Search Results (8,034)

In response to the complexity and uncertainty in assessing the safety and economic impacts of the Changed Product Rule (CPR) in civil aviation products’ airworthiness certification, this paper constructs a comprehensive evaluation model based on a cost–benefit analysis framework. In previous research, studies on aircraft modification costs have consistently been conducted from the perspective of design organizations, focusing on modeling and optimizing the one-time engineering costs of the modifications themselves or remaining confined to the level of safety performance without addressing the calculation of economic value. The model proposed in this paper considers the entire aircraft service lifecycle and uniformly quantifies potential impacts into monetary terms for comparison. The model encompasses safety improvements, cost estimation, and discounted cash flow analysis, aiming to provide decision-makers with quantitative tools for determining the applicability of the “impracticality exception” standard. This ensures that modifications to aviation products balance safety with economic viability. Through case studies involving fuel tank access panel design changes and Auxiliary Power Unit (APU) inlet duct fire protection requirements, the effectiveness and practicality of the model are validated, offering an empirical foundation for future policy formulation and industry regulation. Nevertheless, the parameters in the model depend on historical data, and appropriate parameters must be carefully selected. Although the model has taken into account the entire lifecycle of the aircraft, it is still based on static assumptions and fails to consider the impact of the rapid development of the aviation industry over time. Ongoing model refinement, international data collection, and integration of non-economic factors remain key directions for future research. Full article

Structuring oils using oleogels (OGs) represents a promising strategy for developing semi-solid lipid matrices with applications in food and other soft systems. This study evaluated the thermal stability and physicochemical properties of an oleogel (OG) formulated with extra virgin olive oil (EVOO) and beeswax (BW, 6%). The oleogel and olive oil samples were initially characterized by thermogravimetric analysis (TGA/DTG). The beeswax and oleogel samples were initially characterized by texture analysis. An antioxidant capacity (ORAC) analysis was initially applied to the beeswax sample. An initial rheometric analysis was applied to the oleogel sample. Fatty acid profiling and infrared spectroscopy were applied initially and finally to the oleogel and olive oil samples. During the thermal processing (80 °C, 14 days) of the oleogel and olive oil, analyses of the percentage of polar compounds, refractive index, and absorption parameters (K₂₃₂ and K₂₇₀) were performed. The oleogel exhibited a soft, pseudoplastic network, with lower hardness and mechanical strength than pure beeswax. Gelation modified the thermo-oxidative stability of EVOO, showing lower levels of polar compounds (from day 7 of heating; p = 0.028) and a slight delay in the onset of thermal degradation (Tonset), suggesting partial protection against the formation of polar degradation compounds. Furthermore, the evolution of K₂₃₂ indicated differences in the formation of primary oxidation products (p = 0.027) over the 14 days of heating, while K₂₇₀ showed no differences in the formation of secondary oxidation compounds. This reflects the complex interaction between the gelled matrix and the lipid deterioration mechanisms. Overall, the results demonstrate that the incorporation of beeswax allows for a partial reduction in degradation compounds in high-temperature processes, producing technologically functional oleogels that offer a potential alternative source for structuring solid fats. This work provides relevant evidence for the rational design of oleogels based on unrefined oils and opens new opportunities for their application in food systems and gelled matrices with thermal processing requirements. Full article

End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely primarily on manually crafted features, expert-defined thresholds, and rule-based decision logic. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and transfer learning (TL), have emerged as promising solutions to overcome these limitations by enabling data-driven, adaptive, and scalable quality inspection. This paper presents a comprehensive and structured review of the latest advances in intelligent EoL quality inspection for electric motor production. It systematically surveys the non-invasive measurement techniques that are commonly employed in industrial environments and examines the evolution from traditional signal processing-based inspection to AI-based approaches. ML methods for feature selection and classification, DL models for raw signal-based fault detection, and TL strategies for data-efficient model adaptation are critically analyzed in terms of their effectiveness, robustness, interpretability, and industrial applicability. Furthermore, this work identifies key challenges that prevent the widespread adoption of AI-based EoL inspection systems, including dependence on expert knowledge, limited availability of labeled fault data, generalization between motor variants and production condition, and the lack of standardized evaluation methodologies. Based on the identified research gaps, this review outlines research directions and emerging concepts for developing robust, interpretable, and data-efficient intelligent inspection systems suitable for real-world manufacturing environments. By synthesizing recent advances and highlighting open challenges, this review aims to support researchers and experts in designing next-generation intelligent EoL quality control systems that enhance production efficiency, reduce operational costs, and improve product reliability. Full article

Depleted oil and gas reservoirs, owing to their large storage capacity and well-established infrastructure, are attractive sites for storing green energy carriers such as natural gas, hydrogen, and compressed air. During injection–production cycling in underground gas storage (UGS), variations in effective stress can cause repeated stress disturbances in the reservoir and surrounding rock, which may trigger borehole sand production. In this study, laboratory sand-production simulation tests were conducted to evaluate the effects of cyclic-loading stage, upper stress limit, and cycling frequency on borehole damage and sand-production behavior. The results show that sand production is stage-dependent. During the rapid-hardening and stable stages, the borehole remains largely intact and sand production is negligible. Once the failure and collapse stages are reached, borehole integrity deteriorates and sand production increases sharply, with fine particles becoming dominant. Cumulative sand production increases with the upper stress limit. Increasing the upper limit from 80% to 95% leads to a 2.53-fold increase in produced sand mass, together with a higher fine-sand fraction and a shift in the particle-size distribution (PSD) toward smaller sizes. The cycling frequency also plays an important role. When the frequency decreases, cumulative sand production increases and becomes 53.1% higher than the baseline at 0.001 Hz. Meanwhile, the median particle size (D50) decreases, indicating stronger particle breakage under low-frequency cycling. These findings provide guidance for designing injection–production schemes for UGS and for selecting appropriate sand-control completion strategies. Full article

Wellbore instability in shale formations represents a worldwide challenge in drilling engineering. The development of high-performance shale stabilizers is crucial for enhancing wellbore stability. A core–shell structured shale stabilizer, designated AAD-CaCO₃, was synthesized via inverse emulsion polymerization using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), and dimethyl diallyl ammonium chloride (DMDAAC) as monomers. Nano-CaCO₃ was generated in situ by reacting calcium chloride and sodium carbonate. Sodium bisulfite and ammonium persulfate were used as initiators. The product was characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermogravimetric analysis (TGA). Its effects on the rheological properties and filtration performance of a bentonite-based mud were evaluated. The stabilizer’s efficacy in inhibiting shale hydration swelling and dispersion was evaluated through linear swelling tests and shale rolling dispersion experiments, while its plugging performance was examined via a filtration loss test with a nanoporous membrane and spontaneous imbibition tests. The results indicated that AAD-CaCO₃ possesses a core–shell structure with the nano-CaCO₃ encapsulated by the polymer. It moderately improved the rheology of the bentonite-based mud and significantly reduced both the low-temperature and low-pressure (LTLP) filtration loss and the high-temperature and high-pressure (HTHP) filtration loss. AAD-CaCO₃ could be adsorbed onto shale surfaces through electrostatic attraction, resulting in substantially reduced clay hydration swelling and an increased shale cutting recovery rate. Effective plugging of micro-nanopores in shale was achieved, demonstrating a dual mechanism of chemical inhibition and physical plugging. Full article

Community-based collection of household plastic waste (HPW) is expanding in Japan as a way to produce high-value post-consumer recycled (PCR) materials. These systems set up collection points where residents bring recyclable items and sort them into designated bins. However, the environmental impacts of such systems and their advantages over municipal collection remain insufficiently understood, and discussion on the burden placed on residents is limited. This study empirically analyses HPW collection and recycling in community-based systems and examines approaches to producing high-value PCR from environmental and resident-burden perspectives. Environmental impact assessments were conducted for municipal and community-based collection. The time required for residents to wash items before segregation was also evaluated as a burden using questionnaire surveys. A scenario for collecting and recycling five HPW types in Kobe City, Japan, was developed, and environmental impacts and resident burdens were quantified. Results show that community-based collection achieves 3.75 kg-CO_2eq of avoided annual greenhouse gas emissions per household compared with incineration but requires 1.72 h of annual washing time. High-value PCR production depends on resident cooperation during segregation. Clear communication is essential to achieve environmental and economic benefits while minimising additional burdens on residents. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

Solar drying is an energy-efficient and environmentally friendly method for dehydrating agricultural and medicinal products; however, its performance is strongly affected by fluctuating climatic conditions and nonlinear heat and mass transfer processes. In cabinet-type solar dryers, maintaining the drying air temperature and relative humidity within optimal ranges is particularly critical for medicinal plants such as Plantago major leaves, which are sensitive to overheating and non-uniform drying. In this study, a Mamdani-type fuzzy logic-based intelligent control system is developed and experimentally validated for a cabinet solar dryer operating under real summer climatic conditions in Tashkent, Uzbekistan. The proposed controller regulates fan speed using drying air temperature and relative humidity as inputs. To evaluate its effectiveness, the fuzzy logic controller is benchmarked against a conventionally tuned Proportional–Integral–Derivative (PID) controller under identical operating and climatic conditions. A coupled thermodynamic–hygrometric dynamic model of the drying process is implemented in MATLAB/Simulink (R2024a) to support controller design and analysis. Experimental results demonstrate that the fuzzy logic controller maintains the drying air temperature within the optimal range of 45–50 °C despite significant fluctuations in solar irradiance (650–900 W/m²), whereas the PID-controlled system exhibits noticeable overshoot and oscillations. Compared with PID control, the fuzzy-controlled dryer achieves a smoother reduction in relative humidity, a reduction of approximately 22% in total drying time for the same final moisture content (8–10% wet basis), and an 18% decrease in auxiliary electrical energy consumption. In addition, tray-wise moisture measurements indicate improved drying uniformity under fuzzy control, with moisture variation remaining within ±4%. Overall, the results confirm that fuzzy-logic-based intelligent control provides a robust and energy-efficient solution for cabinet solar dryers operating under hot continental climatic conditions, offering clear advantages over conventional PID control in terms of stability, drying performance, and uniformity. Full article

Silicone-based implants have been widely used in breast reconstruction but have also been associated with poorly understood complications, including pathologic foreign body responses such as capsular contracture. In this study, we leveraged 3D-printing technology to generate silicone-based implants in a novel, anatomically relevant, prepectoral rat model. We used this model to evaluate the response to an extracellular matrix-based product: ovine-derived reinforced tissue matrix (RTM). Two-piece negative molds were developed through computer-aided design and 3D-printed. The molds were filled with various polydimethylsiloxane mixtures and dip-coated to fabricate implants. Implant material characterization revealed that the implants retained the original 3D-printed mold shape and qualitatively demonstrated a shell with an inner solid gel-like structure. Fabricated implants had smooth surfaces, as well as tunable features including implant stiffness (storage modulus). From initial studies in our rat model, placement of bilateral prepectoral implants allowed assessment of both muscle- and skin-facing capsules and were well-tolerated for at least 12 weeks. Comparison of the foreign body response between RTM-covered and uncovered (control) implants in this model revealed that the capsule thickness did not differ between groups at the 12-week endpoint. However, RTM reduced contractile fibroblasts (alpha-smooth muscle actin) and macrophages (Iba1) compared to the control. Our findings suggested that RTM may improve capsule quality by attenuating cells involved in fibrosis, even when total capsule thickness remains unchanged. However, these changes to cells involved in fibrosis were only observed at this early endpoint and may not predict long-term clinical outcomes. Full article

Circular economy (CE) strategies in the agri-food sector hold strong potential for reducing waste, enhancing resource efficiency, and promoting sustainable value creation. However, early-stage assessment of innovative valorisation pathways remains challenging due to limited data availability and heterogeneous sustainability trade-offs. This study presents a multi-criteria evaluation tool designed to identify sustainability hotspots and support the preliminary screening of CE solutions based on easily obtainable information. The tool combines a structured literature review with expert-based scoring across environmental (ENV), economic (EC), and social (SOC) dimensions. Its applicability was demonstrated through the following three case studies: (i) reconstitution of cheese approaching expiration, (ii) extraction of polyphenols from grape-wine residues via subcritical water extraction, and (iii) biodegradable mulching film production from grape-wine pomace. Results show that the tool successfully differentiates sustainability performance across value chain areas Residue, Final Product, and Process (RES, FP, and PRO) and reveals critical gaps requiring further investigation. Scenario 3 achieved the higher overall score (69.7%) due to fewer regulatory constraints, whereas Scenarios 1 and 2 (61.2% and 54.5%, respectively) are penalised due to the more regulations for human consumption. The proposed tool offers a practical and efficient method to support researchers and industry stakeholders in identifying CE strategies with the highest potential for sustainable development. Full article

Natural ventilation is a common cooling strategy in open dairy barns, but its efficiency largely depends on external wind directions and speeds. Misalignment between external airflow and fan jets often led to non-uniform air distribution, reduced local cooling efficiency, and an elevated risk of heat stress in cows. However, few studies have systematically examined the combined effects of wind directions and speeds on airflow and heat dissipation. Most research isolates natural or mechanical ventilation effects, neglecting their interaction. Accurate computational fluid dynamics (CFD) modeling of the coupling between outdoor and indoor airflow is crucial for designing and evaluating mixed ventilation systems in dairy barns. To address this gap, this study systematically analyzed the effects of external wind directions (0°, 45°, 90°, 135°, 180°) and speeds (1, 3, 5, 7, 10 m s⁻¹) on fan jet distribution and convective heat transfer around dairy cows using the open-source CFD platform OpenFOAM. By evaluating body surface airflow and regional convective heat transfer coefficients (CHTCs), this study quantitatively linked barn-scale airflow to animal heat dissipation. Results showed that both wind directions and speeds markedly influenced airflow and heat exchange. Under 0° wind direction, dorsal airflow reached 6.2 m s⁻¹ and CHTCs increased nearly linearly with wind speeds, indicating strong synergy between the fan jet and external wind. Crosswinds (90° wind direction) enhanced abdominal airflow (approximately 5.2 m s⁻¹), whereas oblique and opposing winds (135–180°) caused stagnation and reduced convection. The dorsal-to-abdominal CHTCs ratio ($R_{d/a}$) increased to about 1.6 under axial winds but decreased to 1.1 under cross-flow, reflecting reduced thermal asymmetry. Overall, combining axial and lateral airflow paths improves ventilation uniformity in naturally or mechanically ventilated dairy barns. The findings provide theoretical and technical support for optimizing ventilation design, contributing to energy efficiency, animal welfare, productivity, and the sustainable development of dairy farming under changing climatic conditions. Full article

Industrial heritage—particularly large modern cement plants—serves as a crucial witness to the architectural and technological evolution of modern urbanization. In Europe, North America, and East Asia, many decommissioned cement factories have been transformed into cultural venues, creative districts, or urban landmarks, while a greater number of sites still face the risks of functional decline and spatial disappearance. In China, early large-scale cement plants have received limited attention in international industrial heritage research, and their conservation and adaptive reuse practices remain underdeveloped. This study takes the Huaxin Cement Plant, founded in 1907, as the research object. As the birthplace of China’s modern cement industry, it preserves the world’s only complete wet-process rotary kiln production line, representing exceptional rarity and typological significance. Combining social media perception analysis with the Hidalgo-Giralt four-quadrant model, the study aims to clarify the plant’s value positioning and propose a design-oriented pathway for spatial activation. Based on 378 short videos and 75,001 words of textual data collected from five major platforms, the study conducts a value-tag analysis of public perceptions across five dimensions—historical, technological, social, aesthetic, and economic. Two composite indicators, Cultural Representativeness (CR) and Utilization Intensity (UI), are further established to evaluate the relationship between heritage value and spatial performance. The findings indicate that (1) historical and aesthetic values dominate public perception, whereas social and economic values are significantly underrepresented; (2) the Huaxin Cement Plant falls within the “high cultural representativeness/low utilization intensity” quadrant, revealing concentrated heritage value but insufficient spatial activation; (3) the gap between value cognition and spatial transformation primarily arises from limited public accessibility, weak interpretive narratives, and a lack of immersive experience. In response, the study proposes five optimization strategies: expanding public access, building a multi-layered interpretive system, introducing immersive and interactive design, integrating into the Yangtze River Industrial Heritage Corridor, and encouraging community co-participation. As a representative case of modern Chinese industrial heritage distinguished by its integrity and scarcity, the Huaxin Cement Plant not only enriches the understanding of industrial heritage typology in China but also provides a methodological paradigm for the “value positioning–spatial utilization–heritage activation” framework, bearing both international comparability and disciplinary methodological significance. Full article

The growing interest in using edible flowers as functional ingredients has increased the demand for reliable and sustainable strategies to recover and characterize their bioactive compounds. Torch ginger is a tropical species rich in anthocyanins. In this study, an ultrasound-assisted extraction (UAE) method was developed, optimized, and validated for the efficient recovery of anthocyanins from torch ginger flowers, with a clear focus on food-related applications. A Box–Behnken experimental design was applied to evaluate the influence of solvent composition, temperature, solvent-to-sample ratio, and pH on anthocyanin yield, using chromatographic responses. Solvent composition and solvent-to-sample ratio were identified as the most influential parameters, and effective extraction was achieved under mild temperature and pH conditions. The optimized conditions consisted of 84% methanol in water as the extraction solvent, a temperature of 30 °C, a solvent-to-sample ratio of 20:1 (mL g⁻¹), and a pH of 5.6. Kinetic studies revealed that a 5 min extraction time maximized recovery while preventing compound degradation. The method was successfully applied to different torch ginger varieties, revealing a strong correlation between flower color and anthocyanin concentration. This research provides a fast, reliable, and environmentally friendly approach for assessing anthocyanin content in torch ginger flowers. The results support the valorization of this edible flower as a potential source of natural colorants and bioactive ingredients, contributing to ingredient selection, quality control, and the future development of functional foods and clean-label products. Full article

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article