and treatment outcomes for facet-mediated pain. Full article

This study aimed to develop high-performance, ultra-low Pt-loading 2.1 wt% vs. 20 wt% for commercial Pt/C) oxygen reduction reaction (ORR) catalysts. Utilizing carbon nanotubes (CNTs) as templates, a PANI layer was coated onto the surface to serve as a nitrogen-doped anchoring layer for metal species. Physical and structural characterizations demonstrated that the PANI-derived nitrogen-doped carbon layer uniformly encapsulates the CNT skeleton. This architecture not only achieved highly uniform Pt nanoparticle dispersion but also induced strong metal–support electronic interactions via deep-seated Ni atoms, effectively optimizing the electronic structure of the surface Pt. Electrochemical results showed that Pt/Ni-N-CNT delivers superior ORR activity in an acidic electrolyte, with a half-wave potential of 0.846 V (vs. RHE) and limiting diffusion current density outperforming commercial Pt/C (0.81 V vs. RHE), demonstrating excellent oxygen reduction kinetics. Full article

Under the global carbon-neutrality target, the technology of carbon capture, utilization, and storage-enhanced oil recovery (CCUS-EOR) faces a severe challenge of carbonation-induced degradation of oil-well cement in harsh downhole environments. Traditional cement suffers serious structural failure under high-temperature and high-pressure CO₂ conditions, whereas single-nanoparticle or polymer modification cannot meet long-term safety requirements. Meanwhile, the comparative study between the “matrix modification strategy” and the “cement system replacement strategy” is still insufficient under real CCUS-EOR conditions. In this study, experimental investigations including macroscopic performance testing, phase analysis, and multi-scale microstructural characterization were conducted. This study systematically evaluates the carbonation resistance of polyaniline@titanium dioxide-modified cement (P@T) and calcium aluminate phosphate cement (CAP). The results show that the carbonation resistance follows the descending order: CAP > P@T > silica-fume-containing Class G oil-well cement (PT). CAP seems to demonstrate a potential “corrosion-induced densification” effect. After 90 days of corrosion, its compressive strength increases to 62.5 MPa, and its permeability decreases to 13.3% of the initial value, indicating continuously improved performance. P@T indicates the possible decoupling of high carbonation degree (CaCO₃ content of 25.26%) and microstructural stability through a structural regulation mechanism of “physical filling–homogeneous distribution of carbonation products”. In contrast, PT undergoes complete structural failure after 60 days. This study fills a gap in comparative evaluation between modification and replacement schemes, reveals the multi-scale structural regulatory effects of P@T and the intrinsic stability of CAP, and provides two reliable cement solutions—“modification enhancement” and “system replacement”—for CCUS-EOR environments. The scientific validity is demonstrated through multi-scale characterization, offering key theoretical and technical support for ensuring long-term wellbore integrity. Full article

Clinical monitoring of chronic liver disease (CLD) is currently hindered by the invasiveness of conventional biopsies. While breath-borne volatile organic compound (VOC) analysis offers a promising non-invasive alternative, the metabolic profiles of sedentary populations often overlap significantly with those of healthy individuals, making latent pathologies difficult to identify. To overcome this high-resolution diagnostic challenge, this study developed an integrated framework that couples high-performance semiconductor sensing technology with a machine learning-based analytical baseline. During the biomarker screening phase, GC-MS was utilized to analyze over 2000 VOCs, identifying 20 markers associated with CLD. These were further optimized into a robust feature panel including ammonia, isoprene, dimethyl sulfide (DMS), and limonene. For several critical metabolic features exhibiting high diagnostic potential, preliminary identifications were conducted by referencing NIST database matches and relevant literature. To maintain analytical rigor and account for the inherent complexity of trace volatile metabolites in biological samples, these signals are treated as putative metabolic features and characterized by their retention times. Regarding hardware, an InN-based sensor with Pt-AlN surface modification was fabricated, achieving a limit of detection (LOD) for ammonia below 0.2 ppm. Crucially, while the InN sensor was validated for specific core markers such as ammonia, the current AI classification model is trained on a refined 7-VOC panel derived from the comprehensive GC-MS data. To resolve diagnostic overlaps, a three-state dynamic sampling protocol (resting, exercise, and recovery) was implemented to isolate biomarkers that remain physiologically stable. By integrating multi-dimensional VOC features (e.g., isoprene and DMS) with sensor-validated data through DBSCAN and Random Forest algorithms, the framework successfully captured non-linear metabolic fingerprints. Machine learning results confirm that the framework effectively distinguished sedentary controls from CLD patients, achieving a macro-average AUC of 0.96. This integration provides a high-precision technical pathway for early-stage liver disease screening. Full article

This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 framework for objective screening, establishing a functional ML role taxonomy, and mapping uncertainty sets to computational tractability. Following PRISMA guidelines, searches across Scopus, Sage, and Dimensions identified 82 eligible studies validated through a three-point quality assessment scale. Bibliometric analysis indicates that the VRP has evolved into an interdisciplinary field that combines the power of rigorous RO with the integration capabilities of ML to achieve sustainability and resilience goals. Based on the results of the literature review, it was found that ML plays four crucial functional roles: as an end-to-end problem solver, a tool for predicting input parameters, a guide for search subroutines, and a mechanism for constructing more precise uncertainty sets. Various frameworks such as Adjustable Robust Optimization (ARO), Distributionally Robust Optimization (DRO), and Data-Driven Robust Optimization (DDRO) have been reported in various studies to offer improved cost efficiency and robustness compared to conventional static RO models by utilizing data more dynamically to reduce the level of conservatism. The integration of these environmental factors is carried out through emission and energy consumption parameters, which systematically give rise to operational trade-offs. This SLR has several limitations, including database and language limitations, the absence of cross-reference validation in EmbedSLR 2.0, and limitations in quality assessment. This publication is funded by the Universitas Padjadjaran through the LPDP on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract No. 4303/B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025), as well as the Universitas Padjadjaran Research Grant under Research Grant for Graduate Students (Hibah Riset Melibatkan Mahasiswa Pascasarjana - RMMP) with contract number 5598/UN6.3.1/PT.00/2025. This systematic review was registered on the Open Science Framework (OSF) on 8 May 2026. Full article

This study investigates the dielectric behavior and NO_x storage properties of Pt/Ba–Al₂O₃ NO_x storage materials using microwave cavity perturbation, operando DRIFTS, and impedance spectroscopy with respect to their applicability in a radio-frequency-based NO_x dosimeter-type sensor. Dielectric losses (ε″) are identified as the most sensitive indicator of NO_x storage, exhibiting a clear linear correlation with both the accumulated NO_x dose and the utilization of Ba storage sites. Approximately 35% of the available Ba sites participate in nitrite and nitrate formation, and the absolute dielectric loss response increases proportionally with the Ba content of the NOx storage catalyst. In contrast, the permittivity (ε′) shows only minor changes, which are mainly influenced by temperature. Temperature-dependent experiments reveal stable NO_x storage with negligible desorption up to 350 °C, whereas pronounced desorption processes at 400 °C significantly limit the linear dosimeter behavior. Operando DRIFTS measurements on Pt/Ba–Al₂O₃ functional films confirm temperature-dependent formation of nitrites and nitrates, with nitrates dominating the NO_x storage at elevated temperatures. Capacitance measurements show a slight increase during NO_x storage, indicating a moderate increase in permittivity. Overall, Pt/Ba–Al₂O₃ NO_x storage materials exhibit a robust, quantitatively interpretable dielectric response that is well suited for radio-frequency-based, dosimeter-type NO_x sensing. Full article

While aluminum (Al) continues to be a cornerstone for microelectronic interconnect technologies, its chronic tendency toward hillock growth and thermal instability necessitates a transition toward high-performance nanostructured material architectures. This research tackles these reliability bottlenecks by achieving a molecular-level integration of graphene nanoplatelets (GNPs) within Al matrices, a strategy designed to fortify structural resilience. Adopting a green chemistry approach, we synthesized Al-GNP (0.25 vol.%) composite thin films through Pulsed Laser Deposition (PLD) using precursors derived from recycled aluminum. A major obstacle—the formation of the deleterious Al₄C₃ intermetallic phase—was effectively suppressed by ensuring a homogeneous supramolecular dispersion via a specialized dual protocol (ultrasonication and magnetic stirring) during the powder metallurgy stage. Comprehensive physicochemical characterization, utilizing HR-TEM and XRD, verified the structural integrity of the multilayer GNPs (d-spacing = 4.6 Å). Furthermore, surface metrology analysis uncovered a radical shift in growth kinetics: whereas pure Al grew via a “spiky” Volmer-Weber mechanism (Sku = 31.17), the carbon-based inclusion stabilized the film evolution, tempering the kurtosis to Sku = 7.74. Analytical cross-sectional EDS confirmed both stoichiometric fidelity and the achievement of void-free Si/Pt/Al-GNP interfaces. These outcomes prove that a precise nanoscale tailoring of surface morphology via carbonaceous reinforcements significantly bolsters microstructural stamina. Consequently, these PLD-deposited composites emerge as sustainable, cutting-edge candidates for the next generation of microelectronic packaging and interfacial chemistry applications. Full article