Search Results (3,031)

Ground source heat pump (GSHP) systems contribute to sustainable building decarbonization while confronting two intertwined challenges: long-term ground thermal imbalance in shallow borefields and the requirement for coordinated operation between thermal systems and electrical grid dynamics. Hybrid shallow–deep borefield configurations have been proposed to mitigate thermal imbalance for sustainable geothermal resource exploitation, yet their grid-interactive demand–response potential remains unexplored. Here, we develop a coupled thermal–electrical model for a shallow–deep mixed GSHP (SDBHE) system equipped with water-tank thermal storage, validated against scaled sand-tank experiments (3.5–8.3% error), and assess its year-round performance under time-of-use electricity tariffs for a 200,000 m² residential district in cold-climate conditions. The SDBHE system reduces the required shallow borehole count by 28% and total drilling length by 22% compared with a shallow-only baseline, saving 11% on operational electricity costs over 10 years. Integrating water-tank thermal storage with a 50% load-shifting strategy yields an additional 10.9–11% cost reduction without degrading the system’s coefficient of performance. Under higher load-shifting ratios, the combined capital and operational savings reach 19–29%, with the optimal allocation assigning the incremental high-price-period load preferentially to deep boreholes (COP 6.29 versus 5.25 for shallow). These results demonstrate that integrating shallow and deep geothermal tiers with thermal storage enables both capital-efficient borefield design and economically viable demand-side grid participation. The findings are bound by the cold-climate residential context and the rule-based control scheme; field-scale validation and lifecycle cost analysis are needed to generalize the conclusions. Full article

Reliable electricity supply in Colombia’s Non-Interconnected Zones requires sustainable and low-cost energy technologies. Vertical-axis hydrokinetic turbines are promising for this purpose; however, their relatively low power coefficient remains a major challenge. This study combines transient 2D and 3D $k$–$\omega$ SST computational fluid dynamics (CFD) simulations with hydraulic channel experiments to investigate a hybrid Darrieus–Savonius turbine. A 27-case Design of Experiments (DoE) based on 2D CFD was first applied to screen the effects of rotor radius ratio (RR), attachment angle (AA), and water velocity. Within the investigated design space, the configuration with $\mathrm{R}\mathrm{R}=0.5$ and $\mathrm{A}\mathrm{A}=0^{\circ }$ produced the most favorable average performance. The selected configuration was subsequently analyzed using 3D CFD and experimentally evaluated at TSR values of 1.0, 1.1, and 1.2. At TSR = 1.0, the 3D model predicted ${\mathrm{C}}_{\mathrm{P}}=0.1525$, closely matching the experimental value of 0.1541 with a relative error of 1.05%. The results demonstrate that 2D CFD is useful for computationally efficient parameter screening and qualitative trend identification, but it overpredicts absolute performance because it neglects blade tip vortices, spanwise flow, and volumetric wake interactions. Three-dimensional CFD is therefore required for reliable performance prediction and analysis of the complex flow structures governing hybrid hydrokinetic turbine behavior. Full article

Ornamental aquaculture represents a high-value segment of global aquaculture and plays an important role in income diversification for small-scale producers. Despite its economic relevance, investment decisions in ornamental fish farming are often supported by limited economic information and rarely incorporate risk and uncertainty into economic assessments. This study developed and applied a stochastic framework to evaluate the economic performance and investment risk of intensive ornamental aquaculture using Betta splendens production as a representative case study. A representative production system was developed from technical and economic surveys conducted on 20 commercial family operated farms located in one of Brazil’s major ornamental fish production clusters. The production system comprised three greenhouse units with an estimated annual output of 162,288 marketable fish. Production costs were estimated using conventional cost-accounting procedures, whereas economic risk was assessed through Monte Carlo simulation incorporating uncertainty in biological, productive, and market variables. The estimated total production cost was US$0.14 fish⁻¹, while the weighted average selling price reached US$0.18 fish⁻¹, resulting in a benefit–cost ratio of 1.33, a profitability of 25.09%, and an annual return on invested capital (ROIC) of 23.90% under the deterministic baseline scenario. Monte Carlo simulation estimated a mean annual ROIC of 22.46%, with a 98.53% probability of positive economic returns. Sensitivity analysis identified labor demand, the selling price of premium males, and reproductive productivity as the principal drivers of economic risk. The results indicate that economic returns in B. splendens farming are primarily influenced by managerial efficiency, labor requirements, and market conditions rather than production volume alone. The proposed framework provides a robust tool for evaluating economic viability and investment risk in ornamental fish farming under uncertainty and may support decision-making in small-scale aquaculture systems. Full article

Water contamination with surface-active agents, such as sodium lauryl sulfate (SLS), represents a serious environmental concern, driving the need for efficient and low-cost alternative adsorbents as a step toward sustainable waste valorization. In this study, waste biomass derived from plum stones, date stones, and walnut shells was successfully transformed into activated carbons via chemical activation using potassium hydroxide (KOH) at 850 °C with a 1:1 impregnation ratio. The synthesized materials underwent comprehensive physicochemical characterization utilizing TG-DSC, elemental analysis, Boehm titration, SEM, TEM, and nitrogen physisorption (BET), whereas their adsorption performance was evaluated against aqueous SLS solutions across various concentrations. The obtained results reveal a predominantly microporous structure with a high specific surface area, reaching up to 1059.01 m²/g for ACdate. The equilibrium adsorption data were well described by the Langmuir isotherm model, which yielded model-estimated asymptotic adsorption capacities (q_m) of 219.70 mg/g for ACwalnut, 178.25 mg/g for ACdate, and 57.80 mg/g for ACplum. These values represent Langmuir-derived model parameters rather than experimentally attained adsorption capacities within the investigated concentration range. Notably, despite having a lower specific surface area than ACdate, ACwalnut exhibited the highest Langmuir-estimated q_m, which may be associated with its structural balance and well-developed mesoporous network (0.210 cm³/g), facilitating the intraparticle transport of SLS molecules. These findings highlight that high efficiency originates from a synergistic combination of accessible porosity, a mesoporous transport network, hydrophobic character, and specific surface functional groups, demonstrating the exceptional potential of these activated carbons for anionic surfactant wastewater remediation. Full article

In this work, a formaldehyde-free bio-based wood adhesive was successfully fabricated using epoxidized soybean oil (ESO) cross-linked with maleic anhydride (MA) and carboxylated lignin sulfonate (CLS). The effect of CLS substitution dosage on the bonding performance was systematically investigated. The results indicated that the dry shear strength and wet shear strength of the adhesive exhibited a typical non-monotonic variation with increasing CLS content, reaching the maximum values of 1.79 MPa and 1.38 MPa, respectively, at a CLS substitution ratio of 40 mol% relative to MA. These mechanical properties fully meet and exceed the requirements of the Chinese national standard for wood adhesives. Orthogonal experiments were further conducted to optimize the hot-pressing process parameters and the optimal conditions were determined as follows: hot-pressing temperature of 130 °C, pressing time of 10 min, glue spread of 280 g/m², and pre-mixing time of 70 min. FTIR, DSC, and TGA characterizations confirmed the complete curing reaction of the adhesive system. The introduced CLS served as both a reactive curing agent and an efficient catalytic component, which effectively reduced the curing temperature, while the incorporation of MA significantly improved the thermal stability of the cured adhesive. This study provides a feasible strategy for the preparation of high-performance, low-cost, and environmentally friendly bio-based wood adhesives. Full article

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

Kolmogorov–Arnold networks (KANs) replace the fixed activations of a multilayer perceptron (MLP) with learnable univariate edge functions. We evaluate KANs as Payne-style label-to-flux emulators using the public 1000-spectrum Kurucz grid released with The Payne. Two capacity-matched KAN–MLP pairs (approximately 2.3 and 18–19 million parameters) were tuned separately, trained for the same 120,000-update budget across three seeds, and evaluated on a fixed 200-spectrum holdout. At the smaller capacity, the KAN did not outperform the tuned MLP. At the larger capacity, KAN-L achieved the lowest flux errors among the models trained from scratch: its mean absolute error was 35% below the best small model, all three large-MLP layouts remained less accurate, and its residual tails were lighter. In injection–recovery tests, KAN-L improved the recovery of effective temperature, surface gravity, and metallicity at all the tested signal-to-noise ratios relative to the small matched MLP; the best large-MLP control was not included in this test. The learned KAN response shapes were reproducible across seeds. These gains required substantially greater training and inference costs. In an APOGEE Data Release 17 deployment, surface-gravity offsets decreased by approximately 0.09 dex for all the tested emulators when calibrated APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) values were replaced by the raw spectroscopic scale, indicating a common reference-scale contribution. KANs are therefore not efficient drop-in replacements for MLP emulators, but the large KAN tested here improved spectral emulation accuracy and residual-tail robustness. Full article

Background/Objectives: Free-text veterinary pathology diagnoses contain essential information for cancer registration but are difficult to convert into standardized ontology-based codes because of linguistic variability, contextual modifiers, and large ontology search spaces. This study evaluated a hybrid lexical–semantic architecture for the automated assignment of Vet-ICD-O-Canine-1 morphology codes. Methods: A retrospective single-registry benchmark included 211 diagnoses from the São Paulo Animal Cancer Registry. Of these, 190 contained sufficient morphological information for expert-reviewed reference coding, whereas 21 generic or insufficiently specified descriptions were retained as an exploratory challenge subset. Fuzzy lexical matching retrieved Top-10, Top-20, or Top-30 candidates from the complete 971-entry morphology ontology, followed by semantic selection using Claude Haiku 4.5 and structured JSON output. Performance and computational efficiency were compared to direct full-ontology inference. Results: Among the evaluated fuzzy metrics, token_set_ratio achieved the highest Top-30 reference-code retrieval rate of 89.5%. End-to-end exact-match agreement increased from 73.7% with Top-10 to 79.5% with Top-20 and 85.8% with Top-30 (95% CI, 80.1–90.0%). Top-30 generated non-null codes for 93.2% of the 190 evaluable diagnoses and achieved a conditional exact-match agreement of 92.1%. By contrast, the direct full-ontology baseline achieved 71.2% conditional exact-match agreement (42/59) among non-null predictions and 22.1% end-to-end exact-match agreement (42/190) when incorrect predictions, null outputs, and technical failures were considered non-concordant outcomes. Compared to direct full-ontology inference, Top-30 reduced input-token consumption by 92.4%, total token consumption by 92.2%, and inference cost by 91.3%, while avoiding the 118 API rate-limit failures observed with the direct baseline. Among the 21 insufficiently specified diagnoses, Top-30 returned null codes in 38.1% and non-null codes in 61.9%. Conclusions: Ontology-guided candidate reduction improved coding agreement, computational efficiency, and operational robustness within this retrospective single-registry benchmark. However, the reported performance estimates require confirmation in larger independent datasets, and an upstream data-sufficiency or abstention mechanism is needed before prospective operational deployment. Full article

Mechanical force offers a distinctive nonequilibrium mode of energy input for lignocellulosic biomass valorization through localized, transient action. This review systematically examines the multiscale physicochemical effects of mechanical force, its synergistic coupling with chemical pretreatments, and its role in enhancing enzymatic hydrolysis. The principal contribution of mechanical force is not merely particle-size reduction, but the exposure of active sites and improvement in substrate accessibility at the molecular level. Coupling mechanical force with chemical pretreatment enables the efficient component fractionation under mild conditions while mitigating irreversible lignin condensation. In high-solids enzymatic hydrolysis, a periodic mechanical energy input can tear fiber bundles, release constrained water, and renew reaction interfaces, thereby allowing enzymes to sustain a high catalytic efficiency at extremely low liquid-to-solid ratios and reducing the dependence on large amounts of free water. An economic analysis indicates that feedstock and enzyme costs dominate the overall process economics. Accordingly, mechanical-force strategies should prioritize the maximized sugar yield and reduced enzyme loading under a controlled energy input. Future research should focus on continuous operation, the balance between mechanical deconstruction and lignin structural integrity, and multidimensional evaluation frameworks that integrate the energy consumption, sugar yield, enzyme dosage, and full-process energy balance. Full article

Activated carbon is a highly porous adsorbent material that is often used to treat wastewater using physical and chemical adsorption. Agricultural and urban biomass waste valorization to activated carbon is a low-cost, renewable solution to commercial adsorbents, and can help prevent waste from tree pruning from being dumped in landfills or openly burned. In this study, the ability of the ground and unground Ficus nitida leaves to efficiently adsorb Rhodamine B dye and total chromium from model aqueous solutions was investigated. Chemical activation was performed using phosphoric acid (H₃PO₄) at different impregnation ratios (1:1, 2:1, and 4:1). Samples obtained as a result of the above activation were labeled G1–G3 (ground) and UG1–UG3 (unground). The adsorption test showed that the samples with the highest activation ratio (G3 and UG3) gave the best results, removing 92% and 94% RhB, respectively, in 20 minutes. After 24 h, sample G3 showed the best efficiency of 73.31% (13.35 ppm remaining) in chromium removal, where the adsorption kinetics were well described by the pseudo-second-order model (R² > 0.98), indicating that there may be some chemical interactions occurring during the adsorption process along with physisorption, and the RhB adsorption isotherms for sample UG3 were well described by the Langmuir isotherm (R² > 0.95). The higher activation ratio and grinding increased the carbon content (up to 90% C for G3), surface functional groups, and textural properties (BET surface area of 699 m²/g and total pore volume of 3.06 cm³/g). Furthermore, reusability tests over five consecutive cycles demonstrated the excellent recyclability of sample G3, retaining removal efficiencies of 80.5% for RhB and 50.2% for total chromium. The results revealed that Ficus nitida leaf-based AC can be used as an efficient, economical, and reusable adsorbent material for sustainable environmental cleanup and water purification systems and will create a circular economy for waste management. Full article

Hydraulic structures disturb natural flow patterns, reducing water conveyance efficiency and increasing hydraulic energy losses, thereby affecting the sustainable management of water structures. Entrance-zone geometry, particularly upstream wing-wall configuration and canal inside slope, plays a critical role in controlling flow behavior, energy dissipation, upstream afflux, and hydraulic performance. However, the coupled effects of these geometric parameters have not been systematically investigated. Therefore, this study employed a validated HEC-RAS model to evaluate the combined influence of canal inside slope and upstream wing-wall configuration on the hydraulic performance of irrigation water structures and to support sustainable hydraulic design. Four wing-wall configurations (box, broken, curved, and splayed) and three canal inside slopes (1:1, 3:2, and 2:1) were analyzed under a fixed contraction ratio of 0.6 and upstream Froude numbers ranging from 0.12 to 0.18 under steady subcritical flow conditions. The model was validated against measurements from a 1:10 laboratory flume, demonstrating excellent agreement, with an average variation of 5.75% and coefficients of determination (R²) ranging from 0.97 to 0.99. Gradual entrance transitions significantly improved hydraulic performance by reducing flow disturbances and enhancing flow uniformity. For a canal inside slope of 1:1, the curved wing-wall configuration reduced relative heading-up and energy loss by 18.02% and 46.83%, respectively, whereas the splayed configuration achieved the best overall performance, with corresponding reductions of 27.63% and 73.11% compared with the conventional box configuration. Furthermore, dimensionless predictive equations were developed for the principal hydraulic performance indicators, achieving R² values of 0.96–0.99 and RMSE values of 0.001–0.01. The proposed framework improves water conveyance efficiency, minimizes hydraulic losses, and provides a validated, cost-effective numerical tool for evaluating alternative design scenarios, reducing reliance on extensive physical experimentation while supporting sustainable irrigation structures and long-term water resources management. Full article

Wenchang chicken, a premium indigenous yellow-feathered broiler in China, faces production constraints due to high feed costs and soybean meal dependence. Black soldier fly larvae meal (BSFLM) is a promising alternative protein source, but its effects on Wenchang chickens are largely unknown. This study evaluated the effects of dietary BSFLM on growth performance, meat quality, intestinal health, and cecal microbiota. A total of 192 fifty-day-old female Wenchang chickens were randomly assigned to diets with 0%, 5%, 10%, or 15% full-fat BSFLM for 42 days. The 15% BSFLM inclusion increased final body weight (p < 0.05) while reducing the feed conversion ratio (p < 0.01) and feed cost per kg gain (p < 0.001). Regarding meat quality, 15% BSFLM enhanced redness (a*) and reduced shear force, 48 h drip loss, and cooking loss (p < 0.05). It also elevated antioxidant enzyme activities, upregulated Nrf2–Keap1, improved intestinal villus architecture and tight junction proteins, and reduced serum diamine oxidase (p < 0.05). Cecal microbiota underwent distinct restructuring, with dose-dependent proliferation of beneficial taxa. Overall, BSFLM exerts beneficial effects on the feed efficiency, meat quality, and intestinal health of Wenchang chickens, and the 15% inclusion level achieved relatively better comprehensive outcomes. This study provides a scientific reference for insect protein utilization and contributes to sustainable high-quality Wenchang chicken production. Full article

Searching by numerical simulation for structures with optimal damping properties among viscoelastic hollow cylindrical bodies with a filler is usually associated with a large amount of computation. Formulating the mechanical problem as one of natural vibrations and natural wave propagation makes it possible to evaluate the dissipative properties of such a structure independently of external force and kinematic actions, and thereby to reduce the computational cost substantially. The solution of the natural vibration problem for a piecewise homogeneous viscoelastic hollow cylindrical body with a filler yields complex natural frequencies, the real part of which represents the vibration frequency and the imaginary part the damping factor (attenuation rate). The mechanical behavior of the viscoelastic material is described by the linear Boltzmann–Volterra hereditary theory with a three-parameter Koltunov–Rzhanitsyn relaxation kernel, within which the material characteristics are represented by complex dynamic moduli—the shear modulus and the bulk modulus—that, as a rule, depend on frequency. In the natural vibration problem these moduli become functions of the real part of the sought complex natural frequency alone, which makes the standard eigenvalue procedures of commercial finite-element codes inapplicable. The paper presents an algorithm that removes this difficulty. The dispersion relation of the piecewise homogeneous cylinder is obtained analytically in the form of a complex determinant of order 12 for a two-layer and 18 for a three-layer configuration, the elements of which are Bessel and Neumann functions of complex argument; the global stiffness and mass matrices needed for the general configuration can be assembled automatically in a general-purpose finite-element code such as ABAQUS; the resulting complex characteristic equation is solved by Muller’s method—every iteration of which evaluates the determinant by Gaussian elimination with partial pivoting, so that no expansion of the determinant is required. The efficiency of the algorithm is demonstrated for a two-layer viscoelastic hollow cylindrical body with a filler, the outer load-carrying layer being made of Kh12 steel and the inner layer (the filler) of 30 L steel. The real and imaginary parts of the complex natural frequencies, of the phase velocities and of the attenuation are obtained as functions of the dimensionless wave number, of Poisson’s ratio, of the ratio of the layer radii and of the ratio of the instantaneous elastic moduli of the layers. Full article

Digitization is reshaping how banks operate, yet whether it improves corporate financial performance remains unsettled. This study examines how the digital transformation that banks disclose relates to the structure of their key financial performance indicators. Using a balanced panel of 73 listed Gulf Cooperation Council (GCC) banks over 2020–2025 (438 bank–year observations), digital transformation is measured by a text-mined digital disclosure index (DDI, 0–100) constructed from annual reports and decomposed into nine themes. Bank fixed-effects regressions with Driscoll–Kraay standard errors, one-year-lagged specifications, and two-step system GMM are estimated across profitability, net interest margin, cost efficiency, credit risk and capital adequacy. Disclosed digitization more than doubled over the window, but its associations with performance are conditional rather than uniformly positive. Within banks, a higher DDI value is associated with wider net interest margins, yet also with lower profitability, higher cost-to-income ratios, modestly higher credit risk, and thinner capital buffers. This pattern is consistent with an investment or build-out phase in which the costs of digital transformation are visible before any efficiency or stability dividend and in which margins are the single offsetting benefit. The six-year window observes only this cost-bearing segment and not any later recovery, so the study documents the investment-phase drag rather than a completed cycle. The theme decomposition indicates that the margin association is closest to regulatory technology, cybersecurity, broad transformation and payments. Because the design is observational, the results are interpreted as within-bank associations rather than causal effects, and, although precisely estimated, these associations are economically modest. The study contributes a transparent theme-decomposed measure of bank digitization and evidence on its limits for corporate financial performance in an emerging-market banking region. Full article

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets. Full article