Search Results (569)

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The increasing demand for agricultural products, intensified by natural resource degradation and the lack of human labor in the agri-food sector, favors the adoption of advanced automated technologies in the entire farm-to-fork chain. Despite skepticism, 3D (three-dimensional) printing is amongst the methods that have drawn increasing attention and encourage expectations for tackling the aforementioned challenges. In this context, the current work has a multiperspective character. Firstly, it sheds light on the recent progress in the 3D printing fabrication area and focuses on laboratory-implemented parts improving the efficiency of typical agricultural processes. These cost-effective solutions vary from covers for damaged electric water pumps and joints for greenhouse structures to adjustable ventilation grilles, automatic irrigation valves and specialized fruit-harvesting grippers. Secondly, it reports on lessons learned, highlighting potential strengths/weaknesses during the fabrication process, assisted by complementary feedback collected via questionnaires from agricultural engineering students, their professors, and farmers. Experiences gained justify the optimism about the capacity of 3D printing to foster agriculture, but there are still concerns about the easiness of the 3D printing process and the ability of the 3D-printed parts to withstand harsh agricultural field conditions. Finally, it indicates future directions for the incorporation of 3D printing in agriculture toward increased sustainability pathways. Full article

The circular economy paradigm offers a critical framework for addressing agricultural sustainability challenges, yet limited empirical evidence exists regarding how ecological innovations create simultaneous value across environmental, economic, and social dimensions. This study examines stakeholder value creation mechanisms through a 200-day longitudinal case study (March–October 2025) of Fuyang, China’s ecological transformation utilizing exciton-mineral technology for livestock waste valorization. The mixed-methods approach combined environmental monitoring, economic performance data, social surveys (n = 4523), and governance document analysis across operations processing 3000–4500 tons of poultry waste monthly. Results indicated significant environmental improvements including 99.4% odor reduction (NH₃: 999 → 5.6 ppm), 387% soil biodiversity increase, and 42% methane emission reduction. Economic benefits included +20% farmer net profit and +57% egg price premium. Social outcomes encompassed 96.2% resident satisfaction and complete elimination of odor complaints. Governance innovation established China’s first permit-free bio-mineral production system. The findings suggest that ecological innovations embedding circularity as automatic outcomes, rather than requiring behavioral coordination, can accelerate circular agriculture transitions beyond policy mandates, pointing to a potentially scalable model for sustainable production–consumption systems in developing economies. Full article

The deployment of green hydrogen production is increasingly considered a strategic opportunity for energy-exporting countries. However, beyond technological and environmental aspects, large-scale industrial projects may generate complex and uncertain social and economic impacts at the regional level. This study investigates the potential social implications of introducing a green hydrogen production plant in the Department of Paysandú, Uruguay, using a System Dynamics modeling approach. It proposes an initial system model designed to establish a foundational Modeling and Simulation framework. The model explicitly represents feedback mechanisms linking public finance, education, labor competencies, productivity, and social behavior impact, allowing the exploration of long-term socio-economic trajectories under alternative institutional and policy conditions. It is used as an exploratory decision-support tool to assess conditional pathways, trade-offs, and risks. Results indicate that positive social outcomes, such as human capital accumulation and regional income growth, are possible but not automatic; they depend critically on governance capacity, fiscal sustainability, labor market coordination, and social acceptance, and may be attenuated or delayed under adverse scenarios. While this framework provides a strategic engineering lens on the social dimension, it represents a first step toward a comprehensive decision-making tool. The study analyzes a complex system by integrating energy, production, economic, social, and environmental aspects from strategic engineering lens and contributes to the literature by integrating social dimension and institutional constraints into a Modeling and Simulation framework applied to green hydrogen industrialization, offering insights into policy design under uncertainty in emerging energy-export contexts. Full article

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to minimize downtime and energy consumption. However, the increasing use of AI and digital infrastructure has also led to an increase in demand for computing energy, raising concerns about data center efficiency and carbon footprint, leading to the division between Green AI and Red AI. Industry 5.0 expands this paradigm, focusing on human–machine collaboration and sustainable design, where AI supports personalization, circular economy practices, and the integration of renewable energy. Generative AI and digital twins (DTs) enable real-time energy modeling, helping companies simulate outcomes and choose the most sustainable paths. Automation also enables predictive maintenance, extending machine life and reducing material waste. At the same time, AI is contributing to the development of decentralized energy systems, such as smart grids and microgrids, which increase resilience and reduce emissions. A key challenge is balancing the energy efficiency benefits of automation with the sustainability of the AI infrastructure itself, which requires innovation in energy-efficient computing and green algorithms. From this perspective, AI-based automation represents both a solution and a challenge: it accelerates the achievement of sustainable development goals while requiring responsible technological management to ensure long-term ecological sustainability. Full article

Background/Objectives: Automatic evaluation of Parkinson’s disease (PD) progression is an emerging topic that deserves special attention from the research community. Unobtrusive, low-cost technology is essential for monitoring PD patients in remote areas. This paper proposes the use of phonological posteriors to create models that allow the progression of dysarthria level progression to be modelled based on speech recordings. Methods: Eighteen Gated Recurrent Units (GRUs) are used to estimate an equal number of phonological classes assigned to each phoneme pronounced in a given recording. Classification models of PD vs. healthy control (HC) subjects are trained with recordings of the PC-GITA corpus. This information is used in a separate corpus, with longitudinal recordings, to evaluate whether the progression of the dysarthria level, according to the modified Frenchay Dysarthria Assessment (mFDA), is related to abnormal production of specific phonemes. Results: Strident, dental, pause, back, and continuant phonological classes are the ones that better explain dysarthria level progression within time-frames of at least two years, therefore allowing possible monitoring of disease progression. Conclusions: Speech is a low-cost biosignal that can be used to automatically assess PD progression. In particular, this study shows that such an assessment makes it possible to evaluate dysarthria level progression and to find which phonological classes are contributing the most to such a progression. We believe that the findings reported in this paper provide objective evidence about possible abnormalities in broader speech-related processes like respiration, therefore contributing a better understanding of the relationship between speech production patterns and other speech-related processes affected when suffering from PD. Full article

To comply with new pastoral regulations in Benin, herders are increasingly adopting sedentary cattle systems, which may pose environmental risks if poorly managed. This study assessed greenhouse gas (GHG) emissions from three sedentary cattle farm types: zebu (SZF), taurine (STF), and crossbreed (SCF), across two vegetation zones: Sudanian (SZ) and Guineo-Congolian (GCZ) using the GLEAM-i model, online version. Irrespective of the farm type, the animals were exclusively fed on natural pasture. A total of 12 cattle herds were surveyed to collect input data (herd structure, demographic parameters, milk production and composition, and weight data) for the GLEAM-i. The fat and protein content of the milk (determined using a milkotester device), the live weight, and weight at slaughter of animals were entered into the GLEAM-i, which automatically determines the emission intensity values per kg of protein produced. The results revealed that CH₄ was the main GHG emitted (88%), followed by CO₂ (6–7%) and N₂O (6%). The highest and lowest total GHG emissions (kgCO₂-eq/year) were recorded in SZF (188,497) and STF (52,003) farms, respectively. With regard to emission intensity (kgCO₂-eq/kg protein), this varied from 506.59 to 3043.73 for meat and from 588.86 to 3043.73 for milk. Overall, preliminary trends suggest lower emission intensities for taurine in the GCZ and for zebu in the SZ. However, these results would be more meaningful and more accurate if emission values were directly measured from individual animals using the GreenFeed Technology under current production conditions, using various pasture resources and controlled allocation. These would allow us to make firm recommendations for breeding strategies to reduce GHG emissions in Benin’s extensive livestock production system. Full article

Additive manufacturing is increasingly adopted for the industrial production of small series of functional components, particularly in thermoplastic strand extrusion processes such as Fused Filament Fabrication. This transition relies on technological advances addressing key process limitations, including dimensional instability, weak interlayer bonding, extrusion defects, moisture sensitivity, and insufficient melting. Process monitoring therefore focuses on early defect detection to minimize failed builds and costs, while ultimately enabling process optimization and adaptive control to mitigate defects during fabrication. For this purpose, a data processing pipeline for monitoring Optical Coherence Tomography images acquired in Fused Filament Fabrication is introduced. Convolutional neural networks are used for the automatic classification of tomographic cross-sections. A dataset of tomographic images passes semi-automatic labeling, preprocessing, model training and evaluation. A sliding window detects outlier regions in the tomographic cross-sections, while masks suppress peripheral noise, enabling label generation based on outlier ratios. Data are split into training, validation, and test sets using block-based partitioning to limit leakage. The classification model employs a ResNet-V2 architecture with BottleneckV2 modules. Hyperparameters are optimized, with N = 2, K = 2, dropout 0.5, and learning rate 0.001 yielding best performance. The model achieves 0.9446 accuracy and outperforms EfficientNet-B0 and VGG16 in accuracy and efficiency. Full article

Nordic forest management is increasingly emphasizing multi-functional goals, expanding beyond timber production towards non-wood forest products such as wild berries. Wild berry yield maps are based on sample plot data combined with meteorological, remote sensing, and geoinformation data. Automating sample plot data processing is crucial, as manual collection is labor-intensive, time-consuming, and complicated by short berry seasons and fluctuating yields. This study compares two methods for automatic bilberry detection and counting: a deep learning detector YOLO and a machine learning model using the segment anything model (SAM) followed by a random forest classification (SAM-RF). Both system camera and drone imagery were evaluated as input data. YOLOv8 clearly outperformed SAM–RF in berry detection, achieving an $R^2$ of 0.98 and an RMSE of 3.8 berries when evaluated against annotated system camera images, compared to an $R^2$ of 0.80 for SAM–RF. System camera imagery consistently produced higher accuracy than drone imagery due to higher image clarity and more optimal viewing angles, with YOLOv8 achieving an $R^2$ of 0.95 against field counts, compared to 0.81 for drone images. The results also indicate that the primary error source in berry counting arises from the fact that many berries are not visible in the captured images. The results from the data analysis support the use of the developed technologies in yield modeling and even in implementing future ‘follow-me’ drone berry assistants. Full article

For businesses to optimize management decisions in the digital transformation, a process inherently characterized by symmetry between feedback collection and strategic adjustment, it is essential to automatically extract fine-grained opinions from large volumes of unstructured evaluations. However, traditional evaluation management techniques often fail to reflect this symmetrical balance between user perception and organizational response, primarily due to their inefficiency in processing unstructured textual data. Moreover, existing aspect–opinion mining algorithms exhibit limited practical generalization performance due to poor robustness against noise and semantic variations in real-world reviews. To address these gaps, this paper proposes MixContrast, an aspect–opinion mining method based on mix contrastive learning, which integrates mixed sample construction with data augmentation to generate continuous semantic transition samples. By symmetrically aligning positive and negative samples through a contrastive learning mechanism, MixContrast enhances representation learning and improves model generalization. Experiments conducted on cosmetics and multi-domain e-commerce review datasets demonstrate that MixContrast significantly outperforms several strong baseline models in both aspect and opinion extraction tasks. Theoretical analysis shows that MixContrast enhances robustness by ensuring Lipschitz continuity and enabling gradient decomposition in the representation space. Based on MixContrast predictions, we conduct a correlation analysis among aspects, opinions, and sentiment tendencies, delivering real-time quantitative support for marketing strategy formulation, product optimization, and service enhancement. Beyond advancing aspect–opinion mining technology, this work enables data-driven, symmetrical integration of technical insights with managerial decision-making, holding significant theoretical and practical value for digitally transforming enterprises. Full article

Artificial intelligence is increasingly being used in all branches of the media system and has transformed the way specialists in this field work in recent years. Currently, applications of artificial intelligence are used across a range of processes involved in the production, editing, distribution, and consumption of media content. These include technologies such as generative chatbots, automated transcription, writing, translation, and editing tools, as well as applications for image and video creation. All of these types of applications have taken over a significant portion of the traditional activities carried out by media professionals. From a technological point of view, these uses primarily rely on machine learning, natural language processing, and computer vision techniques, complemented by generative models that automatically analyze, generate, and interpret text, sound, and images. Although these technologies contribute to increased efficiency, faster work, and reduced operating costs, they also pose significant risks, particularly regarding the spread of false information. From a theoretical perspective, artificial intelligence goes beyond the status of a technological tool, being conceptualized as a communicational actor that actively intervenes in the generation, structuring, and circulation of messages, influencing the relationships between producers, content, and audiences in the current media environment. Full article

The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of two sets of samples of 60 and 92 ripe dragon fruits, extracting non-destructive parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. Fruit destructive characterization parameters were also measured. The first set of samples was used to develop a discriminant classification model. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the fruits into three quality characteristics based on the ripening index and weight. In a second step, a step-by-step discriminant analysis was conducted to classify the fruits into the three quality categories (based on the laboratory-measured weight, soluble solid content and total acidity) using the non-destructive magnitudes extracted from the image analysis. The proposed classification system achieved an accuracy of nearly 85% of well-classified dragon fruits, effectively segregating dragon fruits into the three established categories. Furthermore, the established model could select the very high-quality dragon fruit (riper and larger fruits) with 93% of correctly identified products. A comparable procedure was subsequently applied to the additional set of samples (set 2), obtaining consistent results and confirming that image analysis magnitudes related to size and color enable fruit classification into the predefined weight- and ripeness-based categories. Compared to conventional destructive methods, this non-destructive approach offers a promising, cost-effective, and reliable solution for quality assessment. The findings highlight the potential for integrating smart technologies into fruit classification processes, during automatic harvest and postharvest operations, ultimately improving efficiency, reducing labor costs, and enhancing product consistency in the dragon fruit industry. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

Objectives: The industrial production of lanolin alcohol currently employs batch saponification, which suffers from high energy consumption, prolonged processing time, and excessive solid waste generation, rendering it incompatible with green chemistry principles. This study aimed to develop an efficient, sustainable saponification process by addressing these limitations through integrating large language models (LLMs) with microfluidic technology. Methods: An LLM-based intelligent agent called SapoMind (version 1.0) was constructed. SapoMind employs LLMs as its software core and a continuous-flow microreactor as the experimental platform. Its performance was enhanced through supervised fine-tuning. The system enables automated recommendation of saponification process parameters, autonomous experimental design, and automatic execution of experiments. Saponification conditions were automatically optimized considering product quality, energy consumption, material consumption, and time consumption. Results: The optimal continuous-flow saponification conditions were determined as 70 °C reaction temperature and 9 min residence time, producing lanolin alcohol complying with European Pharmacopoeia standards. Compared to batch processing, the optimized process reduced carbon emissions by 53% and solid waste by 37%, with the greenness score increasing from 82 to 93. Conclusions: This study demonstrates the effectiveness of LLM-driven intelligent agents in optimizing green chemical processes. SapoMind offers significant environmental and operational benefits for lanolin alcohol production. Full article

Integrating people with hearing disabilities into schools is one of the biggest problems that Latin American societies face. Mexican Sign Language (MSL) is the main language and culture of the deaf community in Mexico. However, its use in formal education is still limited by structural inequalities, a lack of qualified interpreters, and a lack of technology that can support personalized instruction. This study outlines the conceptualization and development of a mobile application designed as an adaptive assistive technology for learning MSL, utilizing a combination of computer vision techniques, deep learning algorithms, and personalized pedagogical interaction. The suggested system uses convolutional neural networks (CNNs) and pose-estimation models to recognize hand gestures in real time with 95.7% accuracy. It then gives the learner instant feedback by changing the difficulty level. A dynamic learning engine automatically changes the level of difficulty based on how well the learner is doing, which helps them learn signs and phrases over time. The Scrum agile methodology was used during the development process. This meant that educators, linguists, and members of the deaf community all worked together to design the product. Early tests show that sign recognition accuracy and indicators of user engagement and motivation show favorable performance and are at appropriate levels. This proposal aims to enhance inclusive digital ecosystems and foster linguistic equity in Mexican education through scalable, mobile, and culturally relevant technologies, in addition to its technical contributions. Full article