Search Results (8,398)

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Pneumatic artificial muscles exhibit pronounced hysteresis in the force-contraction domain, which complicates accurate force modeling under pressure-dependent operation. This work presents a discrete-time quasi-static hysteresis model for a compact pneumatic artificial muscle using a memristive system-based branch-memory formulation. The model combines separate loading and unloading force surfaces through a bounded internal state and is evaluated on experimental data acquired at a force-change rate of $4\mathrm{N}/\mathrm{s}$. Measurements were performed at 13 pressure levels from 0 to 0.6 MPa in 0.05 MPa increments, with 32 unloading points and 32 loading points per pressure level and five repetitions for each operating condition. Representative branch curves were obtained by median reduction in the repeated measurements, and the loading and unloading surfaces were identified with the five-parameter Sárosi–Fabulya exponential-bilinear function. The state update parameter was evaluated over a fixed grid, and the best loop reconstruction on the present dataset was obtained for the hard-switching case $\alpha =1$. Benchmark comparisons with Prandtl–Ishlinskii, discrete Preisach, Maxwell-slip, and sampled Bouc–Wen-type models show that Preisach and Bouc–Wen provide higher loop-reconstruction accuracy. The proposed memristive formulation should not be interpreted as a best-fit benchmark model, but as a low-order global branch-memory representation that preserves pressure dependence and branch asymmetry within a single analytical framework over the investigated quasi-static operating range. Full article

Accurate and reproducible delineation of stream bankfull geometry remains a persistent challenge in environmental planning. To address this gap, we developed the Cross-Sections and Dimensions Tool, a semi-automated, slope-based method for extracting stream cross-sections and estimating bankfull width, elevation and depth using high-resolution elevation data. The tool applies a configurable slope threshold to identify bank edges, generates perpendicular cross-sections from a stream centreline, and stores all outputs in a structured geodatabase to ensure transparency and reproducibility. Validation against manually delineated bankfull polygons across 191 km of stream length in Greater Melbourne, Australia, demonstrated strong spatial agreement, with an average F1 score (a measure of prediction-observation overlap) of 74% and a mean absolute error of 0.64 m in bankfull elevation. The tool was most reliable in larger streams (Strahler order 5 and above) with low to moderate vegetation canopy cover (<80%). We also investigated the practical visibility limits of small or indistinct channels typically encountered by human mappers and verified that the tool did not produce unrealistic channel delineations. This approach advances geomorphic feature extraction by grounding bankfull delineation in deterministic geometry rather than hydrological recurrence or data-driven modelling. In practice, it enables scalable, transparent, and repeatable analysis of stream morphology for ecological assessment, infrastructure planning, and waterway management. Full article

Four algal adsorbents were prepared from two types of green sea algae (Ulva lactuca and Ulva pertusa), either by treatment with concentrated sulfuric acid or without treatment. A comparative study of Au(III) adsorption in an HCl medium was performed. While both untreated adsorbents showed good performance at low HCl concentrations, the treated adsorbents achieved quantitative adsorption and high selectivity for Au(III) across a broad range of HCl concentrations. The adsorption of Au(III) onto the algal biomass adsorbents followed the typical Langmuir monolayer adsorption model. At an HCl concentration of 0.010 M, the maximum adsorption capacities were 1.14, 0.86, 6.57, and 6.28 mol kg^–1 for DUL, DUP, TUL, and TUP, respectively. A kinetic study conducted at different temperatures was consistent with the pseudo-first-order kinetic model and enabled estimation of the activation energy of the adsorption reaction. Structural changes before and after treatment were analyzed using FT-IR spectroscopy. Confirmation of Au(III) adsorption and its subsequent reduction to the elemental state was achieved through XRD and SEM/EDX analyses as well as digital imaging of the Au-loaded adsorbents. Finally, the adsorbed and reduced Au was successfully desorbed using an acidic thiourea solution. Full article

Low-carbon technology innovation serves as the core driver for multiple countries striving to achieve their dual-carbon goals. Therefore, building efficient low-carbon technology innovation networks and accelerating low-carbon technological innovation have become key focuses of academic research. Leveraging patent and publication data, this study constructs dual networks for low-carbon basic and applied research. It employs Exponential Random Graph Models (ERGMs) and Multilevel Exponential Random Graph Models (MERGMs) to explain the different formation factors and dependency relationships within dual networks. Building on this, this study introduces the NK model to analyze the order of effects of these network formation factors and dependencies. The findings reveal the following: (1) The formation factors of dual low-carbon innovation networks differ significantly. For the basic research network (BRN), the key formation factors—in order of effect—are collaboration stability, transitive closure, partner addition, the Matthew effect, and knowledge siphoning. For the applied research network (ARN), the key formation factors—in order of effect—are historical collaboration, collaboration stability, partner addition, cognitive proximity, and knowledge siphoning. (2) The BRN and ARN exhibit an asymmetric dependency. The dependence of the BRN on the ARN is manifested as structural symbiosis, whereas the ARN, guided by the BRN, demonstrates the transmission of collaborative relationships. This study elucidates the complex formation mechanisms and dependency patterns of low-carbon technology innovation networks, providing a theoretical foundation and decision-making support for the differentiated governance of network structures and the optimized allocation of innovation resources. Full article

A sustainable strategy was developed to valorize rabbit manure waste by synthesizing a porous quaternary Ni-Co-Zn-Fe layered double hydroxide/biochar nanocomposite (QL-RMB) for the efficient removal of Mn(VII) in the form of permanganate (MnO₄⁻) from aqueous solutions. The QL-RMB adsorbent exhibited a well-developed mesoporous structure with uniformly dispersed nanoparticles, achieving 73% MnO₄⁻ removal within 60 min under optimized conditions (pH 3.0; dosage 0.5 g L⁻¹). Adsorption followed pseudo-second-order kinetics and was best described by the Freundlich isotherm model (R² > 0.98), yielding a maximum Langmuir adsorption capacity (q_max) of 45.13 mg g⁻¹. Statistical physics modeling confirmed a multi-ionic, vertically oriented adsorption configuration, while thermodynamic analysis demonstrated that the process was spontaneous and exothermic, governed by electrostatic attraction, anion exchange, and surface complexation. The QL-RMB composite exhibited excellent MnO₄⁻ selectivity in the presence of competing ions (selectivity coefficients: 24.96 for Fe³⁺, 31.59 for Ni²⁺, 23.56 for Zn²⁺) and retained significant removal efficiency (73.96%) after five regeneration cycles. In a circular economy approach, the Mn (VII)-spent adsorbent (QL-RMB/Mn) was valorized as an electrocatalyst for urea electro-oxidation, achieving a current density of ~127.19 mA cm⁻² for pristine QL-RMB, which increased to ~217.07 mA cm⁻² after Mn(VII) adsorption (QL-RMB/Mn) in 1 M KOH/1 M urea. Batch scale-up studies revealed an efficiency of 42.55 g or 95% MnO₄⁻ removal from 50 L water, with a low estimated production cost of 0.0602 USD g⁻¹. Environmental sustainability was confirmed by the National Environmental Methods Index (NEMI), modified Green Analytical Procedure Index (Mo-GAPI), Eco-scale (score: 77), and Analytical GREEness (AGREE) assessment frameworks. Full article

Background: Food security systems are central to nutritional health and Sustainable Development Goal 2 (SDG 2), yet existing assessments have paid limited attention to cross-dimensional coordination within food security systems. This study assessed both system performance and coordination in low- and middle-income countries (LMICs) during 2019–2021. Methods: Based on a multidimensional 25-indicator framework, the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach was used to evaluate system performance. Spearman’s rank correlation and Bland–Altman agreement analyses against the SDG 2 Index and the Under-Five Mortality Rate (U5MR) were used to examine the validity. The coupling coordination degree (CCD) model was used to assess coordination across the four dimensions of food security: availability, access, utilization, and stability. Results: Among all included LMICs, composite scores ranged from 0.103 to 0.698. Regionally, Europe and Central Asia showed the strongest overall performance (mean = 0.54), whereas Sub-Saharan Africa exhibited the lowest levels (mean = 0.27). The dimensions of access and stability were identified as the principal global bottlenecks of overall food security system development. The proposed index correlated positively with the SDG 2 Index (R = 0.662, p < 0.001) and inversely with the U5MR (R = −0.769, p < 0.001). The coupling degrees were consistently high but exceeded coordination levels across regions, indicating that strong interdependence among dimensions did not necessarily translate into balanced or synergistic system development. Conclusions: Food security systems in LMICs are constrained by weaknesses in the access and stability dimensions, as well as by insufficient cross-dimensional coordination. Strengthening them requires integrated, cross-sectoral strategies that enhance both system performance and interdimensional coordination. Full article

Improving cultivated land use efficiency is widely regarded as a core issue in ensuring national food security. As one of the key policy instruments supporting agricultural development, agricultural subsidies are considered to play an important role in promoting cultivated land use efficiency. Using micro-survey data from 449 farm households in a typical black soil region of Heilongjiang Province, this study employs the stochastic frontier analysis (SFA) model, the fractional logit model, and the mediation effect model to explore the potential impact of agricultural subsidies on black soil cultivated land use efficiency, as well as the potential mediating pathway at farm scale. The results suggest the following conclusions: (1) Different types of agricultural subsidies appear to have heterogeneous effects on black soil cultivated land use efficiency. Specifically, producer subsidies and total agricultural subsidies appear to exhibit nonlinear relationships with black soil cultivated land use efficiency; however, within the sample range, the overall effects tend to be negative, whereas cultivated land fertility protection subsidies are also associated with lower black soil cultivated land use efficiency. (2) Farm scale appears to serve as a potential mediating pathway linking producer subsidies and total agricultural subsidies to cultivated land use efficiency. (3) Under different conditions of land fragmentation and farm scale, the mediating pathway at farm scale appears to vary. A mediating pathway is observed among highly fragmented landholdings and small-scale farmers, whereas it is not evident among low fragmentation landholdings and large-scale farmers. Based on these findings, this study suggests that the study area may consider optimizing the structure of agricultural subsidies to promote moderate-scale farming and to improve the coordination mechanism between agricultural technical training and regulatory supervision in order to enhance black soil cultivated land use efficiency. Full article

A mesostructured activated carbon (PL–AAC) was engineered from palm leaf biomass via a specific chemical activation protocol and systematically evaluated as a bifunctional adsorbent–catalyst for the advanced oxidative removal of methyl orange (MO) from aqueous media. Physicochemical characterization confirmed the successful transformation of the lignocellulosic precursor into a hierarchically porous carbon framework, exhibiting enhanced surface area (2 → 56 m²/g), increased pore volume (0.0106 → 0.0227 cm³/g), and a dominant mesopore distribution (~3–5 nm). FTIR analysis revealed the presence of oxygen-containing functional groups (hydroxyl, carbonyl, and carboxyl), while SEM images demonstrated the formation of interconnected pore channels. Nitrogen adsorption–desorption isotherms showed Type IV behavior with H4 hysteresis, confirming the presence of narrow slit-shaped mesopores and micropores. This study introduces the novel application of palm leaf-derived activated carbon as a dual-function material that integrates adsorption and catalytic oxidation within a single system. Under acidic conditions (pH 2–3), PL–AAC in the presence of H₂O₂ achieved near-complete MO removal (≈98–100%), driven by the synergistic interaction between adsorption and in situ generation of reactive hydroxyl radicals. Kinetic analysis revealed that the degradation follows a pseudo-second-order model (R² = 0.916), indicating that surface-mediated interactions govern the process. Furthermore, PL–AAC maintained high catalytic efficiency over four regeneration cycles with negligible performance loss, demonstrating excellent stability and reusability. These findings highlight the effective valorization of palm leaf waste into a sustainable, low-cost, and high-performance material for advanced wastewater treatment applications. Full article

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. Full article

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article

In order to maximize the use of low-temperature heat sources for refrigeration, a Li-Br absorption chiller combined with single-effect absorption refrigeration cycle and two-stage absorption refrigeration cycle (STAC) was developed. Experimental research on STAC was conducted on a prototype with a refrigeration capacity of 500 KW. A numerical model validated by experimental data was used to study the refrigeration performance of STAC under variable operating conditions. Compared to single-effect units and two-stage units, STAC demonstrates remarkable heat source conservation capability and adaptability to a broad spectrum of heat source temperatures. This advantage renders the STAC unit more adaptable to new energy or waste heat scenarios characterized by unstable heat sources. As the inlet temperature of the hot water increases, the temperature difference between the inlet and outlet of the hot water also increases. When the inlet temperature of the hot water is 70 °C, 90 °C and 120°C, the temperature difference between the inlet and outlet of the hot water is 10 °C, 30°C and 70 °C, respectively. Both increasing the inlet temperature of hot water and decreasing the temperature of cooling water will enhance the cooling capacity and coefficient of performance (COP) of STAC. As the flow rate of chilled water increases, the refrigeration capacity of STAC will also increase, but the COP will first increase and then decreases Full article

Poly(ethylene terephthalate) (PET) composites reinforced with reduced graphene oxide (rGO) were investigated in order to elucidate the influence of nanofiller concentration and compatibilization on the viscoelastic relaxation behavior across the glass transition. Composites containing 0.1 and 0.5 wt% rGO were prepared by melt blending, and selected systems incorporated 5 wt% of an ionomeric polyester (PETi) as compatibilizer to enhance interfacial adhesion. The thermomechanical response was characterized using dynamic mechanical analysis (DMA) as a function of temperature. Experimental results revealed a strong dependence of stiffness, damping, and glass transition behavior on filler concentration and interfacial interactions. While low rGO loading produced minor changes, the incorporation of 0.5 wt% rGO significantly increased the glassy modulus and shifted the glass transition temperature, indicating restricted segmental mobility. Compatibilized systems exhibited further stiffness enhancement and modified relaxation dynamics due to improved stress transfer and interphase development. To capture the distributed nature of the relaxation processes, the glass transition region was modeled using a fractional Zener model (FZM) with two spring-pot elements within a cooperative relaxation framework. The model successfully reproduced the experimental $E^{\prime }$ and $tan\delta$ curves and revealed systematic variations in the fractional exponents and cooperative parameters. The results demonstrate that the introduction of rGO and compatibilizer progressively transforms the relaxation spectrum of PET from a relatively uniform segmental process into a heterogeneous, interfacially mediated viscoelastic response that is naturally described by fractional rheology. Full article

Sustainable renovation of existing residential building stocks is essential to reduce greenhouse gas emissions, improve energy performance, and support long-term climate-neutral housing strategies. However, decisions based only on operational indicators may overlook important product-stage embodied impacts, especially in highly integrated renovation solutions. This study evaluates how alternative renovation pathways for a public residential building portfolio in the Comunitat Valenciana (Spain) perform from a stock-level sustainability perspective, comparing five INFINITE industrialised retrofit kits (Kit 1–Kit 5) with five paired conventional renovation scenarios (S1–S5). A bottom-up building stock modelling workflow is applied, combining building-energy simulation to quantify operational performance and emissions (B6) with a screening life-cycle assessment of product-stage embodied carbon reported as GWP (A1–A3). To relate upfront and in-use impacts, the study computes carbon payback, cumulative emissions avoided, and a horizon-based partial life-cycle climate indicator, PLC(H), assessed for 2030, 2035, and 2050. The results show a clear sustainability trade-off: renovation packages that sharply reduce operational emissions often require higher upfront embodied carbon, shifting net climate benefits towards longer time horizons. Low-embodied options provide earlier benefits, with Kit 1 reducing PLC(H) by 15.5% by 2030, whereas deeper decarbonisation packages achieve stronger long-term outcomes, with S5 reducing PLC(H) by 70.7% by 2050. A bounded electricity-decarbonisation sensitivity further shows that these long-horizon rankings are affected by lower grid-emission factors, particularly for highly electrified pathways, although the strongest 2050 pathways remain robust across the tested cases. Overall, the findings show that sustainable stock-level renovation planning should jointly consider operational and embodied carbon, carbon payback, and milestone-based cumulative impacts in order to support balanced portfolio sequencing between broadly deployable fast-payback measures and selective deep retrofits. Full article

The objective of this study was to assess and model the condition and resilience of the Cunas River using integrated indices and multivariate statistics in order to determine the impact of anthropogenic pressure and enhance water security in the Peruvian Andes. Stations in the upper, middle, and lower reaches of the river were monitored during the rainy and dry seasons, applying quality indices (NSF-WQI, WA-WQI, CCME-WQI, and I-WQI), principal component analysis (PCA), hierarchical cluster analysis (HCA), and Spearman’s rank correlation (ρ) to assess the intensity and direction of associations between physical–chemical parameters. The results reveal severe degradation in the lower section of the river, with critical hypoxia and extreme coliform levels during the dry season, drastically exceeding the levels in the upper reach. The I-WQI demonstrated superior performance (322.24; Unfit) by being more sensitive than the NSF-WQI (53.15–59.87). PCA confirmed that low flow explains the greatest variance in pollution (PC1 71.55%), while HCA identified maximum synergy (rescaling distance < 1) between biochemical oxygen demand (BOD₅) and total phosphorus, indicating the collapse of self-purification capacity. The HCA identified a maximum synergy between BOD₅ and total phosphorus during the low-flow season, while the PCA confirmed that low discharge intensifies pollutant concentrations. These findings support the need for resilience-based governance that prioritizes the protection of natural infrastructure. Full article