Search Results (67,953)

Sustainable management of biodegradable waste is a key component of the circular economy and resource recovery strategies. Composting is a complex biological process whose efficiency depends on numerous operational and physicochemical factors. Under industrial conditions, continuous laboratory monitoring of waste properties is often limited by time and cost constraints, necessitating reliable predictive tools to support process management. This study investigates the use of artificial neural networks (ANNs), decision trees (C&RT), and principal component analysis (PCA) for optimizing the composting of biodegradable waste under industrial-scale conditions. The research was conducted at a full-scale mechanical–biological treatment facility in Poland processing both the organic fraction mechanically derived from mixed municipal waste and separately collected biowaste. A dataset containing 23 records was developed from operational parameters (airflow, water addition, turning frequency, and process duration) and physicochemical properties of composted waste, including moisture content (MC), loss on ignition (LOI), total organic carbon (TOC), respiration activity (AT4), and higher heating value (HHV). The best-performing neural model achieved a predictive accuracy of 0.999 (coefficient of determination R² in the test set). For each of the neural networks, goodness of fit indices were also determined: MAE and RMSE. PCA confirmed strong relationships among key waste properties, while decision tree analysis identified airflow as the dominant operational factor affecting MC, LOI, and TOC, whereas turning frequency had the strongest influence on AT4. The results demonstrate that machine learning tools can effectively support industrial composting optimization by predicting operational parameters required to achieve desired waste stabilization characteristics, providing practical decision-support solutions for composting plant operators. It is recommended to implement single-output MLP models for dynamic, real-time process control and C&RT rules as emergency procedures. This study aligns with circular economy principles and the Sustainable Development Goals by demonstrating the potential of artificial intelligence to enhance sustainable biodegradable waste management, resource recovery, and industrial composting performance. Full article

Large-scale administrative hospital discharge data is a key resource for secondary health systems research, yet reuse is constrained by barriers of access, quality, interoperability, and semantic comparability. This paper presents and validates a reproducible, declarative, loss-aware harmonization framework for public discharge data that avoids full migration to a comprehensive common data model. The framework comprises a lightweight 14-field canonical model, versioned JSON crosswalks, a shared execution engine, a resilient file reader, schema validation, idempotency tests, value-domain checks, and an information-loss map. It was evaluated on public record-level discharge data from five jurisdictions on three continents (Korea, Brazil, Mexico, Chile, and New York State), comprising 561,966,231 harmonized records from 2001 to 2025. Validation demonstrated conformance to the declared source profiles for 82 of 99 files and full canonical conformance for 39, idempotency across all 99 files, 99.99% conformance with permitted value domains under an explicitly stated aggregation, and detection of source-level defects such as truncated files, malformed rows, and completeness anomalies. A marker-condition query for ischemic stroke (ICD-10 I63) showed that a single case definition executes consistently on the four sources retaining raw ICD-10 codes. The results show that, for heterogeneous administrative data, the key value lies not in scale alone but in the auditability of transformations, explicit loss documentation, and reproducibility of the harmonization process. Full article

The development of sustainable coatings that combine reprocessability with active functionalities remains a central challenge for the composites sector. In this work, a healable, electrically conductive coating was formulated using epoxidized castor oil (ECO) as a bio-based matrix, dibutyl phosphate (DBP) as a transesterification catalyst, and short recycled carbon fibres (RCFs, 2 mm in length) as a conductive filler at loadings of 10 and 20 phr. Formulations were UV-cured via cationic photopolymerization and characterized across the full liquid-to-solid processing chain. FT-IR and photo-DSC showed that increasing RCF content progressively reduced curing rate and conversion, an effect attributed to light scattering/absorption by the fibres and restricted chain mobility, although gel content remained above 98% in all cases. DMTA showed that RCF did significantly affect the glass transition temperature but markedly increased the rubbery storage modulus and apparent crosslink density, consistent with a physical reinforcement mechanism. Stress relaxation tests confirmed the dynamic bond exchange behaviour in all formulations, with the apparent activation energy decreasing from 112 kJ/mol for the neat resin to 33–34 kJ/mol upon RCF incorporation. This significant reduction suggests that the presence of RCF facilitates the bond-exchange process, potentially through interfacial interactions between the polymer network and the fibre surface. However, the specific molecular mechanism responsible for this effect cannot be established from the present data. Electrical conductivity peaked at 10 phr RCF (3.6 × 10⁻³ S/m), enabling measurable Joule heating, while the 20 phr formulation showed reduced conductivity linked to voids and lower conversion. Thermally triggered healing at 120 °C for 6 h restored mechanical integrity, which is higher than reference values, demonstrating the coating’s capacity for repeated repair through its dynamic covalent network. Full article

Through-going structures are widely distributed in rock masses of underground engineering, and their dip angles act as the core factor affecting the stress field and mechanical response of surrounding rock. To reveal the mechanical mechanism of rock masses containing through-going structures with different dip angles, this study adopts a combined method of theoretical derivation, indoor model testing and numerical simulation. Firstly, a plane strain mechanical model is established to classify Tectonically-induced Stress, Residual Gravitational Stress and engineering-induced stress, and the theoretical formulas for stress components, stress residual coefficient and stress deflection angle are derived. Secondly, rock-like specimens with through-going structures of various dip angles are prepared and biaxial compression tests are carried out to monitor mechanical parameters such as surrounding rock strain and peak strength. Finally, a large-scale numerical model is built by FLAC2D (version 7.0) software to simulate the whole process of stress equilibrium and excavation unloading of rock mass under a normal stress of 20 MPa. Then the data of principal stress, stress components, stress residual coefficient and deflection angle under different dip angles are extracted. The results show that the dip angle of through-going structure exerts a prominent regulatory effect on the rock mass stress field. With the increase of the dip angle, the Tectonically-induced Stress decreases continuously while the Residual Gravitational Stress rises gradually. The variation trend of stress deflection angle is highly consistent with structural dip angle, and the influence of Residual Gravitational Stress on deflection angle is limited. Due to the differences in loading modes and model sizes between indoor tests and numerical simulations, the evolution laws of stress residual coefficient show opposite trends, but both results verify the dominant effect of structural dip angle. Combined with theoretical, experimental and numerical results, the proposed theoretical system can effectively describe the stress evolution law of rock masses with through-going structures, which provides theoretical reference and technical support for the stability analysis of surrounding rock in similar underground engineering. Full article

Phytoene, a colorless precursor of carotenoids, has attracted increasing attention because of its favorable bioavailability, antioxidant activity, and potential applications in functional foods, nutraceuticals, and animal nutrition. However, its industrial utilization remains limited by low natural abundance and the dependence of current production strategies on genetic engineering or metabolic pathway manipulation. In this study, we identified and characterized a naturally occurring Brevundimonas vesicularis strain (Bv-xms2024) exhibiting pronounced phytoene accumulation without genetic modification. The strain was comprehensively characterized using morphological, biochemical, molecular, genomic, metabolomic, and transcriptional analyses. Quantitative LC–MS/MS analysis demonstrated that Bv-xms2024 accumulated phytoene to 420.42 ± 98.11 μg/g dry biomass after 96 h of cultivation, substantially exceeding the levels of downstream carotenoids, including β-carotene and astaxanthin. Optimization of cultivation parameters identified 25 °C, pH 7.0, and 96 h as the optimal conditions for phytoene accumulation, while serial passaging confirmed stable production over 20 generations. Genome annotation identified the carotenoid biosynthetic gene repertoire, while RT-qPCR analysis revealed a temporal shift from early upregulation of crtE and crtB to later upregulation of downstream pathway genes, consistent with the observed phytoene-dominant carotenoid profile. Short-term tolerance evaluations in mice and chickens revealed no observable adverse effects under the tested conditions. Collectively, these findings identify Bv-xms2024 as a promising natural microbial resource for phytoene production and provide a basis for further process development and strain-level safety evaluation. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article

Chicken soup is widely appreciated for its nutritional value and flavor. This study investigated the effects of ultrasonic-assisted stewing (UAS) on flavor and non-volatile compounds (NVCs) using gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis. GC-MS identified 39 volatile organic compounds (VOCs). At 120 min of stewing, the total VOCs concentration in the UAS group reached 150.08 mg/kg, approximately 5.10-fold that of the control group (29.41 mg/kg). Multivariate analysis combined with odor activity values further screened 10 differential VOCs. Aroma recombination and omission tests confirmed that 7 key VOCs, including (E)-2-octenal and (E,E)-2,4-decadienal, contributed to the meaty and fatty sensory attributes. UPLC-MS/MS analysis detected 5032 NVCs across 20 categories, among which 322 key differential NVCs, including small peptides, fatty acids, and nucleotides, showed significant differences between the UAS and control groups. Overall, UAS enhanced the flavor complexity of chicken soup by facilitating the release of flavor precursors, including small peptides, nucleotides, and lipid-derived NVCs, and promoting the formation of key VOCs. This study provides theoretical and technical support for the application of UAS in soup processing. Full article

Cemented paste backfill (CPB) is widely used in mining, but its high brittleness, low toughness, and limited ductility can cause it to crack and spall, or even damage the overall structure, thereby limiting its application in deep underground mine excavations. To this end, this study investigates the damage and failure mechanisms of cemented gangue–rubber paste backfill (CGRPB) and analyses its energy evolution characteristics. The aims are to: (i) assess the CGRPB mechanical properties, i.e., toughness, ductility, and brittleness due to incorporating rubber; (ii) analyze the fracture propagation process of CGRPB from an energy evolution perspective. Therefore, monotonic and cyclic compression tests were conducted on CGRPB samples containing 0%, 5%, and 10% recycled rubber powder. This study focuses on analyzing compressive strength, failure modes, stress–strain responses, energy evolution and the damage evolution process. Key findings include: (1)the effect of rubber incorporation on strength is dosage- and curing-age-dependent; a moderate rubber content (5%) improves early-age strength, whereas excessive rubber addition reduces strength due to increased porosity and weakened load-bearing capacity; (2) samples with rubber significantly reduce the length, number, and width of cracks, achieving better structural integrity; (3) introducing rubber improves the pre-peak deformation capacity of the samples; (4) the strain growth magnitude is positively correlated with the rubber content, enhancing their toughness and ductility; (5) adding rubber effectively reduces the damage propagation rate within the sample; (6) under loading, rubber elastic deformation in samples dissipates energy, which describes the approximately linear energy storage and dissipation trend; (7) among the investigated rubber contents, 5% rubber incorporation achieved a favorable balance between mechanical strength, toughness, and ductility. Full article

The scientific problem addressed in this study is how a continuous mechanical architecture can maintain stable lettuce handling while improving treatment-medium access to irregular, overlapping leaf surfaces. We formulate this problem as a morphology-informed design and evaluation task. The proposed machine integrates soil removal, a reserved vision-based yellow-leaf detection and root-trimming station, multi-angle disinfection, water–air washing, combined airflow drying, film wrapping, weighing, and boxing modules on a chain-conveyor platform with bowl-shaped fixtures. The evaluation follows a design-to-evidence workflow: lettuce morphology and process requirements are mapped to module geometry; chain, lead-screw, gear, and motor parameters are checked analytically; an application-oriented geometric spray-coverage model tests fixed versus swinging bilateral nozzles; static finite element analysis screens the frame under defined design loads; and prototype assembly verifies spatial compatibility. The covered-surface proxy increased from 7.24% for fixed bilateral spraying to 13.58% for a ±35° swinging case under explicit screening assumptions, while the frame analysis gave 0.0224 mm maximum deformation and 7.30 MPa maximum von Mises stress. These outputs support a preliminary, mechanically feasible platform and a testable explanation for why adjustable spray orientation may improve access to complex lettuce surfaces. They do not constitute measured cleaning, microbial, trimming, drying, packaging, throughput, or reliability performance. Full article

Chronic kidney disease (CKD) is sustained by a network of sterile inflammation, oxidative and metabolic stress, uremic toxin retention, gut barrier dysfunction, and maladaptive immune activation. These processes contribute to kidney fibrosis, cardiovascular injury, wasting, and excess mortality, but inflammatory biomarkers do not by themselves establish therapeutic causality. This narrative review integrates mechanistic and therapeutic evidence using an explicit three-layer translational hierarchy. Renin–angiotensin system inhibitors, sodium–glucose cotransporter-2 inhibitors, finerenone, and glucagon-like peptide-1 receptor agonists improve cardiorenal outcomes and have plausible anti-inflammatory actions, although inflammatory mediation remains unproven. Interleukin-1 blockade provides cardiovascular proof of principle and small dialysis feasibility data. Interleukin-6 ligand inhibition produces marked human target engagement; however, headline results from the completed phase 3 ZEUS trial showed no reduction in three-point major adverse cardiovascular events with ziltivekimab despite biomarker suppression, while serious infections were more frequent. POSIBIL6ESKD continues to test clazakizumab in inflamed dialysis patients. Direct NLRP3 inhibition has entered early human CKD development, whereas senescence-directed and microbiota-based approaches remain less mature. Future progress requires inflammatory endotyping, repeated biomarker assessment, mechanistically aligned outcomes, and rigorous infection surveillance. ZEUS underscores that pathway suppression must deliver clinical benefit beyond contemporary standard therapy. Full article

This study examines how firms convert dynamic capabilities into green innovation (GI) by developing green core competence (GCC). Drawing on resource-based theory and dynamic capability theory and incorporating core competence logic, the study investigates the direct effects of absorptive capacity (AC) and technological capability (TC) on GI, the mediating role of GCC, and the moderating role of green organizational culture (GOC) in the capability-to-competence process. Survey data were collected from 324 managers in China’s electronic information manufacturing industry, a technology-intensive sector facing pressures for technological upgrading and environmental improvement. Partial least squares structural equation modeling was used to test the proposed mediation and moderation model. The results show that AC and TC both positively affect GI and GCC, while TC has stronger effects. GCC positively affects GI and partially mediates the relationships between AC and GI and between TC and GI. GOC further strengthens the positive effects of AC and TC on GCC. These findings suggest that GI depends not only on the possession of dynamic capabilities but also on their conversion into a green-specific competence base. The study offers a contextualized explanation of how technology-intensive manufacturing firms organize internal capabilities to support sustainable manufacturing practices. Full article

Sourdough fermentation is an important technological process in bread production, influencing acidification, product quality, and storage stability. The present study developed and comparatively evaluated two-strain and multi-strain bacterial starter cultures for application in different flour matrices and under semi-industrial bread-making conditions. The starter cultures comprised selected strains of Lactiplantibacillus plantarum, Lacticaseibacillus rhamnosus, Levilactobacillus brevis, Limosilactobacillus fermentum, Fructilactobacillus sanfranciscensis, and Propionibacterium freudenreichii subsp. shermanii. Baker’s yeast was added separately during final dough preparation and was not part of the bacterial starter combinations. Starter performance was assessed through viable cell counts, titratable acidity, in vitro antimicrobial activity, dough fermentation time, bread volume, descriptive sensory evaluation, and the onset of visually detectable microbial spoilage under the tested storage conditions. The selected combinations adapted to the investigated flour matrices, maintained high viable cell concentrations, supported acidification, and were successfully applied in semi-industrial bread production. Several formulations showed favorable technological and descriptive sensory characteristics and delayed the appearance of visible bacterial and fungal spoilage compared with the corresponding controls. Overall, the findings demonstrate the practical potential of the developed multi-strain starter cultures for application across different flour matrices and provide a strong basis for further technological refinement and mechanistic characterization of these sourdough systems. Full article

The development of hydrogel bioinks that combine structural stability with biological compatibility remains a major challenge in extrusion-based 3D printing for tissue engineering. In this proof-of-concept study, bacterial cellulose (BC) obtained from kombucha fermentation was explored as a sustainable nanofibrillar component for alginate/chondroitin sulfate (CS)/silicon-substituted hydroxyapatite (Si-HA) composite inks. Following alkaline purification, mechanical processing, and freeze-drying, BC was characterized by scanning electron microscopy (SEM), ATR-FTIR spectroscopy, and X-ray diffraction (XRD), revealing a highly entangled nanofibrillar architecture with high crystallinity (85.4%) and strong hydrogen-bonding potential. Four hydrogel formulations were developed as a comparative 2 × 2 matrix, contrasting BC-containing systems with methylcellulose (MC)-containing reference systems at two Si-HA loadings. Reduced-viscosity measurements of the uncrosslinked precursor formulations showed higher values at the lower Si-HA loading in both formulation series. All formulations could be extruded as acellular inks into grid-like constructs and retained identifiable macroporous architectures after ionic crosslinking. Swelling increased between 24 and 48 h, while mass loss remained limited after the initial 24 h incubation period. In direct-contact testing with L929 fibroblasts, cell viability remained above 84% after 48 h, meeting the ISO 10993-5 non-cytotoxicity criterion. These findings support the feasibility of incorporating physically processed kombucha-derived BC into alginate-based composite inks. Full article

Terrain types significantly affect the dynamics and locomotion performance of hexapod robots during walking. Terrain classification is a key solution to modify gait patterns in different terrains and recognize hazardous conditions. Perceiving the terrain with proprioceptive sensing is a robust and reliable approach in extreme conditions. In this paper, an efficient terrain classification approach for a hexapod robot is proposed. The proposed method combines a deep classification framework including the long short-term memory (LSTM) network and an effective statistical feature extraction. Proprioceptive inertial measurement unit (IMU) data is only used as the sensing system for the robot–terrain interaction. In the feature extraction process, four meaningful characteristic features, namely, the mean, median, Lomb–Scargle periodogram power spectral density (LPSD), and Welch’s power spectral density (WPSD), are extracted from the body orientation data using a sliding-window method. These features are combined and fed into the network to perform the training and testing processes. In the experiments, the proposed method is evaluated with commonly used soft computing and deep learning models. The classification performance for the concrete, pebble, and waxed tile terrains reaches 100% with the proposed method. The overall accuracy, precision, sensitivity, specificity, F1-score, and Matthew correlation coefficient are recorded as 95.45%, 96.36%, 95.28%, 98.86%, 95.49%, and 94.61%, respectively. These results demonstrate that the proposed approach delivers reliable classification performance with a low-cost and easy-to-implement solution. Full article

Background/Objectives: Skull vibration-induced nystagmus (SVIN) is a robust sign of vestibular asymmetry, yet its relationship with tests of impulsive canal function, short-latency otolithic reflexes, and sustained visuo-vestibular processing remains incompletely defined. To describe four clinical–physiological cases in which SVIN was dominant or disproportionate relative to the canal, otolithic-reflex, and sustained-domain profiles, and to propose a temporal-domain interpretation of this physiological non-congruence. Methods: Four patients with persistent disequilibrium, episodic vertigo, or selective vestibular-reflex dissociation underwent multidomain neuro-otological assessment, including bedside examination, video head impulse test/head impulse paradigm (vHIT/HIMP), suppression head impulse paradigm (SHIMP) when available, cervical and ocular vestibular-evoked myogenic potentials (cVEMPs and oVEMPs), rotatory or caloric testing when available, and optokinetic or other visuo-vestibular paradigms. SVIN was elicited by 100-Hz mastoid vibration and characterized by direction, dimensionality, stimulation-site dependence, reproducibility, and slow-phase velocity when available. Results: SVIN remained clinically informative across patients with preserved, minimally abnormal, or selectively dissociated canal and otolithic-reflex findings. The recurring feature was not normality of all conventional tests, but physiological non-congruence between the vibration-induced ocular response and the canal, otolithic-reflex, or sustained-domain profile. One patient showed normal six-canal vHIT and symmetric VEMPs despite an exceptionally large fixed-direction SVIN; another showed selective cVEMP absence with preserved oVEMPs and normal vHIT. Atypical multidimensional SVIN coexisted with marked optokinetic directional asymmetry in the adolescent case. Conclusions: SVIN is not a surrogate for vHIT, VEMPs, caloric, rotatory, or velocity-storage-related testing. It should be interpreted as a distinct phase-locked probe of vibration-sensitive vestibular imbalance. Irregular afferents provide the supported physiological framework, whereas dimorphic afferents represent a plausible, hypothesis-generating bridge population capable of contributing to otherwise unexplained clinicophysiological dissociations. Full article