Search Results (68,013)

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

Cardiovascular disease (CVD) remains the leading cause of global mortality, highlighting the need for continuous vital sign monitoring. Photoplethysmography (PPG) is well suited for wearable devices. Smart rings, benefiting from dense capillary distribution and minimal tissue interference, can capture high-quality PPG signals with comfort, making them a promising next-generation wearable. However, ring rotation relative to the finger alters the optical path, especially for multi-wavelength light, thus reducing accuracy. This paper proposes a rotation-robust PPG sensor for smart rings. Monte Carlo simulations analyze photon transmission under different LED–photodiode (PD) angles, showing that at ±60, green, red, and infrared light achieve optimal penetration into the microcirculation layer. Considering non-ideal conditions, the green-light angle is adjusted to ±30, and a symmetrical sensor design is adopted. A prototype smart ring is developed, capable of recording 4-channel PPG, 3-axis acceleration, and 4-channel temperature signals at 100, 25, and 0.2 Hz, respectively. The system achieves reliable PPG acquisition with only 0.59 mA average current consumption. In continuous testing, heart rate estimation reached mean absolute errors of 0.82, 0.79, and 0.78 bpm for green, red, and IR light. The results provide a reference for future smart ring development. Full article

Australia consumes approximately 29 million m³ of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO_2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

The integration of renewable energy into industrial processes is crucial for reducing the carbon footprint of conventional hydrogen production. This work presents detailed design, optical–thermal simulation, and performance analysis of a solar parabolic dish (SPD) system for supplying high-temperature steam to a Steam Methane Reforming (SMR) plant. A 5 m diameter dish with a focal length of 3 m was designed and optimized using COMSOL Multiphysics (version 6.2) and MATLAB (version R2023a). Optical ray tracing confirmed a geometric concentration ratio of 896×, effectively focusing solar irradiation onto a helical cavity receiver. Thermal–fluid simulations demonstrated the system’s capability to superheat steam to 551 °C at a mass flow rate of 0.0051 kg/s, effectively meeting the stringent thermal requirements for SMR. The optimized SPD system, with a 5 m dish diameter and 3 m focal length, was designed to supply 10% of the total process heat (≈180 GJ/day). This contribution reduces natural gas consumption and leads to annual fuel savings of approximately 141,000 SAR (Saudi Riyal), along with a substantial reduction in CO₂ emissions. These quantitative results confirm the SPD as both a technically reliable and economically attractive solution for sustainable blue hydrogen production. Full article

As trade in services continues to play an increasingly important role in international trade, effectively integrating its advancement with green development has become a key issue for China in shaping a new development paradigm. This study treats the service trade pilot city policy as a quasi-natural experiment, employing the Difference-in-Differences (DID) method to investigate the policy’s impact on urban green energy use. The findings indicate that the policy significantly boosted green energy consumption in pilot cities. Heterogeneity analysis reveals more pronounced policy effects in eastern regions and provinces with smaller populations. Furthermore, synergistic effects emerge when this policy interacts with artificial intelligence (AI) technology policies and urban environmental policies, further amplifying green energy consumption outcomes. Consequently, this study proposes recommendations including strengthening institutional innovation in green services trade within pilot zones, establishing cross-regional green collaboration networks, and promoting multi-policy coordination. These findings offer valuable insights for developing countries seeking to achieve sustainable development through services trade liberalization. Full article

In resource-constrained edge agricultural environments, the accurate recognition of toxic weeds poses dual challenges related to model lightweight design and the few-shot generalization capability. To address these challenges, a multi-strategy recognition framework is proposed, which integrates a lightweight backbone network, a pseudo-labeling guidance mechanism, and a contrastive boundary enhancement module. This approach is designed to improve deployment efficiency on low-power devices while ensuring high accuracy in identifying rare toxic weed categories. The proposed model achieves a real-time inference speed of 18.9 FPS on the Jetson Nano platform, with a compact model size of 18.6 MB and power consumption maintained below 5.1 W, demonstrating its efficiency for edge deployment. In standard classification tasks, the model attains 89.64%, 87.91%, 88.76%, and 88.43% in terms of precision, recall, F1-score, and accuracy, respectively, outperforming existing mainstream lightweight models such as ResNet18, MobileNetV2, and MobileViT across all evaluation metrics. In few-shot classification tasks targeting rare toxic weed species, the complete model achieves an accuracy of 80.32%, marking an average improvement of over 13 percentage points compared to ablation variants that exclude pseudo-labeling and self-supervised modules or adopt a CNN-only architecture. The experimental results indicate that the proposed model not only delivers strong overall classification performance but also exhibits superior adaptability for deployment and robustness in low-data regimes, offering an effective solution for the precise identification and ecological control of toxic weeds within intelligent agricultural perception systems. Full article

T cells play a pivotal role in cancer research, particularly in immunotherapy, which harnesses the immune system to target malignancies. However, conventional expansion methods face limitations such as high reagent consumption, contamination risks, and difficulties in maintaining suspension cells in dynamic culture environments. This study presents a microfluidic system for long-term culture of non-adherent cells, featuring automated perfusion and image acquisition. The system integrates deep learning-based image analysis, which quantifies cell coverage and estimates cell numbers, and efficiently processes large volumes of data. The performance of this deep learning approach was benchmarked against the widely used Trainable Weka Segmentation (TWS) plugin for Fiji. Additionally, two distinct lab-on-a-chip (LOC) devices were evaluated independently: the commercial ibidi^® LOC and a custom-made PDMS LOC. The setup supported the proliferation of Jurkat cells and primary human T cells without significant loss during medium exchange. Each platform proved suitable for long-term expansion while offering distinct advantages in terms of design, cell seeding and recovery, and reusability. This integrated approach enables extended experiments with minimal manual intervention, stable perfusion, and supports multi-reagent administration, offering a powerful platform for advancing suspension cell research in immunotherapy. Full article

The apparel industry is changing dynamically and quickly to manufacturing sustainable fashion products and the development of sustainable design strategies that minimize material consumption at the source. This study addresses a critical research gap by quantitatively evaluating the impact of fusing traditional South Asian garment construction (the kameez) with varied, Western-inspired sleeve geometries on key manufacturing metrics. Thirty-three distinct women’s garment styles, comprising three kameez types (simple, princess-cut, open-front) each paired with eleven different sleeve designs, were developed in the apparel industry to study the effect of fabric efficiency, wastage, and cost-effectiveness. The virtual patterns and markers were drafted and accomplished through Garment Gerber Technology (GGT) software to analyze fabric consumption, fabric efficiency, and cost-effectiveness. The results revealed that paneled kameez styles, such as the princess-cut and open-front, are significantly more material-efficient, achieving average fabric efficiencies of up to 83.95%, compared to the monolithic simple kameez, which averaged only 75.68%. Among sleeve types, multi-constructions like the slit sleeve and cuff sleeve proved most efficient (achieving up to 86.91% efficiency), while voluminous, single-piece designs like the umbrella sleeve consumed the most fabric and were the least efficient. Open-front kameez slit sleeves (OFSL3), simple kameez slit sleeves (SSL3), and princess-cut kameez slit sleeves (PCSL3), were better and more sustainable selections since they were most efficient in fabric efficiency (i.e., 86.91%, 86.17%, and 86.09%). Furthermore, the simple kameez style has the highest fabric wastage (above 22%), while the princess kameez style has the least (below 19%). The simple kameez slit sleeves design (SSL3) has the minimum wastage, while the simple kameez umbrella sleeves design (SSL4) has the maximum wastage. From a cost perspective, the open-front kameez slit sleeve (OFSL1) was identified as the most economical design, whereas the simple kameez with an umbrella sleeve (SSL4) was the most expensive. Statistical analysis confirmed that the differences between kameez styles were significant (p < 0.05). Thus, adoption of specific, sustainable, deliberate design choices and incorporating paneling into the garment body and utilizing multi-piece sleeve constructions offer a quantifiable and strategic approach for manufacturers to reduce material waste, optimize fabric utilization, and improve production cost-effectiveness. Full article

In thermal engineering applications, finned surfaces are extensively employed to enlarge the effective heat transfer area and thereby enhance the efficiency of heat exchangers. The present study examines cylindrical tubes externally fitted with rectangular and triangular fins under the condition of a constant transverse cross-sectional area and total fin volume. For all five rectangular fin configurations analyzed, the cross-sectional area and total volume were kept constant, while for the five triangular fin configurations, these parameters were also maintained constant but were approximately half of those of the rectangular fins due to geometric characteristics. Both analytical calculations and numerical simulations using ANSYS Fluent were conducted to evaluate thermal performance across different fin thicknesses and heights. Results show that rectangular fins provide up to 9.75% higher heat flux than triangular fins at the optimal thickness; however, this improvement requires nearly a twofold increase in material consumption. The analysis further indicates that most heat transfer occurs near the fin base, where convective efficiency is highest, with effectiveness diminishing along the extended surface. These findings highlight the importance of selecting fin geometries that balance thermal performance with material economy. In conclusion, this study provides practical insights for designing more efficient and cost-effective heat exchangers. Full article

The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a survey was administered to university students (n = 280). Participant scores for the survey sections containing five-point Likert-type questions on climate change awareness were calculated using min–max normalization. The normalized data was then processed using the k-means algorithm, a well-known technique in unsupervised machine learning. This resulted in a classification (clustering) related to climate change awareness. The number of clusters was determined using the Silhouette index. Three clusters identified using k-means and Silhouette index ($S\approx 0.55)$ revealed the knowledge and application levels of student groups regarding climate change awareness. As a result of clustering, it was determined that Cluster-3 students (n = 134, 47.9%), defined as having a high level of knowledge and application, had a higher impact value in their overall assessments of green space-focused issues related to climate change awareness compared to the overall assessments of students in other clusters. Some notable findings concerning the attitudes of Cluster-3 students highlight climate change awareness-related practices. These include minimizing water consumption to levels necessary for ecosystem water management (mean = 95.7, std. deviation = 10.9) and exercising controlled, sustainable daily energy use to alleviate pressure on green spaces (mean = 94.4, std. deviation = 12.5). This study offers practical insights for policymakers, educators, and institutions, emphasizing the need to enhance climate education and to promote the active involvement of younger generations in shaping sustainable environments. Full article

Producing solid biofuels with high calorific value and high storage stability under limited energy consumption has become a crucial focus in the global energy field. Low temperature torrefaction below 300 °C is a common method for producing solid biofuels. However, this approach limits the carbon content and higher heating value (HHV) of the resulting biochar. Sodium percarbonate is a solid oxidant that can assist in the pyrolysis of organic molecules during the torrefaction to increase carbon content of biochar. Incorporating sodium percarbonate as a strategic additive presents a viable means to address the constraints associated with the torrefaction technologies. This study blended sodium percarbonate with spent coffee grounds (SCGs) to prepare torrefied SCG solid biofuels with high calorific value and high carbon content. Based on the Taguchi method with L9 orthogonal arrays, torrefaction temperature is identified as the most influential factor affecting higher heating value (HHV). Results from FTIR, water activity, hygroscopicity, and mold observation confirmed that torrefied SCGs blended with 0.5 wt% sodium percarbonate (0.5TSSCG) exhibited good storage stability. They were not prone to mold growth under ambient temperature and pressure. 0.5TSSCG with a carbon content of 61.88 wt% exhibited a maximum HHV of 29.42 MJ∙kg⁻¹. These findings indicate that sodium percarbonate contributes to increasing the carbon content and HHV of torrefied SCGs, enabling partial replacement of traditional coal consumption. Full article

The transportation sector plays a pivotal role in economic development but is also a major contributor to environmental degradation due to its reliance on fossil fuels. This study explores the relationship between transport-related CO₂ emissions, economic growth, road and rail freight transport, industry, trade openness, fossil fuel consumption, financial development, and renewable energy in ten landlocked countries from 1990 to 2022. Using panel cointegration tests and PMG-ARDL techniques, the findings reveal a bidirectional causality between CO₂ emissions, road freight, financial development, and industry. Road freight transport significantly boosts economic growth but also intensifies emissions, while renewable energy effectively mitigates transport-related CO₂. The results emphasize the need for policymakers to balance economic advancement with sustainable energy and emission reduction strategies. Achieving economic-energy sustainability is essential for fostering a green and clean environment without compromising growth. Full article

The increasing consumption of artificially sweetened beverages among young people, coupled with prevalent digital technology use, presents growing public health concerns regarding potential effects on health and behavior. This study aimed to determine the concentrations of three commonly used artificial sweeteners—acesulfame K, saccharin, and aspartame—in soft drinks available on the market in Osijek, Croatia, to assess their compliance with European Union regulations, and to investigate the consumption patterns and possible associations with internet addiction among university students. Laboratory analysis of 43 beverages was performed using high-performance liquid chromatography with diode array detection, while a cross-sectional survey of 792 students collected data on sociodemographic characteristics, beverage consumption, and internet use. Acesulfame K was the most frequently detected sweetener, followed by aspartame and saccharin, with mean concentrations of 50.1 mg/L, 22.7 mg/L, and 19.76 mg/L, respectively. Overall, 85.7% of the students stated that they consumed artificially sweetened drinks, with an average consumption of 0.2 L/day. Internet addiction was found in 39.8% of the participants, but no significant correlation was found between beverage consumption and internet addiction (p = 0.177). All measured concentrations of sweeteners were below the legal limits. These results suggest that while exposure to artificial sweeteners in beverages is within safe limits, further research is needed to assess cumulative intake and its potential impact on behavioral health in young adults. Full article