Industrial water network vulnerability analysis using dynamic inoperability input-output model.

Industrial parks provide opportunities for Process Integration among different enterprises. Inter-Plant Water Network Integration is an effective strategy for water conservation. However, increased interplant linkages can make the entire system vulnerable to cascading failures in case of loss of water flow in some plants. The potential indirect impact of water shortages on such integrated systems may not be evident without the use of appropriate models. This work defines inoperability as the fractional loss of water flow relative to normal operations. A comparison between the applicability of demand-driven versus supply-driven Inoperability Input-output Model (IIM) is conducted. Then, a Vulnerability Assessment Framework which integrates vulnerability indicators into the Dynamic Input-Output Model (DIIM) is developed to analyse failure propagation in water networks in an industrial park. The DIIM is then applied to simulate the cascading effects of disturbances. From a time perspective, the vulnerabilities of the industrial parks With Integrated Optimal Water Network (WWN) and Without Integrated Optimal Water Network (WOWN) are assessed considering robustness, adaptability, and recoverability as the indicators. The results indicate that supply-driven IIM is more suitable for cascading failure analysis of water networks. The average inoperability at 16% from supply-driven IIM is higher than that from demand-driven IIM. In the freshwater disturbance scenario, the dependence of the plant on freshwater is proportional to the rate of inoperability change, the time to reach a new equilibrium. In this study, the robustness of WWN is about fivefold that of WOWN, but the recovery rate is only one-sixth of the latter. This work can help identify system vulnerabilities and provide a scientific insight for the development of park-wide water management strategies.

Energy, environmental, economic and social equity (4E) pressures of COVID-19 vaccination mismanagement: A global perspective.

Vaccination now offers a way to resolve the COVID-19 pandemic. However, it is critical to recognise the full energy, environmental, economic and social equity (4E) impacts of the vaccination life cycle. The full 4E impacts include the design and trials, order management, material preparation, manufacturing, cold chain logistics, low-temperature storage, crowd management and end-of-life waste management. A life cycle perspective is necessary for sustainable vaccination management because a prolonged immunisation campaign for COVID-19 is likely. The impacts are geographically dispersed across sectors and regions, creating real and virtual 4E footprints that occur at different timescales. Decision-makers in industry and governments have to act, unify, resolve, and work together to implement more sustainable COVID-19 vaccination management globally and locally to minimise the 4E footprints. Potential practices include using renewable energy in production, storage, transportation and waste treatment, using better product design for packaging, using the Internet of Things (IoT) and big data analytics for better logistics, using real-time database management for better tracking of deliveries and public vaccination programmes, and using coordination platforms for more equitable vaccine access. These practices raise global challenges but suggest solutions with a 4E perspective, which could mitigate the impacts of global vaccination campaigns and prepare sustainably for future pandemics and global warming.

A causal eco-industrial park hierarchical transition model with qualitative information: Policy and regulatory framework leads to collaboration among firms.

Eco-industrial parks promise to reduce environmental and social impacts and improve the economic performance of industrial parks. However, the transition from industrial parks to eco-industrial parks is still not well understood. This study contributes to developing valid hierarchical eco-industrial park transition attribute sets with qualitative information, as prior studies lack an exploration of the attributes in the transition of eco-industrial parks in Hungary. In nature, eco-industrial park transition attributes have causal and hierarchical interrelationships and are described with qualitative information. The assessment involves an analysis of the industrial symbiosis principles by using linguistic preferences. However, multiple attributes are involved in the assessment; therefore, this study proposes the Delphi method to develop a valid attribute set and applies fuzzy set theory to translate qualitative information into crisp values. The fuzzy decision-making trial evaluation laboratory method is used to visualize the attributes' causal interrelationships under uncertainties. The results indicate that the policy and regulatory framework leads to collaboration among firms in the eco-industrial park transition model. In practice, price reforms, management commitment, strategic planning, cognitive barriers and the integration of external information are the practical criteria for improvement. Theoretical and practical implications are also discussed.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems.

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Nef interaction with actin compromises human podocyte actin cytoskeletal integrity.

The HIV-1 accessory protein Nef is considered to play an important role in the development of a podocyte phenotype in HIV-1 associated nephropathy. We hypothesized that Nef may be altering the podocyte phenotype both structurally and functionally. To elucidate the involved mechanisms, podocyte proteins interacting with Nef were identified using GST pull down assay and yeast two hybrid assay. The GST pull down assay on protein extracts made from stable colonies of conditionally immortalized human podocytes expressing Nef (Nef/CIHP) displayed a band at 45 kD, which was identified as actin by mass spectrometry. Yeast two hybrid assay identified the following Nef-interacting proteins: syntrophin, filamin B, syntaxin, translational elongation factor 1, and zyxin. The Nef-actin and Nef-zyxin interactions were confirmed by co-localization studies on Nef/CIHP stable cell lines. The co-localization studies also showed that Nef/CIHP stable cell lines had a decreased number of actin filaments (stress fibers), displayed formation of lamellipodia, and increased number of podocyte projections (filopodia). Nef/CIHP displayed an enhanced cortical F-actin score index (P<0.001) and thus indicated a reorganization of F-actin in the cortical regions. Microarray analysis showed that Nef enhanced the expression of Rac1, syndecan-4, Rif, and CDC42 and attenuated the expression of syndecan-3 and syntenin. In addition, Nef/CIHPs displayed a diminished sphingomyelinase (ASMase) activity. Functionally, Nef/CIHPs displayed diminished attachment and enhanced detachment to their substrate. These findings indicate that Nef interaction with actin compromises the podocyte cytoskeleton integrity.

Unintended environmental consequences and co-benefits of economic restructuring.

Current economic restructuring policies have ignored unintended environmental consequences and cobenefits, the understanding of which can provide foundations for effective policy decisions for green economy transformation. Using the input-output life cycle assessment model and taking China as an example, we find that household consumption, fixed capital formation, and export are main drivers to China's environmental impacts. At the product scale, major contributors to environmental impacts vary across different types of impacts. Stimulating the development of seven strategic emerging industries will cause unintended consequences, such as increasing nonferrous metal ore usage, terrestrial acidification, photochemical oxidant formation, human toxicity, and terrestrial ecotoxicity. Limiting the surplus outputs in the construction materials industry and metallurgy industry may only help mitigate some of the environmental impacts caused by China's regulated pollutants, with little effect on reducing other impacts, such as marine eutrophication, terrestrial acidification, photochemical oxidant formation, and particulate matter formation. However, it will bring cobenefits by simultaneously reducing mineral ore usage, human toxicity, marine ecotoxicity, and terrestrial ecotoxicity. Sustainable materials management and integrated policy modeling are possible ways for policy-making to avoid unintended consequences and effectively utilize cobenefits.

Cost and water footprint trade-off in a supply chain optimization model.

Abstract: Water footprint (WF) is an appropriate tool to help any water-intensive industrial system to adapt to climate change. WF is a metric where the direct and indirect freshwater consumption of a country, firm, activity, or product are quantified. Most of existing WF literature emphasizes the assessment of products, not the optimal decision making in the supply chain. To address this research gap, a bi-objective optimization model is developed for supplier selection in a supply chain that minimizes costs and WF. Apart from determining the sources of raw materials to use in producing the products, the model also determines the actions to be taken by the firm in case of supply shortages. The model is demonstrated using three illustrative case studies which show that WF embedded in the raw materials can influence the actions to be taken when addressing issues on raw material availability. The WF becomes significant in the decisions in this bi-objective optimization problem when it is given a weight of at least 20% (or the weight of the cost is at most 80%) for case study 1 and at least 50% for case study 2. When the assigned weight in cost reaches the point where WF becomes significant, the increase in the assigned weight in WF has an inverse impact on the total cost. Case study 3 demonstrates the stochastic variant of the model. Supplementary Information: The online version contains supplementary material available at 10.1007/s10098-023-02549-5.

Network Modeling for Post-Entry Management of Invasive Pest Species in the Philippines: The Case of the Colorado Potato Beetle, Leptinotarsa decemlineata (Say, 1824) (Coleoptera: Chrysomelidae).

Crop shifting is considered as an important strategy to secure future food supply in the face of climate change. However, use of this adaptation strategy needs to consider the risk posed by changes in the geographic range of pests that feed on selected crops. Failure to account for this threat can lead to disastrous results. Models can be used to give insights on how best to manage these risks. In this paper, the socioecological process graph technique is used to develop a network model of interactions among crops, invasive pests, and biological control agents. The model is applied to a prospective analysis of the potential entry of the Colorado potato beetle into the Philippines just as efforts are being made to scale up potato cultivation as a food security measure. The modeling scenarios indicate the existence of alternative viable pest control strategies based on the use of biological control agents. Insights drawn from the model can be used as the basis to ecologically engineer agricultural systems that are resistant to pests.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

Management of status epilepticus in Malaysia: A national survey of current practice and treatment gap.

INTRODUCTION: Early and effective treatment is fundamental in status epilepticus (SE) management. At the initiative of the Epilepsy Council of Malaysia, this study aimed to determine the treatment gap in SE across different healthcare settings in Malaysia. METHODS: A web-based survey was sent to clinicians involved in the management of SE, across all states and at all levels of healthcare services. RESULTS: A total of 158 responses were received from 104 health facilities, including 23 tertiary government hospitals (95.8% of all government tertiary hospitals in Malaysia), 4 (80.0%) universities, 14 (6.7%) private, 15 (11.5%) district hospitals and 21 clinics. Intravenous (IV) diazepam was available in 14 (93.3%) district and 33 (80.5%) tertiary hospitals for prehospital management. Non-IV benzodiazepine (rectal diazepam and intramuscular midazolam) was not widely available in prehospital services (75.8% and 51.5%). Intramuscular midazolam was underutilised (60.0% in district and 65.9% in tertiary hospitals). IV sodium valproate and levetiracetam were only available in 66.7% and 53.3% of the district hospitals, respectively. Electroencephalogram (EEG) services were available in only 26.7% of the district hospitals. Non-pharmacological therapies such as ketogenic diet, electroconvulsive therapy, and therapeutic hypothermia were not available in most district and tertiary hospitals for refractory and super-refractory SE. CONCLUSIONS: We identified several gaps in the current practice of SE management, including limited availability and underutilization of non-IV midazolam in prehospital services, underutilization of non-IV midazolam and other second-line ASMs, and lack of EEG monitoring in district hospitals and limited treatment options for refractory and super-refractory SE in tertiary hospitals.

Data set on oil palm plantation production and LUC emissions under different management strategies.

Oil palm plantations are the fundamental units in a palm supply chain. The fresh fruit bunch (FFB) yield at a plantation varies based on the maturity (age) of the oil palm trees. Failure to account for the maturity can lead to a demand-supply mismatch. To address this issue, Rajakal et al. (2021) have developed a mathematical optimisation model to determine the optimal maturity of the plantations needed to meet the crude palm oil demand. This article presents the data set on the FFB production and land use change (LUC) emissions at the plantations. The model was coded and solved in LINGO 18.0. The data can be used for further investigation in optimising other related activities in a palm supply chain.

Modelling vicious networks with P-graph causality maps.

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.

Forecasting plastic waste generation and interventions for environmental hazard mitigation.

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

The lost art of short communications in academia.

Short communications are an integral part of academic journal publishing since they serve as a forum for scholarly debate on recently published journal articles. Their prestige and popularity, however, have been declining in the present academic setting. In this short note, we offer several reasons for this phenomenon.

Growth-dependent signals drive an increase in early G1 cyclin concentration to link cell cycle entry with cell growth.

Entry into the cell cycle occurs only when sufficient growth has occurred. In budding yeast, the cyclin Cln3 is thought to initiate cell cycle entry by inactivating a transcriptional repressor called Whi5. Growth-dependent changes in the concentrations of Cln3 or Whi5 have been proposed to link cell cycle entry to cell growth. However, there are conflicting reports regarding the behavior and roles of Cln3 and Whi5. Here, we found no evidence that changes in the concentration of Whi5 play a major role in controlling cell cycle entry. Rather, the data suggest that cell growth triggers cell cycle entry by driving an increase in the concentration of Cln3. We further found that accumulation of Cln3 is dependent upon homologs of mammalian SGK kinases that control cell growth and size. Together, the data are consistent with models in which Cln3 is a crucial link between cell growth and the cell cycle.

Joint Feature Optimization and Fusion for Compressed Action Recognition.

Recent methods including CoViAR and DMC-Net provide a new paradigm for action recognition since they are directly targeted at compressed videos (e.g., MPEG4 files). It avoids the cumbersome decoding procedure of traditional methods, and leverages the pre-encoded motion vectors and residuals in compressed videos to complete recognition efficiently. However, motion vectors and residuals are noisy, sparse and highly correlated information, which cannot be effectively exploited by plain and separated networks. To tackle these issues, we propose a joint feature optimization and fusion framework that better utilizes motion vectors and residuals in the following three aspects. (i) We model the feature optimization problem as a reconstruction process that represents features by a set of bases, and propose a joint feature optimization module that extracts bases in the both modalities. (ii) A low-rank non-local attention module, which combines the non-local operation with the low-rank constraint, is proposed to tackle the noise and sparsity problem during the feature reconstruction process. (iii) A lightweight feature fusion module and a self-adaptive knowledge distillation method are introduced, which use motion vectors and residuals to generate predictions similar to those from networks with optical flows. With these proposed components embedded in a baseline network, the proposed network not only achieves the state-of-the-art performance on HMDB-51 and UCF-101, but also maintains its advantage in computational complexity.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis.

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts' evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

Socio-ecological network structures from process graphs.

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.