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Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation. [ABSTRACT FROM AUTHOR]

Notwithstanding the overall improvement in the ecological condition of the Qilian Mountains, there are localized occurrences of grassland degradation, desertification, and salinization. Moreover, timely and accurate acquisition of desertification information is a fundamental prerequisite for effective monitoring and prevention of desertification. Leveraging the Google Earth Engine (GEE) platform in conjunction with machine learning techniques, this study aims to identify and extract the spatiotemporal dynamics of desertification in the Qilian Mountain National Park (QMNP) and its surroundings (QMNPs) spanning from 1988 to 2023. Results show that based on the random forest algorithm, the multi-index inversion methodology achieves a commendable overall accuracy of 91.9% in desertification extraction. From 1988 to 2023, the gravity center of light desertification shifts southeastward, while centers characterized by moderate, severe, and extremely severe desertification display a westward retreat with fluctuations. The area of sandy land shows an expansion trend in the medium term, but after 2018, desertification in QMNPs reversed. As of 2023, the sandy land area measured 16,897.35 km2, accounting for 18.29% of the total area of QMNPs. The insights garnered from this study provide a valuable reference for regional desertification prevention and control in the future. [ABSTRACT FROM AUTHOR]

Lightweight concrete is widely used in the construction industry due to its low density and high strength. In this paper, lightweight concrete was prepared by a simple two-step method. Firstly, the light calcium carbonate reinforced epoxy macrospheres (LCR-EMS) material was obtained by adhering calcium lighter carbonate powder to the expanded polystyrene foam spheres (EPS) material using the "balling method". In the second step, the LCR-EMS was mixed with water, cement, and the hollow glass microspheres (HGMS) material using the "molding method" to obtain lightweight concrete. The combination of macroscopic photographs and microscopic morphology shows that the LCR-EMS material itself is uniformly encapsulated and well bonded to the matrix. Test results show that the density of the lightweight concrete decreases with an increase in the volume fraction of stacked LCR-EMS, the diameter, and the proportion of HGMS in the matrix, but it decreases with a decrease in the number of layers of LCR-EMS. The compressive strength of lightweight concrete exhibits a completely opposite trend. When three layers of LCR-EMS were used as filler material, the density and compressive strength of the concrete were 1.246 g/cm3 and 8.19 MPa, respectively. The density and maximum compressive strength of lightweight concrete were 1.146 g/cm3 and 6.37 Mpa, respectively, when filled with 8–9 mm-2L-90 svol% of LCR-EMS and 40 wt% of HGMS in the matrix. Compared with lightweight concrete filled with 90% EPS, the density increased by 20% while the compressive strength increased by 300%. [ABSTRACT FROM AUTHOR]

To investigate the tube thinning of gas wells in the northwestern oilfield, the failed tubing was analyzed by using material property testing, SEM, EDS, and XRD. A novel model that was specific to the service life of tubing in terms of the wall thickness of tubing was established. The model is based on the circumferential stress of tubing. The safety factor against internal pressure and corrosion rate are considered in the model. Our results make clear that the chemical composition, non-metallic inclusion, hardness, and tensile strength of the pipe meet the requirements of relevant standards. The corrosion products on the inner and outer wall of the tubing are mainly FeCO3 and BaSO4, while CaCO3 exists in the outer wall. Additionally, we prove that the corrosion process of the failed tubing is CO2 corrosion. The tubing under the packer suffers from water-accumulation corrosion, and the tubing above the packer suffers from water-carrying corrosion. It is observed that the failed tubing arises under-deposit corrosion in local areas. According to the model calculation, the safe service life of tubing above the packer is 20.6 years. However, the safe service life of tubing below the packer is only 4.9 years. [ABSTRACT FROM AUTHOR]

The die-stacking structure of 3D network-on-chips (3D NoC) leads to high power density and unequal thermal conductance between different layers, which results in low reliability and performance degradation of 3D NoCs. Congestion-aware adaptive routing, which is capable of balancing the network's traffic load, can alleviate congestion and thermal problems so as to improve the performance of the network. In this study, we propose a traffic- and thermal-aware Q-routing algorithm (TTQR) based on Q-learning, a reinforcement learning method. The proposed algorithm saves the local traffic status and the global temperature information to the Q1-table and Q2-table, respectively. The values of two tables are updated by the packet header and saved in a small size, which saves the hardware overhead. Based on the ratio of the Q1-value to the Q2-value corresponding to each direction, the packet's output port is selected. As a result, packets are transferred to the chosen path to alleviate thermal problems and achieve more balanced inter-layer traffic. Through the Access Noxim simulation platform, we compare the proposed routing algorithm with the TAAR routing algorithm. According to experimental results using synthetic traffic patterns, our proposed methods outperform the TAAR routing algorithm by an average of 63.6% and 41.4% in average latency and throughput, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, there is a great deal of interest in the development of practical optimization models and intelligent solution algorithms for solving disassembly-line balancing problems. Based on the importance of energy efficiency of product disassembly and the trend for green remanufacturing, this paper develops a new optimization model for the energy-efficient disassembly-line balancing problem where the goal is to minimize the energy consumption generated during the disassembly-line operations. Since the proposed model is a complex optimization problem known as NP-hard, this study develops an improved metaheuristic algorithm based on the water cycle algorithm as a recently developed successful metaheuristic inspired by the natural water cycle phenomena of diversion, rainfall, confluence, and infiltration operations. A local search operator is added to the main algorithm to improve its performance. The proposed algorithm is validated by the exact solver and compared with other state-of-the-art and recent metaheuristic algorithms. A case study in a turbine reducer with different parameters is solved to show the applicability of this paper. Finally, our results confirm the high performance of the proposed improved water cycle algorithm and the efficiency of our sensitivity analyses during some sensitivity analyses. [ABSTRACT FROM AUTHOR]

Three-dimensional network-on-chip (NoC) is the primary interconnection method for 3D-stacked multicore processors due to their excellent scalability and interconnect flexibility. With the support of 3D NoC, 3D non-uniform cache architecture (NUCA) is commonly used to organize the last-level cache (LLC) due to its high capacity and fast access latency. However, owing to the layered structure that leads to longer heat dissipation paths and variable inter-layer cooling efficiency, 3D NoC experiences a severe thermal problem that has a big impact on the reliability and performance of the chip. In traditional memory-to-LLC mapping in 3D NUCA, the traffic load in each node is inconsistent with its heat dissipation capability, causing thermal hotspots. To solve the above problem, we propose a temperature-balanced NUCA mapping mechanism named TB-NUCA. First, the Bayesian optimization algorithm is used to calculate the probability distribution of cache blocks in each node in order to equalize the node temperature. Secondly, the structure of TB-NUCA is designed. Finally, comparative experiments were conducted under random, transpose-2, and shuffle traffic patterns. The experimental results reveal that, compared with the classical NUCA mapping mechanism (S-NUCA), TB-NUCA can increase the mean-time-to-failure (MTTF) of routers by up to 28.13% while reducing the maximum temperature, average temperature, and standard deviation of temperature by a maximum of 4.92%, 4.48%, and 20.46%, respectively. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simonk-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27× compression and reduce 74.3% of the multiply–accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset. [ABSTRACT FROM AUTHOR]

The reliability of memory is an important issue. The rapid development of transistor technology makes the memory more prone to soft errors. Several recent efforts have proposed various designs to avoid the corruption of stored data by using Error Correction Codes (ECC). However, these designs tend to focus on one indicator, which means they cannot balance the electrical timing, area and power consumption constraints with the increasing of the chip-scale and the operating frequency. In this paper, we propose a design named TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design. We achieve higher energy-efficiency and lower hardware cost by using a two-dimensional error correction codes, and evaluate the design by considering the overall system performance. Comparing with the traditional Hamming code, the evaluation shows that the TECED reduces most of fifty percent of the area overhead and twenty-eight point five percent power consumption of the memory at a specific storage capacity. [ABSTRACT FROM AUTHOR]

Tensioning is an important process for producing prestressed concrete beams and directly affects bridge performance and driving safety. Active sensing and management of tensioning process data can improve the efficiency of quality monitoring and level of prestressed concrete beams. To realize remote collection and quality monitoring of tensioning process data, a framework for data sensing and processing of tensioning system based on the Internet of Things (IoT) is proposed in this study. Firstly, we investigate the technical framework and techniques of the system and designs a work flow of sensing, transport, and application service layers. The architecture of the tensioning system is presented. Then we propose a data acquisition and preprocessing method for the sensing layer, put forwards the data-pushing mechanism of the transport layer, and designs the function and work flow of the application service layer. After that,.NET platform and Android Studio are used to implement the tensioning system based on Browser/Server (B/S) architecture and mobile terminals. Finally, the case results of the system in seven precast beam fields in the Hubei section of Zhengzhou–Wanzhou high-speed Railway are given, which show that the developed system realizes collection, active pushing, and remote monitoring of tensioning process data. [ABSTRACT FROM AUTHOR]

High-temperature (150-170 °C) pretreatment of lignocellulosic biomass with mineral acids is well established for xylan breakdown. Fe2+ is known to be a cocatalyst of this process although kinetics of its action remains unknown. The present work addresses the effect of ferrous ion concentration on sugar yield and degradation product formation from corn stover for the entire two-step treatment, including the subsequent enzymatic cellulose hydrolysis. The feedstock was impregnated with 0.5% acid and 0.75 mM iron cocatalyst, which was found to be optimal in preliminary experiments. The detailed kinetic data of acid pretreatment, with and without iron, was satisfactorily modelled with a four-step linear sequence of first-order irreversible reactions accounting for the formation of xylooligomers, xylose and furfural as intermediates to provide the values of Arrhenius activation energy. Based on this kinetic modelling, Fe2+ turned out to accelerate all four reactions, with a significant alteration of the last two steps, that is, xylose degradation. Consistent with this model, the greatest xylan conversion occurred at the highest severity tested under 170 °C/30 min with 0.75 mM Fe2+, with a total of 8% xylan remaining in the pretreated solids, whereas the operational conditions leading to the highest xylose monomer yield, 63%, were milder, 150 °C with 0.75 mM Fe2+ for 20 min. Furthermore, the subsequent enzymatic hydrolysis with the prior addition of 0.75 mM of iron(II) increased the glucose production to 56.3% from 46.3% in the control (iron-free acid). The detailed analysis indicated that conducting the process at lower temperatures yet long residence times benefits the yield of sugars. The above kinetic modelling results of Fe2+ accelerating all four reactions are in line with our previous mechanistic research showing that the pretreatment likely targets multiple chemistries in plant cell wall polymer networks, including those represented by the C–O–C and C–H bonds in cellulose, resulting in enhanced sugar solubilization and digestibility. [ABSTRACT FROM AUTHOR]

Wound dressings have become a crucial treatment for wound healing due to their convenience, low cost, and prolonged wound management. As cutting-edge biomaterials, marine polysaccharides are divided from most marine organisms. It possesses various bioactivities, which allowing them to be processed into various forms of wound dressings. Therefore, a comprehensive understanding of the application of marine polysaccharides in wound dressings is particularly important for the studies of wound therapy. In this review, we first introduce the wound healing process and describe the characteristics of modern commonly used dressings. Then, the properties of various marine polysaccharides and their application in wound dressing development are outlined. Finally, strategies for developing and enhancing marine polysaccharide wound dressings are described, and an outlook of these dressings is given. The diverse bioactivities of marine polysaccharides including antibacterial, anti-inflammatory, haemostatic properties, etc., providing excellent wound management and accelerate wound healing. Meanwhile, these biomaterials have higher biocompatibility and biodegradability compared to synthetic ones. On the other hand, marine polysaccharides can be combined with copolymers and active substances to prepare various forms of dressings. Among them, emerging types of dressings such as nanofibers, smart hydrogels and injectable hydrogels are at the research frontier of their development. Therefore, marine polysaccharides are essential materials in wound dressings fabrication and have a promising future. [ABSTRACT FROM AUTHOR]

Due to the high throughput and high computing capability of convolutional neural networks (CNNs), researchers are paying increasing attention to the design of CNNs hardware accelerator architecture. Accordingly, in this paper, we propose a block parallel computing algorithm based on the matrix transformation computing algorithm (MTCA) to realize the convolution expansion and resolve the block problem of the intermediate matrix. It enables high parallel implementation on hardware. Moreover, we also provide a specific calculation method for the optimal partition of matrix multiplication to optimize performance. In our evaluation, our proposed method saves more than 60% of hardware storage space compared with the im2col(image to column) approach. More specifically, in the case of large-scale convolutions, it saves nearly 82% of storage space. Under the accelerator architecture framework designed in this paper, we realize the performance of 26.7GFLOPS-33.4GFLOPS (depending on convolution type) on FPGA(Field Programmable Gate Array) by reducing bandwidth and improving data reusability. It is 1.2×–4.0× faster than memory-efficient convolution (MEC) and im2col, respectively, and represents an effective solution for a large-scale convolution accelerator. [ABSTRACT FROM AUTHOR]

Ferrous ion co-catalyst enhancement of dilute-acid (DA) pretreatment of biomass is a promising technology for increasing the release of sugars from recalcitrant lignocellulosic biomass. However, due to the reductive status of ferrous ion and its susceptibility to oxidation with exposure to atmosphere, its effective application presumably requires anaerobic aqueous conditions created by nitrogen gas-purging, which adds extra costs. The objective of this study was to assess the effectiveness of oxidative iron ion, (i.e., ferric ion) as a co-catalyst in DA pretreatment of biomass, using an anaerobic chamber to strictly control exposure to oxygen during setup and post-pretreatment analyses. Remarkably, the ferric ions were found to be as efficient as ferrous ions in enhancing sugar release during DA pretreatment of biomass, which may be attributed to the observation that a major portion of the initial ferric ions were converted to ferrous during pretreatment. Furthermore, the detection of hydrogen peroxide in the liquors after DA/Fe ion pretreatment suggests that Fenton reaction chemistry was likely involved in DA/Fe ion pretreatments of biomass, contributing to the observed ferric and ferrous interchanges during pretreatment. These results help define the extent and specification requirements for applying iron ions as co-catalysts in DA pretreatments of biomass. [ABSTRACT FROM AUTHOR]

Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases. [ABSTRACT FROM AUTHOR]