An adjusted energy-saving quantity calculation method for building energy-efficient retrofit

Aiming at a comprehensive assessment of energy-saving retrofitting effect on existing buildings,a calculation method is developed to adjust energy-saving quantity in standard condition for comparison under the same conditions. A mathematical model,method theory and calculation steps are given. Error analysis results show that this method can be applied accurately to practical engineering projects. In a case study of energy-saving quantity assessment before and after retrofitting on a certain hospital in Shanghai,with energy simulation software TRNSYS,detailed application of this method is introduced and analyzed. The method is applied to the case of energy-saving quantity assessment to a hospital in Shanghai before and after retrofitting with the energy simulation software TRNSYS.

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.

Excavator Energy-saving Efficiency Based on Diesel Engine Cylinder Deactivation Technology

The hydraulic excavator energy-saving research mainly embodies the following three measures： to improve the performance of diesel engine and hydraulic component, to improve the hydraulic system, and to improve the power matching of diesel-hydraulic system-actuator. Although the above measures have certain energy-saving effect, but because the hydraulic excavator load changes frequently and fluctuates dramatically, so the diesel engine often works in high-speed and light load condition, and the fuel consumption is higher. Therefore, in order to improve the economy of diesel engine in light load, and reduce the fuel consumption of hydraulic excavator, energy management concept is proposed based on diesel engine cylinder deactivation technology. By comparing the universal characteristic under diesel normal and deactivated cylinder condition, the mechanism that fuel consumption can be reduced significantly by adopting cylinder deactivation technology under part of loads condition can be clarified. The simulation models for hydraulic system and diesel engine are established by using AMESim software, and fuel combustion consumption by using cylinder-deactivation-technology is studied through digital simulation approach. In this way, the zone of cylinder deactivation is specified. The testing system for the excavator with this technology is set up based on simulated results, and the results show that the diesel engine can still work at high efficiency with part of loads after adopting this technology; fuel consumption is dropped down to 11% and 13% under economic and heavy-load mode respectively under the condition of driving requirements. The research provides references to the energy-saving study of the hydraulic excavators.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

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.

"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.

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.

Operation optimization strategy of existing residential building energy-saving renovation market:From the perspective of subject behavior

The energy-saving renovation of existing residential buildings is a crucial measure to achieve the strategic goal of energy conservation and emission reduction in China and build ecologically livable cities.This article focuses on the perspective of subject behavior,starting from analyzing the current situation and difficulties of the operation of the energy-saving renovation market for existing residential buildings in China,drawing on the practical experience of the operation of the existing residential building energy-saving renovation market abroad.Based on principles such as systematicity,humanization,feasibility,and sustainability,the article constructs an operation optimization system of the existing residential building energy-saving renovation market from the perspective of subject behavior.In order to provide a reference for the healthy and orderly operation of the existing residential building energy-saving renovation market,this paper proposes implementation strategies for optimizing the operation of the existing residential building energy-saving renovation market.Suggestions are proposed from four aspects:optimizing the market environment,innovating the financing model,building the information sharing platform,and utilizing the synergies of the main subjects.

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.

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.

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.

Energy-Saving Construction Technologies for Buildings

The development of the construction industry is shifting towards low-carbon construction,so it is necessary to improve and optimize related construction concepts,methods,and processes.By improving resource and energy control efficiency in building projects,minimizing construction waste,and reducing environmental impact,a foundation for the sustainable development of the industry can be established.This paper mainly analyzes the significance of low-carbon energy-saving construction technology and the control factors of construction,summarizes the status quo of the development of building energy-saving construction,and puts forward strategies for applying building energy-saving construction technology.These strategies serve to achieve low-carbon and energy-saving goals to promote the healthy development of energy-saving construction.

Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.

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.

Application of Energy-Saving Materials in Architectural Design

The conventional process of building construction is associated with issues such as the waste of construction materials and environmental pollution.Sustainable development highlights the importance of energy conservation and eco-friendly practices.It is essential to use energy-efficient and green materials in building designs to ensure the healthy growth of construction companies.This article discusses the advantages and principles of incorporating energy-saving materials in architectural design.It examines the strategies and critical control points for using energy-saving materials in architectural design,offering guidance for the sustainable development of the construction industry.

An Efficient and Lightweight YOLOv8s Strawberry Maturity Detection Model

The manual picking of strawberries is inefficient and costly,limiting scalability and economic benefits.Mechanizing this process reduces labor demands,improves working conditions,and modernizes the strawberry industry.Target detection technology,crucial for mechanized picking,must accurately determine strawberry maturity.This study presents an enhanced YOLOv8s model addressing current machine learning issues like accuracy,parameters,and complexity.The improved model replaces the Bottleneck structure in C2f with the FasterNet network,integrates an efficient multi-scale attention mechanism,and uses the Ghost module in the backbone to reduce computational load while maintaining performance.It also introduces Wise-IoU for bounding box regression loss,improving recognition accuracy.The YOLOv8s-FEGW model achieves a 93.8%mAP in detecting strawberry ripeness,with significant reductions in parameters(36.8%),complexity(34.6%),and model size(37.7%),alongside a 12.7% Frames Per Second(FPS)boost.These enhancements result in excellent detection capabilities,supporting agricultural automation and intelligence.