Search Results (15,058)

Microbial lipid production from renewable carbon sources, particularly lignocellulosic hydrolysates, is a promising alternative to plant-derived oils and fats for food applications, as it can minimize the land use by utilizing agricultural wastes and byproducts from food production. In this context, a standard approach to prevent oxygen limitation at reduced air gassing rates during long-term aerobic microbial processes is to operate bioreactors at increased pressure for elevating the gas solubility in the fermentation broth. This study investigates the effect of absolute pressures of up to 2.5 bar on the conversion of the carbon sources (glucose, xylose, and acetate), growth, and lipid biosynthesis by Cutaneotrichosporon oleaginosus converting a synthetic nutrient-rich lignocellulosic hydrolysate at low air gassing rates of 0.1 vessel volume per minute (vvm). Increasing pressure delayed xylose uptake, reduced acetic acid consumption, and reduced biomass formation. Lipid accumulation decreased with increasing pressure, except for fermentations at 1.5 bar, which achieved a maximum lipid content of 83.6% (±1.6, w/w) (weight per weight in %). At an absolute pressure of 1.5 bar, a lipid yield from glucose, xylose, and acetic acid of 38% (w/w) was reached after 6 days of fermentation. The pressure sensitivity of C. oleaginosus may pose challenges on an industrial scale due to the dynamic changes in pressure when the yeast cells pass through the bioreactor. Increasing liquid heights in full-scale bioreactors will result in increased hydrostatic pressures at the bottom, substantially reducing lipid yields, e.g., to only 23% (w/w) at 2.0–2.5 bar, as shown in this study. However, further scale-up studies with dynamic pressure regimes (1–2.5 bar) may help to evaluate scale-up feasibility. Full article

The present study investigates the potential of poplar (Populus spp.) biomass from phytoremediation plantations as a feedstock for downdraft fixed bed gasification. The biomass was characterized in terms of moisture, ash content, elemental composition (C, H, N, O), and calorific values (HHV and LHV), confirming its suitability for thermochemical conversion. Gasification tests yielded a volumetric syngas production of 1.79 Nm³ kg⁻¹ biomass with an average composition of H₂ 14.58 vol%, CO 16.68 vol%, and CH₄ 4.74 vol%, demonstrating energy content appropriate for both thermal and chemical applications. Alkali and alkaline earth metals (AAEM), particularly Ca (273 mg kg⁻¹) and Mg (731 mg kg⁻¹), naturally present enhanced tar reforming and promoted reactive gas formation, whereas heavy metals such as Cd (0.27 mg kg⁻¹), Pb (0.02 mg kg⁻¹), and Bi (0.01 mg kg⁻¹) were detected only in trace amounts, posing minimal environmental risk. The results indicate that poplar pruning residues from phytoremediation sites can be a renewable and sustainable energy resource, transforming a waste stream into a process input. In this perspective, the integration of soil remediation with syngas production constitutes a tangible model of circular economy, based on the efficient use of resources through the synergy between environmental remediation and the valorization and sustainable management of marginal biomass—i.e., pruning residues—generating environmental, energetic, and economic benefits along the entire value chain. Full article

Current approaches to dynamic sign language recognition commonly rely on dense landmark representations, which impose high computational cost and hinder real-time deployment on resource-constrained devices. To address this limitation, this work proposes a computationally efficient framework for real-time dynamic Mexican Sign Language (MSL) recognition based on a multimodal minimal angular-geometry representation. Instead of processing complete landmark sets (e.g., MediaPipe Holistic with up to 468 keypoints), the proposed method encodes the relational geometry of the hands, face, and upper body into a compact set of 28 invariant internal angular descriptors. This representation substantially reduces feature dimensionality and computational complexity while preserving linguistically relevant manual and non-manual information required for grammatical and semantic discrimination in MSL. A real-time end-to-end pipeline is developed, comprising multimodal landmark extraction, angular feature computation, and temporal modeling using a Bidirectional Long Short-Term Memory (BiLSTM) network. The system is evaluated on a custom dataset of dynamic MSL gestures acquired under controlled real-time conditions. Experimental results demonstrate that the proposed approach achieves 99% accuracy and 99% macro F1-score, matching state-of-the-art performance while using fewer features dramatically. The compactness, interpretability, and efficiency of the minimal angular descriptor make the proposed system suitable for real-time deployment on low-cost devices, contributing toward more accessible and inclusive sign language recognition technologies. Full article

Background/Objectives: Volatile organic compounds (VOCs) are emerging as non-invasive biomarkers of metabolic and disease-related processes, yet their reliable detection from complex biological matrices such as urine remains analytically challenging. This study aimed to establish a robust, non-targeted headspace solid-phase microextraction gas chromatography–mass spectrometry (HS–SPME GC–MS) workflow optimized for very small-volume urinary samples. Methods: We systematically evaluated the effects of pH adjustment and NaCl addition on VOC extraction efficiency using a 75 µm CAR/PDMS fiber and a sample volume of only 0.75 mL. Method performance was further assessed using concentration-dependent experiments with representative VOC standards and by application to real human urine samples analyzed in technical triplicates. Results: Acidification to pH 3 markedly improved extraction performance, increasing both total signal intensity and the number of detectable VOCs, whereas alkaline conditions and additional NaCl produced only minor effects. Representative VOC standards showed compound-specific linear dynamic ranges with minimal carry-over within the relevant analytical range. Application to real urine samples confirmed high analytical reproducibility, with triplicates clustering tightly in principal component analysis and most metabolites exhibiting relative standard deviations below 25%. Conclusions: The optimized HS–SPME GC–MS method enables comprehensive, non-targeted urinary VOC profiling from limited sample volumes. This workflow provides a robust analytical foundation for exploratory volatilomics studies under sample-limited conditions and supports subsequent targeted method refinement once specific compounds or chemical classes have been prioritized. Full article

Macroalgae are increasingly recognized as a valuable source of nutrients and bioactive compounds for animal nutrition, including for aquatic species. However, the complex structure of the macroalgal cell wall limits the accessibility of intracellular components, restricting their use in feeds. To overcome this limitation, macroalgal hydrolysis using various technological treatments has been tested, often employing a low solid-to-water ratio, which complicates downstream processing due to phase separation. In contrast, high-solids loading hydrolysis has the advantage of producing a single and consolidated fraction, simplifying subsequent processing and application. The present study assessed the effectiveness of high-solids loading water or alkaline (0.5 and 1N NaOH) autoclaving for 30 or 60 min, applied alone or followed by sequential enzymatic hydrolysis, using a xylanase-rich enzymatic complex aimed at promoting cell wall disruption and increasing the extractability of intracellular components in the red macroalga Palmaria palmata with minimal free water. The 1N NaOH treatment for 30 min decreased neutral and acid detergent fiber while increasing Folin–Ciocalteu total phenolic content (GAE) (expressed as gallic acid equivalent) and the water-soluble protein fraction and decreased crude protein, indicating enhanced extractability of these components. Microscopic examination showed relatively mild structural changes on the surface of P. palmata after high-solids loading alkaline (1N NaOH) autoclaving for 30 min. Following alkaline or water treatment, the enzymatic complex hydrolysis further increased the Folin–Ciocalteu total phenolic content (GAE), with minimal effects on NDF, ADF, or crude protein. Overall, these results showed that high-solids loading alkaline autoclaving, with or without subsequent enzymatic hydrolysis, effectively disrupts P. palmata cell walls and induces substantial modifications while simplifying processing by avoiding phase separation. Full article

Coffee grounds can be used in agriculture as a bio-input to enhance soil fertility and biodiversity in the long term. Furthermore, the use of coffee grounds in agriculture is a sustainable practice because it reuses an organic waste product as natural fertilizer and minimizes the environmental impact resulting from the improper disposal of waste. This study aimed to identify the effects of coffee grounds on the growth and yield of iceberg lettuce plants. The experiment was conducted in a greenhouse using 4 kg of substrate in containers with a 5.356 dm³ capacity, following a completely randomized design in a 2 × 2 factorial arrangement. The primary treatment consisted of plants grown in two types of substrate: soil and sand (01) and soil, sand, and 10% coffee grounds (02). The secondary treatment corresponded to irrigation with water (01) and a 10% coffee ground extract solution (02). Coffee grounds incorporated into the soil increase soil fertility; however, they reduce lettuce growth due to the toxicity of the compounds present and should not be used without prior treatment. Processing coffee grounds into irrigation solutions shows promise due to its high potential for use as an agricultural bio-input in lettuce production. This solution enhances the growth and development of the species, resulting in vigorous plants with market value. Full article

The diffusion and trapping of particles in complex porous media are fundamental processes in materials science and bioengineering. This study systematically investigates the influence of pore radius on particle capture kinetics within a three-dimensional cylindrical pore containing randomly distributed absorbing traps. Numerical simulations were performed for a wide range of pore radii (from 3a to 81a, a is a minimal length of the problem, arbitrary unit) and trap concentrations M (from 100 to 5090, these numbers are determined by the pore geometry) using a random walk algorithm. The particle lifetime (τ), characterizing the capture rate, was calculated and analyzed. Results reveal three distinct capture regimes dependent on trap concentration: a diffusion-limited regime at low concentration M (<1000), a transition regime at medium M (1000 < M < 2000), and a trap-density-dominated saturation regime at high M (>2000). For each regime, optimal approximating functions for τ(M) were identified. Furthermore, empirical relationships between the approximating coefficients and the pore radius were derived, which enable the prediction of particle lifetimes. The findings demonstrate that while the pore radius significantly impacts capture kinetics at low trap densities, its influence diminishes as trap concentration increases, converging towards a universal behavior dominated by trap density. Full article

Global polymer production has rapidly escalated in response to the increasing global demand. These materials are highly regarded due to their superior strength-to-weight ratio, cost-effectiveness, and ease of manufacturing. This study presents a systematic review aimed at addressing the environmental challenges associated with polymer production. It also seeks to develop a Sustainable Conceptual Model for Environmental Impact Assessment (EIA), integrating key sustainability factors, which are often overlooked in existing frameworks. A systematic literature review was conducted following PRISMA guidelines, covering peer-reviewed studies published between 2015 and 2025 in the Scopus and Web of Science databases. Critical gaps in conventional EIA practices for polymer manufacturing were identified, forming the basis for the proposed integrated sustainability framework. The proposed model provides a structured methodology for assessing key sustainability dimensions across polymer production, enabling a more comprehensive evaluation of environmental impacts throughout the polymer production process. As validation for the model, a pilot study with 68 industry experts was analyzed through reliability testing, confirmatory factor analysis, and regression-based hypothesis testing. The results supported the proposed model. Industries can utilize the model to develop targeted sustainability strategies, minimize environmental footprints, and inform policymaking efforts aimed at improving the environmental performance of polymer manufacturing. Full article

Few-Shot Text Classification (FSTC) aims to classify text accurately into predefined categories using minimal training samples. Recently, prompt-tuning-based methods have achieved promising results by constructing verbalizers that map input data to the label space, thereby maximizing the utilization of pre-trained model features. However, existing verbalizer construction methods often rely on external knowledge bases, which require complex noise filtering and manual refinement, making the process time-consuming and labor-intensive, while approaches based on pre-trained language models (PLMs) frequently overlook inherent prediction biases. Furthermore, conventional data augmentation methods focus on modifying input instances while overlooking the integral role of label semantics in prompt tuning. This disconnection often leads to a trade-off where increased sample diversity comes at the cost of semantic consistency, resulting in marginal improvements. To address these limitations, this paper first proposes a novel Bayesian Mutual Information-based method that optimizes label mapping to retain general PLM features while reducing reliance on irrelevant or unfair attributes to mitigate latent biases. Based on this method, we propose two synergistic generators that synthesize semantically consistent samples by integrating label word information from the verbalizer to effectively enrich data distribution and alleviate sparsity. To guarantee the reliability of the augmented set, we propose a Low-Entropy Selector that serves as a semantic filter, retaining only high-confidence samples to safeguard the model against ambiguous supervision signals. Furthermore, we propose a Difficulty-Aware Adversarial Training framework that fosters generalized feature learning, enabling the model to withstand subtle input perturbations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on most few-shot and full-data splits, with F1 score improvements of up to +2.8% on the standard AG’s News benchmark and +1.0% on the challenging DBPedia benchmark. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article

The growing demand for sustainable cosmetic products and the rapid expansion of the pet care market have driven the search for environmentally friendly, safe, and effective alternatives to conventional formulations. In this study, an ecofriendly solid shampoo for pets was developed using exclusively natural ingredients and a microbial biosurfactant produced by Starmerella bombicola ATCC 22214 as a surface-active component. The biosurfactant was combined with renewable anionic and nonionic surfactants, conditioning agents, natural oils and butters, and minimal water content to obtain a compact, solid formulation. The shampoo was produced through a controlled multi-phase process and subsequently characterized by physicochemical, microbiological, toxicological, and performance analyses. The formulation exhibited stable pH values suitable for pet skin, low moisture content, absence of free alkalinity, high solid content, and satisfactory foaming capacity. Cleaning efficiency tests demonstrated effective removal of artificial sebum from pet fur while preserving softness and shine. Microbiological assays confirmed the absence of bacterial and fungal contamination, and toxicological evaluations revealed no cytotoxicity and low eye irritation potential. In addition, the shampoo showed 100% biodegradability and maintained physicochemical and organoleptic stability over six months of storage. Overall, the results demonstrate that the developed solid shampoo represents an innovative, safe, and biodegradable alternative that reduces water consumption and plastic packaging, contributing to sustainable development in the pet cosmetics sector. Full article

Background/Objectives: The Rett Syndrome Behavior Questionnaire (RSBQ) is a widely used caregiver-reported instrument for assessing behavioral and neurological features of Rett syndrome (RTT). However, a validated Korean version has not been available. This study aimed to translate the RSBQ into Korean (K-RSBQ) and to evaluate its psychometric properties in a Korean RTT population. Methods: The RSBQ was translated and back-translated using standardized procedures and refined through a Delphi process. Primary caregivers of individuals with clinically diagnosed RTT completed an online survey including the K-RSBQ and the Childhood Autism Rating Scale (CARS). Test–retest reliability was assessed in a subset of caregivers who completed the questionnaire twice within one week, and inter-rater reliability was evaluated when an additional caregiver was available. Results: Sixty-six primary caregivers participated. The K-RSBQ demonstrated high internal consistency for the total score (Cronbach’s α = 0.912) and moderate-to-high consistency across most subscales. Test–retest reliability for the total score was moderate (weighted κ = 0.594), while inter-rater reliability between primary and secondary caregivers was generally low. The hand behavior subscale showed low and non-significant test–retest reliability. The K-RSBQ total score exhibited a low-to-moderate correlation with the CARS total score, and the general mood subscale showed a moderate correlation with the CARS emotional response item. Caregivers reported minimal difficulty in understanding the questionnaire items. Conclusions: The K-RSBQ demonstrates acceptable internal consistency and test–retest reliability when administered to primary caregivers, with preliminary evidence supporting its construct validity. Although limitations exist regarding criterion validation and inter-rater agreement, the K-RSBQ represents a feasible and culturally adapted tool for assessing RTT-related behavioral features in Korean clinical and research settings. Full article

The essential oil (EO) of Mentha spicata L. (spearmint) exhibits pronounced biological activity, making it valuable for applications in agrochemistry as an insecticidal agent, in perfumery and cosmetics, and as a natural preservative in the food industry. However, maintaining the integrity and yield of EO during post-harvest processing and extraction remains a key technological challenge. This study aimed to enhance the vacuum-drying (VD) process of spearmint using calcium chloride as a desiccant and to optimize the conditions of supercritical CO₂ extraction (SC-CO₂), including EO separation and the evaluation of its solubility under dynamic extraction conditions. The incorporation of calcium chloride into the VD process reduced drying duration by 21.1% and processing costs by 31.0%, while increasing EO yield by 11%. A decrease in separator pressure from 70 to 10 bar during SC-CO₂ extraction resulted in nearly a threefold increase in EO yield by minimizing the loss of volatile constituents. The solubility of spearmint EO in supercritical CO₂ was successfully described by the Chrastil model and correlated with carvone solubility. The maximum total phenolic content (72.3 ± 2.2 mg gallic acid equivalent per gram) was observed at a CO₂ density of 353.91 kg/m³. The solubility of EO was studied directly using the plant matrix under dynamic conditions. Full article

The explosive growth of artificial intelligence (AI) services has led to massive scaling of GPU computing clusters, causing sharp rises in power consumption and carbon emissions. Although hardware-level accelerator enhancements and deep neural network (DNN) model compression techniques can improve power efficiency, they often encounter deployment barriers and risks of accuracy loss in practice. To address these issues without altering hardware or model architectures, we propose a novel Carbon-Aware Resource Management (CA-RM) framework for GPU clusters. In order to minimize the carbon emission, the CA-RM framework dynamically adjusts energy usage by combining real-time GPU core frequency scaling with intelligent workload placement, aligning computation with the temporal availability of renewable generation. We introduce a new metric, performance-per-carbon (PPC), and develop three optimization formulations: carbon-constrained, performance-constrained, and PPC-driven objectives that simultaneously respect DNN model training deadlines, inference latency requirements, and carbon emission budgets. Through extensive simulations using real-world renewable energy traces and profiling data collected from NVIDIA RTX4090 GPU running representative DNN workloads, we show that the CA-RM framework substantially reduces carbon emission while satisfying service-level agreement (SLA) targets across a wide range of workload characteristics. Through experimental evaluation, we verify that the proposed CA-RM framework achieves approximately 35% carbon reduction on average, compared to competing approaches, while still ensuring acceptable processing performance across diverse workload behaviors. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article