Stages Of Life Cycle Research Articles

Life cycle assessment of enhanced geothermal systems with CO2 as a working fluid—polish case study

AbstractLife-cycle assessment (LCA) is a methodology used to quantify the sustainability of a product, system, or process over its lifetime. The approach allows us to determine energy and material consumption at all life cycle stages, from raw material extraction to the end of a product's life, including the design, production, operation, and end-of-life stages. The LCA aims to assess the overall environmental impact of a facility, consider its strengths and weaknesses, and identify possible solutions to reduce the environmental burden sustainably. This research focuses on a novel approach to using carbon dioxide (CO2) as a working medium. The following research combines two key aspects of electricity production and carbon dioxide sequestration, a solution that can contribute to producing clean energy and reducing CO2 in the atmosphere. This paper aimed to assess the impact of enhanced geothermal systems (EGS) through a life-cycle analysis carried out under Polish conditions for the Gorzow block. It includes differentiating the main impact categories and key system components that indicate the most vulnerable areas. A framework available in the literature and the modelling results performed within the EnerGizerS project were adapted to carry out the study. Calculations were performed using SimaPro software. The work was performed for EGS with supercritical CO2 (sCO2-EGS) as the working fluid in a configuration involving direct expansion in a turbine for electricity production. An environmental impact assessment was conducted, including estimating the carbon footprint for such an installation and different working fluid mass flows. The main objective of the environmental analysis is to examine how the project will affect the various environmental elements (air, water, soil) or forms of nature conservation and to identify ways to prevent, reduce or minimise the effects of the planned investment. The study results show that the construction phase, which includes well drilling and hydraulic fracturing, has the most significant impact on the environment with climate change values for different working fluid mass flows. This phase dominates the indicators obtained, which are considered typical for renewable energy sources. Graphical Abstract

Dynamics analysis of Filippov pest control model with two economic thresholds

Understanding the stages of the pest life cycle, from the juvenile class to the adult class, is crucial for developing effective pest control strategies. Targeted interventions can be tailored to each life stage, maximizing control efficiency and reducing the excessive use of pesticides; therefore, they help minimize damage to crops. With this motivation, we present a new strategy that uses the Filippov framework to describe the two stages of the pest life cycle, from the juvenile class to the adult class, with two economic thresholds. These thresholds serve as a vital signal for the timely implementation of resistance strategies. The first threshold measure is dependent on the density of juvenile pests allowed, while the second threshold is based on the density of allowed adult pests. This model assists pest control by concurrently managing both juvenile and adult populations, effectively keeping them below dangerous thresholds. By employing a dual approach to population regulation, the system ensures a comprehensive strategy for pest management. The model's dynamic behaviours, existence, and stability of various equilibria are addressed both analytically and numerically. Furthermore, we investigate sliding segment regions to determine the timing of control measures for both juvenile and adult pest populations. Additionally, we discuss the dynamics of the sliding mode, covering both local and global sliding bifurcations. The results indicate that the utilization of two economic thresholds in pest management is beneficial, as they can help prevent pest outbreaks by maintaining the density of juvenile and adult pests at desired levels.

Restoration of submerged vegetation modulates microbial communities to decrease nitrogen and phosphorus loads in sediment-water systems

The release of nitrogen and phosphorus from sediments, known as internal nutrient loading, plays a crucial role in determining the eutrophic state of lakes and the timeframe for their ecological recovery. The restoration of submerged macrophytes is considered an effective measure to improve lake eutrophication. However, the impact of submerged macrophytes restoration on the storage and transformation mechanisms of nitrogen and phosphorus in lake sediment-water systems has not yet been systematically studied. This study constructed two submerged macrophyte communities with species richness of either two or five and monitored the physicochemical characteristics, nitrogen and phosphorus dynamics, and the structural and functional changes of sediment microbiomes in the plant-water-sediment system during three growth stages of the plants (May, July, and October). Our results demonstrated that the presence of submerged vegetation effectively reduced the nitrogen and phosphorus loads in the sediment-water system, encompassing their chemical forms, active concentrations, and release fluxes. Simultaneously, the restoration of submerged vegetation altered the composition of sediment microbial communities and the nitrogen and phosphorus cycling functions. Following the restoration, the abundance of functional genes associated with nitrogen fixation, organic nitrogen metabolism, nitrate reduction, and nitrification exhibited an average decrease of 2.95 %. In contrast, the abundance of genes involved in denitrification and nitrogen limitation response regulation increased by 20.24 %, while those related to phosphorus cycling processes showed a 7.29 % increase. Additionally, submerged macrophyte communities with varying richness differentially affected lake nitrogen and phosphorus loads, as well as the structure and function of sediment microbiomes, primarily related to the life cycle stages of the submerged macrophytes. These findings highlight the crucial role of submerged plants in maintaining lake nutrient balance and sediment microbiomes, providing valuable insights into how the restoration of submerged vegetation affects nutrient cycling in aquatic ecosystems.

The Impact of the Business Life Cycle on the Relationship between Excessive ESG and Firm Value

This study aims to investigate the impact of the business life cycle on the relationship between excessive ESG and firm value through regression analysis. The study targeted Korean firms from 2016 to 2021, and the findings revealed a negative correlation between excessive ESG investment and firm value. After including the business life cycle in the analysis, no relationships were found during the introduction and decline stages between these two factors. However, excessive ESG positively impacts firm value during both the growth and mature stages of the business life cycle. In the growth stage, firms need a strategy that distinguishes them from other competitors. Thus, ESG investments serve as a method for differentiation in a competitive market, resulting in a positive association between the two factors. Additionally, firms in the mature stage possess substantial cash flow, enabling them to engage in a wider array of socially responsible initiatives. Therefore, in both stages, excessive ESG aligns with strategic objectives and is critical for enhancing firm value. This study suggests that companies can strategically leverage ESG investments to maximize firm value according to the different stages of the business life cycle.

Interest-bearing capital and monetary policy: a heterogeneous agent Marxian model

PurposeIn terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.Design/methodology/approachBased on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.FindingsThe research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.Originality/valueClarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.

Creating Digital Twin of the Cheese Production Line Using Virtual Reality Technology

The main stages of the project life cycle are considered, its schedule is presented in the form of a Gantt chart. A method for designing the line using virtual reality technology is proposed which includes four stages: designing a 3D CAD model, importing a model, creating a scene, testing the system. To evaluate the approach, a user survey was conducted which proves the need to use a digital twin for designing the cheese production line.

Development and Validation of a Mechanistic, Weather-Based Model for Walnut Blight Caused by Xanthomonas arboricola pv. juglandis.

Walnut blight, caused by Xanthomonas arboricola pv. juglandis (Xaj), occurs worldwide in almost all areas where the Persian walnut (Juglans regia) is grown, causing significant reductions in nut yield via defoliation and fruit drop. The disease control relies on the calendar-based, repeated use of chemical bactericides, negatively impacting economic and environmental sustainability and potentially inducing Xaj resistance to chemicals. This study developed and validated a mechanistic model incorporating the main stages of the pathogen's life cycle and the influence of weather under orchard conditions to improve the scheduling of disease control interventions. The model can simulate: i) the mobilization of primary inoculum; ii) the infection caused by bacteria; and iii) the lesion formation and production of secondary inoculum. We evaluated the model against 21 independent walnut blight epidemics in Italy (nine epidemics on leaves in five orchards) and the USA (12 epidemics on fruit in six orchards). Overall, the model provided accurate predictions for both the occurrence and non-occurrence of infection, with a precision and F1-score of 0.866 and 0.844, respectively. The model could accurately predict disease progression across the season, with a concordance correlation coefficient between observed and predicted disease severities of 0.951, a root mean square error of 0.069, and a coefficient of residual mass of 0.024. After further validation, the model can serve as a decision-making tool for the risk-based timing of chemical sprays in walnut blight management.

Agglomeration externalities, industry life cycle and firm survival: evidence from Chinese manufacturing start-ups

ABSTRACT This paper examines the impact of the industrial life cycle on the relationship between agglomeration externalities and the survival of manufacturing start-ups. The empirical study in the Chinese context finds that at the growth and intermediate stages of the industrial life cycle, specialisation and related variety reduce the survival risk of start-ups, whereas unrelated variety does not affect the survival risk. At the mature stage, specialisation and related variety do not affect the survival risk, but unrelated variety reduces the survival risk. This paper enriches the research on the benefits of agglomeration externalities from the perspective of industry evolution.

TcCARP3 modulates compartmentalized cAMP signals involved in osmoregulation, infection of mammalian cells, and colonization of the triatomine vector in the human pathogen Trypanosoma cruzi.

Cyclic AMP signaling pathways are poorly understood in the stercorarian parasite Trypanosoma cruzi . Specifically, the mechanisms driving the activation of TcACs in response to microenvironmental stress are completely unknown. This study unveils the role of TcCARP3 in modulating the content of cAMP through the interaction with several TcACs and putative cAMP effectors in T. cruzi . Particularly, TcCARP3 interacts with TcAC1 in the main developmental stages of this parasite's life cycle, where both proteins display a dual localization pattern. These results provide new evidence supporting the compartmentalization of cAMP signals in trypanosomes. Moreover, our data unequivocally demonstrates that TcCARP3 is required for key cellular processes for parasite survival, such as response to osmotic stress, host cell invasion, intracellular replication, and the ability to colonize the hindgut of the triatomine vector. In summary, we found that TcCARP3 is an adenylate cyclase regulator, necessary for the life cycle progression of T. cruzi .

RNA Modifications in Pathogenic Viruses: Existence, Mechanism, and Impacts.

RNA modification is a key posttranscriptional process playing various biological roles, and one which has been reported to exist extensively in cellular RNAs. Interestingly, recent studies have shown that viral RNAs also contain a variety of RNA modifications, which are regulated dynamically by host modification machinery and play critical roles in different stages of the viral life cycle. In this review, we summarize the reports of four typical modifications reported on viral RNAs, including N6-methyladenosine (m6A), 5-methylcytosine (m5C), N4-acetylcytosine (ac4C), and N1-methyladenosine (m1A), describe the molecular mechanisms of these modification processes, and illustrate their impacts on viral replication, pathogenicity, and innate immune responses. Notably, we find that RNA modifications in different viruses share some common features and mechanisms in their generation, regulation, and function, highlighting the potential for viral RNA modifications and the related host machinery to serve as the targets or bases for the development of antiviral therapeutics and vaccines.

Integrating Circular Economy Principles in the Electric Motor Industry: A Comprehensive Framework and Case Study for Sustainable Supply Chain Management

AbstractThis study develops and validates an innovative assessment framework designed to integrate circular economy principles into the operations and supply chain of the electric motor industry. Given the rising demand for electric motors across various sectors, effective end-of-life management has become critical for enhancing sustainability. The primary objective of the proposed framework is to create a holistic approach that improves motor sustainability by addressing all stages of the product life cycle, from material sourcing and production to utilization and end-of-life management. The study employs a case study methodology, focusing on an electric motor company, to validate the framework’s applicability and effectiveness. Key findings reveal that emphasizing motor recovery significantly prolongs product lifecycles, reducing waste and conserving resources. The framework’s adaptability suggests its potential application across various business contexts, though it requires customization to fit specific production, logistics, and resource allocation needs. Recommendations include further research to refine the framework for other industries and the potential for policy changes that support circular economy practices. The article provides practical insights into overcoming barriers to CE adoption, emphasizing the need for strategic management capabilities, policy support, and industry-specific customization. This comprehensive approach not only addresses current sustainability challenges within the electric motor industry but also serves as a model for other sectors seeking to implement circular economy principles.

Modular Display of Plasmodium yoelii Circumsporozoite Surface Protein and Merozoite Surface Protein-1 on Norovirus-like Particles.

Recently, virus-like particles have been regarded as a promising platform for displaying foreign peptides or proteins on their surface. In this study, a dual-protein-displaying platform based on the norovirus-like particle (NoV-LP) was developed using SpyTag (SpT)/SpyCatcher (SpC) protein bioconjugation. A short 14-amino-acid SpT peptide was added to the C-terminus of VP1, with a rigid "EAAAK" spacer in between. Antigenic proteins from a rodent malaria parasite, Plasmodium yoelii, specifically the circumsporozoite protein (PyCSP) and the 19 kDa C-terminal region of merozoite surface protein 1 (PyMSP119), were displayed on the surface of NoV-LPs in both monovalent and bivalent formats. The immunogenicity of these VLP-based vaccines was assessed, and they were found to induce antigen-specific IgG responses against both PyCSP and PyMSP119 in BALB/c mice in the absence of an adjuvant, at levels comparable to those induced by subunit antigenic proteins with an alum adjuvant added. Interestingly, the bivalent vaccine raised IgG responses at a similar titer to the monovalent vaccine. This finding hints that the NoV-LP possesses an inherent adjuvanted property in the presence of a foreign antigen. The measured anti-PyCSP and anti-PyMSP119 antibodies through ELISA indicate that surface display of PyCSP and PyMSP119 on SpTagged-NoV-LP has the potential for further development as a bivalent vaccine against two different life-cycle stages of malaria.

Detection of advanced persistent threat: A genetic programming approach

Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyze network traffic data for APT detection, this article introduces a novel approach that utilizes Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimizing its models to effectively learn and adapt to complex APT behaviors. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71% improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP’s capabilities, providing a fresh and effective perspective on countering these persistent threats.

Prospective LCA of brown seaweed-based bioplastic: Upscaling from pilot to industrial scale

Seaweed-based bioplastics are considered a possible alternative to conventional fossil-based plastics due to their potential environmental advantages. The cultivation of seaweed is a fast-growing practice that requires no arable land, freshwater, or fertilizers, perceiving it as an advantageous option for bioresource production. However, research on the environmental impacts of seaweed-based bioplastics is still limited, highlighting the need for a life cycle assessment (LCA) to evaluate their potential. In this article, a prospective LCA is conducted to assess the environmental impacts of brown seaweed-based bioplastic production, from pilot to industrial scale. Upscaling techniques are combined for each life-cycle stage, using interviews to upscale seaweed production and process simulation for the biorefinery and film fabrication steps, and the end-of-life scenario is modelled as composting. All the processes were upscaled to 4000 tonnes (t) per year in 2030 and 2 million tonnes (Mt) per year in 2035, including marginal suppliers of brown seaweed. The results show that the production of 1 kg of brown seaweed-based bioplastic resulted in approximately 1.37 kg CO2-eq. in the best-performing scenario, producing 2 Mt per year in 2035 accounting for the carbon uptake, which is lower than low-density polyethylene (LDPE) with an impact of 3.6 kg CO2-eq. The impact in marine eutrophication in the 2 Mt scenario is −0.009 kg N-eq., and −0.002 kg P-eq. in freshwater eutrophication. This study provides for the first time estimates of prospective industrial-scale impacts of the emerging seaweed-based bioplastic and shows how different upscaling techniques can be successfully combined, i.e., interviews and process simulation, to conduct a prospective LCA of seaweed-based bioplastics. The results demonstrate the potential of seaweed-based bioplastics as a sustainable alternative to conventional plastics.

Intelligent manufacturing and corporate green transformation

The rapid evolution of intelligent manufacturing technologies presents opportunities and challenges for corporate environmental sustainability. Although these advancements offer potentially improved efficiency and reduced environmental impact, their actual effect on firms' green transformation remains understudied, particularly in the Chinese market. Hence, this research fills this gap by focusing on the mediating roles of financing constraints and knowledge diversification. Utilizing a comprehensive dataset of 18,390 firm-year observations from Chinese A-share listed companies between 2007 and 2022, we employ fixed-effects regression models and mediation analyses to examine these relationships. Our findings reveal a notable positive impact of intelligent manufacturing on green transformation, partially mediated by reduced financing constraints and increased knowledge diversification. Furthermore, we uncover important heterogeneities across firm lifecycle stages and ownership structures, with mature firms and state-owned enterprises benefiting more from intelligent manufacturing in their green transformation efforts. These results contribute to the growing literature on sustainable Industry 4.0 and offer valuable insights for policymakers and corporate leaders seeking to leverage technological advancements for environmental sustainability.

Using Chronic Kidney Disease as a Model Framework to Estimate Healthcare-Related Environmental Impact.

While the economic and clinical burden of chronic diseases are well documented, their environmental impact remains poorly understood. We developed a framework to estimate the environmental impact of a disease care pathway using chronic kidney disease (CKD) as an example. A life cycle assessment framework was developed for the CKD care pathway and validated by experts. Life cycle stages were characterised for resource utilisation based on a literature review and ecoinvent database inputs, in ten countries. The ReCiPe impact assessment method was used to calculate impacts across multiple environmental dimensions. At CKD stage5, kidney replacement therapies (KRT) have highest impact; emissions ranged between 3.5 and 43.9kg carbon dioxide equivalents (CO2e) per session depending on dialysis modality, and 336-2022kgCO2e for kidney transplant surgery, depending on donor type. Hospitalisations have a substantial environmental impact: a 1-day intensive care stay had highest impact (66.4-143.6kgCO2e), followed by a 1-day hospital stay (28.8-63.9kgCO2e) and an 8-h emergency room visit (14.4-27.5kgCO2e). Patient transport to and from healthcare sites was a key driver of environmental impact for all life cycle stages, representing up to 99.5% of total CO2e emissions. Full care pathways should be analysed alongside specific healthcare processes. Application of this framework enables quantification of the environmental benefits of preventative medicine and effective management of chronic diseases. For CKD, early diagnosis, and proactive management to reduce the need for KRT and hospitalisations could improve patient outcomes and reduce environmental burden.

Inhibition of protein kinase D and its substrate phosphatidylinositol-4 kinase III beta blocks common human coronavirus replication.

Human coronaviruses can lead to a range of clinical symptoms, from asymptomatic infection to severe illness and death, with a limited array of antiviral drugs available. Protein kinase D (PKD) is involved in various cellular processes, such as cell proliferation, apoptosis, and membrane fission of the Golgi apparatus. However, the specific role of PKD in the human coronavirus life cycle remains unclear. In this study, we found that PKD inhibitors effectively attenuated human coronavirus replication at the trans-Golgi network (TGN) stage in the viral life cycle. Furthermore, inhibiting PKD reduced PI4KIIIβ activation, thereby blocking viral replication in the host cells. Importantly, PI4KIIIβ inhibitors also blocked human coronavirus replication. Thus, PKD may represent a promising therapeutic target against both current circulating and future emerging coronaviruses.

A knowledge graph-based framework to automate the generation of building energy models using geometric relation checking and HVAC topology establishment

Building Energy Models (BEM) are widely utilized throughout all stages of a building's lifecycle to understand and enhance energy usage. However, creating these models demands significant effort, particularly for larger buildings or those with complex HVAC systems. While a substantial amount of information can be extracted from Building Information Models (BIM) — which are increasingly accessible and provide necessary data for geometric and HVAC contexts — this information is not readily usable in setting up BEM and typically requires manual translation. To address this challenge, this paper introduces a BIM-to-BEM (BIM2BEM) framework that focuses on automating the generation of HVAC parts of BEM models from BIM data. Core to the methodology is the extraction of HVAC system topologies from the BIM model and the creation of a knowledge graph with the HVAC topology. The topology transformation unfolds in three key stages: first, a geometry-induced knowledge graph is established by examining the geometric relationships among HVAC elements; second, this graph is converted into an informative HVAC topology with enhanced properties from additional data sources; and finally, the informative topology is simplified into a BEM-oriented HVAC topology compliant with BEM platforms such as EnergyPlus. A case study of a large university building with a complex HVAC system showcases that the proposed framework achieves automatic and precise generation of building performance simulation models. The model's predictions are then validated against actual measurements from the building.

Plasmodium falciparum protein phosphatase PP7 is required for early ring-stage development.

Plasmodium falciparum causes malaria and is responsible for more than 600,000 deaths each year. Although effective drugs are available to treat disease, the spread of drug-resistant parasites endangers their future efficacy. It is hoped that a better understanding of the biology of malaria parasites will help us to discover new drugs to tackle the resistance problem. Our work is focused on the cell signaling mechanisms that control the development of the parasite throughout its complex life cycle. All signal transduction pathways are ultimately regulated by reversible protein phosphorylation by protein kinase and protein phosphatase enzymes. In this study, we investigate the function of calcium-dependent protein phosphatase PP7 and show that it is essential for the development of ring-stage parasites following the invasion of human erythrocytes. Our results contribute to the understanding of the erythrocytic stages of the parasite life cycle that cause malaria pathology.

Climate change and plant pathogens: Understanding dynamics, risks and mitigation strategies

AbstractClimate change is reshaping the interactions between plants and pathogens, exerting profound effects on global agricultural systems. Elevated tropospheric ozone levels due to climate change hinder plant photosynthesis and increase vulnerability to biotic invasion. The prevailing atmospheric conditions, including temperature and humidity, profoundly influence fungal pathogenesis, as each stage of a pathogen's life cycle is intricately linked to temperature variations. Likewise, climate change alters bacterial behaviour, fostering increased production of extracellular polysaccharides by plant‐pathogenic bacteria in warmer temperatures. Heat‐adapted bacteria, such as Burkholderia glumea and Ralstonia solanacearum, are emerging as significant global threats as temperature rise. Viruses, too, respond dynamically to climate shifts, with certain species favouring warmer climates for replication, resulting in expanded geographical ranges and modified transmission patterns. Nematodes, formidable constraints in crop production, exhibit temperature‐dependent life cycles and would have potentially accelerated proliferation and distribution as global warming progresses. Molecular‐level changes in pathogenesis, induced by temperature fluctuations, influence various pathogens, thereby impacting their virulence and interactions with host plants. Modelling studies predict changes in disease risks and distributions under future climate scenarios, highlighting the necessity of integrating climate data into crop disease models for accurate forecasts. Mechanistic and observational models illustrate pathogen behaviours amidst changing environmental conditions, providing crucial insights into future disease dynamics. In addition, controlled experiments study disease responses under simulated climate scenarios, underscoring the urgency for comprehensive research to devise effective resistance strategies against severe plant diseases.