Search Results (776)

Spiking Neural Networks (SNNs), with their event-driven and energy-efficient characteristics, have shown great promise in processing data from neuromorphic sensors. However, the sparse and non-stationary nature of event-based data poses significant challenges to optimization, particularly when using conventional algorithms such as AdamW, which assume smooth gradient dynamics. To address this limitation, we propose MemristiveAdamW, a novel algorithm that integrates memristor-inspired dynamic adjustment mechanisms into the AdamW framework. This optimization algorithm introduces three biologically motivated modules: (1) a direction-aware modulation mechanism that adapts the update direction based on gradient change trends; (2) a memristive perturbation model that encodes history-sensitive adjustment inspired by the physical characteristics of memristors; and (3) a memory decay strategy that ensures stable convergence by attenuating perturbations over time. Extensive experiments are conducted on two representative event-based datasets, Prophesee NCARS and GEN1, across three SNN architectures: Spiking VGG-11, Spiking MobileNet-64, and Spiking DenseNet-121. Results demonstrate that MemristiveAdamW consistently improves convergence speed, classification accuracy, and training stability compared to standard AdamW, with the most significant gains observed in shallow or lightweight SNNs. These findings suggest that memristor-inspired optimization offers a biologically plausible and computationally effective paradigm for training SNNs on event-driven data. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

As avionics advance, heat dissipation becomes more challenging. Microencapsulated phase change material slurry (MPCMS), with its latent heat transfer properties, offers a potential solution. However, the low thermal conductivity of microencapsulated phase change material (MPCM) limits heat transfer rates, and most studies focus on improving conductivity, with little attention given to convective enhancement. This study prepared MPCMS with an MPCM mass fraction (W_m) of 10% and 20%, investigating melting heat transfer under mechanical stirring at 0–800 RPM and heat fluxes of 8.5–17.0 kW/m². Stirring significantly alters MPCMS heat transfer behavior. As rotational speed increases, both wall-to-slurry and internal temperature differences decrease. Stirring extends the time at which the heating wall temperature (T_w) stays below a threshold. For example, at W_m = 10% MPCM and 8.50 kW/m², increasing speed from 0 to 800 RPM raises the holding time below 70 °C by 169.6%. The effect of MPCM mass fraction on heat transfer under stirring is complex: at 0 RPM, 0% > 10% > 20%; at 400 RPM, 10% > 0% > 20%; and at 800 RPM, 10% > 20% > 0%. This is because as W_m increases, the latent heat and volume expansion coefficients of MPCMS rise, promoting heat transfer, while viscosity and thermal conductivity decrease, hindering it. At 0 RPM, the net effect is negative even at W_m = 10%. Stirring enhances internal convection and significantly improves heat transfer. At 400 RPM, heat transfer is positive at W_m = 10% but still negative at W_m = 20%. At 800 RPM, both W_m levels show positive effects, with slightly better performance at W_m = 10%. In addition, at the same heat flux, higher speeds maintain T_w below a threshold longer. Overall, stirring improves MPCMS cooling performance, offering an effective means of convective enhancement for avionics thermal management. Full article

Artificial light has been shown to enhance the fishing efficiency of fishing gear by attracting marine organisms. This study introduces a novel approach by incorporating noctilucent materials into crab pots and evaluates their effects on catch performance. Based on the crab pots commonly used on the coast, four types of crab pots were tested: ordinary crab pots (Con-pot), ordinary crab pots equipped with noctilucent sticks (Exp-pot 1), crab pots equipped with noctilucent nets (Exp-pot 2), and crab pots equipped with both noctilucent nets and sticks (Exp-pot 3). The results showed that the noctilucent material exhibits 6 h persistent emission in darkness after just 10 min of solar charging. Exp-pot 3 could significantly enhance fishing efficiency, which increased by 63.84% compared to the Con-pot. The proportion of crabs in Exp-pot 3 was the highest (86.35%), and the individual weight of crabs in Exp-pot 3 was the heaviest (61.5 g), which was 38.30% heavier than that in the Con-pot. Notably, Exp-pots 2 and 3 demonstrated superior selectivity with higher W₅₀ values (53.01 g and 54.49 g), narrower SRs (33.04–72.98 g and 32.95–76.03 g), effectively balancing target catch retention with undersized crab release, indicated that noctilucent nets exhibited stronger weight selectivity for crabs compared to noctilucent sticks. These results demonstrate that functional materials have broad potential applications in fishing gear, which could enhance the catch efficiency and individual size of crab caught. Full article

High-resolution spatiotemporal prediction information on fishing vessel activities is essential for formulating and effectively implementing fisheries policies that ensure the sustainability of marine resources and fishing practices. This study focused on the tuna longline fishery in the Western and Central Pacific Ocean (130° E–150° W, 20° S–20° N) and constructed a CLA U-Net deep learning model to predict fishing effort (FE) distribution based on 2017–2023 FE records and environmental variables. Two modeling schemes were designed: Scheme 1 incorporated both historical FE and environmental data, while Scheme 2 used only environmental variables. The model predicts not only the binary outcome (presence or absence of fishing effort) but also the magnitude of FE. Results show that in short-term predictions, Scheme 1 achieved F1 scores of 0.654 at the 0.5°-1-day scale and 0.763 at the 1°-1-day scale, indicating substantial improvement from including historical FE data. In medium-term predictions, Scheme 1 and Scheme 2 reached maximum F1 scores of 0.77 and 0.72, respectively, at the optimal spatiotemporal scale of 1°-30 days. The analysis also quantified the relative importance of environmental variables, with sea surface temperature (SST) and chlorophyll-a (Chl-a) identified as the most influential. These findings provide methodological insights for spatiotemporal prediction of fishing effort and support the refinement of fisheries management and sustainability strategies. Full article

Recently, with the rapid development of underwater resource exploration and underwater activities, underwater acoustic (UA) target recognition has become crucial in marine resource exploration. However, traditional underwater acoustic recognition systems face challenges such as low energy efficiency, poor accuracy, and slow response times. Systems for UA target recognition using deep learning networks have garnered widespread attention. Convolutional neural network (CNN) consumes significant computational resources and energy during convolution operations, which exacerbates the issues of energy consumption and complicates edge deployment. This paper explores a high-energy-efficiency UA target recognition system. Based on the DenseNet CNN, the system uses fine-grained pruning for sparsification and sparse convolution computations. The UA target recognition CNN was deployed on FPGAs and chips to achieve low-power recognition. Using the noise-disturbed ShipsEar dataset, the system reaches a recognition accuracy of 98.73% at 0 dB signal-to-noise ratio (SNR). After 50% fine-grained pruning, the accuracy is 96.11%. The circuit prototype on FPGA shows that the circuit achieves an accuracy of 95% at 0 dB SNR. This work implements the circuit design and layout of the UA target recognition chip based on a 65 nm CMOS process. DC synthesis results show that the power consumption is 90.82 mW, and the single-target recognition time is 7.81 ns. Full article

Retinal vessel segmentation is critical for early diagnosis of diabetic retinopathy, yet existing deep models often compromise accuracy for complexity. We propose DSAE-Net, a lightweight dual-stage network that addresses this challenge by (1) introducing a Parameterized Cascaded W-shaped Architecture enabling progressive feature refinement with only 1% of the parameters of a standard U-Net; (2) designing a novel Skeleton Distance Loss (SDL) that overcomes boundary loss limitations by leveraging vessel skeletons to handle severe class imbalance; (3) developing a Cross-modal Fusion Attention (CMFA) module combining group convolutions and dynamic weighting to effectively expand receptive fields; and (4) proposing Coordinate Attention Gates (CAGs) to optimize skip connections via directional feature reweighting. Evaluated extensively on DRIVE, CHASE_DB1, HRF, and STARE datasets, DSAE-Net significantly reduces computational complexity while outperforming state-of-the-art lightweight models in segmentation accuracy. Its efficiency and robustness make DSAE-Net particularly suitable for real-time diagnostics in resource-constrained clinical settings. Full article

Additive manufacturing of metal components is predominantly based on fusion-welding processes involving melting and solidification. However, processing high-strength aluminium alloys presents challenges, including reduced mechanical properties and increased susceptibility to hot cracking. To address these issues, alternative solid-state processing methods for aluminium are being explored worldwide. One such method is wire-based friction-stir additive manufacturing, which builds on the principles of friction-stir welding. This study focused on assessing a range of pin tool designs to promote improved mixing between the filler material and substrate. The best results were achieved using a two-stirring-probe configuration, which was then employed to fabricate a multilayer wall made of EN AW-6063 aluminium alloy. The resulting structure showed significant grain refinement, with the deposited layers having an average grain size approximately four times smaller than that of the substrate, indicating dynamic recrystallisation. Tensile testing of the intermediate layer revealed a strength of 147 MPa and 10% elongation, corresponding to 77% of the filler wire strength. These findings highlight the potential of the W-FSAM process for producing near-net-shape, high-quality lightweight metal components with refined microstructures and reliable mechanical performance. Full article

Valorisation of spent yeast biomass post-fermentation requires energy-intensive autolysis or enzymatic hydrolysis that reduces the net benefit. Here, we present a simple and reproducible method for generating functional yeast extract recycled from Komagataella phaffii biomass without a requirement of a pre-treatment process. Spent yeast pellets from fermentations were freeze-dried to produce a fine powder that can be used directly at low concentrations, 0.0015% (w/v), together with 2% peptone (w/v), to formulate complete media ready for secondary fermentations. This media formulation supported growth rates of yeast culture that were statistically indistinguishable (p-value > 0.05) from cultures grown in standard YPD media containing commercial yeast extract, and these cultures produced equivalent titres of recombinant β-glucosidase (0.998 Abs_405nm commercial extract vs. 0.899 Abs_405nm recycled extract). Additionally, nutrient analyses highlight equivalent levels of sugars (~23 g/L), total proteins, and cell yield per carbon source (~2.17 g) with this recycled yeast extract media formulation when compared to commercial media. This method reduces process complexity and cost and enables the circular reuse of yeast biomass. The protocol is technically straightforward to implement, using freeze drying that is commonly available in research laboratories, representing a broadly applicable and sustainable alternative to conventional media supplementation that achieves a circular approach within the same fermentation system. Full article

This study evaluates the technical, economic, and environmental feasibility of grid-connected and off-grid hybrid energy systems designed to meet the energy demands of a greenhouse facility. Various system configurations were developed based on combinations of solar, wind, diesel, and battery storage technologies. The analysis considers a daily electricity consumption of 369.52 kWh and a peak load of 52.59 kW for the greenhouse complex. Among the grid-connected systems, the grid/PV configuration was identified as the most optimal, offering the lowest Net Present Cost (NPC) of USD 282,492, the lowest Levelized Cost of Energy (LCOE) at USD 0.0401/kWh, and a reasonable emissions reduction of 54.94%. For off-grid scenarios, the generator/PV/battery configuration was the most cost-effective option, with a total cost of USD 1.19 million and an LCOE of USD 0.342/kWh. Environmentally, this system showed a strong performance, achieving a 64.58% reduction in CO₂ emissions; in contrast, fully renewable systems such as PV/wind/battery and wind/battery configurations succeeded in reaching zero-emission targets but were economically unfeasible due to their very high investment costs and limited practical applicability. Sensitivity analyses revealed that economic factors such as inflation and energy prices have a critical effect on the payback time and the Internal Rate of Return (IRR). Full article

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m⁻². Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations. Full article

This paper presents the optimal design and cost–benefit analysis of an off-grid solar photovoltaic system integrated with a hybrid energy storage system for a Category 3 rural healthcare facility in Elands Bay, South Africa. The optimal configuration, designed in Homer Pro, consists of a 16.1 kW solar PV array, 10 kW lithium-ion battery, 23 supercapacitor strings (2 modules per string), 50 kW fuel cell, 50 kW electrolyzer, 20 kg hydrogen tank, and 10.8 kW power converter. The daily energy consumption for the selected healthcare facility is 44.82 kWh, and peak demand is 9.352 kW. The off-grid system achieves 100% reliability (zero unmet load) and zero CO₂ emissions, compared to the 24,128 kg/year of CO₂ emissions produced by the diesel generator. Economically, it demonstrates strong competitiveness with a levelized cost of energy (LCOE) of ZAR24.35/kWh and a net present cost (NPC) of ZAR6.05 million. Sensitivity analysis reveals the potential for a further 20–40% reduction in LCOE by 2030 through anticipated declines in component costs. Hence, it is established that the proposed model is a reliable and viable option for off-grid rural healthcare facilities. Full article

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly varied terrain. This study produced the actual albedo databases by using albedo retrieval look-up tables. And then we investigated the spatiotemporal variations in land surface albedo and its corresponding radiative effects in the Yellow River Basin from 2000 to 2022 using MODIS-derived reflectance data. We employed time-series, trend, and anomaly detection analyses alongside surface downward shortwave radiation measurements to quantify the radiative forcing induced by land-cover changes. Our key findings reveal that (i) the basin’s average surface albedo was 0.171, with observed values ranging from 0.058 to 0.289; the highest variability was noted in the Loess Plateau during winter—primarily due to snowfall and low temperatures; (ii) a notable declining trend in the annual average albedo was observed in conjunction with rising temperatures, with annual values fluctuating between 0.165 and 0.184 and monthly averages spanning 0.1595 to 0.1853; (iii) land-cover transitions exerted distinct radiative forcing effects: conversions from grassland, shrubland, and wetland to water bodies produced forcings of 2.657, 2.280, and 2.007 W/m², respectively, while shifts between barren land and cropland generated forcings of 4.315 and 2.696 W/m². In contrast, transitions from cropland to shrubland and from grassland to shrubland resulted in minimal forcing, and changes from impervious surfaces and forested areas to other cover types yielded negative forcing, thereby exerting a net cooling effect. These findings not only deepen our understanding of the interplay between land-cover transitions and radiative forcing within the Yellow River Basin but also offer robust scientific support for regional climate adaptation, ecological planning, and sustainable land use management. Full article

Managing plastic waste is a great challenge for today’s society, and it is increasingly necessary to find solutions to the large amount of plastic waste dumped annually in the oceans. The main objective of this research is to perform a comprehensive characterisation of different gypsum-based materials incorporating recycled PP/HDPE pellets from the recycling of discarded fishing nets in the Mediterranean Sea. For this purpose, composites were developed with a partial substitution of the original material by these pellets, up to 30% by volume, while maintaining a water/gypsum ratio of 0.65 by mass. The results showed that even in the most unfavourable case, with a 30% replacement in volume by these recycled pellets, flexural (2.72 MPa) and compressive (7.15 MPa) strengths higher than those required by the standards were obtained, with good integration of the residue in the matrix. Also, there was a decrease in total water absorption of up to 20.5% compared to traditional gypsum. The thermal behaviour study showed that a minimum conductivity value of 292.3 mW/m K was obtained, implying a decrease of 14.9% from the control series. In addition, a life cycle analysis was conducted, obtaining a reduction in environmental impact of up to 13.1% in terms of CO₂ equivalent emissions. Overall, the composites obtained represent a sustainable alternative to producing prefabricated plates and panels for building construction. Full article

Since the hydrogen-production process is not yet fully efficient, this paper proposes a poly-generation system that is driven by a geothermal energy source and utilizes a combined Kalina/organic Rankine cycle coupled with an electrolyzer unit to produce, simultaneously, power and green hydrogen in an efficient way. A comprehensive thermodynamic analysis and an exergetic evaluation are carried out to assess the effect of key system parameters (geothermal temperature, high pressure, ammonia–water concentration ratio, and terminal thermal difference) on the performance of concurrent production of power and green hydrogen. Thereby, two configurations are investigated with/without the separation of turbines. The optimal ammonia mass fraction of the basic solution in KC is identified, which leads to an overall optimal system performance in terms of exergy efficiency and green hydrogen production rate. In both configurations, the optimal evaluation is made possible by conducting a genetic algorithm optimization. The simulation results without/with the separation of turbines demonstrate the potential of the suggested cycle combination and emphasize its effectiveness and efficiency. Exemplary, for the case without the separation of turbines, it turns out that the combination of ammonia–water and MD2M provides the best performance with net power of 1470 kW, energy efficiency of 0.1184, and exergy efficiency of 0.1258 while producing a significant green hydrogen amount of 620.17 kg/day. Finally, an economic study allows to determine the total investment and payback time of $3,342,000 and 5.37 years, respectively. The levelized cost of hydrogen (LCOH) for the proposed system is estimated at 3.007 USD/kg H₂, aligning well with values reported in the literature. Full article