Whole System Research Articles

Receptor-like proteins: decision-makers of plant immunity

AbstractReceptor-like proteins (RLPs) are crucial pattern-recognition receptors on the surface of plant cells, which are involved in almost all processes of the plant life cycle. Recently, the evolution of high-throughput sequencing technology has strengthened the appraisal and identification of increasing numbers of RLPs and has primarily improved our understanding of the roles of RLPs in various biological processes. Here, we review the classification and evolutionary characteristics of RLPs and their regulatory roles in pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). In particular, we summarize the ligands recognized by RLPs, their co-receptors, and downstream signalling cascades mediated by RLPs. To summarize, this review offers beneficial guidance for researchers in at-a-glance comprehension of the function of RLPs. It also puts forward the prospect of mining broad-spectrum candidate genes in light of the research on the disease resistance mechanism of RLPs and current challenges in disease resistance breeding.

Life Cycle Assessment and Process Optimization of Precipitated Nanosilica—A Case Study in China

To mitigate environmental emissions in the industrial nanosilica sector and promote its sustainable development, the life cycle assessment (LCA) method is employed to evaluate the environmental impacts throughout the life cycle of industrial precipitated nanosilica. This LCA spans from the acquisition and transportation of raw materials to the production of nanosilica. By identifying the critical contributing factors, effective optimization strategies have been proposed to enhance the environmental performance of the nanosilica life cycle. The effects of electricity, alkalis, acids, and steam on the life cycle emission factors of nanosilica were examined. The results indicate that substituting traditional coal power and steam with cleaner alternatives like wind energy, hydroelectric power, and solar power (both photovoltaic and thermal), as well as biogas steam, can lead to a significant reduction in the life cycle emission factors of nanosilica, ranging from 50% to 90%. Notably, the types of acids and alkalis used only significantly reduce certain environmental factors. These findings provide valuable theoretical insights and practical guidance for the industrial nanosilica sector, particularly in the areas of energy conservation, emission reduction, and the transition towards a lower-carbon economy.

Apple Pomace Integrated Biorefinery for Biofuels Production: A Techno-Economic and Environmental Sustainability Analysis

The combination of techno-economic process modelling and life cycle assessment is an integrated methodology that addresses quantitative operational data, and evaluates the emissions associated with any process under development. In particular, the valorisation of waste streams within the context of the circular economy could be considered a valid and promising approach, especially regarding techno-economic and environmental indicators. This manuscript aims to evaluate the integral valorisation of apple pomace from the processing industry into bioethanol, and vinasses (a byproduct of the distillation process) into biogas and digestate as biofertiliser. In addition to biogas production, lagooning and composting were considered as strategies for vinasse management. After the conceptual design of the process options was completed, the environmental profile of bioethanol production was estimated across different scenarios. When biogas production was integrated to reduce the biorefinery’s energy demand, the carbon footprint was 1.13 kg CO2eq∙kg−1. This footprint increased to values around four when lagooning and composting were used as vinasse management strategies. Although the economic dimension posed a significant limitation due to high investment costs, the eco-efficiency analysis showed that the scenario of the co-production of bioethanol and biogas is the best alternative. Despite the promising results, further research is needed to explore the recovery of additional co-products to develop a high-potential strategy for apple pomace.

Data and model synergy-driven rolling bearings remaining useful life prediction approach based on deep neural network and Wiener process

Abstract Various Remaining Useful Life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling bearing. Hybrid methods that integrate the advantages of model-based and data-driven approaches have garnered significant attention. However, the effective integration of the two methods to address the randomness in rolling bearing full lifecycle processes remains a significant challenge. To overcome the challenge, this paper proposes a data and model synergy-driven RUL prediction framework that includes two data and model synergy approaches. Firstly, a convolutional stacked bidirectional long and short-term memory network with temporal attention mechanism is established to construct health index. The RUL prediction is achieved based on HI and polynomial model. Secondly, a three-phase prediction model based on the Wiener process is established by considering the evolution law of different degradation phases. Then, two synergy methods are designed. Strategy 1: HI is adopted as the observation value for online updating of physics degradation model parameters under Bayesian framework, and the RUL prediction results are obtained from the physics degradation model. Strategy 2: The RUL prediction results from the data-driven and physics-based model are weighted linearly combined to improve the overall prediction accuracy. The effectiveness of the proposed model is verified using two bearing full lifecycle datasets. The results indicate that the proposed approach can accommodate both short-term and long-term RUL predictions, outperforming state-of-the-art single models.

Identification and characterization of host factor VCPIP1 as a multi-functional positive regulator of hepatitis B virus.

Hepatitis B virus (HBV) encodes the regulatory protein HBx that plays crucial roles in viral life cycle and cellular processes through interacting with viral and cellular proteins. Identifying HBx-interacting proteins may reveal novel aspects of host-virus interactions. In this work, proximity labeling coupled with proteomic analysis identified multiple HBx-interacting host factors, and among these, valosin-containing protein-interacting protein 1 (VCPIP1) was confirmed to directly bind HBx and reduce its proteasomal degradation, corroborating a recent report. In addition to upregulating HBx-expressing HBV, we showed that VCPIP1 also positively regulates mutant HBV lacking HBx expression. This novel HBx-independent function of VCPIP1 was shown to involve its association with one viral promoter/enhancer element, which upregulated the latter's promoter and enhancer activities. These results establish VCPIP1 as a positive regulator of HBV that acts through multiple, diverse mechanisms and might also contribute toward revealing novel cellular functions of VCPIP1.

Ionic Liquid-Based Green Solvents for Extraction and Purification of Natural Plant Products

Introduction: This research paper explores the environmental sustainability of ionic liquid-based green solvents in the extraction and purification of natural plant products, with a focus on their entire life cycle. The objectives of the study were to assess the environmental impact of ionic liquid synthesis, energy consumption, water usage, emissions, recycling rates, policy effects, and stakeholder perceptions. Methods: Methodologically, we conducted a comprehensive Life Cycle Assessment (LCA) that involved primary data collection through field surveys and interviews with key stakeholders in the ionic liquid production and usage industry across various regions in India. The data were analyzed using specialized LCA software tools to quantify environmental impacts. Key findings include the identification of synthesis as a major contributor to environmental impact, emphasizing the need for greener synthesis methods. Results: The study revealed the significant carbon footprint, energy consumption, and water usage during production, highlighting opportunities for improvement. Emissions data underscored the importance of emission control measures, particularly for greenhouse gases and volatile organic compounds. Recycling and reuse were identified as environmentally friendly disposal methods. Policy compliance varied among stakeholders, indicating room for stricter regulations. Stakeholder perceptions varied, with researchers having the most positive outlook. Implications of the findings extend to sustainable chemistry practices, emphasizing interdisciplinary collaboration and the importance of considering the entire life cycle of chemical processes. Conclusion: This research contributes to a deeper understanding of green solvents and provides a foundation for promoting sustainable practices in industrial processes in India and globally.

Fog Computing Enabled Adaptive Prognosis of Cutting Tool Remaining Life through Multi-source Data

Abstract Cutting tool remaining life during machining processes is essential for achieving sustainable production. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilising multi-source data to address these challenges effectively. The key innovations include: (1) The proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardised data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life, best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite Element Analysis (FEA) is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimisation mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64% and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in enhancing the operational efficiency and reliability of manufacturing systems is validated through advanced data integration and deep learning techniques.

Integrating Process Simulation and Life Cycle Assessment for Enhanced Process Efficiency and Reduced Environmental Impact in Ferromanganese Production

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO2 emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO2 emissions.

Synoptic Scale Controls and Aerosol Effects on Fog and Low Stratus Life Cycle Processes in the Po Valley, Italy

AbstractFog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Research on the Application of Digital Twin Technology in Equipment Maintenance

Digital twin technology is to make full use of the data such as physical model, sensor update, running history, integrate the multi-subject multi-physical quantity, multi-scale, multi-probability simulation process, complete the mapping in virtual space, thus reflect the whole life cycle process of the corresponding entity equipment. In this paper, the digital twin of a certain type of equipment system is studied to realize the operation condition monitoring, fault intelligent diagnosis and so on, so as to improve the maintenance efficiency and effect of the equipment.

Modular Microgrid Technology with a Single Development Environment Per Life Cycle

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project’s duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc.

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Extending cutting tool remaining life through deep learning and laser shock peening remanufacturing techniques

Predicting and extending the remaining life of cutting tools during machining processes is crucial for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to diverse working conditions throughout the machining process lifecycle. This paper introduced a comprehensive framework that effectively addressed the challenges by integrating multi-source data and using deep learning techniques. The system integrated power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines with the following innovations: (1) A standardized data fusion method was developed to integrate multi-source data sources, the hybrid graph convolutional network (GCN) with attention mechanisms was employed to improve the prognosis accuracy of cutting tool remaining life, best accuracy of 98.56% and average accuracy of 97.71% were achieved. (2) The optimization of laser shock peening (LSP) remanufacturing parameters using the bees algorithm showed good performance, a fitness value of 0.95 was achieved with convergence within 15 iterations. (3) Monitoring of the LSP remanufacturing process was designed based on sound and vibration data for optimal remanufacturing performance. (4) The remanufacturing approach in extending the remaining life of cutting tool was validated through FEA analysis and experimental testing, cutting tool life was extended by 29.32% to achieve a sustainable manufacturing process.

Anticipatory Thinking and AI-Driven Assessments: A Balanced Approach to AI Integration in Education Aligned with Saudi Vision 2030

Anticipatory Thinking and AI-Driven Assessments: A Balanced Approach to AI Integration in Education Aligned with Saudi Vision 2030

The Impact of Retrofitting Natural Gas-Fired Power Plants on Carbon Footprint: Converting from Open-Cycle Gas Turbine to Combined-Cycle Gas Turbine

Since retrofitting existing natural gas-fired (NGF) power plants is an essential strategy for enhancing their efficiency and controlling greenhouse gas emissions, this paper compares the carbon footprint of natural gas-fired power generation from an NGF power plant in Brazil (BR-NGF) with and without retrofitting. The former scenario entails retrofitting the BR-NGF power plant with combined-cycle gas turbine (CCGT) technology. In contrast, the latter involves continuing the BR-NGF power plant operation with open-cycle gas turbine (OCGT) technology. Our analysis considers the BR-NGF power plant’s life cycle (construction, operation, and decommissioning) and the natural gas’ life cycle (natural gas extraction and processing, liquefaction, liquefied natural gas transportation, regasification, and combustion). Moreover, it is based on data from primary and secondary sources, mainly the Ecoinvent database and the ReCiPe 2016 method. For OCGT, the results showed that the BR-NGF power plant and the natural gas life cycles are responsible for 620.87 gCO2eq./kWh and 178.58 gCO2eq./kWh, respectively. For CCGT, these values are 450.04 gCO2eq./kWh and 129.30 gCO2eq./kWh. Our findings highlight the relevance of the natural gas’ life cycle, signaling additional opportunities for reducing the overall carbon footprint of natural gas-fired power generation.

Exploring resource recovery from diverted organics: Life cycle assessment comparison of options for managing the organic fraction of municipal solid waste

Diversion of the organic fraction of municipal solid waste (OFMSW) from landfills is increasing. Previous life cycle assessment studies have evaluated subsets of OFMSW management options, but conclusions are inconsistent, and none have evaluated diverse applications of material by-products. The primary objective of this work was to identify sustainability-based improvements to the selection, design, implementation, and operation of organics waste diversion management technologies. Process modeling and life cycle assessment were used to compare OFMSW composting, anaerobic digestion, and pyrolysis, with biochar used as a landfill cover, leachate treatment sorbent, and land applicant. Material and energy flows, calculated by newly developed models for the defined functional unit (1 kg MSW over a 20-year timeframe), were translated to environmental performance using ecoinvent and USLCI databases and TRACI method. Additionally, uncertainty, sensitivity, and scenario analyses were conducted to evaluate the implications of model uncertainties, design decisions, and resource recovery tradeoffs. OFMSW pyrolysis usually (65 % of uncertainty assessment simulations) had the best global warming performance mostly due to energy recovery and biochar's carbon sequestration benefit, which was independent of fate. Pyrolyzing the biosolids from OFMSW anaerobic digestion recovered the most energy and had the best performance in 34 % of uncertainty simulations. Material recovery amounts were large (e.g., more biochar was produced than required for novel uses) and warrant feasibility considerations. Global warming performance was more sensitive to uncertainty in carbon sequestration and primary energy production than in fertilizer offset, energy conversion, or heat offset approach. Practical implications include the potential for biochar supply to outweigh demand, and inconsistent revenue from the sale of recovered energy and carbon credits; future research could focus on evaluating the relative social and economic sustainability of the OFMSW management technologies.

Efficient arsenic removal from water using iron-impregnated low-temperature biochar derived from henequen fibers: performance, mechanism, and LCA analysis

The present study aims to investigate the low-energy consumption and high-efficiency removal of arsenic from aqueous solutions. The designed adsorbent Fe/TBC was synthesized by impregnating iron on torrefaction henequen fibers. Isothermal adsorption experiments indicated maximum adsorption capacities of 7.30 mg/g and 8.98 mg/g for arsenic(V) at 25.0 °C and 40.0 °C, respectively. The interference testing showed that elevated levels of pH, HCO3− concentration, and humic acid content in the solution could inhibit the adsorption of arsenic by Fe/TBC. Characterization of the adsorbent before and after adsorption using FTIR and SEM–EDS techniques confirmed arsenic adsorption mechanisms, including pore filling, electrostatic interaction, surface complexation, and H-bond adhesion. Column experiments were conducted to treat arsenic-spiked water and natural groundwater, with effective treatment volumes of 550 mL and 8792 mL, respectively. Lastly, the life cycle assessment (LCA) using OpenLCA 2.0.3 software was performed to treat 1 m3 of natural groundwater as the functional unit. The results indicated relatively significant environmental impacts during the Fe/TBC synthesis stage. The global warming potential resulting from the entire life cycle process was determined to be 0.8 kg CO2-eq. The results from batch and column experiments, regeneration studies, and LCA analysis indicate that Fe/TBC could be a promising adsorbent for arsenic(V).

Evaluation of two acrylic acid production processes from renewable crude glycerol: Rigorous process design, techno-economic evaluation, and life cycle assessment

This study aims to propose and evaluate two acrylic acid (AA) production processes that utilize bio-based crude glycerol as a feedstock. Scheme 1 employs acrolein as an intermediate, while Scheme 2 utilizes allyl alcohol as an intermediate. For process intensification, Scheme 1 employs a one-pot reactor configuration that couples the glycerol dehydration and oxidation reactions and uses an additional reactor to suppress the formation of side products. Scheme 2 implements a membrane that helps prevent the multiple azeotropic distillation between water and other substances. Furthermore, both processes were systematically optimized based on varying levels of information available in the process (i.e., three objectives for Scheme 1, single objective for Scheme 2). From a techno-economic evaluation, Scheme 2 (2.136 USD/kg) slightly outperforms Scheme 1 (2.514 USD/kg) in terms of the minimum required selling price (MRSP). Both values are significantly higher than the market price (1.24–1.32 USD/kg) based on the conventional process that uses propene as a feedstock. Subsequently, a cradle-to-gate life cycle assessment was conducted to compare these processes across five impact categories (i.e., global warming potential, fossil source scarcity, human non-carcinogenic toxicity, water consumption, and terrestrial acidification). We have observed that Scheme 1 can be more sustainable than Scheme 2 and the conventional process if the issue associated with the acrolein left in the wastewater can be addressed. In contrast, Scheme 2 is far from environmentally friendly even compared to the conventional process, primarily because of the use of formic acid as a co-reactant.

A micro‐level model for crisis management in tourism destinations: An interdisciplinary approach

AbstractCrisis management models aim to prevent, prepare and successfully overcome a crisis. The tourism industry is particularly prone to crises and thus requires adequate models to deal with crises and disasters. Several researchers developed crisis management models for tourism crises. As scholars call for a more holistic approach, this systematic literature review examines 25 crisis management models with organisational and tourism focus between 1979 and 2019 applicable to the tourism field. A textual narrative synthesis is used as methodological approach to analyse and understand the identified models, not only formally but also content‐wise. The findings show that crisis terminology is not consistent, that the crisis lifecycle and crisis management processes are used disparately and that stakeholder involvement within the models is low. As a result, a new model for destination crisis management is created on a micro‐level with aligned terms and phases.