Investigation into the Methodology and Implementation of Life Cycle Engineering under China’s Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China.In pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing industries.An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology.This strategy aims to attain an optimal solution for material performance,resource consumption,and environmental impact.In this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and reutilization.The meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing process.This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an example.The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

Growth of RB Population in the Conversion Phase of Chlamydia Life Cycle

Upon infecting a host cell,the reticulate body(RB)form of the Chlamydia bacteria simply proliferates by binary fission for an extended period.Available data show only RB units in the infected cells 20 hours post infection(hpi),spanning nearly half way through the development cycle.With data collected every 4 hpi,conversion to the elementary body(EB)form begins abruptly at a rapid rate sometime around 24 hpi.By modeling proliferation and conversion as simple birth and death processes,it has been shown that the optimal strategy for maximizing the total(mean)EB population at host cell lysis time is a bang-bang control qualitatively replicating the observed conversion activities.However,the simple birth and death model for the RB proliferation and conversion to EB deviates in a significant way from the available data on the evolution of the RB population after the onset of RB-to-EB conversion.By working with a more refined model that takes into account a small size threshold eligibility requirement for conversion noted in the available data,we succeed in removing the deficiency of the previous models on the evolution of the RB population without affecting the optimal bang-bang conversion strategy.

Life cycle assessment as a prospective tool for sustainable agriculture and food planning at a local level

Owing to the far-reaching environmental consequences of agriculture and food systems,such as their contribution to climate change,there is an urgent need to reduce their impact.International and national governments set sustainability targets and implement corresponding measures.Nevertheless,critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues.Yet,at the subnational level,local authorities rarely apply a systemic environmental assessment to enhance their action plans.This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning.The objective is to identify significant direct and indirect environmental hotspots,their origins,and formulate effective mitigation strategies.The methodology is applied to the administrative department of Finistere,a strategic agricultural region in North-Western France.Multiple environmental criteria including climate change,fossil resource scarcity,toxicity,and land use are modeled.The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources,such as livestock feed or diesel consumption.Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies,resulting in a 25%decrease in the climate change indicator.However,the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator.These results highlight the innovative application of life cycle assessment led at a local level,offering insights for the further advancement of systematic and prospective local agri-food assessment.Additionally,they provide guidance for local authorities to enhance the sustainability of planning strategies.

Crystalline and amorphous metal sulfide composite electrode materials with long cycle life:Preparation and performance of hybrid capacitors

Crystalline@amorphous NiCo_(2)S_(4)@MoS_(2)(v-NCS@MS)nanostructures were designed and constructed via an ethylene glycol-induced strategy with hydrothermal synthesis and solvothermal method,which simultaneously realized the defect regulation of crystal NiCo_(2)S_(4) in the core.Taking advantage of the flexible protection of an amor-phous shell and the high capacity of a conductive core with defects,the v-NCS@MS electrode exhibited high specif-ic capacity(1034 mAh·g^(-1) at 1 A·g^(-1))and outstanding rate capability.Moreover,a hybrid supercapacitor was assembled with v-NCS@MS as cathode and activated carbon(AC)as anode,which can achieve remarkably high specific energy of 111 Wh·kg^(-1) at a specific power of 219 W·kg^(-1) and outstanding capacity retention of 80.5%after 15000 cycling at different current densities.

A Review of the Life Cycle Analysis for Plastic Waste Pyrolysis

Pyrolysis is a rapidly expanding chemical-based recyclable method that complements physical recycling. It avoids improper disposal of post-consumer polymers and mitigates the ecological problems linked to the production of new plastic. Nevertheless, while there is a consensus that pyrolysis might be a crucial technology in the years to come, more discussions are needed to address the challenges related to scaling up, the long-term sustainability of the process, and additional variables essential to the advancement of the green economy. Herein, it emphasizes knowledge gaps and methodological issues in current Life Cycle Assessment (LCA), underlining the need for standardized techniques and updated data to support robust decision-making for adopting pyrolysis technologies in waste management strategies. For this purpose, this study reviews the LCAs of pyrolytic processes, encompassing the complete life cycle, from feedstock collection to end-product distribution, including elements such as energy consumption, greenhouse gas emissions, and waste creation. Hence, we evaluate diverse pyrolysis processes, including slow, rapid, and catalytic pyrolysis, emphasizing their distinct efficiency and environmental footprints. Furthermore, we evaluate the impact of feedstock composition, process parameters, and scale of operation on the overall sustainability of pyrolysis-based plastic waste treatment by integrating results from current literature and identifying essential research needs. Therefore, this paper argues that existing LCA studies need more coherence and accuracy. It follows a thorough evaluation of previous research and suggests new insights into methodologies and restrictions.

A Building Information Modeling-Life Cycle Cost Analysis Integrated Model to Enhance Decisions Related to the Selection of Construction Methods at the Conceptual Design Stage of Buildings

Life Cycle Cost Analysis (LCCA) provides a systematic approach to assess the total cost associated with owning, operating, and maintaining assets throughout their entire life. BIM empowers architects and designers to perform real-time evaluations to explore various design options. However, when integrated with LCCA, BIM provides a comprehensive economic perspective that helps stakeholders understand the long-term financial implications of design decisions. This study presents a methodology for developing a model that seamlessly integrates BIM and LCCA during the conceptual design stage of buildings. This integration allows for a comprehensive evaluation and analysis of the design process, ensuring that the development aligns with the principles of low carbon emissions by employing modular construction, 3D concrete printing methods, and different building design alternatives. The model considers the initial construction costs in addition to all the long-term operational, maintenance, and salvage values. It combines various tools and data through different modules, including energy analysis, Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to execute a comprehensive assessment of the financial implications of a specific design option throughout the lifecycle of building projects. The development of the said model and its implementation involves the creation of a new plug-in for the BIM tool (i.e., Autodesk Revit) to enhance its functionalities and capabilities in forecasting the life-cycle costs of buildings in addition to generating associated cash flows, creating scenarios, and sensitivity analyses in an automatic manner. This model empowers designers to evaluate and justify their initial investments while designing and selecting potential construction methods for buildings, and enabling stakeholders to make informed decisions by assessing different design alternatives based on long-term financial considerations during the early stages of design.

NCAPD2 serves as a potential prognostic biomarker for lung adenocarcinoma and promotes cell proliferation,migration,invasion and cell cycle in vitro

Objectives:The pro-oncogenic effects of NCAPD2 have been extensively studied across various tumor types;however,its precise role within the context of lung adenocarcinoma(LUAD)remains elusive.This study aims to elucidate the biological functions of NCAPD2 in LUAD and unravel the underlying mechanistic pathways.Methods:Utilizing bioinformatics methodologies,we explored the differential expression of NCAPD2 between normal and tumor samples,along with its correlations with clinical-pathological characteristics,survival prognosis,and immune infiltration.Results:In the TCGA-LUAD dataset,tumor samples demonstrated significantly elevated levels of NCAPD2 expression compared to normal samples(p<0.001).Clinically,higher NCAPD2 expression was notably associated with advanced T,N,and M stages,pathologic stage,gender,smoking status,and diminished overall survival(OS).Moreover,differentially expressed genes(DEGs)associated with NCAPD2 were predominantly enriched in pathways related to cell division.Immune infiltration analysis revealed that NCAPD2 expression levels were linked to the infiltration of memory B cells,naïve CD4+T cells,activated memory CD4+T cells,and M1 macrophages.In vitro experiments demonstrated that silencing NCAPD2 suppressed LUAD cell proliferation,migration,invasion,epithelial-mesenchymal transition(EMT),and cell cycle progression.Conclusions:In summary,NCAPD2 may represent a promising prognostic biomarker and novel therapeutic target for LUAD.

Tailoring Mg^(2+)Solvation Structure in a Facile All-Inorganic[Mg_(x)Li_(y)Cl2_(x+y)·nTHF]Complex Electrolyte for High Rate and Long Cycle-Life Mg Battery

A high-performance all-inorganic magnesium-lithium chloride complex(MLCC)electrolyte is synthesized by a simple room-temperature reaction of LiCl with MgCl_(2) in tetrahydrofuran(THF)solvent.Molecular dynamics simulation,density functional theory calculation,Raman spectroscopy,and nuclear magnetic resonance spectroscopy reveal that the formation of[Mg_(x)Li_(y)Cl_(2x+y)·nTHF]complex solvation structure significantly lowers the coordination number of THF in the first solvation sheath of Mg^(2+),which significantly enhances its de-solvation kinetics.The MLCC electrolyte presents a stable electrochemical window up to 3.1 V(vs Mg/Mg^(2+))and enables reversible cycling of Mg metal deposition/stripping with an outstanding Coulombic efficiency up to 99%at current densities as high as 10 mA cm^(-2).Utilizing the MLCC electrolyte,a Mg/Mo_(6)S_(8) full cell can be cycled for over 10000 cycles with a superior capacity retention of 85 mA h g^(-1) under an ultrahigh rate of 50 C(1 C=128.8 mA g^(-1)).The facile synthesis of highperformance MLCC electrolyte provides a promising solution for future practical magnesium batteries.

Childlife干预在儿童隐匿阴茎围术期护理中的应用

分析儿童隐匿阴茎围术期护理中采取Child life干预的效果。方法 选取2022年1月-2023年2月纳入的隐匿性阴茎儿童60例作为观察对象,经随机抽签法分组,对照组、观察组各有30例。两组分别采取常规护理、Child life干预,观察围术期患儿的心理状态,并评估患儿术后疼痛评分及护理满意度、护理依从性。结果 护理前,组间SAS及SDS评分相比,p>0.05;护理后,观察组SAS及SDS评分比对照组低,p<0.05;观察组Wong-Baker分值低于对照组,p<0.05;观察组依从性及护理满意度高于对照组,p<0.05。结论 在隐匿性阴茎儿童围术期护理中采取Child life护理模式可提升患儿的依从性、护理满意度,并改善患儿心理状态,减轻其疼痛感。

Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

The inquiry process of traditional medical equipment maintenance management is complex,which has a negative impact on the efficiency and accuracy of medical equipment maintenance management and results in a significant amount of wasted time and resources.To properly predict the failure of medical equipment,a method for failure life cycle prediction of medical equipment was developed.The system is divided into four modules:the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage,the middle stage,and the later stage;the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module;and the main function of the fault diagnosis module is based on the normal value of the relevant sensitive data in the whole life cycle management module.The inference machine diagnoses the operation data of the equipment;the fault prediction module constructs a fine prediction system based on the least square support vector machine algorithm and uses the AFS-ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters;the optimal model is then used to predict the failure of medical equipment.Comparative experiments are designed to determine whether or not the design system is effective.The results demonstrate that the suggested system accurately predicts the breakdown of ECG diagnostic equipment and incubators and has a high level of support and dependability.The design system has the minimum prediction error and the quickest program execution time compared to the comparison system.Hence,the design system is able to accurately predict the numerous causes and types of medical device failure.

Life Cycle Assessment Introduced by Using Nanorefrigerant of Organic Rankine Cycle System for Waste Heat Recovery

The use of nanorefrigerants in Organic Rankine Cycle(ORC)units is believed to affect the cycle environment performance,but backed with very few relevant studies.For this purpose,a life cycle assessment(LCA)has been performed for the ORC system using nanorefrigerant,the material and energy input,characteristic indicators and comprehensive index of environmental impact,total energy consumption and energy payback time(BPBT)of the whole life cycle of ORC system using Al_(2)O_(3)/R141b nanorefrigerant were calculated.Total environmental comprehensive indexes reveal that ECER-135 index decrease by 1.5%after adding 0.2%Al_(2)O_(3)nanoparticles to R141b.Based on the contribution analysis and sensitivity analysis,it can be found out ORC system manufacturing is of the most critical stage,where,the ECER-135 index of ORC component production is the greatest,followed by the preparation process of R141b,transportation phase,and that of Al_(2)O_(3)nanoparticles preparation is small.The retirement phase which has good environmental benefits affects the result significantly by recycling important materials.Meanwhile,the main cause and relevant suggestion for improvement were traced respectively.Finally,the environmental impacts of various power generations were compared,and results show that the power route is of obvious advantage.Among the renewable energy,ORC system using Al_(2)O_(3)/R141b nanorefrigerant with minimal environmental impact is only 0.67%of coal-fired power generation.The environmental impact of current work is about 14.34%of other nations’PV results.

Carbon footprint accounting for cigar production processes: A life cycle assessment perspective

Although the tobacco industry is a significant contributor to energy consumption and carbon emissions its negative environmental impact has received inadequate attention globally.Cigarette factories are a key link in the tobacco industry’s production chain,and using data provided by a cigarette factory in China we conduct a life cycle assessment to account for the carbon footprint of cigar production in cigarette factories.The results of the assessment show that factory air conditioning is the most important contributor to the environmental load of the cigar manufacturing process,while electricity is the key factor that contributes the greatest envi‐ronmental load across all of the processes in the product life cycle.In addition,packaging,including small boxes and cigarette cartons,has a significant impact on the industry’s environmental footprint due to its use of raw materials.We find the carbon footprint of the entire production process for cigar products to be 383.59 kg CO_(2) eq.Based on our findings,we suggest ways to optimize cigar/cigarette factory processes to re‐duce carbon emissions that can help to promote sustainable development in related industries.

Zr-doping stabilizes spinel LiMn_(2)O_(4)as a low cost long cycle life cathode for lithium ion batteries

The present commercial spinel LiMn_(2)O_(4) delivers only 90 m Ah/g–115 m Ah/g,far lower than the theoretical specific capacity.It degrades fast caused by the Jahn–Teller effect,Mn dissolution and related side reactions that consume Li inventory.In this work,Zr doping is employed to improve the structural stability and electrochemical performance of spinel LiMn_(2)O_(4).Li_(1.06)Mn_(1.94-x)Zr_xO_4(x=0,0.01,0.02,0.04)have been successfully synthesized by a simple solid-state reaction method and evaluated as cathode for lithium ion batteries(LIB).Li_(1.06)Mn_(1.92)Zr_(0.02)O_4 is superior cathode material with a high capacity of 122 m Ah/g at 1-C rate;long cycle stability,98.39%retention after 100 cycles at 1-C rate,excellent high rate performance 107.1 m Ah/g at 10-C rate,and high temperature performance 97.39%retention after 60 cycles.These are thought to be related to Zr doping effectively stabilizing the spinel LiMn_(2)O_(4),by forming stronger Zr–O bonds in the octahedron,suppressing the Jahn–Teller effect,thus improving electrochemical performance.

Carbon emissions reduction potentiality for railroad transportation based on life cycle assessment

This study addresses the comparative carbon emissions of different transportation modes within a unified evaluation framework,focusing on their carbon footprints from inception to disposal.Specifically,the entire life cycle carbon emissions of High-Speed Rail(HSR),battery electric vehicles,conventional internal combustion engine vehicles,battery electric buses,and conventional internal combustion engine buses are analyzed.The life cycle is segmented into vehicle manufacturing,fuel or electricity production,operational,and dismantlingrecycling stages.This analysis is applied to the Beijing-Tianjin intercity transportation system to explore emission reduction strategies.Results indicate that HSR demonstrates significant carbon emission reduction,with an intensity of only 24%-32% compared to private vehicles and 47%-89% compared to buses.Notably,HSR travel for Beijing-Tianjin intercity emits only 24% of private vehicle emissions,demonstrating the emission reduction benefits of transportation structure optimization.Additionally,predictive modeling reveals the potential for carbon emission reduction through energy structure optimization,providing a guideline for the development of effective transportation management systems.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

Study on metal recovery process and kinetics of oxidative leaching from spent LiFePO_(4)Li-batteries

A green environmental protection and enhanced leaching process was proposed to recover all elements from spent lithium iron phosphate(LiFePO_(4)) lithium batteries.In order to reduce the influence of Al impurity in the recovery process,NaOH was used to remove impurity.After impurity removal,the spent LiFePO_(4) cathode material was used as raw material under the H_(2)SO_(4) system,and the pressure oxidation leaching process was adopted to achieve the preferential leaching of lithium.The E-pH diagram of the Fe-P-Al-H_(2)O system can determine the stable region of each element in the recovery process of spent LiFePO_(4)Li-batteries.Under the optimal conditions(500 r·min^(-1),15 h,363.15 K,0.4 MPa,the liquid-solid ratio was 4:1 ml·g^(-1)and the acid-material ratio was 0.29),the leaching rate of Li was 99.24%,Fe,Al,and Ti were 0.10%,2.07%,and 0.03%,respectively.The Fe and P were precipitated and recovered as FePO_(4)·2H_(2)O.The kinetic analysis shows that the process of high-pressure acid leaching of spent LiFePO_(4) materials depends on the surface chemical reaction.Through the life cycle assessment(LCA)of the spent LiFePO_(4) whole recovery process,eight midpoint impact categories were selected to assess the impact of recovery process.The results can provide basic environmental information on production process for recycling industry.

Effect of drying-wetting cycles on pore characteristics and mechanical properties of enzyme-induced carbonate precipitation-reinforced sea sand

Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.

Striatum-specific mechanisms regulate neuronal cell cycle re-entry:the choice between life and death

Mitochondria dysfunction occurs in the aging brain as well as in several neurodegenerative disorders and predisposes neuronal cells to enhanced sensitivity to neurotoxins.In particular,defects in any of the mitochondria respiratory chain complexes lead to impaired adenosine triphosphate production resulting in diseases that often affect the central nervous system.For instance,innate deficits in succinate dehydrogenase(SDH)mitochondria respiratory chain complex II activity caused by genetic mutations in SDH subunits lead to early-onset neurodegeneration(Jain-Ghai et al.,2013),while several adult-onset genetic neurodegenerative disorders are associated with variable levels of complex II deficiency in the central nervous system(Túnez et al.,2010).Also,chemically induced complex II deficiency leads to neurodegeneration.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.