Search Results (2,326)

Sparse Matrix–Vector Multiplication (SpMV) is a critical computational kernel in high-performance computing (HPC) and artificial intelligence (AI). However, its irregular memory access patterns lead to frequent cache misses on multi-level cache hierarchies, significantly degrading performance. Scratchpad memory (SPM), a software-managed, low-latency on-chip memory, offers improved data locality and control, making it a promising alternative for irregular workloads. To enhance SpMV performance, we propose a vectorized execution framework targeting SPM-augmented processors. Recognizing the limitations of traditional formats for vectorization, we introduce Sorted-Row-Block ELL (SRB-ELL), a new matrix storage format derived from ELLPACK (ELL). SRB-ELL stores only non-zero elements, partitions the matrix into row blocks, and sorts them by block size to improve load balance and SIMD efficiency. We implement and evaluate SRB-ELL on a custom processor architecture with integrated SPM using the gem5 simulator. Experimental results show that, compared to vectorized CSR-based SpMV, the SRB-ELL design achieves up to 1.48× speedup and an average of 1.19×. Full article

Methane emissions from freshwater reservoirs represent a globally relevant greenhouse gas source, which are estimated to range from 3% to 10% of all global anthropogenic methane emissions. However, there is high uncertainty in estimating reservoir emissions on local to global scales due to a combination of data paucity in key regions, particularly in the Southern Hemisphere, and challenges monitoring emission pathways. The key to improved spatially and temporally representative estimation of emission rates is to better understand the primary drivers of emission pathways, in particular, ebullition. We examine ebullition from 15 freshwater storages located in the Southern Hemisphere subtropical (South East Queensland) and temperate (Tasmania) regions using a combination of optical methane detection to develop the high-resolution mapping of ebullition zones and floating chamber incubation within ebullition zones to quantify areal emission rates. We demonstrate the equivalent water level, through air pressure or physical water level change, as a key driver of ebullition and examine the implications for spatially and temporally representative estimation of reservoir emissions. This study represents the largest broadscale ebullition survey undertaken across Australian temperate and subtropical reservoirs. The study findings are of broad relevance to scientists and corporate and government entities navigating the complexities of estimating greenhouse gas emissions from reservoirs and related policy instruments. Full article

Arabinogalactan proteins (AGPs) and extensins influence cell wall assembly and regulate plant cell mechanical properties through interactions with extracellular matrix polymers. These proteins may play a key role in the biochemical events underlying postharvest treatments aimed at controlling fruit texture and turgor loss associated with senescence-related disorders. We studied the temporal and spatial accumulation patterns of extensin and AGP isoforms constitutively expressed along with the profiling of nucleotide sugars UDP-galactose, UDP-arabinose, UDP-glucuronic acid, and UDP-rhamnose in Mara des Bois strawberries under different storage conditions. We also assessed the expression timing of AGP-encoding genes (FvAFP4, FvAGP5) and genes involved in key steps of post-translational glycosylation (FvP4H1, FvGAT20, FvGAT7). Whereas extensins are down-regulated, AGPs are transcriptionally regulated by cold and cold-high CO₂ and post-translationally modulated after transfer to 20 °C. Based on their subcellular localization, molecular properties, isoform-specific glycosylation, UDP-sugar availability, and timing-regulated expression, AGPs are likely involved in cell wall assembly and modulation of mechanical properties. Consequently, they may influence fruit texture and enhanced softening resistance, potentially counteracting senescence-associated disorders through CO₂-responsive signaling mechanisms. Conversely, the decrease in both UDP-galactose levels and AGPs gene expression in non-cold-stored senescent strawberries at 20 °C further supports their relevance in AGPs biosynthesis regulation and underscores their potential as markers for improving postharvest storage strategies. Full article

Simultaneous Localization and Mapping (SLAM) systems require accurate and globally consistent mapping to ensure the long-term stable operation of robots or vehicles. However, for the commercial applications of indoor sweeping robots, the system needs to maintain accuracy while keeping computational and storage requirements low to ensure cost controllability. This paper proposes a dual-point-cloud-registration SLAM based on line features for the mapping of a mobile robot, named DPCR-SLAM. The front-end employs an improved Point-to-Line Iterative Closest Point (PLICP) algorithm for point cloud registration. It first aligns the point cloud and updates the submap. Subsequently, the submap is aligned with the regional map, which is then updated accordingly. The back-end uses the association between regional maps to perform graph optimization and update the global map. The experimental results show that, in the application scenario of indoor sweeping robots, the proposed method reduces the map storage space by 76.3%, the point cloud processing time by 55.8%, the graph optimization time by 77.7%, and the average localization error by 10.9% compared to the Cartographer, which is commonly used in the industry. Full article

The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T_PLAND), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C_PLAND) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments. Full article

The construction sector is a major contributor to environmental degradation due to high energy consumption and CO₂ emissions. This study presents a low-tech, sustainable construction system based on the manual fabrication of curved laminated bamboo beams, assembled with screws and steel straps, without adhesives or heavy machinery. The case study is part of a bamboo roof structure built within Jericoacoara National Park, Brazil, using Dendrocalamus asper for its mechanical strength and carbon storage capacity. The construction process of three vertical lower laminated curved beams (Vig.CLIV-1, CLIV-2, and CLIV-3) was systematized into two main phases—preparation and construction. Due to the level of detail involved, only Vig.CLIV-1 is fully presented, broken down into work items, processes, and sub-processes to identify critical points for quality control and time efficiency. Comparative analysis of the three beams complements the findings, highlighting differences in logistics, labor performance, and learning outcomes. The results demonstrate the potential of this handcrafted system to achieve high geometric accuracy in complex site conditions, with low embodied energy and strong replicability. Developed by bamboo specialists from Colombia and Peru with support from local assistants, this experience illustrates the viability of low-impact, appropriate construction solutions for ecologically sensitive contexts and advances the integration of sustainable, replicable practices in architectural design. Full article

Internal erosion is a significant issue caused by water flow within soils, resulting in structural collapse of hydraulic structures, particularly in coarse soils located near rivers. These soils typically exhibit granulometric instability due to low clay content, resulting in poor hydraulic and mechanical properties. To mitigate this problem, cement treatment is applied as an alternative to soil removal, reducing transportation and storage costs. The hole erosion test (HET) and Crumbs tests, shearing behaviour through consolidated undrained (CU) triaxial, and microstructure analyses regarding scanning electron microscopy (SEM), mercury intrusion porosimeter (MIP) and thermogravimetric analysis (TGA) were conducted for untreated and treated coarse soil specimens with varying cement contents (1%, 2%, and 3%) and curing durations (1, 7, and 28 days). The findings indicate a reduction in the loss of eroded particles and overall stability of treated soils, along with an improvement in mechanical properties. SEM observations reveal the development of hydration gel after treatment, which enhances cohesion within the soil matrix, corroborated by TGA analyses. MIP reveals the formation of a new class of pores, accompanied by a reduction in dry density. This study demonstrates that low cement addition can transform locally unsuitable soils into durable construction materials, reducing environmental impact and supporting sustainable development. Full article

The Moon’s unique environment, strategic position, and resource abundance make it a key target for deep space exploration. As lunar missions evolve from research to long-term habitation, leveraging local resources is essential to reduce dependence on Earth-based supply chains. Despite significant studies on the lunar environment and in-situ resource utilization (ISRU), a unified framework that integrates these findings remains lacking. This article addresses this gap by systematically reviewing and synthesizing current research to support sustainable lunar development. It first explores the use of extreme lunar environmental factors such as thermal gradients, weak magnetic fields, subsurface cavities, and geographic advantages. It then examines lunar water and mineral resource development, highlighting methods for detection, extraction, purification, and storage, alongside strategies for utilizing various minerals. The article further reviews recent progress in in-situ manufacturing, construction technologies, energy regeneration, and closed-loop life-support systems vital for lunar base establishment. These advances are crucial for creating sustainable infrastructure and maintaining life on the Moon. Finally, the paper outlines the challenges and limitations associated with ISRU and offers perspectives on future directions, aiming to inform the design of next-generation lunar missions and facilitate permanent human presence on the Moon. Full article

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems. Full article

Tea tree seeds are highly sensitive to dehydration and cannot be stored for extended periods, making surface defect detection crucial for preserving their germination rate and overall quality. To address this challenge, we propose Cgc-YOLO, an enhanced YOLO-based model specifically designed to detect small-scale and complex surface defects in tea seeds. A high-resolution imaging system was employed to construct a dataset encompassing five common types of tea tree seeds, capturing diverse defect patterns. Cgc-YOLO incorporates two key improvements: (1) GhostBlock, derived from GhostNetV2, embedded in the Backbone to enhance computational efficiency and long-range feature extraction; and (2) the CPCA attention mechanism, integrated into the Neck, to improve sensitivity to local textures and boundary details, thereby boosting segmentation and localization accuracy. Experimental results demonstrate that Cgc-YOLO achieves 97.6% mAP50 and 94.9% mAP50–95, surpassing YOLO11 by 2.3% and 3.1%, respectively. Furthermore, the model retains a compact size of only 8.5 MB, delivering an excellent balance between accuracy and efficiency. This study presents a robust and lightweight solution for nondestructive detection of tea seed defects, contributing to intelligent seed screening and storage quality assurance. Full article

A diverse array of soil microorganisms exhibit plant growth-promoting (PGP) traits, many of which enhance root growth and development. These microorganisms include various taxa of bacteria, fungi, microalgae and yeasts—some of which are currently used in biofertilizers and biostimulant formulations. Recent studies have begun to unravel the complex communication between plant roots and beneficial microorganisms, revealing mechanisms that modulate root nitrogen (N) uptake beyond atmospheric N₂ fixation pathways. Root N uptake is tightly regulated by plants through multiple mechanisms. These include transcriptional and post-transcriptional control of plasma membrane-localized N transporters in the epidermis, endodermis, and xylem parenchyma. Additionally, N uptake efficiency is influenced by vacuolar N storage, assimilation of inorganic N into organic compounds, and the maintenance of electrochemical gradients across root cell membranes. Many of these processes are modulated by microbial signals. This review synthesizes current knowledge on how soil microorganisms influence root N uptake, with a focus on signaling molecules released by soil beneficial microbes. These signals include phytohormones, volatile organic compounds (VOCs), and various low-molecular-weight organic compounds that affect transporter expression, root architecture, and cellular homeostasis. Special attention is paid to the molecular and physiological pathways through which these microbial signals enhance plant N acquisition and overall nutrient use efficiency. Full article

The advancement of efficient energy conversion and storage technologies is fundamentally linked to the development of electrochemical systems, including fuel cells, batteries, and electrolyzers, whose performance depends on key electrocatalytic reactions: hydrogen evolution (HER), oxygen evolution (OER), oxygen reduction (ORR), carbon dioxide reduction (CO₂RR), and nitrogen reduction (NRR). Beyond catalyst design, the electrolyte microenvironment significantly influences these reactions by modulating charge transfer, intermediate stabilization, and mass transport, making electrolyte engineering a powerful tool for enhancing performance. This review provides a comprehensive analysis of how fundamental electrolyte properties, including pH, ionic strength, ion identity, and solvent structure, affect the mechanisms and kinetics of these five reactions. We examine in detail how the electrolyte composition and individual ion contributions impact reaction pathways, catalytic activity, and product selectivity. For HER and OER, we discuss the interplay between acidic and alkaline environments, the effects of specific ions, interfacial electric fields, and catalyst stability. In ORR, we highlight pH-dependent activity, selectivity, and the roles of cations and anions in steering 2e⁻ versus 4e⁻ pathways. The CO₂RR and NRR sections explore how the electrolyte composition, local pH, buffering capacity, and proton sources influence activity and the product distribution. We also address challenges in electrolyte optimization, such as managing competing reactions and maximizing Faradaic efficiency. By comparing the electrolyte’s effects across these reactions, this review identifies general trends and design guidelines for enhancing electrocatalytic performance and outlines key open questions and future research directions relevant to practical energy technologies. Full article

Background: Static cold storage is the standard method of kidney preservation following donation after circulatory death (DCD). A previous study on rodent models demonstrated the efficacy of storing DCD kidneys at 22 °C in an extracellular-type solution (ETK). We evaluated the efficacy of storing warm-ischemic kidneys at 22 °C in MHC-inbred miniature swine. Methods: After 2 h warm ischemia, the kidneys were preserved in ETK for one hour at either 4 °C or 22 °C and then subjected to ex vivo normothermic machine perfusion (NMP) for 2 h (n = 3 in each group). The same warm-ischemic kidneys, preserved in ETK (n = 3 in each group) or intracellular-type solution (UW; n = 2 in each group) at either 4 °C or 22 °C, were transplanted into MHC-matched recipients. Results: Compared with kidneys preserved at 4 °C, those preserved at 22 °C showed significantly better physiological and metabolic indices during ex vivo NMP. Furthermore, renal function was significantly higher in transplanted kidneys, and graft biopsies on postoperative day 4 showed more localized necrosis in the renal tubules when kidneys were stored at 22 °C. In contrast, recipient animals with kidneys stored in UW solution did not survive for more than 7 days. Conclusions: Two-hour warm-ischemic kidneys from miniature swine showed improved preservation at 22 °C than at 4 °C when an extracellular-type solution was used. Full article

The world’s energy system faces major challenges due to transitions from fossil fuels to other alternatives. An important part of the transition is energy-efficient homes that partially produce their own electricity. This paper explores the energy interactions between heating, cooling, and electricity usage in a planned residential area in Sweden where a significant portion of the electricity is generated by solar PV systems. Conventional district heating and cooling systems and a low-temperature district heating system that uses return cascading technology were compared with heat pump systems. Electricity sharing in an energy community has a low impact on the calculated national energy efficiency metric. It is also shown that electrifying space heating with heat pumps improves the calculated energy efficiency metric, but heat pumps increase the peak power demand in the winter due to high heat demand and a lack of solar production. Using heat pumps for heating domestic hot water and compressor chillers for cooling offers a more balanced use/production of electricity since the electric cooling load is mostly met by local solar production, as shown by an increase in self-consumption of 8% and stable self-sufficiency. There is, however, a time mismatch between production and the peak electricity demand, which could be addressed by using energy storage systems. Full article

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article