Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Novel interface engineering of LDH-based materials on Mg alloy for efficient photocatalytic systems considering the geometrical linearity of condensed phosphates

This study presents a facile and rapid method for synthesizing novel Layered Double Hydroxide(LDH)nanoflakes,exploring their application as a photocatalyst,and investigating the influence of condensed phosphates'geometric linearity on their photocatalytic properties.Herein,the Mg O film,obtained by plasma electrolysis of AZ31 Mg alloys,was modified by growing an LDH film,which was further functionalized using cyclic sodium hexametaphosphate(CP)and linear sodium tripolyphosphate(LP).CP acted as an enhancer for flake spacing within the LDH structure,while LP changed flake dispersion and orientation.Consequently,CP@LDH demonstrated exceptional efficiency in heterogeneous photocatalysis,effectively degrading organic dyes like Methylene blue(MB),Congo red(CR),and Methyl orange(MO).The unique cyclic structure of CP likely enhances surface reactions and improves the catalyst's interaction with dye molecules.Furthermore,the condensed phosphate structure contributes to a higher surface area and reactivity in CP@LDH,leading to its superior photocatalytic performance compared to LP@LDH.Specifically,LP@LDH demonstrated notable degradation efficiencies of 93.02%,92.89%,and 88.81%for MB,MO,and CR respectively,over a 40 min duration.The highest degradation efficiencies were observed in the case of the CP@LDH sample,reporting 99.99%for MB,98.88%for CR,and 99.70%for MO.This underscores the potential of CP@LDH as a highly effective photocatalyst for organic dye degradation,offering promising prospects for environmental remediation and water detoxification applications.

Facile preparation and efficient MnxCoy porous nanosheets for the sustainable catalytic process of soot

The pursuit of high-performance is worth considerable effort in catalysis for energy efficiency and environmental sustainability. To develop redox catalysts with superior performance for soot combustion, a series of Mn_(x)Co_(y) oxides were synthesized using MgO template substitution.This method greatly improves the preparation and catalytic efficiency and is more in line with the current theme of green catalysts and sustainable development. The resulting Mn_(1)Co_(2.3) has a strong activation capability of gaseous oxygen due to a high concentration of Co^(3+) and Mn^(3+). The Mn doping enhanced the intrinsic activity by prompting oxygen vacancy formation and gaseous oxygen adsorption. The nanosheet morphology with abundant mesoporous significantly increased the solid–solid contact efficiency and improved the adsorption capability of gaseous reactants. The novel design of Mn_(1)Co_(2.3)oxide enhanced its catalytic performance through a synergistic effect of Mn doping and the porous nanosheet morphology, showing significant potential for the preparation of high-performance soot combustion catalysts.

An efficient and universal protoplast-based transient gene expression system for genome editing in Brassica crops

Protoplast-based transient gene expression system has been widely used in plant genome editing because of its simple operation and less time-consuming.In order to establish a universal protoplast-based transient transfection system for verifying activities of genome editing vectors containing targets in Brassica,we systematically optimized factors affecting protoplast isolation and transient gene expression.We established an efficient protoplast-based transient gene expression system(PTGE)in Chinese cabbage,achieving high protoplast yield of 4.9×10^(5)·g^(-1)FW,viability over 95%,and transfection efficiency of 76%.We showed for the first time that pretreatment of protoplasts with a hypotonic MMG could significantly enhance the transfection efficiency.Furthermore,protoplasts incubated at 37℃ for 6 min improved the transfection efficiency to 86%.We also demonstrated that PTGE worked well(more than 50%transfection efficiency)in multiple Brassica species including cabbage,Pak Choi,Chinese kale,and turnip.Finally,PTGE was used for validating the activities of CRISPR/Cas9 vectors containing targets in Chinese cabbage,cabbage,and pak choi,demonstrating the broad applicability of the established PTGE for genome editing in Brassica crops.

Efficient cache replacement framework based on access hotness for spacecraft processors

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access behaviors.However,modern cache management rarely considers this fine-grained feature,which impacts the effective cache capacity of contemporary high-performance spacecraft processors.To harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was proposed.This framework reconstructs the cache replacement policy,facilitating data migration between the main cache and the auxiliary cache.Unlike traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache utilization.The evaluation shows impressive performance improvement,especially on workloads with irregular access patterns.Benefiting from fine granularity,the proposal achieves superior storage efficiency compared with commonly used cache management schemes,providing a potential optimization opportunity for modern resource-constrained processors,such as spacecraft processors.Furthermore,the framework complements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

Fast,simple,efficient Agrobacterium rhizogenes-mediated transformation system to non-heading Chinese cabbage with transgenic roots

Non-heading Chinese cabbage, a variety of Brassica campestris, is an important vegetable crop in the Yangtze River Basin of China. However,the immaturity of its stable transformation system and its low transformation efficiency limit gene function research on non-heading Chinese cabbage. Agrobacterium rhizogenes-mediated(ARM) transgenic technology is a rapid and effective transformation method that has not yet been established for non-heading Chinese cabbage plants. Here, we optimized conventional ARM approaches(one-step and two-step transformation methods) suitable for living non-heading Chinese cabbage plants in nonsterile environments. Transgenic roots in composite non-heading Chinese cabbage plants were identified using phenotypic detection, fluorescence observation, and PCR analysis. The transformation efficiency of a two-step method on four five-day-old non-heading Chinese cabbage seedlings(Suzhouqing, Huangmeigui, Wuyueman, and Sijiu Caixin) was 43.33%-51.09%, whereas using the stout hypocotyl resulted in a transformation efficiency of 54.88% for the 30-day-old Sijiu Caixin.The one-step method outperformed the two-step method;the transformation efficiency of different varieties was above 60%, and both methods can be used to obtain transgenic roots for functional studies within one month. Finally, optimized ARM transformation methods can easily,quickly, and effectively produce composite non-heading Chinese cabbage plants with transgenic roots, providing a reliable foundation for gene function research and non-heading Chinese cabbage genetic improvement breeding.

"Three-in-One" Ecological Development and Efficient Utilization Model of Aggregates

Green mining and the formation of an effective and efficient development model have become key issues that aggregates enterprises around the world need to solve urgently.On the basis of analyzing the development status of aggregates industry in Xiluodu area,the paper studied the main problems faced in the construction of green aggregates mines at present,and proposed a"three-in-one"ecological,intelligent and efficient green mine construction model for"ecological development","green logistics"and"solid waste recycling"of aggregates.The study has certain theoretical value and practical significance for the construction of green aggregates mine in Xiluodu area.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Establishment of NaLuF_(4):15%Tb-based low dose X-PDT agent and its application on efficient antitumor therapy

X-ray excited photodynamic therapy(X-PDT)is the bravo answer of photodynamic therapy(PDT)for deep-seated tumors,as it employs X-ray as the irradiation source to overcome the limitation of light penetration depth.However,high X-ray irradiation dose caused organ lesions and side effects became the major barrier to X-PDT application.To address this issue,this work employed a classic-al co-precipitation reaction to synthesize NaLuF_(4):15%Tb^(3+)(NLF)with an average particle size of(23.48±0.91)nm,which was then coupled with the photosensitizer merocyanine 540(MC540)to form the X-PDT system NLF-MC540 with high production of singlet oxygen.The system could induce antitumor efficacy to about 24%in relative low dose X-ray irradiation range(0.1-0.3 Gy).In vivo,when NLF-MC540 irradiated by 0.1 Gy X-ray,the tumor inhibition percentage reached 89.5%±5.7%.The therapeutic mechanism of low dose X-PDT was found.A significant increase of neutrophils in serum was found on the third day after X-PDT.By immunohistochemical staining of tumor sections,the Ly6G^(+),CD8^(+),and CD11c^(+)cells infiltrated in the tumor microenvironment were studied.Utilizing the bilat-eral tumor model,the NLF-MC540 with 0.1 Gy X-ray irradiation could inhibit both the primary tumor and the distant tumor growth.De-tected by enzyme linked immunosorbent assay(ELISA),two cytokines IFN-γand TNF-αin serum were upregulated 7 and 6 times than negative control,respectively.Detected by enzyme linked immune spot assay(ELISPOT),the number of immune cells attributable to the IFN-γand TNF-αlevels in the group of low dose X-PDT were 14 and 6 times greater than that in the negative control group,respectively.Thus,it conclude that low dose X-PDT system could successfully upregulate the levels of immune cells,stimulate the secretion of cy-tokines(especially IFN-γand TNF-α),activate antitumor immunity,and finally inhibit colon tumor growth.

Priority Based Energy Efficient MAC Protocol by Varying Data Ratefor Wireless Body Area Network

Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.

Low-Cost Insulation for Energy Efficient Buildings in Terai Region of Nepal

There is a huge amount of energy savings potential in public building sector that has yet to be realized.By prioritizing energy efficiency in its own buildings and thus promoting the development of required knowledge in terms of new technology and construction methods,the public sector will lead the way in efforts to increase the rate of renovations.The low-cost insulation strategies and a comparison of cost with existing insulation materials has been described in this study.We have repeatedly faced energy crises and will continue to do so in the future if appropriate action is not taken in a timely manner.Properly implementing energy-saving initiatives in for achieving thermal comfort in buildings as well as reducing the energy costs would undoubtedly inspire the residential sector,resulting in significant reductions in energy usage.Simulations were carried out to study insulation layers on various building components like exterior walls,floor and roofs,generating different scenarios for a building as a base model,which were then compared and analysed to verify the literature used to develop the cases.The proposed recommendations,which have been validated,are certain to increase building energy efficiency,achieve thermal comfort in low cost than what is currently being used.

基于改进EfficientNetB0模型的葡萄叶部病害识别方法

为了高效、准确地识别葡萄叶部病害,文中提出了LE-EfficientNet模型,在EfficientNetB0模型基础上,采用大核注意力(LKA)机制替换原模型部分MBConv模块中的压缩激励网络(SENet),接着利用跳跃连接在最后一层卷积层后面融入高效通道注意力机制(ECA),结合三种注意力机制让网络更高效地提取葡萄叶部病害的局部重要信息,并引用Adam优化器替换原模型的SGD优化器,提升了分类模型的泛化能力。在PlantVillage葡萄叶部病害数据集上训练,结果表明,LE-EfficientNet模型相比原模型准确率提升了1.58%,总体精度提升了1.62%,召回率提升了1.46%,F_(1)分数提升了1.53%,并且参数量仅有10.18 MB,比原模型参数量降低2.7 MB,与其他经典网络模型相比,性能评估指标均有不同程度的提升,该研究为葡萄叶部病害识别提供了新的参考与借鉴。

Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

Joint UAV 3D deployment and sensor power allocation for energy-efficient and secure data collection

Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.

基于FACAM-Efficient Net的通信信号调制方式识别

针对在复杂电磁空间中,通信信号识别方法存在噪声干扰、识别率不足、网络模型复杂等问题,提出基于高阶累积量可视化时频图像的FACAM-Efficient Net信号调制方式识别方法,通过高阶累积量时频分析方法,将不同调制的通信信号转换成可视化时频图像,用作网络模型的训练和测试,根据可视化时频图像的图像特征,提出FACAM频域通道混合注意力机制,并引入FACAM改进Efficient Net的网络主体结构,更好地提取时频图像的频域和通道的特征。实验结果显示,本文提出的调制识别方法在低信噪比下有较好的识别效果。

基于改进E-EfficientNet的古陶瓷纹饰分类模型

为进一步提高古陶瓷纹饰分类精度,提出一种基于改进EfficientNet的古陶瓷纹饰分类模型。该模型通过引入高效率注意力机制(ECA)模块改进原主干网络EfficientNet-B0,有效捕获通道间的交互信息,利用跳跃连接在特征提取的最后一层加入ECA模块,获得古陶瓷纹饰注意力特征图,并利用迁移学习和Adam优化算法在古陶瓷纹饰数据集上进行实验验证。结果表明,改进后的E-EfficientNet模型在古陶瓷纹饰数据集上的识别准确率达到了99.26%,较改进前提高了2.48%;与同类轻量化模型ShuffleNet-V2和MobileNet-V3对比,识别准确率分别提高了2.10%和2.91%;与其他经典模型VGG、ResNet对比,不仅参数量大幅度减少,识别准确率均明显提高,可有效用于古陶瓷纹饰分类。

Gene expression pattern of K transporter GhHAK5 gene of potassium efficient and in-efficient cotton cultivars based on morphological physiognomies as affected by potassium nutrition and reduced irrigation

Background Under K deficiency the uptake and distribution pattern in plant cells is mediated through different transport proteins and channels which were controlled by specific gene family.Therefore,a hydroponic experiment was conducted under control condition for testing the gene expression pattern of the K transporter under adequate and low K supply levels.After that,a 2-year field experiment was conducted to evaluate five selected cotton cultivars(four K-efficient cultivars,viz.,CIM-554,CYTO-124,FH-142,IUB-2013,and one K non-efficient,BH-212) screened from the initial hydroponics culture experiment and two levels of potassium(0 K_(2)O kg·ha^(-1) and 50 K_(2)O kg·ha^(-1)) were tested under reduced irrigation(50% available water content;50 AWC) and normal irrigation conditions(100% available water content;100 AWC).Result Results revealed that the transcript levels of GhHAK5aD in roots were significantly higher in K^(+) efficient cultivars than that in K^(+) non-efficient cultivars.The GhHAK5aD expression upon K^(+) deficiency was higher in roots but lower in shoots,indicating that GhHAK5aD could have a role in K^(+) uptake in roots,instead of transport of K^(+) from root to shoot.Similarly,under field conditions the cultivar FH-142 showed an increase of 22.3%,4.9%,2.4%,and 1.4% as compared with BH-212,IUB-2013,CYTO-124,and CIM-554,respectively,in seed cotton yield(SCY) with K application under reduced irrigation conditions.With applied K,the FH-142 showed an increase in net photosynthetic rate by 57.3% as compared with the rest of the cultivars under reduced irrigation over K control.However,the overall performance indicators of K-efficient cultivars like FH-142,CYTO-124,CIM-554,and IUB-2013 were better than BH-212(K in-efficient) under reduced irrigation conditions with applied K at 50 kg·ha^(-1).Fiber quality trait improved significantly with K application under water deficit.The increase in micronaire was 3.6%,4.7%,7.8%,3.4%,and 6.7% in BH-212,IUB-2013,CIM-554,CYTO-124,and FH-142,respectively,with K application at 50 kg·ha^(-1) over without K application under reduced irrigation conditions during the cotton growing season.Similarly,the cultivars FH-142 increased by 12% with K application under reduced irrigation as compared with other cultivars.The performance of K-efficient cultivars under reduced irrigation conditions was 30% better in SCY and quality traits with the application of K at 50 kg·ha^(-1) as compared with K-non-efficient cultivars.Similarly,water use efficiency(WUE)(40.1%) and potassium use efficiency(KUE)(20.2%) were also noted higher in case of FH-142 as compared with other cultivar with K application under reduced conditions.Conclusion Higher expression of GhHAK5aD gene was observed in K-efficient cultivars as compared with K-nonefficient cultivars in roots indicates that GhHAK5aD may be contributing to genotypic differences for K^(+) efficiency in cotton.K-efficient cotton cultivars can be used for the low-K environments and can also be recommended for general cultivars.

Surface Passivation Toward Efficient and Stable Perovskite Solar Cells

Although metal halide perovskites are increasingly popular for the next generation of efficient photovoltaic devices,the inevitable defects from the preparation process have become the notorious barrier to further improvement of performance,which increases non-radiative recombination and lowers the power conversion efficiency of solar cells.Surface passivation strategies have been affirmed as one of the most practical approaches to suppress these defects.Therefore,it is necessary to have a detailed review on the surface passivation to reveal the improvements of the devices.Herein,the mechanism and recent advances of surface passivation have been systematically summarized with respect to various passivation approaches,including the Lewis acid–base,the low-dimensional perovskite,inorganic molecules,and polymers.Finally,the review also offers the research trend and prospects of surface passivation.