Search Results (1,048)

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Flammulina filiformis, one of the most delicious and commercially important mushrooms, demonstrates remarkable adaptability to diverse agricultural wastes. However, it is unclear how different substrates affect the degradation of lignocellulosic biomass and the production of lignocellulolytic enzymes in F. filiformis. In this study, label-free comparative proteomic analysis of F. filiformis cultivated on sugarcane bagasse, cotton seed shells, corn cobs, and glucose substrates was conducted to identify degradation mechanism across various substrates. Label-free quantitative proteomics identified 1104 proteins. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of protein expression differences were predominantly enriched in energy metabolism and carbohydrate metabolic pathways. Detailed characterization of carbohydrate-active enzymes among the identified proteins revealed glucanase (GH7, A0A067NSK0) as the key enzyme. F. filiformis secreted higher levels of cellulases and hemicellulases on sugarcane bagasse substrate. In the cotton seed shells substrate, multiple cellulases functioned collaboratively, while in the corn cobs substrate, glucanase predominated among the cellulases. These findings reveal the enzymatic strategies and metabolic flexibility of F. filiformis in lignocellulose utilization, providing novel insights for metabolic engineering applications in biotechnology. The study establishes a theoretical foundation for optimizing biomass conversion and developing innovative substrates using targeted enzyme systems. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

This study aimed to investigate the underlying mechanisms by which dandelion extract inhibits the proliferation of breast cancer MDA-MB-231 cells. Dandelion root and leaf extracts were prepared using a heat reflux method and subjected to solvent gradient extraction to obtain fractions with different polarities. MTT assays revealed that the ethyl acetate fraction exhibited the strongest inhibitory effect on cell proliferation. LC-MS analysis identified 12 potential active compounds, including sesquiterpenes such as Isoalantolactone and Artemisinin, which showed significantly lower toxicity toward normal mammary epithelial MCF-10A cells compared to tumor cells (p < 0.01). Mechanistic studies demonstrated that the extract induced apoptosis in a dose-dependent manner, with an apoptosis rate as high as 85.04%, and significantly arrested the cell cycle at the S and G2/M phases. Label-free quantitative proteomics identified 137 differentially expressed proteins (|FC| > 2, p < 0.05). GO enrichment analysis indicated that these proteins were mainly involved in cell cycle regulation and apoptosis. KEGG pathway analysis revealed that the antitumor effects were primarily mediated through the regulation of PI3K-Akt (hsa04151), JAK-STAT (hsa04630), and PPAR (hsa03320) signaling pathways. Moreover, differential proteins such as PI3K, AKT1S1, SIRT6, JAK1, SCD, STAT3, CASP8, STAT2, STAT6, and PAK1 showed strong correlation with the core components of the EA-2 fraction of dandelion. Molecular docking results demonstrated that these active compounds exhibited strong binding affinities with key target proteins such as PI3K and JAK1 (binding energy < −5.0 kcal/mol). This study elucidates the multi-target, multi-pathway synergistic mechanisms by which dandelion extract inhibits breast cancer, providing a theoretical basis for the development of novel antitumor agents. Full article

Alkaline earth elements have emerged as crucial electronic modifiers for regulating active sites in catalytic systems, yet the influence of metal–support interactions (MSIs) between alkaline earth compounds and active metals remains insufficiently understood. This study systematically investigated Pt nanoparticles supported on alkaline earth carbonates (Pt/MCO₃, M = Mg, Ca, Ba) for low-temperature propylene combustion. The Pt/BaCO₃ catalyst exhibited outstanding performance, achieving complete propylene conversion at 192 °C, significantly lower than Pt/MgCO₃ (247 °C) and Pt/CaCO₃ (282 °C). The enhanced activity stemmed from distinct MSI effects among the supports, with Pt/BaCO₃ showing the poorest electron enrichment and lowest propylene adsorption energy. Through kinetic analyses, ¹⁸O₂ isotope labeling, and comprehensive characterization, the reaction was confirmed to follow the Mars–van Krevelen (MvK) mechanism. Pt/BaCO₃ achieves an optimal balance between propylene and oxygen adsorption, a critical factor underlying its superior activity. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

To circumvent the computational bottlenecks associated with the intermediate steps (e.g., least squares fitting) in conventional sine wave similarity principles and directly acquire the energy metrics required for stabilized sinusoidal waveform characterization, this study leverages time domain probability distribution theory. From a complementary advantage perspective, a novel transformer inrush current identification criterion is developed using the Wasserstein distance metric. The methodology employs feature discretization to extract target/template signals, transforming them into state vectors for sample labelling. By quantifying inter-signal energy distribution disparities through this framework, it achieves a precise waveform similarity assessment in sinusoidal regimes. The theoretical analysis and simulations demonstrate that the approach eliminates frequency domain computations while maintaining implementation simplicity. Compared with conventional sine wave similarity methods, the solution streamlines protection logic and significantly enhances practical applicability with accelerated response times. Furthermore, tests conducted on field-recorded circuit breaker closing waveforms using MATLAB R2022a confirm the effectiveness of the proposed method in improving transformer protection performance. Full article

Nearly zero-energy buildings (nZEBs) are central to global carbon reduction strategies, and Taiwan is actively promoting their adoption through building energy performance labeling, particularly in the retrofit of existing buildings. Under Taiwan’s nZEB framework, qualification requires both an A⁺ energy performance label and over 50% energy savings from retrofit technologies. This study proposes an integrated assessment framework for retrofitting small- to medium-sized office buildings into nZEBs, incorporating diagnostics, technical evaluation, policy alignment, and resource integration. A case study of a bank branch in Kaohsiung involved on-site energy monitoring and EnergyPlus V22.2 simulations to calibrate and assess the retrofit impacts. Lighting improvements and two HVAC scenarios—upgrading the existing fan coil unit (FCU) system and adopting a completely new variable refrigerant flow (VRF) system—were evaluated. The FCU and VRF scenarios reduced the energy use intensity from 141.3 to 82.9 and 72.9 kWh/m²·yr, respectively. Combined with rooftop photovoltaics and green power procurement, both scenarios met Taiwan’s nZEB criteria. The proposed framework demonstrates practical and scalable strategies for decarbonizing existing office buildings, supporting Taiwan’s 2050 net-zero target. Full article

This study investigates the influence of monthly income level (low, medium, and high) on consumer behavior regarding a newly launched cream cheese product enriched with berry by-products. A panel of 345 participants was surveyed, and data were analyzed using the Kruskal–Wallis and Mann–Whitney tests. Most consumers were environmentally aware, recognizing the impact of personal food waste and expressing support for food products incorporating by-products. Respondents also favored the use of renewable energy and reducing greenhouse gas emissions in the food industry. Higher income levels were associated with greater health awareness and increased acceptance of cream cheese with berry by-products, with the high-income group showing a greater willingness to pay a premium. Health benefits and the product’s natural character were the main advantages identified. Individuals with lower incomes were more open to trying unfamiliar foods when ingredient details were not provided, while higher-income respondents expressed greater hesitation and distrust toward new products. Willingness to try novel items decreased with income level. Statistically significant differences (p < 0.05) were found between income groups for label reading, support for mountain dairies, and the influence of product origin, health benefits, nutrient diversity, pricing concerns, and consumer confidence in purchasing cream cheese with berry by-products. These findings are important for understanding how income affects consumer perceptions and willingness to consume innovative, sustainable food products like berry-enriched cream cheese, highlighting key areas for targeted marketing and product development. Full article

Lignin, the most abundant renewable aromatic biopolymer on Earth, is rapidly emerging as a powerful enabler of next-generation sustainable technologies. This review shifts the focus to the latest industrial breakthroughs that exploit lignin’s multifunctional properties across energy, agriculture, healthcare, and environmental sectors. Lignin-derived carbon materials are offering scalable, low-cost alternatives to critical raw materials in batteries and supercapacitors. In agriculture, lignin-based biostimulants and controlled-release fertilizers support resilient, low-impact food systems. Cosmetic and pharmaceutical industries are leveraging lignin’s antioxidant, UV-protective, and antimicrobial properties to create bio-based, clean-label products. In water purification, lignin-based adsorbents are enabling efficient and biodegradable solutions for persistent pollutants. These technological leaps are not merely incremental, they represent a paradigm shift toward a materials economy powered by renewable carbon. Backed by global sustainability roadmaps like the European Green Deal and China’s 14th Five-Year Plan, lignin is moving from industrial residue to strategic asset, driven by unprecedented investment and cross-sector collaboration. Breakthroughs in lignin upgrading, smart formulation, and application-driven design are dismantling long-standing barriers to scale, performance, and standardization. As showcased in this review, lignin is no longer just a promising biopolymer, it is a catalytic force accelerating the global transition toward circularity, climate resilience, and green industrial transformation. The future of sustainable innovation is lignin-enabled. Full article

Copper pyrithione (CuPT), an emerging biocide used in ship antifouling coatings, may accumulate in marine sediments and pose risks to non-target organisms. However, current research on CuPT toxicity remains limited. Litopenaeus vannamei, one of the world’s most important aquaculture shrimp species, relies heavily on its hepatopancreas for energy metabolism, detoxification, and immune responses. Due to their benthic habitat, these shrimps are highly vulnerable to contamination in sediment environments. This study investigated the toxicological response in the hepatopancreas of L. vannamei exposed to CuPT (128 μg/L) for 3 and 48 h. Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) fluorescence staining revealed increased apoptosis, deformation of hepatic tubule lumens, and the loss of stellate structures in the hepatopancreas after CuPT 48 h exposure. A large number of differentially expressed genes (DEGs) were identified by transcriptomics analysis at 3 and 48 h, respectively. Most of these DEGs were related to detoxification, glucose transport, and immunity. Metabolomic analysis identified numerous significantly different metabolites (SDMs) at both 3 and 48 h post-exposure, with most SDMs associated with energy metabolism, fatty acid metabolism, and related pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of metabolomics and transcriptome revealed that both DEGs and SDMs were enriched in arachidonic acid metabolism, fatty acid biosynthesis, and glycolysis/gluconeogenesis pathways at 3 h, while at 48 h they were enriched in the starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, and galactose metabolism pathways. These results suggested that CuPT disrupts the energy and lipid homeostasis of L. vannamei. This disruption compelled L. vannamei to allocate additional energy toward sustaining basal physiological functions and consequently caused the accumulation of large amounts of reactive oxygen species (ROS) in the body, leading to apoptosis and subsequent tissue damage, and ultimately suppressed the immune system and impaired the health of L. vannamei. Our study elucidates the molecular mechanisms of CuPT-induced metabolic disruption and immunotoxicity in L. vannamei through integrated multi-omics analyses, providing new insights for ecological risk assessment of this emerging antifoulant. Full article

Background: The growing demand for nutritionally balanced, gluten-free products has encouraged the development of innovative formulations that deliver both sensory quality and functional benefits. Combining rice and legume flours offers promising alternatives to mimic gluten-like properties while improving nutritional value. This study aimed to develop a gluten-free fusilli noodle using extruded flours based on mixtures of Japanese rice (JR) and chickpea (CP) particles. Methods: A 2³ factorial design with augmented central points was applied to evaluate the effects of flour ratio (X₁, CP/JR, 20–40%), feed moisture (X₂, 24–30%), and extrusion temperature (X₃, 80–120 °C) on responses from process properties (PPs), extruded flours (EFs), and noodle properties (NPs). Results: Interaction effects of X₃ with X₁ or X₂ were observed on responses. On PP, X₁ at 120 °C reduced the mechanical energy input (181.0 to 136.2 kJ/kg) and increased moisture retention (12.0 to 19.8%). On EF, X₁ increased water-soluble solids (2.3 to 4.2 g/100 g, db) and decreased water absorption (8.6 to 5.7 g/g insoluble solids). On NP, X₁ also affected their cooking properties. The mass increase was greater at 80°C (140 to 174%), and the soluble-solids loss was greater at 120 °C (9.3 to 4.5%). The optimal formulation (X₁–X₂–X₃: 40–30%–80 °C) yielded noodles with improved elasticity, augmented protein, and enhanced textural integrity. Conclusions: Extruded flours derived from 40% chickpea flour addition and processed under mild conditions proved to be an effective strategy for enhancing both the nutritional and technological properties of rice-based noodles and supporting clean-label alternative products for gluten-intolerant and health-conscious consumers. Full article

A fluorescent sandwich assay was devised to quantify CK-MB. In a typical immunoassay, antibodies bind to the target, and the detected signal is quantified according to the target’s concentration. We innovated a unique fluorescence assay known as the “enzyme-linked aptamer assay” (ELAA) by substituting antibodies with a pair of high-affinity aptamers labelled with biotin, namely apt. A1 and apt. A2. Avidin-labelled ALP binds to biotin-labelled aptamers, hydrolyzing its substrate, 2-phosphoascorbic acid trisodium salt, resulting in the formation of ascorbic acid. The catalytic hydrolysate functions as a reducing agent, causing the deterioration of MoS₂ nanosheets. This results in the transformation of MoS₂ nanosheets into nanoribbons, leading to the release of quenched AGQDs. The reestablishment of fluorescence is triggered by Förster Resonance Energy Transfer (FRET) between the MoS₂ nanoribbons and AGQDs, enhancing the sensitivity of disease biomarker detection. The working range for detection falls between 2.5 nM and 160 nM, and the limit of detection (LOD) for CK-MB is verified at 0.20 nM. Full article

Striae distensae (SD), or stretch marks, are a common aesthetic concern with limited effective treatment options. This prospective, single-center, open-label study aimed to evaluate the efficacy and safety of 1470 nm non-ablative laser therapy in improving skin texture and reducing SD dimensions. Twenty healthy female volunteers (aged 19–56) with SD of varying stages underwent three laser sessions at three-week intervals. Treatments were delivered using energy densities of 28–35 mJ per point with spot spacing of 0.8–1.2 mm, uniformly delivered over the affected SD lesions. Assessments were performed at baseline, Day 14, Day 35, Day 56–70, and Day 118–132. SD depth and width were measured using high-frequency ultrasound; aesthetic improvement was assessed using the Global Aesthetic Improvement Scale (GAIS), alongside clinical and photographic evaluations. A statistically significant, progressive reduction in SD size was observed: mean depth decreased from 0.34 mm (SD = 0.16) to 0.18 mm (SD = 0.15), and width decreased from 6.58 mm (SD = 2.65) to 4.40 mm (SD = 2.52) by Day 118–132 (p < 0.01 for both). Most participants reported improvement on GAIS at each follow-up. No severe adverse events occurred; only mild, transient erythema and edema were noted. In conclusion, 1470 nm non-ablative laser therapy showed significant efficacy and a favorable safety profile in SD treatment, offering a promising non-invasive option based on fractional thermal stimulation and selective dermal absorption. Full article