Search Results (7,092)

Temporal reasoning in dynamic, data-intensive environments increasingly demands expressive yet tractable logical frameworks. Traditional approaches often rely on negation to express absence or contradiction. In such contexts, negation-as-failure is commonly used to infer negative information from the lack of positive evidence. However, for open and distributed systems such as IoT networks and the Semantic Web, negation-as-failure semantics become unreliable due to incomplete and asynchronous data. This has led to growing interest in negation-free fragments of temporal rule-based systems, which preserve monotonicity and enable scalable reasoning. This paper investigates the expressive power of negation-free Metric Temporal Logic (MTL), a temporal logic framework designed for rule-based reasoning over time. We show that the “always” operators ⊞ and ⊟, often treated as syntactic sugar for combinations of other temporal constructs, can be eliminated using “once”, “since” and “until” operators. Remarkably, even the “once” operators can be removed, yielding a fragment based solely on “until” and “since”. These results challenge the assumption that negation is necessary for expressing universal temporal constraints and reveal a robust fragment capable of capturing both existential and invariant temporal patterns. Furthermore, the results induce a reduction in the syntax of MTL, which, in turn, can provide benefits for both theoretical study as well as for implementation efforts. Full article

Electrification is vital for economic growth, poverty reduction, and improved quality of life. Over 80% of Afghanistan’s rural population lacks electricity. Despite increasing interest in decentralized energy systems, there remains a lack of site-specific studies that jointly assess the technical, economic, and policy feasibility of decentralized solar PV for rural electrification in Afghanistan. This study addresses that gap through a mixed-method case study of Syahgel, Ghazni, combining a household survey of 30 households, PVsyst-based system sizing, economic evaluation, and policy analysis. The study compares multi-tier Solar Home Systems (SHSs) with a community microgrid under local demand and affordability conditions. The results show that SHSs, with entry-level costs starting from USD 95, are more suitable for small, dispersed settlements, while microgrids remain relevant for larger or more concentrated communities. Financing mechanisms, including subsidies and interest-free loans, can improve affordability by up to 75%, while electrification can reduce annual fuelwood expenditure by approximately USD 51.5 per household and generate broader health, educational, and livelihood benefits. The findings highlight the need for integrated policy reform, targeted financial support, and context-sensitive system design to support sustainable and inclusive rural electrification in Afghanistan. Full article

The escalating power density in Active Phased Array Radar has made the thermal management of Transmitter and Receiver (T/R) modules a critical bottleneck for radar performance. To address the thermal resistance of traditional cold plates, this study investigates an innovative embedded cooling strategy utilizing micro-pyramid arrays and advanced nanofluids. Thermal performance was evaluated using maximum temperature, maximum temperature difference and surface temperature standard deviation (ST). Higher pyramid density markedly enhances temperature uniformity, an effect that scales positively with the power load. Under a 100 W condition, the 8-circle micro-pyramids configuration (the densest structure with roughness Ra = 1.3) achieved a 22.58 K reduction in maximum temperature and a 22.5% improvement in temperature uniformity compared to the 2-circle structure, and outperformed the 4-circle structure by 16.98 K and 17.9%, respectively. Furthermore, a comparative analysis of nanofluids (Al₂O₃, CuO, graphene, and h-BN) is conducted and it is found that graphene nanofluid exhibits the best overall heat transfer enhancement because of its high thermal conductivity and moderate reduction in specific heat capacity. The thermal performance of the nanofluid is evaluated by comparing the maximum temperatures of the heat source at the 8-circle structure. The synergistic coupling of graphene nanofluid with the 8-circle array yields a remarkable 35.38% enhancement in temperature uniformity at 100 W. The enhancement mechanisms are mainly attributed to intrinsic thermophysical properties of the nanoparticles and convection caused by denser pyramid array. The aforementioned findings provide important guidance for the thermal management design of antenna and other high-density integrated electronic systems with embedded cold plate design demand. Full article

Road transportation is a major contributor to global CO₂ emissions, yet the influence of road geometry on vehicular emissions remains insufficiently quantified under real-world conditions. This study investigates the effects of horizontal and vertical alignments on CO₂ emissions of a light-duty gasoline passenger vehicle using Portable Emissions Measurement System (PEMS) data collected along a 62.4 km highway section. Six geometric parameters longitudinal grade, cross slope, horizontal curve radius, horizontal curve length, vertical curve radius, and vertical curve length were analyzed in combination with second-by-second vehicle dynamics. The results indicate that transient CO₂ emissions exhibit substantial variability, with instantaneous emission rates exceeding 7.0 g/s under high-load conditions. Longitudinal slope gradient shows the strongest linear association with emission rate (r = 0.63), while speed and acceleration exhibit weaker but statistically significant correlations (r = 0.21 and r = 0.28, respectively). Vehicle Specific Power (VSP), representing integrated tractive power demand, demonstrates stronger association with instantaneous CO₂ emissions than individual kinematic variables. In contrast, cross slope and horizontal curvature parameters display minimal direct correlations under the tested highway conditions. A nonlinear polynomial regression model modestly improves explanatory performance relative to a linear formulation (R² = 0.21 versus 0.15; RMSE approximately 56 g/km), although a substantial portion of variability remains unexplained, reflecting the complexity of transient real-world processes. Overall, vertical alignment and transient driving conditions dominate CO₂ emission variability, while horizontal parameters play supplementary roles. These findings provide empirical evidence for refining emission models and highlight the importance of incorporating vertical alignment into sustainable roadway design and carbon reduction strategies. Full article

The escalating demand for sustainable, nutrient-dense feeds underscores the need to valorize the agro-industrial byproducts utilizing innovative bioconversion strategies. In this context, we have studied the feasibility of incorporating tomato (Solanum lycopersicum) cultivation residues into Black Soldier Fly (BSF) larvae diets to produce high-protein insect meals. These residues are known to contain the naturally occurring toxic steroidal alkaloids tomatidine and α-tomatine, prohibiting their incorporation into human and animal diets. Herein, the tomato cultivation biomass was dried and mill-ground, and its varying volumes were incorporated into standard poultry feed (seven diet levels with 0–100% biomass inclusion) and tested in BSF-larvae-rearing trials to produce insect meals. The optimal results with respect to larvae growth, protein accumulation (highest value = 30.61%), lipid–fiber content, and antioxidant capacity were determined for insect meals obtained from BSF larvae reared with a ration composed of 40% tomato plant biomass. In addition, the toxicity of this insect meal was substantially low, as a consequence of the observed groundbreaking reduction in the contained toxic steroidal alkaloids α-tomatine and its aglycone tomatidine. The results herein reveal the efficacy of the BSF-larvae-rearing process in acting as a biological filter for the bioconversion of the toxic tomato cultivation waste into a functional, safe, and protein-rich livestock feed, supporting the principles of a circular economy. Full article

The rapid expansion of the high electricity-intensive industries like data center has led to a structural increase in industrial electricity demand, thereby increasing the need for demand response (DR) to enhance power system flexibility. However, in the industrial sector, DR strategies based solely on simple load curtailment can impose productivity losses on participating customers. To address this limitation, this study proposes an SMP-based load shifting linear programming (LP) optimization model that enables DR curtailment to translate into electricity cost reduction through clustered DR resources formed by combining load resources at the industrial complex level. The decision variables representing hourly load shifting are adjusted under constraints defined by the hourly average demand and flexibility of the load resources, and the averages and fluctuations of SMP. The objective function is optimized to minimize the total electricity cost. Since the demand flexibility varies by season, experiments are conducted about various clustered DR resources on a seasonal basis. When resources with similar hourly average demand and flexibility are combined, the resulting load shifting plans are found to yield the greatest electricity cost reduction (Scenario 2—0.79 M KRW). These results confirm that the proposed load shifting LP model can provide a practical approach for DR operation planning. Full article

Climate change is expected to impact rainfall amount, seasonality, and dry/wet patterns, with direct implications for rainwater harvesting systems. This study aims to quantify how future rainfall may affect rainwater harvesting systems across Brazil by combining multi-model climate projections with a daily water balance model. A single-family social housing archetype (60 m² roof area; four occupants; 150 L/day/person; non-potable demand equal to 30% of total demand) was simulated for 652 Brazilian cities, using bias-corrected daily rainfall from the CLIMBra dataset and nineteen climate models. Historical conditions were compared with near-future and far-future projections under the SSP2-4.5 and SSP5-8.5 scenarios. Historically, the greater potential for potable water savings has occurred in wetter, less seasonal climates, such as those in the North. In contrast, more seasonal and drought-prone areas, such as the Northeast, showed lower reliability. In future climates, most models indicate relative reductions in the potential for potable water savings in the North, Northeast, and Centre–West, with larger reductions under SSP5-8.5 and in the far-future scenarios. The South shows the most significant divergence between models and may increase the potential for potable water savings in some projections. On the other hand, in the South, the volume of rainwater harvesting system overflow increases under future scenarios. This work contributes to the literature by delivering a national-scale, multi-model, uncertainty-aware evaluation of rainwater harvesting performance under non-stationary rainfall regimes. Full article

With rising global energy demand and the gradual depletion of petroleum-based resources, interest in alternative fuels for internal combustion diesel engines (ICDEs) has increased. In ICDEs, firing-related and mechanical excitations may result in adverse vibration and noise responses. This study examines whether incorporating sour cherry pit pyrolysis oil (SCPO) with n-butanol and 2-ethylhexyl nitrate (2-EHN) may reduce vibration and noise under constant-load, steady-state operating conditions compared with neat diesel (D100). For the experimental tests, five fuel types were prepared: one neat diesel fuel and four blended fuels with a constant diesel fraction of 40% and a fixed 2-ethylhexyl nitrate (2-EHN) content of 5%, while the SCPO and n-butanol fractions were varied (D40/SCPO0/B55/2-EHN5, D40/SCPO5/B50/2-EHN5, D40/SCPO10/B45/2-EHN5, and D40/SCPO15/B40/2-EHN5). Experiments were performed using a single-cylinder ICDE at a fixed load of 10 Nm under steady-state conditions at engine speeds of 1500, 1800, 2400, 3000, and 3600 rpm. For each operating condition, vibration and noise data were recorded over a 10.4 s window. Experimental findings indicate that D40/SCPO10/B45/2-EHN5 yielded the lowest mean overall RMS vibration, with a 37.5% reduction relative to neat diesel (D100), and the lowest equivalent sound level (LAeq) among the tested fuels. Under the investigated steady-state constant-load conditions, the D40/SCPO10/B45/2-EHN5 fuel blend demonstrates the potential to achieve lower measured vibration and noise levels than neat diesel. Full article

The growing scale and complexity of industrial infrastructure make efficient and reliable inspections a critical challenge. Inspection task demands often vary dynamically, requiring efficient and demand-responsive inspection strategies to ensure stable operation. However, existing UAV inspection approaches typically deploy UAV base stations (UAV-BSs) based on fixed inspection frequencies, which are inadequate for adapting to such dynamic demands and may reduce inspection efficiency. Moreover, these approaches often rely on historical inspection data, whose empirical distributions may deviate from the true distributions, thereby compromising solution robustness. To address these issues, this paper proposes a two-stage distributionally robust optimization (TDRO) framework for joint UAV-BS deployment and inspection routing in dynamic environments. The framework accounts for uncertainties in both inspection frequency and distributional perturbations. Uncertainty sets constructed based on probability metrics are employed to capture deviations between empirical and true distributions, forming the foundation of the two-stage distributionally robust optimization model. The resulting model is solved using column-and-constraint generation (C&CG) integrated with column generation (CG), yielding robust deployment decisions and an effective trade-off between total system cost and inspection efficiency. Simulation results show that the framework effectively addresses inspection frequency uncertainty, reducing the total objective by 5.50% on average, with a further 2.16% reduction when distributional perturbations are considered. Full article

The retrofit of existing residential buildings plays a critical role in reducing energy consumption and carbon emissions in the building sector. However, previous retrofit evaluations often fail to account for the age-related thermal and lighting requirements of residents in aging residential buildings, thereby overlooking the substantial behavioral heterogeneity that shapes retrofit effectiveness. This study evaluates the comprehensive performance of different building envelope retrofit strategies, considering occupants’ thermal and visual comfort, from the perspectives of energy efficiency, economic feasibility, and environmental sustainability. First, age-specific differences in occupancy patterns, thermal preferences, and lighting requirements between elderly and non-elderly comparison group occupants were systematically extracted from the literature. Then, a typical high-rise residential building was modeled in EnergyPlus to serve as the reference building, within which the differentiated occupant behavior models were implemented, and the pre-retrofit condition was defined as the baseline scenario. Next, six commonly applied exterior wall insulation materials and different glass configurations and window frames were parameterized and evaluated under varying insulation thicknesses and remaining building service life scenarios. Finally, the energy-saving performance, economic benefits, and carbon reduction potential of envelope retrofit measures were quantitatively assessed across three primary functional zones (bedroom, living room, and study), using area-normalized indicators. The results indicate that, in the retrofit of existing residential buildings, bedrooms and study rooms exhibit greater retrofit benefits than living rooms, primarily due to longer occupancy durations and higher heating demand. In terms of retrofit strategies, exterior wall insulation consistently outperforms window retrofitting in energy-saving potential, with energy-saving rates of approximately 3.2–4.3% depending on functional zone, material type, and insulation thickness. Among the evaluated materials, vitrified microbead insulation performs best overall in terms of energy, economic, and carbon benefits at 40–60 mm thickness. These findings support occupant-informed, low-carbon retrofit decision-making for existing residential buildings. Full article

Industrial-scale Anaerobic Digestion (AD) is a key technology for the energy recovery of agro-industrial and municipal waste and for the mitigation of greenhouse gas emissions; however, the actual operational performance of industrial biodigesters continues to show significant discrepancies with respect to the theoretical values reported in the scientific literature. In this context, there is still a lack of systematic analysis to identify which operating parameters are consistently monitored in industrial settings and which remain insufficiently explored, particularly those that describe the overall state of the digestion environment. To address this gap, a systematic literature review was conducted in the Scopus database for the period 2000–2026, complemented by a bibliometric analysis using VOSviewer software v1.6.18. 3. After applying inclusion criteria focused exclusively on industrial-scale and pilot systems, 1327 documents corresponding to the category of operating parameters were selected and analyzed using keyword co-occurrence networks and evaluation of occurrence frequencies and total link intensities. The analysis shows a marked concentration of the literature on a small set of classic parameters, highlighting pH (154 occurrences, 3667 link intensities), temperature (147 occurrences, 3255 link intensities), and ammonia (131 occurrences, 2824 link intensities) as the most recurrent variables in the industrial operation of anaerobic digesters. Complementarily, parameters such as chemical oxygen demand, total and volatile solids, and hydrogen sulfide have progressively increased their presence since 2015, mainly associated with effluent quality assessment, nutrient recovery, and overall process sustainability. In contrast, variables that integrate the state of the environment, such as electrical conductivity, oxidation-reduction potential, and the rheological properties of digestate, appear in less than $5\%$ of the studies analyzed, despite their ability to integrate information on stability, buffer capacity, and overall operating conditions. Taken together, these findings highlight an imbalance between the intensive use of traditional parameters and the limited incorporation of integrative indicators in industrial monitoring, suggesting that their systematic inclusion, together with the development of soft sensors and predictive models, could contribute to improving operational control and reducing the gap between the theoretical performance and actual behavior of industrial biodigesters. Full article

The influence of pre-deformation on the microstructure and mechanical properties of a Mg-rich high-Cu Al-Mg-Si-Cu alloy was systematically investigated by hardness measurement, tensile test, and atomic resolution high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). With the increase in pre-deformation strain (0–10%), the hardness and strength of the alloy after PB hardening increased progressively, accompanied by a continuous reduction in tensile elongation. Notably, increasing pre-deformation strain from 2% to 10% did not bring a significant enhancement in bake hardening response, despite the gradual improvement in the strain hardening capability of the alloy. An optimal pre-deformation strain of 5% is identified, which enabled the alloy to achieve a superior and industrially feasible combination of strength and ductility, balancing practical forming demand (T4 temper) and service performance (PB state). Pre-deformation can significantly affect the morphology and atomic structure of precipitates for the alloy. Dislocations introduced by pre-deformation acted as heterogeneous nucleation sites, inducing the formation of elongated and string-like precipitates along dislocation lines. A distinct Cu segregation behavior was observed in the pre-deformed alloy with the majority of Cu atoms segregated at the precipitate/α-Al interface, which was in sharp contrast to their dominant distribution within the precipitate interior in the non-pre-deformed alloy. These findings provide new insights into deformation-assisted precipitation regulation in Mg-rich high-Cu Al-Mg-Si-Cu alloys and offer practical guidance for optimizing the strength–ductility synergy of such alloys for automotive lightweight manufacturing applications. Full article

Edge intelligence enables significant latency reduction and enhances the timeliness of model-based fault diagnosis. However, existing deep learning-driven bearing fault diagnosis models are ill-suited for deployment on edge devices, primarily due to three critical limitations: (1) Lightweight models typically exhibit inadequate anti-noise performance, failing to meet the reliability requirements of real-world engineering scenarios. (2) Models with superior anti-noise capabilities often demand high-performance hardware for operation, thereby restricting their deployment on resource-constrained edge devices. (3) These models adopt a fixed input length, which makes it difficult to guarantee diagnostic accuracy across diverse application scenarios—attributed to variations in sampling frequencies, bearing parameters, and other relevant factors. To address these challenges, this paper proposes a lightweight anti-noise diagnostic framework (LADF) for edge-intelligent bearing fault diagnosis in complex engineering environments. The LADF comprises three core modules: a dynamic input module (DIM), a lightweight network module (LNM), and a denoising branch. Specifically, the DIM is designed based on the envelope spectrum, leveraging its inherent demodulation characteristics to dynamically adapt to input signals across diverse scenarios. Group convolution and layer normalization are employed to construct the LNM, ensuring robust diagnostic performance while achieving efficient computation. The denoising branch constrains the feature extractor via a loss function, enabling it to learn generalized fault features under varying noise environments and thereby enhancing the anti-noise capability of the framework. Finally, the proposed LADF is validated through test rig experiments on two datasets of train axle box bearings. Comparative analysis with state-of-the-art models demonstrates that the LADF achieves superior diagnostic stability and anti-noise performance while maintaining a more lightweight architecture, making it well-suited for edge deployment in railway bearing fault diagnosis. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

In emergency communication networks, service demands and user mobility change dynamically. Low service rates and limited coverage are significant challenges that hinder the effectiveness of emergency services. Due to the flexibility, low deployment cost, and adjustable coverage range of unmanned aerial vehicles (UAVs), UAV-assisted emergency communication networks can serve as a viable method to address these challenges. Given the strong coupling between UAV trajectory optimization and resource allocation, joint optimization is crucial to meet dynamic service demands and user mobility. In this paper, we establish a user mobility model based on the Maxwell–Boltzmann distribution and a service model based on the Poisson process. We formulate an optimization problem to maximize the data transmission rate of emergency services. To address the challenges of high-dimensional continuous action spaces, we propose a shared feature extraction-enhanced PPO (SPOR) algorithm for joint trajectory optimization and resource allocation. Simulation results show that the proposed SPOR algorithm significantly outperforms benchmark methods. Specifically, it achieves at least a 20% improvement in data transmission rate, a 28% improvement in emergency communication service ratio, and a 12% reduction in average service distance. Full article