Search Results (52)

Public buildings play a critical role in national decarbonization strategies and green energy transitions due to their high energy consumption densities, large occupant capacities, and potential to drive public awareness. Optimizing energy efficiency in these structures not only alleviates the financial burden on public budgets but also serves as a benchmark for sustainable urban development. To investigate the energy-saving potentials, thermal comfort dynamics, and financial feasibilities within this sector, this study selects two university buildings from two different countries with distinct climatic, structural, and operational profiles as comparative case studies: university buildings in Türkiye (TR) and Italy (IT), respectively. A total of seven tailored retrofitting scenarios were developed based on country-specific legislative frameworks and subsidy mechanisms: the Minimum Environmental Criteria (CAM) and Conto Termico 3.0 for Italy, and the Public Buildings Energy Efficiency Project (KABEV), Energy Performance Contracting (EPC), and Nearly Zero Energy Buildings (NSEB) mandates for Türkiye. The scenarios evaluate deep building envelope insulation, high-efficiency window replacements, lighting automation (LED with daylighting controls), mechanical ventilation with heat recovery units (HRV), air-to-water heat pump integrations, and rooftop photovoltaic (PV) installations using calibrated DesignBuilder simulation models. The quantitative results demonstrate that country-specific green building incentives drastically enhance both the energy performance and financial viability of deep retrofits. For the Turkish case study, the comprehensive near-zero energy building (nZEB) retrofitting package (TR-4) successfully reduced annual primary energy consumption by 75% (from 284 to 71 kWh/m²·year) and cut annual thermal comfort discomfort hours by 72% (from 3147 to 880 h), yielding a Subsidized Net Present Value (NPV) of +310,600 €. Similarly, for the Italian case study, the holistic retrofit combined with rooftop photovoltaic integration (IT-4) achieved an 80% energy reduction (dropping from 128 to 25.6 kWh/m²·year), minimized annual discomfort hours to 45 h, and generated a Subsidized NPV of +425,500 €. Furthermore, national incentive mechanisms shortened simple payback periods by more than half, establishing that targeted public policy is vital to accelerate public sector building decarbonization while ensuring long-term fiscal profitability. Full article

Inconel 718 exhibits poor machinability due to its high strength and low thermal conductivity, which induce severe thermo-mechanical loads. Conventional cooling strategies struggle to concurrently regulate heat dissipation and interface lubrication. This paper proposes a machining method based on Active Thermal Conductive Media (ATCM), which simultaneously exerts the Rehbinder mechanochemical effect and solid-phase enhanced heat transfer effect by pre-coating a liquid graphene film on the workpiece surface. Orthogonal turning tests were conducted using a K313 carbide tool at a cutting speed of 30 m/min, cutting width of 2 mm, and undeformed chip thickness of 0.1 mm. The cutting force, cutting temperature, cutting power, and tool wear characteristics under six machining conditions—dry cutting, flood cutting, Minimum Quantity Lubrication (MQL), Cryogenic MQL (CMQL), Nanofluid MQL (NMQL), and ATCM-assisted cutting—are systematically compared. The results show that ATCM achieves a 21.6% reduction in cutting force, a 20% reduction in cutting temperature, and a 34.9% reduction in cutting power through the synergistic coupling effect of reduced heat generation and enhanced heat dissipation, with adhesive wear and diffusion wear of the cutting tool significantly suppressed. Full article

Forecasting multi-asset volatility spillovers and turning the forecasts into risk-aware portfolios requires methods that uncover directional information flow between assets, compress the state into a minimal sufficient representation, deliver calibrated uncertainty, and respect explicit tail-risk limits. We propose TDV (Transfer-entropy, Dynamic-graph-attention, Variational-information-bottleneck), an information-theoretic artificial intelligence framework that couples a time-varying transfer entropy network with a graph attention encoder regularised by a variational information bottleneck, and demonstrates the practical value of the calibrated predictive distribution through a downstream entropy-regulated, CVaR-constrained portfolio application. We establish three theoretical results: $L^2$ consistency of the k-nearest-neighbour transfer entropy estimator on $\alpha$-mixing returns with rate $O_P\left(n^{-2/(2+d)}\right)$, a PAC–Bayes generalisation bound of order $O\left(\sqrt{\left(I\right(X;Z)+log(1/\delta \left)\right)/n}\right)$ for the bottleneck-encoded forecaster, and asymptotic CVaR feasibility of the plug-in allocation. In simulations across sparse Granger networks, contagion DCC–GARCH ensembles, and regime-switching factor models, the framework cuts spillover forecasting errors by 24 to 42 percent against LSTM, vanilla GAT, and Transformer baselines, and it recovers 1.6 additional nats of mutual information with the realised connectedness matrix. On a 32-asset global panel covering 2014 to 2025, the model delivers an out-of-sample $R^2$ of 0.331, an annualised Sharpe ratio of 1.46 against 0.83 for an equally weighted benchmark, a maximum drawdown of 7.8 percent, and 95 percent CVaR reductions of 28 to 36 percent across sub-periods relative to a shrinkage minimum-variance baseline. Full article

This work examines the machining responses of dry turning in ultrasonic-assisted stir-squeeze cast A356 hybrid nanocomposites reinforced with zirconia (ZrO₂) and graphene oxide (GO). Accordingly, flank wear (VBc) ranged from 0.061 to 0.238 mm, influenced by abrasion, adhesion, built-up edge (BUE) formation, and diffusion mechanisms. Cutting speed had the most significant effect on flank wear (65.65%), followed by depth of cut (18.2%) and feed rate (11.13%), supported by a well-fitted regression model (R² = 0.987; p < 0.05). Surface roughness (Ra) ranged from 1.733 to 7.012 μm, with cutting speed, feed rate, and depth of cut contributing 70.42%, 15.43%, and 9.56%, respectively. The cutting temperature was limited to 127 °C, primarily influenced by cutting speed (60.68%), whereas cutting power varied between 0.353 and 0.644 kW, mainly governed by cutting speed (68.71%) and depth of cut (25.92%). The chip morphology showed a segmented sawtooth pattern due to cyclic fracture initiation during material removal. Multi-criteria optimization using complex proportional assessment (COPRAS) identified v = 90 m/min, f = 0.06 mm/rev, and d = 0.1 mm as the optimal parameters, yielding a tool life of 22.6 min and a machining cost of INR 58.69 per item. This research is further focused on the implementation of different cooling lubrication techniques utilizing environmentally friendly cutting fluids, including Minimum-Quantity Lubrication and nano-MQL, among other types of environments. Full article

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. Full article

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking “cutting edge micro-geometry—material separation behavior (separation point/minimum uncut chip thickness)—microstructural evolution of the machined surface.” This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel. Full article

Diamond coring bits exhibit stable rock-breaking and coring processes as well as a long service life. However, when drilling in complex and challenging formations are characterized by high hardness, strong plasticity, and high abrasiveness, issues such as low rock-breaking efficiency, rapid failure, and shortened service life frequently occur. To prevent premature bit failure and enhance rock-breaking efficiency, this study investigated the effects of drilling pressure and rotational speed on rock-breaking performance through bench-scale experiments using typical rock samples. A total of 15 experimental groups were included in this study, with one independent trial performed for each group. ROP is calculated as the ratio of effective drilling depth to time consumed, and MSE is derived based on axial force, torque, and rock-breaking volume. The experimental results indicated that (1) sandstone is more sensitive to rotational speed, whereas limestone and dolomite are more sensitive to drilling pressure; (2) the minimum mechanical specific energy (MSE) of sandstone was achieved at a drilling pressure of 15 kN and rotational speed of 50 r/min; (3) limestone exhibited the lowest MSE at 10 kN drilling pressure and 50 r/min rotational speed; and (4) dolomite showed the minimum energy consumption at 10 kN drilling pressure and 25 r/min rotational speed. On this basis, this paper establishes a cutting mechanics model for single-crystal diamond and a working load calculation model for the entire bit, respectively. The cutting mechanics model for single-crystal diamond is re-established based on Hertzian contact theory and elastic-plastic deformation theory. The findings of this study are expected to provide a working load calculation method for diamond coring bits in typical complex and challenging drilling formations and offer technical support for the design of coring bit cutting structures and the development of customized new products. It should be noted that the conclusions of this study are limited to the experimental parameter range (drilling pressure: 5–15 kN; rotational speed: 25–80 r/min), and their applicability under higher load conditions requires further verification. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph and identifying its hanging nodes. The network is then partitioned into several undirected subgraphs based on identified cut points. Each subgraph is transformed into a flow network according to actual power flow data. An efficient minimum cut set search algorithm is developed to locate potential transmission sections. To assess the risk under extreme conditions, a mixed-integer optimization model is formulated to select sections that are vulnerable to overload-induced tripping during N-K line outages caused by natural disasters. Simulation results on the IEEE RTS 24-bus and IEEE 39-bus systems validate the effectiveness and applicability of the proposed method. Full article

The transition to high-energy-density lithium metal batteries (LMBs) is essential for advancing electric vehicle (EV) technologies beyond the limitations of conventional lithium-ion batteries. A key challenge in scaling LMB production is the precise, contamination-free separation of lithium metal (LiM) anodes, hindered by lithium’s strong adhesion to mechanical cutting tools. This study investigates high-speed, contactless laser cutting as a scalable alternative for shaping double-coated LiM anodes. The effects of pulse duration, pulse energy, repetition frequency, and scanning speed were systematically evaluated using a nanosecond pulsed laser system on 30 µm LiM foils laminated on both sides of an 8 µm copper current collector. A maximum single-pass cutting speed of 3.0 m/s was achieved at a line energy of 0.06667 J/mm, with successful kerf formation requiring both a minimum pulse energy (>0.4 mJ) and peak power (>2.4 kW). Cut edge analysis showed that shorter pulse durations (72 ns) significantly reduced kerf width, the heat-affected zone (HAZ), and bulge height, indicating a shift to vapor-dominated ablation, though with increased spatter due to recoil pressure. Optimal edge quality was achieved with moderate pulse durations (261–508 ns), balancing energy delivery and thermal control. These findings define critical laser parameter thresholds and process windows for the high-speed, high-fidelity cutting of double-coated LiM battery anodes, supporting the industrial adoption of nanosecond laser systems in scalable LMB electrode manufacturing. Full article

Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022–2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January–June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha⁻¹), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R² of 0.802 (RMSE = 983.8 kg ha⁻¹), the protein model an R² of 0.786 (RMSE = 1.71%), and the RFV model an R² of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures—particularly in March and May—and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management—prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity—can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions. Full article

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop. Full article

We construct a family of linear optimal functional-repair regenerating storage codes with parameters $\{m,(n,k),(r,\alpha ,\beta \left)\right\}=\left\{\right(2r-\alpha +1)\alpha /2,(r+1,r),(r,\alpha ,1\left)\right\}$ for any integers $r,\alpha$ with $1\le \alpha \le r$, over any field when $\alpha \in \{1,r-1,r\}$, and over any finite field ${\mathbb{F}}_q$ with $q\ge r-1$ otherwise. These storage codes are Minimum-Storage Regenerating (MSR) when $\alpha =1$, Minimum-Bandwidth Regenerating (MBR) when $\alpha =r$, and represents extremal points of the (convex) attainable cut-set region different from the MSR and MBR points in all other cases. It is known that when $2\le \alpha \le r-1$, these parameters cannot be realized by exact-repair storage codes. Each of these codes come with an explicit and relatively simple repair method, and repair can even be realized as help-by-transfer (HBT) if desired. The coding states of codes from this family can be described geometrically as configurations of $r+1$ subspaces of dimension $\alpha$ in an m-dimensional vector space with restricted sub-span dimensions. A few “small” codes with these parameters are known: one for $(r,\alpha )=(3,2)$ dating from 2013 and one for $(r,\alpha )=(4,3)$ dating from 2024. Apart from these, our codes are the first examples of explicit, relatively simple, optimal functional-repair storage codes over a small finite field, with an explicit repair method and with parameters representing an extremal point of the attainable cut-set region distinct from the MSR and MBR points. Full article

Background/Objectives: The evaluation of the differences in corneal epithelial thickness profiles in healthy eyes and eyes with mild and moderate stages of keratoconus, using optical coherence tomography (OCT). Methods: Fifty-two healthy eyes (group 0), forty-one eyes with mild keratoconus (group I), and thirty eyes with moderate keratoconus (group II) were included in this study. Only one of the patient’s eyes was enrolled, and they were divided into groups using the Amsler–Krumeich (A–K) classification—stage I and II. All patients underwent a visual acuity assessment, slit-lamp examination, corneal tomography, and automatic mapping of corneal thickness and epithelial thickness on a diameter of 9 mm. Corneal tomography with a Placido/Scheimpflug instrument (Sirius, CSO, Florence, Italy) and OCT with a corneal adaptor module (Avanti RTVue XR, Optovue, Lombard, IL, USA) were used. Results: Minimum corneal epithelium thickness was 49.5, 43, and 40 µm in groups 0, I, and II, respectively (Kruskal–Wallis test, p < 0.001). A moderate correlation was found between minimum epithelial thickness and the apex curvature (Pearsons’s coefficient r = −0.62, p < 0.001) and posterior radius of central corneal curvature (Pearsons’s coefficient r = 0.62, p < 0.001). The difference between minimum and maximum epithelial thickness showed a high correlation (r = −0.770, p < 0.001). In groups I and II, on corneal epithelial thickness maps the thinnest sector, located inferiorly and temporally to the center, was surrounded by sectors with increased thickness. Conclusions: At the apex of the cone, the corneal epithelium becomes thinner, and a thicker ring forms around the cone. Although there is a moderate-to-strong correlation to parameters linked with the severity of keratoconus, and minimum epithelial thickness as well as the minimum–maximum difference, it is not possible to establish cut-off values for stages I and II in the Amsler–Krumeich (A–K) classification. Full article

Diamond grinding wheels have been widely used to remove the residual features of cast parts, such as parting lines and pouring risers. However, diamond grains are prone to chemical wear as a result of their strong interaction with ferrous metals. To mitigate this wear, this study proposes the use of a novel water-based hexagonal boron nitride (hBN) as a minimum quantity lubrication (MQL) during the grinding of cast steel and conducted the grinding experiment and molecular dynamics simulation. The experiment demonstrated that compared to dry grinding, the water-based hBN nanofluid can effectively reduce the maximum temperature of a workpiece at contact zone from 408 K to 335 K and change the serious abrasion wear of diamond grain to slightly micro-broken. The molecular dynamics simulation indicates that the flake of hBN can weaken the catalytic effect of iron on the diamond, prevent the diffusion of carbon atom to cast steel, and suppress the graphitization of diamond grain. Additionally, the flake of hBN improves the contact state between the diamond grain and cast steel and reduces the cutting heat and friction coefficient from about 0.5 to 0.25. Thus, the water-based hBN nanofluid as a new MQL was proven to be suitable for the wear inhibition of diamond grain when grinding cast steel. Full article