Search Results (10,124)

Phycobiliproteins are recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications. Moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities. Therefore, the search for new methods of their extraction and isolation is still ongoing. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is a promising but understudied method. The process may be carried out both under mild conditions that are suitable for proteins and also for diluted solutions. This paper presents the results of applying the foam fractionation process to concentrate and separate phycobiliproteins. Allo- and C-phycocyanin from a thermophilic Synechococcus PCC 6715 strain were used in extract form after biomass cultivation and disintegration. Two ways of running the process were investigated: batch mode and continuous mode, the latter of which has not been reported in the literature previously. The results indicate that the method can be applied on a larger scale, as the outcomes of the continuous mode processes were comparable to those of the batch mode. Moreover, the results indicate that the process provides, to a certain extent, the opportunity of separating phycobiliproteins from each other. Full article

Microseismic monitoring is extensively applied in hydraulic fracturing and mineral extraction, with accurate event localization being a critical component. Recently, deep learning approaches have shown promise for microseismic event localization; however, most of these supervised methods depend on large, labeled datasets, which are costly and challenging to acquire. To mitigate this issue, we propose a semi-supervised approach based on a residual convolutional autoencoder (RCAE) for automated microseismic localization, designed to leverage limited labeled data effectively and improve source localization accuracy even with small sample sizes. Our method employs pre-training by masking and reconstructing unlabeled seismic records, while integrating residual connections within the encoder to enhance feature extraction from seismic signals. This enables high localization accuracy with minimal labeled data, resulting in significant cost savings. Experimental results indicate that our method surpasses purely supervised approaches on both a 2D salt dome model and a 3D homogeneous half-space model, validating its effectiveness in microseismic localization. Further comparisons with baseline models highlight the method’s advantages, providing an innovative solution for improving cost-efficiency in practical applications. Full article

Background: Self-harm is a growing public health concern and the strongest predictor of suicide in young people (YP). The “Supporting Adolescents with Self-Harm” (SASH) intervention was developed with YP with lived experience and expert clinicians. It involves rapid follow-up after ED attendance and up to six intervention sessions. The intervention has three components: Therapeutic Assessment (TA) of self-harm; an enhanced safety plan (SP); and Solution-Focused Brief Therapy (SFBT). Depending on the YP’s preference, carers can join sessions. Carers can also receive two individual sessions. The clinical and cost-effectiveness of SASH is being evaluated in a randomised controlled trial across nine emergency departments in three NHS Trusts in London, England. A total of 154 YP were recruited between May 2023 and March 2025 and randomised on a 1:1 ratio to SASH alongside Treatment As Usual (TAU) or TAU. A logic model describes the SASH inputs, activities, mechanisms, outcomes and longer-term impacts. The aim of this paper is to (1) illustrate how TA, SP, and SFBT were implemented in practice by presenting intervention materials and session recordings for four YP cases and one carer case and (2) explore how the case study materials/recordings reflect the intervention mechanisms in the SASH logic model. Methods: Each case focused on a different component of the intervention. Intervention materials (TA self-harm diagram and completed SP) and recorded SFBT sessions with four YP and one carer were analysed using a descriptive case study approach. The TA diagram and SP were extracted from medical records. Audio/video recordings of intervention sessions were identified. Recordings of intervention sessions and qualitative interviews were transcribed. Quotes from qualitative interviews with the same participants were included where relevant. Results: Across the four YP cases, some core themes emerged. The role of friendships for young people, particularly at school, was important in both negative and positive ways. Experiencing difficulties with friends at school led to feelings of sadness and stress, which could become overwhelming, leading to thoughts of self-harm (“I just need to hurt myself”), triggering self-harm behaviour. YP described mood changes and signs that they were becoming stressed, which improved their self-awareness and understanding of the link between their feelings and self-harm behaviour. They reflected on what kept them feeling calm and overcoming their fear of burdening others by sharing how they were feeling, as this helped them not to self-harm. They also described difficult feelings stemming from a need to please everyone or needing validation from others. Overcoming these feelings led to less social anxiety and more confidence. This made it easier to go to school and to be more social with friends/student peers, which in turn improved their mood. Conclusions: These case studies demonstrate how YP improved their self-awareness and understanding of the link between feelings and self-harm behaviour and identified personal strategies for managing difficult feelings and situations. The carer case study demonstrates how sessions with carers can facilitate carers better supporting their YP’s mental health. Supporting YP and carers in this way has the potential to reduce the risk of future self-harm. Full article

With the growing demand for eco-friendly food packaging, poly(butylene adipate-co-terephthalate) (PBAT)/polylactic acid (PLA) composite films have emerged as promising biodegradable alternatives, but their inherent limitations (e.g., poor antioxidant capacity, weak UV stability, and insufficient antimicrobial activity) hinder practical applications. This study aimed to address these drawbacks by incorporating Rosmarinus officinalis L. extract (RM) as a natural multifunctional additive. PBAT/PLA/RM blend films with RM concentrations of 0.1%, 0.3%, 0.5%, and 1% were fabricated via melt extrusion and blown film processing. Key characterizations were conducted to evaluate thermal stability, mechanical properties, morphology, antioxidant activity, UV-shielding performance, antimicrobial efficacy, and biodegradability. The results showed that RM significantly enhanced the antioxidant capacity of the films, with the highest DPPH radical scavenging activity achieved at 0.3% RM. UV-blocking performance improved incrementally with increasing RM concentration, and films containing ≥0.5% RM filtered over 90% of UVA and UVB radiation. All composite films met biodegradability standards, with over 90% degradation observed after 240 days of composting, though RM prolonged the initial degradation stage by inhibiting early microbial activity. However, the antimicrobial effect of RM was limited, and concentrations exceeding 1% caused film stickiness, impeding processing. This work demonstrates that RM is a viable natural additive for functionalizing PBAT/PLA films, offering enhanced antioxidant and UV-shielding properties while maintaining biodegradability, thus providing a promising solution for sustainable food packaging. Full article

The exponential growth of data-driven applications has intensified performance demands on database systems, where cache management represents a critical bottleneck. Traditional cache replacement policies such as Least Recently Used (LRU) and Least Frequently Used (LFU) rely on simple heuristics that fail to capture complex temporal and frequency patterns in modern workloads. This research presents a modular machine learning-enhanced cache management framework that leverages pattern recognition to optimize database performance through intelligent replacement decisions. Our approach integrates multiple machine learning models—Random Forest classifiers, Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Gradient Boosting methods—within a modular architecture enabling seamless integration with existing database systems. The framework incorporates sophisticated feature engineering pipelines extracting temporal, frequency, and contextual characteristics from query access patterns. Comprehensive experimental evaluation across synthetic workloads, real-world production datasets, and standard benchmarks (TPC-C, TPC-H, YCSB, and LinkBench) demonstrates consistent performance improvements. Machine learning-enhanced approaches achieve $8.4\%$ to $19.2\%$ improvement in cache hit rates, $15.3\%$ to $28.7\%$ reduction in query latency, and $18.9\%$ to $31.4\%$ increase in system throughput compared to traditional policies and advanced adaptive methods including ARC, LIRS, Clock-Pro, TinyLFU, and LECAR. Random Forest emerges as the most practical solution, providing $18.7\%$ performance improvement with only $3.1\%$ computational overhead. Case study analysis across e-commerce, financial services, and content management applications demonstrates measurable business impact, including $8.3\%$ conversion rate improvements and USD 127,000 annual revenue increases. Statistical validation ($p<0.001$, Cohen’s $d>0.8$) confirms both statistical and practical significance. Full article

The fiberboard industry remains heavily reliant on synthetic, formaldehyde-based adhesives, which, despite their cost-effectiveness and strong bonding performance, present significant environmental and human health concerns due to volatile organic compound (VOC) emissions. In response to growing sustainability imperatives and regulatory pressures, the development of non-toxic, renewable, and high-performance bio-based adhesives has emerged as a critical research frontier. This review, conducted through both narrative and systematic approaches, synthesizes current advances in green adhesive technologies with emphasis on lignin, tannin, starch, protein, and hybrid formulations, alongside innovative synthetic alternatives designed to eliminate formaldehyde. The Evidence for Policy and Practice Information and Coordinating Centre (EPPI) framework was applied to ensure a rigorous, transparent, and reproducible methodology, encompassing the identification of research questions, systematic searching, keywording, mapping, data extraction, and in-depth analysis. Results reveal that while bio-based adhesives are increasingly capable of approaching or matching the mechanical strength and durability of urea–formaldehyde adhesives, challenges persist in terms of water resistance, scalability, cost, and process compatibility. Hybrid systems and novel crosslinking strategies demonstrate particular promise in overcoming these limitations, paving the way toward industrial viability. The review also identifies critical research gaps, including the need for standardized testing protocols, techno-economic analysis, and life cycle assessment to ensure the sustainable implementation of these solutions. By integrating environmental, economic, and technological perspectives, this work highlights the transformative potential of green adhesives in transitioning the fiberboard sector toward a low-toxicity, carbon-conscious future. It provides a roadmap for research, policy, and industrial innovation. Full article

This study addresses the practical demand for facial recognition of pigs in the food safety and insurance industries, tackling the challenge of low recognition accuracy caused by complex farming environments, occlusions, and similar textures. To this end, we propose an enhanced model, DGS-YOLO, based on YOLOv11n, designed to achieve precise facial recognition of group-raised young pigs. The core improvements of the model include the following: (1) replacing standard convolutions with dynamic convolutions (DMConv) to enhance the network’s adaptive extraction capability for critical detail features; (2) designing a C3k2_GBC module with a bottleneck structure to replace the C3k2 neck, enabling more efficient capture of multi-scale contextual information; (3) introducing the SimAM parameter-free attention mechanism to optimize feature focusing; (4) employing the Shape-IoU loss function to mitigate the impact of bounding box geometry on regression accuracy. Experiments on self-built datasets demonstrate that DGS-YOLO achieves 4%, 2.1%, and 2.3% improvements in accuracy, recall, and mAP50, respectively, compared to the baseline model YOLOv11n. Furthermore, its overall performance surpasses that of Faster R-CNN and SSD in comprehensive evaluation metrics. Especially in limited sample scenarios, the model demonstrates strong generalization ability, with accuracy and mAP50 further increased by 20.1% and 10.3%. This study provides a highly accurate and robust solution for animal facial recognition in complex scenarios. 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

Fish skin, a by-product of commercial fish processing, represents a viable source of type I collagen. Acetic acid has been widely used for the extraction of collagen from fish skin because it can preserve the native structure. However, it requires an extraction time of more than 72 h and complex and time-consuming dialysis steps to remove acetic acid residues from the extracted collagen which can otherwise cause inferior structural modifications. Therefore, this study describes a simple time- and cost-effective method to extract collagen using hydrochloric acid. The experiments focused on understanding the behavior of fish skin and changes in the extraction medium. The extraction procedure developed in this study includes treatment with a 0.01 M hydrochloric acid solution at a 1:20 mass to volume ratio for 5 h, followed by homogenization. The native triple-helical structure of collagen was confirmed by ATR-FTIR and circular dichroism spectroscopy. Thermal stability was confirmed by differential scanning calorimetry. This study also provides guidelines for the application of this knowledge to skin of any fish species of interest: (i) an upper limit of pH 4 during collagen extraction; (ii) a manageable viscosity of the collagen extract solution; and (iii) as few undissolved skin pieces as possible after homogenization. Full article

Background: Early diagnosis of Alzheimer’s disease (AD) is essential for slowing disease progression and mitigating cognitive decline. However, conventional diagnostic methods are often invasive, time-consuming, and costly, limiting their utility in large-scale screening. There is an urgent need for scalable, non-invasive, and accessible screening tools. Methods: We propose a novel screening framework combining a pre-trained multimodal large language model with structured MMSE speech tasks. An artificial intelligence-assisted multilingual Mini-Mental State Examination system (AAM-MMSE) was utilized to collect voice data from 1098 participants in Sichuan and Chongqing. CosyVoice2 was used to extract speaker embeddings, speech labels, and acoustic features, which were converted into statistical representations. Fourteen machine learning models were developed for subject classification into three diagnostic categories: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). SHAP analysis was employed to assess the importance of the extracted speech features. Results: Among the evaluated models, LightGBM and Gradient Boosting classifiers exhibited the highest performance, achieving an average AUC of 0.9501 across classification tasks. SHAP-based analysis revealed that spectral complexity, energy dynamics, and temporal features were the most influential in distinguishing cognitive states, aligning with known speech impairments in early-stage AD. Conclusions: This framework offers a non-invasive, interpretable, and scalable solution for cognitive screening. It is suitable for both clinical and telemedicine applications, demonstrating the potential of speech-based AI models in early AD detection. Full article

An optical fiber chemical sensor using a molecularly imprinted chitosan membrane coated on the surface of a bent optical fiber probe was developed for selectively analyzing 4-nitrophenol (4-NP) in water samples. When the sensor probe is exposed to a water sample, the chitosan MIP membrane extracts/concentrates 4-NP from the water sample into the membrane. The 4-NP extracted into the membrane was detected by passing a light beam through the optical fiber and the interaction of the 4-NP in the membrane with an evanescent wave of light guided through the optical fiber was detected as a sensing signal. This sensor detects the intrinsic optical absorption signal of 4-NP itself as a sensing signal. No chemical reagent was needed in analyzing this compound in a sample. The sensor is reversible, can be used for continuous monitoring of 4-NP in a sample, and has a quick response with a response time of 5 min. The sensor has high sensitivity and selectivity because the MIP membrane selectively concentrates 4-NP by 1.4 × 10⁴ times into the membrane from a sample solution, but blocks out interference species, including its isomers and derivatives, from entering the membrane. The sensor achieved a detection limit of 2.5 ng/mL (0.018 µM), which is lower than most reported analytical techniques for analyzing this compound in water samples. This sensor can discriminate 4-NP from its isomers and derivatives, such as 2-NP, 3-NP, 2-Cl-4-NP, and 2,4-di-NP, with a selectivity factor ranging from 104 to 1922. This is the first reported case of an MIP-based optical fiber chemical sensor with the capability of discriminating an organic compound from its closely related positional isomers, which demonstrates the high selectivity nature of the MIP-based optical fiber chemical sensor technique. The sensor has been used for analyzing 4-NP in a standard addition sample. The obtained recovery rate ranged from 93% to 101%, demonstrating the application potential of this sensor in water quality analysis. Full article

The increasing volume of produced water (PW) generated by oil extraction activities has intensified the need for environmentally sustainable strategies that enable its reuse and valorization. Biotechnological approaches, particularly those involving the microbial production of value-added compounds, offer a promising route for transforming PW from an industrial waste into a useful resource. In this context, bacterial exopolysaccharides (EPS) have gained attention due to their diverse functional properties and applicability in bioremediation, bioprocessing and petroleum-related operations. This study evaluated the potential of Lelliottia amnigena to synthesize EPS using oilfield PW as a component of the culture medium in stirred-tank bioreactors. Three conditions were assessed: a control using distilled water (dW), PW diluted to 25% (PW25%) and dialyzed PW (DPW). Batch experiments were conducted for 24 h, during which biomass growth, EPS accumulation and dissolved oxygen dynamics were monitored. Post-cultivation analyses included elemental and monosaccharide composition, scanning electron microscopy and rheological characterization of purified EPS solutions. EPS production varied among treatments, with dW and DPW yielding approximately 9.6 g L⁻¹, while PW25% achieved the highest productivity (17.55 g L⁻¹). The EPS samples contained fucose, glucose and mannose, with compositional differences reflecting the influence of PW-derived minerals. Despite reduced apparent viscosity under PW25% and DPW conditions, the EPS exhibited physicochemical properties suitable for biotechnological applications, including potential use in fucose recovery, drilling fluids and lubrication systems in the petroleum sector. The EPS also demonstrated substantial adsorption capacity, incorporating salts from PW and contributing to contaminant removal. This study demonstrates that PW can serve both as a substrate and as a source of functional inorganic constituents for microbial EPS synthesis, supporting an integrated approach to PW valorization. These findings reinforce the potential of EPS-based bioprocesses as sustainable green technologies that simultaneously promote waste mitigation and the production of high-value industrial bioproducts. Full article

Global Navigation Satellite System (GNSS) signals are highly vulnerable to spoofing attacks, which can cause positioning errors and pose serious threats to user receivers. Therefore, the development of efficient and reliable spoofing detection techniques has become an urgent requirement for ensuring GNSS security. In spoofing attacks, attackers introduce additional bias in the Doppler shift. However, detection methods that rely on extracting this deviation from raw measurements suffer from limited practicality, and existing alternative detection schemes based on position, velocity, and time (PVT) information exhibit poor adaptability to diverse scenarios. To address these limitations, this paper proposes a spoofing detection method based on the self-consistency verification of the receiver’s clock state (SCV-RCS). Its core statistic is the cumulative difference between the estimated clock bias and the bias obtained by integrating clock drift. By monitoring this consistency, SCV-RCS identifies anomalies in pseudorange and Doppler observations without complex bias extraction or auxiliary hardware, ensuring easy deployment. Simulation and experimental results demonstrate the method’s effectiveness across diverse spoofing scenarios. It achieves the fastest alarm delay of ≤2 s while providing continuous alerting capability in full-channel and partial-channel spoofing. This study provides a robust and reliable solution for GNSS receivers operating in complex spoofing environments. Full article

Background/Objectives: Family members are the principal providers of home-based care for migrant older adults. Linguistic, cultural, and structural barriers within health systems exacerbate the caregiver burden across emotional, physical and financial domains. Although home healthcare services may alleviate this burden, variability in access, cultural safety, and care coordination can also intensify it. This scoping review maps the evidence on the burden experienced by family caregivers who deliver home-based healthcare to migrant older adults and examines how these arrangements affect caregivers’ health and well-being. It synthesizes the literature on facilitators and barriers—including access, cultural-linguistic fit, coordination with formal services, and legal/immigration constraints—and distills implications for policy and practice to strengthen equitable, culturally responsive home care. Method: The Joanna Briggs Institute (JBI) scoping review framework was used to conduct the review. A comprehensive search was performed across six databases (CINAHL, Scopus, Web of Science, PsycINFO, MEDLINE and Sociological Abstracts) for articles published between 2000 and 2025. Studies were selected based on predefined inclusion criteria focusing on the family caregiver burden in providing home healthcare for migrant older adults. Data extraction and thematic analysis were conducted to identify key themes. Results: The review identified 20 studies across various geographical regions, highlighting four key themes: (1) Multidimensional Caregiver Burden, (2) The Influence of Gender, Family Hierarchy, and Migratory Trajectories on Caregiving, (3) Limited Access to Formal and Culturally Appropriate Support, and (4) Health Outcomes, Coping, and the Need for Community-Based Solutions. Conclusions: System-level reforms are required to advance equity in home healthcare for aging migrants. Priorities include establishing accountable cultural-safety training for providers; expanding multilingual access across intake, assessment, and follow-up; and formally recognizing and resourcing family caregivers (e.g., navigation support, respite, training, and financial relief). Investment in community-driven programs, frameworks and targeted outreach—co-designed with migrant communities—can mitigate isolation and improve uptake. While home healthcare is pivotal, structural inequities and cultural barriers continue to constrain equitable access. Addressing these gaps demands coordinated policy action, enhanced provider preparation, and culturally responsive care models. Future research should evaluate innovative frameworks that integrate community partnerships and culturally responsive practices to reduce the caregiver burden and improve outcomes for migrant families. Full article

In external fertilisation, spawning synchrony is often mediated by pheromones. However, their chemical nature is rarely well-established; this is particularly true for bivalves. This study used an electrophysiological technique—the electro-osphradiogram (EOsG)—to investigate the spawning-inducing pheromone (SIP) in the Pacific oyster (Magallana gigas), a species of economic and environmental relevance. Recording the electrophysiological response of the osphradium to conspecific sperm milt and its fractions, we show that the SIP is multicomponent, likely proteinaceous—with at least one component linked to the spermatozoa and the other components in free solution—and all resistant to freezing. At least three active components are involved: one of about 35 kDa, one between 3 and 10 kDa and one of less than 3 kDa. All three, alone, evoke responses from the osphradium, but all three must probably be present to evoke the full biological response—gamete release—in the receiver. All three are likely polar; none were retained by a range of solid-phase extraction cartridges. We suggest that the EOsG will be useful to isolate and identify the individual components of the oyster SIP. Successful identification of the SIP will represent an important step towards more sustainable and efficient bivalve hatchery practices. Full article