Search Results (4,909)

Multiport DC–AC converters are widely used in photovoltaic-energy storage–charging systems, but traditional two-stage schemes face challenges in circuit cost and efficiency improvements. To address this issue, a novel three-port single-stage DC–AC converter is proposed for grid-connected applications. The proposed converter integrates two DC ports and one AC port through circuit multiplexing, eliminating the high-voltage DC bus and reducing system complexity. An unfolding bridge is employed at the AC port, and full bridge circuits are used at DC ports, reducing the number of high-frequency switches. The proposed single-stage topology inherently achieves galvanic isolation and bidirectional power conversion. To achieve accurate grid current regulation and wide-range zero-voltage-switching, a multiple-phase-shift modulation method is developed to ensure a sinusoidal current waveform. The effectiveness of the proposed converter and modulation method is verified through simulation results, demonstrating a peak efficiency of 97% and a total harmonic distortion of 2.91%. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

In X-ray imaging, tissue scattering is an important factor that degrades image clarity, especially using a portable gridless X-ray imaging device. This study focuses on using Monte Carlo simulation to quantify the effect of scatter radiation on image resolution, by analyzing the point spread function (PSF) and the corresponding modulation transfer function (MTF). Lateral energy absorption profiles in tissue and a cesium iodide (CsI) scintillator were calculated at different X-ray tube voltages (70–90 kV) and filter configurations. Results showed that 85.7% of the total scattered radiation is concentrated at a distance of 4 cm from the central axis for the tissue and 67.37% for the CsI scintillator. The MTF remained high at low spatial frequencies (23% at 0.04 cycles/cm) but dropped at mid frequencies (0.015–0.025 at 0.3–0.6 cycles/cm) and was almost zero at high frequencies (0.004 at 0.8 cycles/cm), indicating loss of detail due to scattering. Increasing the thickness of the filter or adding a copper (Cu) filter reduced the contrast at low spatial frequencies (from 23% to 21%). The study quantitatively investigated the MTF degradation in portable X-ray imaging devices without grid, due to scatter. These results may aid in the development of scatter correction algorithms to improve image quality without the need for an anti-scatter grid. Full article

We establish a general no-go theorem demonstrating that all traversable wormhole configurations in Unimodular Gravity necessarily require exotic matter. The proof relies solely on the geometric flaring-out condition, b′(r₀) ≤ 1, which directly implies that ρ(r₀) + p_r(r₀) ≤ 0 at the throat. This condition represents a violation of the Null Energy Condition and, consequently, of the Weak and Strong Energy Conditions, independently of the particular choice of shape function, redshift function, or equation of state. This result holds for both tidal and zero-tidal-force configurations, showing that the requirement of exotic matter is a fundamental geometric consequence of the traversability condition rather than an artifact of specific solution choices. Therefore, Unimodular Gravity shares this fundamental constraint with General Relativity. Full article

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article

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

Rapid urbanization and global growth have made sustainable infrastructure a dire necessity. In hot arid regions, rising heat index levels intensify cooling demand and accelerate construction activity. Reducing emissions from concrete is critical to mitigate climate change. This study integrates BIM in Revit with EC3 to quantify GWP and total use of renewable/non-renewable primary resources at the product stage. A residential building is used to evaluate variations in environmental performance across multiple material scenarios (carbon intensive, energy transition, and green scenarios). Results reveal substantial differences in embodied carbon across scenarios. The carbon intensive scenario accounts for a total GWP of 649 tCO₂e, while the green scenario reduces emissions to 381 tCO₂e, which represents a reduction of 42%. Walls and floors are identified as the dominant contributors to embodied carbon due to high concrete volumes, with raw material extraction accounting for the largest share of emissions. Substituting conventional concrete walls with lightweight concrete walls reduces the total GWP by 28%. In addition, planed timber exhibits near zero emissions due to biogenic carbon storage and shows the highest renewable primary energy use among assessed materials. The proposed framework provides a practical approach for evaluating embodied carbon emissions and supports informed material selection for more sustainable building design. Full article

Indoor CO₂ forecasting supports proactive ventilation control that balances air quality with energy efficiency. While Machine Learning (ML) models have shown strong performance in controlled settings such as schools, their generalization across indoor spaces with diverse occupancy dynamics remains poorly characterized. We present a systematic benchmark of 11 forecasting models spanning simple baselines, statistical methods, classical ML, deep learning, ensembles, and foundation models using 18 weeks of IoT sensor data spanning six real-world use cases: conference rooms, dining halls, hospitals, food markets, offices and student residences. Performance depends strongly on the prediction horizon and on the regularity of occupancy-driven CO₂ patterns. Simple baselines tend to perform best at short horizons (10 min ahead), while ensembles and fine-tuned foundation models provide more robust accuracy at longer horizons (4 h ahead). Remarkably, zero-shot foundation models demonstrate the ability to outperform trained classical models in data-scarce scenarios, challenging the traditional paradigm of localized training. These findings indicate that optimal forecasting strategies are context-dependent and challenge the assumption of universal model superiority. Full article

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

This study develops an interpretable machine learning (ML) surrogate to predict hourly indoor air temperature and discomfort indicators for a representative Mexican social-housing prototype in San Luis Potosí (cold semi-arid, Köppen–Geiger BSk). A four-zone EnergyPlus model with constant window opening (50%) and no internal gains was used to generate a parametric dataset spanning 24 building orientations, seven roof solar absorptance levels, and two neighborhood configurations (surrounded vs. corner). Zone-specific bagged-tree regression models were trained in MATLAB using weather predictors, temporal indicators, and weather-memory features (including outdoor temperature lags and rolling averages). Orientation and roof absorptance were included as explicit design predictors, enabling the surrogate model to generalize across the full combinatorial design space rather than requiring a separate model for each configuration. Interpretability was assessed with SHAP values. Evaluated on orientation–absorptance combinations deliberately held out during training, the surrogate achieved high accuracy across zones of the house (R² = 0.98–0.99; RMSE = 0.31–0.67 °C) with stable, near-zero-centered residuals. When propagated into adaptive-comfort metrics computed directly relative to the monthly neutral temperature $T_n$, ML predictions preserved the main cold and hot discomfort degree-hour patterns across the full design space. The proposed surrogate enables rapid, physically consistent comfort-oriented screening of roof finishes and orientation choices in naturally ventilated social housing. Full article

This study investigates the hydrodynamic responses and energy harvesting performance of a hemispherical point-absorber wave energy converter (WEC) in uniform current. A frequency-domain Rankine source method (RSM) is developed to rigorously account for current-modified free-surface conditions, and an approximate free-surface Green-function method (AFSGM) is implemented to assess practical applicability under weak-current assumptions. The numerical settings for body, free-surface, and radiation-boundary discretizations are determined through convergence tests. Model validation is performed by comparing motion responses against published benchmark results under both zero-current and current conditions. The effects of current and motion constraints are examined for surge–heave free and heave-only cases. Results show that current can amplify the heave response and that surge freedom enhances heave motion through coupling effects, leading to increasing discrepancies between RSM and AFSGM as current strengthens. For heave-only motion, AFSGM provides practically acceptable predictions within $\mid F_r\mid \le 0.045$, while noticeable differences appear near resonance beyond this range, for which RSM is recommended. Energy harvesting is evaluated using a linear PTO damping model, revealing that current alters the capture width ratio (CWR) and shifts the optimal PTO damping and frequency, indicating the necessity of considering current in performance assessment and PTO design. Full article

This paper investigates the spatial asymptotic behavior of solutions to a class of nonlinear parabolic equations defined on an exterior region in ${\mathbb{R}}^3$. By constructing a suitable weighted energy functional and employing a fractional-order differential inequality technique, we establish a sharp Phragmén–Lindelöf type alternative: the solution either ceases to exist at a finite radial distance or decays to zero as the radial variable $r\to \infty$ when the power $p>2$. In the decay case, we derive explicit polynomial type decay estimates. The analysis is conducted in unbounded exterior domains where traditional compactness arguments are not applicable, extending previous studies on semi-infinite cylinders to more complex geometric settings. Our results reveal distinct spatial behaviors compared to those observed in linear or differently nonlinear parabolic problems and can be seen as a version of Saint-Venant principle in exterior regions. Full article

This study takes the relevant literature published in the past decade as the research object, screens the literature by setting clear inclusion and exclusion criteria, and systematically reviews the thermal insulation performance, durability, and prospects for engineering applications of bio-based thermal insulation materials by means of qualitative integration and comparative analysis. With the advantages of low energy consumption, renewability, and biodegradability, bio-based thermal insulation materials have emerged as a green alternative to traditional thermal insulation materials. This paper systematically reviews the research progress of such materials, which are classified into two categories: natural biomass (e.g., straw bales and cork boards) and bio-based composites. The core thermal insulation indicators include thermal conductivity, thermal resistance, and thermal storage coefficient, and the performance is affected by factors such as component ratio, pore structure, temperature, and humidity. The thermal conductivity of some bio-based materials is comparable to that of expanded polystyrene (EPS) and mineral wool. In terms of durability, temperature–humidity cycling, corrosion, biological erosion, and mechanical action are the main causes of performance degradation, and composite modification can effectively improve their stability. Current engineering applications face challenges such as thermal insulation performance being susceptible to humidity, poor construction compatibility, high costs, and a lack of relevant standards. Future research should focus on the development of high-performance composite systems, the investigation of long-term durability mechanisms, the innovation of low-cost green preparation technologies, and the establishment of unified standards, so as to promote the large-scale application of bio-based thermal insulation materials in the construction industry and contribute to the achievement of carbon neutrality goals. Full article