Search Results (1,146)

Wastewater treatment plants (WWTPs) are crucial for environmental protection and public health; however, they are among the most energy-intensive facilities in the water sector. This study examines the correlation between energy consumption and treatment efficiency at the Metamorphosis WWTP (MWWTP) in Attica, Greece, during the years 2022 and 2023. By analyzing influent and effluent characteristics, energy consumption patterns, and the removal efficiencies of key pollutants—Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD₅), and Suspended Solids (SS)—this research provides valuable insights into optimizing wastewater treatment operations. The findings reveal that, despite seasonal variations and fluctuations in influent composition, the facility consistently achieved high pollutant removal rates while maintaining stable energy consumption. The influent BOD₅ increased from 992.8 mg L⁻¹ in 2022 to 1122.3 mg L⁻¹ in 2023. COD rose from 1925.4 mg L⁻¹ to 2594.4 mg L⁻¹, SS from 1280.8 mg L⁻¹ to 1421.2 mg L⁻¹, and total phosphorus from 14.2 mg L⁻¹ to 17.0 mg L⁻¹. Effluent concentrations remained consistently low, with BOD₅ at 6.1 mg L⁻¹ in 2022 and 4.7 mg L⁻¹ in 2023; COD at 23.8 mg L⁻¹ and 25.2 mg L⁻¹, respectively; total nitrogen at 20.2 mg L⁻¹ and 16.7 mg L⁻¹; total phosphorus at 2.4 mg L⁻¹ and 2.6 mg L⁻¹; and SS at 2.4 mg L⁻¹ and 3.5 mg L⁻¹. These results indicate removal efficiencies exceeding 90%. Energy consumption remained stable, recorded at 13,044.9 kWh (0.593 kWh m⁻³ influent) in 2022 and 13,126.1 kWh (0.598 kWh m⁻³ influent) in 2023. These results highlight the importance of integrating energy-efficient strategies and renewable energy solutions to enhance wastewater treatment plant (WWTP) sustainability. This study contributes to ongoing efforts to improve energy optimization in wastewater treatment, supporting global initiatives for carbon footprint reduction and advancing the principles of a circular economy. Full article

Tire shape prediction presents significant engineering challenges due to the complex behavior of cord-rubber composites during manufacturing processes. Fabric cord components undergo thermal shrinkage and permanent deformation that substantially influence final tire dimensions, creating discrepancies between mold geometry and cured tire shape. While Post-Cure Inflation (PCI) helps control these dimensional changes, accurate prediction methods remain essential for reliable performance forecasting. This study addresses this challenge through a systematic experimental characterization of fabric cord behavior under manufacturing conditions. Thermal shrinkage and permanent set were quantified under various combinations of in-mold strain and PCI force, with distinct patterns identified for different cord materials (PET and nylon). Based on these experimental findings, a comprehensive finite element analysis methodology was developed to predict cured tire shape. Validation against 65 tire profiles demonstrated remarkable improvements over conventional approaches, with dimensional error reductions of 54.2% for the outer diameter and 49.5% for the section width. Profile and footprint predictions also showed significantly enhanced accuracy, particularly in capturing geometric features critical for tire–road contact characteristics. The proposed methodology enables more precise tire design optimization, improved performance prediction, and reduced prototype iterations, ultimately enhancing both product development efficiency and final tire performance. Full article

Malting is one of the most energy-intensive stages in beer brewing, yet its environmental impacts remain under-characterized despite recent efficiency gains. Barley and malt transport drive significant greenhouse gas emissions in import-dependent countries, while local, small-scale production can offset those savings through lower process efficiencies or higher resource use. This study conducted a cradle-to-gate Life Cycle Assessment (LCA) of three Italian malthouses—small, medium, and large—using SimaPro 10.2.0.0 and a functional unit of 1 kg of malted barley delivered by bulk truck to local breweries. Primary data on barley, water, methane, and electricity consumption, as well as waste generation, were collected via questionnaires; secondary data were sourced from Ecoinvent and Agri-Footprint. Impact categories were evaluated using the Cumulative Energy Demand (CED) and Product Environmental Footprint (PEF) methodologies. Barley cultivation dominates the footprint (84–92% of total impacts when using local grain). Drying and transport contribute 3.7–4.4% and 0–8.4% of impacts, respectively, depending on facility scale and import share. Smaller malthouses exhibit higher per-kilogram impacts due to lower energy efficiency and transportation modes. Mitigation strategies —including sustainable agriculture, renewable energy adoption, logistics optimization, and process improvements—can substantially reduce impacts. Notably, sourcing barley from low-impact suppliers alone lowers the carbon footprint from 0.80 to 0.66 kg CO_2e/kg, freshwater eutrophication from 227 to 32 CTU_e/kg, land use from 196 to 136 Pt/kg, and overall PEF from 192 to 81 µPt/kg. These results underscore the critical role of feedstock sourcing and process efficiency in decarbonizing malt production and provide a quantitative baseline for targeted sustainability interventions. Full article

Nitrogen (N) cycling in agroecosystems is a complex process regulated by both biological and agronomic factors, with ammonia-oxidizing archaea (AOA) and bacteria (AOB) playing pivotal roles in nitrification. Despite extensive fertilizer applications to achieve maximum crop yields, nitrogen use efficiency (NUE) remains less than ideal, with substantial losses contributing to environmental degradation. This review synthesizes current knowledge on plant-mediated and fertilization-induced shifts in ammonia-oxidizer communities and their implications on nitrogen cycling. We highlight the differential ecological niches of AOA and AOB, emphasizing their responses to plant community composition, root exudates, and allelopathic compounds. Fertilization regimes of inorganic nitrogen inputs and biological nitrification inhibition (BNI) are examined in the context of microbial adaptation and ammonia tolerance. Our review highlights the need for integrated nitrogen management strategies comprising optimized fertilization timing, nitrification inhibitors, and plant–microbe interactions in order to optimize NUE and mitigate nitrogen losses. Future research directions must involve applications of metagenomic and isotopic tracing techniques to unravel the mechanistic AOA and AOB pathways that are involved in regulating these dynamics. An improved understanding of these microbial interactions will inform the creation of more sustainable agricultural systems that aim to optimize nitrogen retention and reduce environmental footprint. Full article

Cloud Data Centers (CDCs) are an essential component of the infrastructure for powering the digital life of modern society, hosting and processing vast amounts of data and enabling services such as streaming, Artificial Intelligence (AI), and global connectivity. Given this importance, their energy efficiency is a top priority, as they consume significant amounts of electricity, contributing to operational costs and environmental impact. Efficient CDCs reduce energy waste, lower carbon footprints, and support sustainable growth in digital services. Consequently, energy efficiency metrics are used to measure how effectively a CDC utilizes energy for computing versus cooling and other overheads. These metrics are essential because they guide operators in optimizing resource use, reducing costs, and meeting regulatory and environmental goals. To this end, this paper reviews more than 25 energy efficiency metrics and more than 250 literature references to CDCs, different energy-consuming components, and configuration setups. Then, some real-world case studies of corporations that use these metrics are presented. Thereby, the challenges and limitations are investigated for each metric, and associated future research directions are provided. Prioritizing energy efficiency in CDCs, guided by these energy efficiency metrics, is essential for minimizing environmental impact, reducing costs, and ensuring sustainable scalability for the digital economy. Full article

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO₂ emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future. Full article

Under the “Healthy China” strategy, the demand for home fitness equipment is increasing, but existing solutions face challenges such as large size, limited functionality, and lack of personalization. This study proposes an innovative integrated design framework for multifunctional home fitness equipment, combining modular design, space optimization, and intelligent health monitoring. The design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing equipment footprint by 30% while enabling seamless transitions between exercise modes. Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. This research demonstrates a scalable and intelligent solution that bridges the gap between multifunctional integration and user-centric health management, offering significant advancements over previous designs. Full article

The amount of municipal solid waste (MSW) produced has increased with population growth and consumption patterns. Currently, most waste goes to dumps, although the Brazilian law requires the final destination to be landfills. The latter does not consider the energy lost by these solutions and the carbon footprint that better destinations could avoid. However, not treating the waste correctly aggravates land availability problems, especially in large cities such as São Paulo. Anaerobic digestion is an alternative to traditional waste management, and in addition to treating residues, it generates energy and recovers the nutrients present in MSW. Thermodynamic analyses are still scarce in the literature despite being a known process. This study performed an exergy analysis of an existing biogas plant at the Institute of Energy and Environment of the University of São Paulo with a processing capacity of 20 tons of MSW per day composed of three reactors (430 m³ each) and one internal combustion engine (ICE) of 75 kW. The plant uses MSW as the substrate for anaerobic digestion and generates electrical energy, biogas, and fertilizer for agriculture (digestate). Additionally, the plant operates in cogeneration, as the anaerobic digestion reactor uses the heat produced to generate electrical energy. The results showed that the exergy present in the substrate is 67,320 MJ/day. The products’ exergy flows and the processes’ efficiencies show that the exergy flow of the biogas (44,488 MJ/day) is significantly higher than the exergy flow of the digestate (1455 MJ/day). When considering the cogeneration process, the exergy flow was similar for heat and electric energy as the final products, with 10,987 MJ/day for electric energy and 5215 MJ/day for electric energy. The exergy efficiency of the digestion process was 68.25%, while that of cogeneration (digestate, heat and electric energy) was 26.23%. These results can help identify inefficiencies and optimize processes in an anaerobic digestion plant. Furthermore, thermodynamic analyses of anaerobic digestion found in the literature are mostly based on theoretical models. Thus, this study fills a gap regarding exergy analysis of actual biogas plants. Full article

Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management systems (HEMS) are positioning themselves as an efficient alternative by integrating artificial intelligence to improve their accuracy. Predictive algorithms that provide accurate data on the future behavior of energy consumption and appliance usage time are required in these HEMS to achieve this goal. This study presents a predictive model based on recurrent neural networks with long short-term memory (LSTM), known to capture nonlinear relationships and long-term dependencies in time series data. The model predicts individual and total household energy consumption and appliance usage time. Training data were collected for 12 months from an HEMS installed in a typical Colombian house, using smart meters developed in this research. The model’s performance is evaluated using the mean squared error (MSE), reaching a value of 0.0168 kWh². The results confirm the effectiveness of HEMS and demonstrate that the integration of LSTM-based predictive models can significantly improve energy efficiency and optimize household energy consumption. Full article

Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (Edge-BS-RoFormer), a novel, lightweight band-split rotary position encoding transformer. While existing deep learning methods face limitations in dynamic UAV noise suppression under such constraints, including insufficient harmonic modeling and high computational complexity, the proposed Edge-BS-RoFormer distinctively synergizes a band-split strategy for fine-grained spectral processing, a dual-dimension Rotary Position Encoding ($\mathrm{RoPE}$) mechanism for superior joint time–frequency modeling, and $\mathrm{FlashAttention}$ to optimize computational efficiency, pivotal for its lightweight nature and robust ultra-low-SNR performance. Experiments on our self-constructed DroneNoise-LibriMix (DN-LM) dataset demonstrate Edge-BS-RoFormer’s superiority. Under a −15 dB SNR, it achieves Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) improvements of 2.2 dB over Deep Complex U-Net (DCUNet), 25.0 dB over the Dual-Path Transformer Network (DPTNet), and 2.3 dB over HTDemucs. Correspondingly, the Perceptual Evaluation of Speech Quality (PESQ) is enhanced by 0.11, 0.18, and 0.15, respectively. Crucially, its efficacy for edge deployment is substantiated by a minimal model storage of 8.534 MB, 11.617 GFLOPs (an 89.6% reduction vs. DCUNet), a runtime memory footprint of under 500MB, a Real-Time Factor (RTF) of 0.325 (latency: 330.830 ms), and a power consumption of 6.536 W on an NVIDIA Jetson AGX Xavier, fulfilling real-time processing demands. This study delivers a validated lightweight solution, exemplified by its minimal computational overhead and real-time edge inference capability, for effective speech enhancement in complex UAV acoustic scenarios, including dynamic noise conditions. Furthermore, the open-sourced dataset and model contribute to advancing research and establishing standardized evaluation frameworks in this domain. Full article

Background and Objectives: Graft impingement against the intercondylar notch has been identified as a significant contributor to graft deterioration and suboptimal outcomes following anterior cruciate ligament (ACL) reconstruction. This study aimed to (1) identify the optimal combination of tunnel positions that minimizes impingement between the ACL graft and femoral intercondylar notch. Materials and Methods: Three-dimensional models of nine normal knees were reconstructed using computed tomography scans obtained at four knee flexion angles (0°, 45°, 90°, and 120°). Virtual ACL grafts with diameters of 7 mm and 9 mm were modeled as cylinders. Nine graft configurations were investigated by varying femoral and tibial footprint locations (anteromedial, central, and posterolateral) in all possible combinations. For each configuration, impingement volume was quantified by measuring the overlap between the intercondylar notch and the virtual graft using Boolean operators in 3D simulation software. The effects of graft diameter, footprint location, and knee flexion angle on impingement volume were analyzed. Results: Maximum impingement volumes were observed at 0° knee extension, with significant reductions at 45° flexion (p < 0.01) and negligible impingement at 90° and 120° flexion. The 9 mm diameter grafts demonstrated significantly greater impingement volumes than 7 mm grafts (p < 0.01). Impingement volumes increased progressively as footprint locations shifted from posterolateral to anteromedial positions in both femoral and tibial components. However, statistically significant differences in impingement volume across footprint locations were observed only for tibial positioning (p < 0.001), not for femoral positioning (p > 0.05). The femoral anteromedial-tibial anteromedial configuration exhibited the highest impingement volume (577.8 ± 171.3 mm³ for 9 mm grafts), while the femoral posterolateral-tibial posterolateral configuration showed the lowest (73.5 ± 85.6 mm³). Conclusions: Tunnel position, graft diameter, and knee flexion angle significantly influence impingement risk in ACL reconstruction. Tibial tunnel position appears more critical than femoral position in minimizing graft impingement. Posterolateral positioning of tunnels, particularly on the tibial side, may reduce impingement volume. Clinical Relevance: This study provides quantitative evidence to guide surgeons in optimizing tunnel placement and graft selection for anatomical single-bundle ACL reconstruction, potentially reducing the risk of graft deterioration and failure due to mechanical impingement. Full article

Sweet corn (Zea mays L. saccharata) has emerged as a valuable crop not only for its economic potential but also for its role in sustainable food systems due to its high consumer demand and adaptability. As global agricultural systems face increasing pressure from climate change, resource scarcity, and nutritional challenges, a strategic synthesis of research is essential to guide future innovation. This review aims to critically assess and synthesize major advancements in sweet corn (Zea mays L. saccharata) research from 2010 to 2025, with the objectives of identifying key genetic improvements, evaluating agronomic innovations, and examining sustainable production strategies that collectively enhance crop performance and resilience. The analysis is structured around three core pillars: genetic improvement, agronomic optimization, and sustainable agriculture, each contributing uniquely to the enhancement of sweet corn productivity and environmental adaptability. In the genetics domain, recent breakthroughs such as CRISPR-Cas9 genome editing and marker-assisted selection have accelerated the development of climate-resilient hybrids with enhanced sweetness, pest resistance, and nutrient content. The growing emphasis on biofortification aims to improve the nutritional quality of sweet corn, aligning with global food security goals. Additionally, studies on genotype–environment interaction have provided deeper insights into varietal adaptability under varying climatic and soil conditions, guiding breeders toward more location-specific hybrid development. From an agronomic perspective, innovations in precision irrigation and refined planting configurations have significantly enhanced water use efficiency, especially in arid and semi-arid regions. Research on plant density, nutrient management, and crop rotation has further contributed to yield stability and system resilience. These agronomic practices, when tailored to specific genotypes and environments, ensure sustainable intensification without compromising resource conservation. On the sustainability front, strategies such as reduced-input systems, organic nutrient integration, and climate-resilient hybrids have gained momentum. The adoption of integrated pest management and conservation tillage further promotes sustainable cultivation, reducing the environmental footprint of sweet corn production. By integrating insights from these three dimensions, this review provides a comprehensive roadmap for the future of sweet corn research, merging genetic innovation, agronomic efficiency, and ecological responsibility to achieve resilient and sustainable production systems. Full article

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO₂-eq. Based on gross emissions (28,306.43 t CO₂-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO₂-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article

Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO₂ footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method for creating a robust auto-tuner for proportional–integral–differential (PID) controller control of the lime saturation factor (LSF) of the raw mix using artificial neural networks (ANNs). This auto-tuner, combined with a previously studied robust PID controller, forms an integrated system that adapts to process changes and maintains low long-term variance in LSF. The ANN links each of the three PID gains to the process dynamic parameters, with the three ANNs also interconnected. We employed the Levenberg–Marquardt method to optimize the ANNs’ synaptic weights and applied the weight decay method to prevent overfitting. The industrial implementation of our control system, using the auto-tuner for 16,800 h of raw mill operation, shows an average LSF standard deviation of 2.5, with fewer than 10% of the datasets exceeding a standard deviation of 3.5. Considering that the measurement reproducibility is 1.44 and assuming a low mixing ratio of the raw meal in the silo equal to 2, the LSF standard deviation in the kiln feed approaches the analysis reproducibility, indicating that disturbances in the raw meal largely diminish in the kiln feed. In conclusion, integrating traditional, well-established tools like PID controllers with newer advanced techniques, such as ANNs, can yield innovative solutions. Full article

The use of natural fibers in hydro turbine rotors promotes sustainability by offering biodegradable, renewable materials with a lower carbon footprint. This study compares the hydrodynamic performance of two rotors in a gravitational vortex turbine: Rotor 1, 3D-printed with polylactic acid (PLA), and Rotor 2, made from fique fiber and epoxy resin using manual molding. To compare the rotors, experimental tests were conducted on a laboratory-scale setup, where the behavior of both rotors was evaluated under different flow regimes. Rotor 1 achieved 61.01% efficiency at an angular velocity ($\omega$) 160 RPM, while Rotor 2 reached only 19.03% at $\omega$ of 165 RPM. The lower performance of Rotor 2 was due to dynamic imbalances and mechanical vibrations, leading to energy losses. These challenges highlight the limitations of manual molding in achieving precise rotor geometry and balance. To improve natural fiber rotor viability, optimizing manufacturing techniques is crucial to enhance dynamic balance and minimize vibrations. Advancements in fabrication could bridge the performance gap between natural and synthetic materials, making bio-based rotors more competitive. This study emphasizes the potential of natural fibers in sustainable energy and the need to refine production methods to maximize efficiency and reliability. Addressing these challenges will help integrate eco-friendly rotors into hydro turbine technologies. Full article