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Emerging infectious diseases of wildlife have markedly increased in the last few decades. Unsustainable, continuous, and rapid alterations within and between coupled human and natural systems have significantly disrupted wildlife disease dynamics. Direct and indirect anthropogenic effects, such as climate change, pollution, encroachment, urbanization, travel, and trade, can promote outbreaks of infectious diseases in wildlife. We constructed a coupled human and natural systems framework identifying three main wildlife disease risk factors behind these anthropogenic effects: (i) immune suppression, (ii) viral spillover, and (iii) disease propagation. Through complex and convoluted dynamics, each of the anthropogenic effects and activities listed in our framework can lead, to some extent, to one or more of the identified risk factors accelerating disease outbreaks in wildlife. In this review, we present a novel framework to study anthropogenic effects within coupled human and natural systems that facilitate the emergence of infectious disease involving wildlife. We demonstrate the utility of the framework by applying it to Fibropapillomatosis disease of marine turtles. We aim to articulate the intricate and complex nature of anthropogenically exacerbated wildlife infectious diseases as multifactorial. This paper supports the adoption of a One Health approach and invites the integration of multiple disciplines for the achievement of effective and long-lasting conservation and the mitigation of wildlife emerging diseases.

Abstract Background Porcine reproductive and respiratory syndrome (PRRS) is a threat to pig production worldwide. Our objective was to understand mechanisms of persistence of PRRS virus (PRRSV) in tonsil. Transcriptome data from tonsil samples collected at 42 days post infection (dpi) were generated by RNA-seq and NanoString on 51 pigs that were selected to contrast the two PRRSV isolates used, NVSL and KS06, high and low tonsil viral level at 42 dpi, and the favorable and unfavorable genotypes at a genetic marker (WUR) for the putative PRRSV resistance gene GBP5. Results The number of differentially expressed genes (DEGs) differed markedly between models with and without accounting for cell-type enrichments (CE) in the samples that were predicted from the RNA-seq data. This indicates that differences in cell composition in tissues that consist of multiple cell types, such as tonsil, can have a large impact on observed differences in gene expression. Based on both the NanoString and the RNA-seq data, KS06-infected pigs showed greater activation, or less inhibition, of immune response in tonsils at 42 dpi than NVSL-infected pigs, with and without accounting for CE. This suggests that the NVSL virus may be better than the KS06 virus at evading host immune response and persists in tonsils by weakening, or preventing, host immune responses. Pigs with high viral levels showed larger CE of immune cells than low viral level pigs, potentially to trigger stronger immune responses. Presence of high tonsil virus was associated with a stronger immune response, especially innate immune response through interferon signaling, but these differences were not significant when accounting for CE. Genotype at WUR was associated with different effects on immune response in tonsils of pigs during the persistence stage, depending on viral isolate and tonsil viral level. Conclusions Results of this study provide insights into the effects of PRRSV isolate, tonsil viral level, and WUR genotype on host immune response and into potential mechanisms of PRRSV persistence in tonsils that could be targeted to improve strategies to reduce viral rebreaks. Finally, to understand transcriptome responses in tissues that consist of multiple cell types, it is important to consider differences in cell composition.

Abstract Building UV completions of lepton-number-violating effective operators has proved to be a useful way of studying and classifying models of Majorana neutrino mass. In this paper we describe and implement an algorithm that systematises this model-building procedure. We use the algorithm to generate computational representations of all of the tree-level completions of the operators up to and including mass-dimension 11. Almost all of these correspond to models of radiative neutrino mass. Our work includes operators involving derivatives, updated estimates for the bounds on the new-physics scale associated with each operator, an analysis of various features of the models, and a look at some examples. We find that a number of operators do not admit any completions not also generating lower-dimensional operators or larger contributions to the neutrino mass, ruling them out as playing a dominant role in the neutrino-mass generation. Additionally, we show that there are at most five models containing three or fewer exotic multiplets that predict new physics that must lie below 100 TeV. Accompanying this work we also make available a searchable database containing all of our results and the code used to find the completions. We emphasise that our methods extend beyond the study of neutrino-mass models, and may be useful for generating completions of high-dimensional operators in other effective field theories. Example code: ref. [37].

Carbon footprint analysis of geopolymer concrete can be used to determine its advantages over conventional Portland cement concrete. However, the influence of allocation assumptions has been neglected in previous geopolymer life cycle assessment studies. This research gap is addressed here through an analysis of the effect of allocation scenarios in the assessment of self-healing geopolymer concrete made from coal fly ash and ground granulated blast furnace slag feedstocks. In addition, an empirical, variable-mix “gray box” model was integrated into the life cycle assessment to allow different blends that meet product property specifications to be considered. The cradle-to-gate life cycle assessment was done using OpenLCA and MS Excel, using inventory data from databases and literature. The allocation assumptions are found to significantly affect the results, with carbon footprints ranging from 208.72 kg eq. CO2 to 395.72 kg eq. CO2 per cubic meter of concrete. Using allocation based on economic value, the price of coal fly ash has a greater effect than that of ground granulated blast furnace slag. The implications of this result on the commercial use of geopolymer concrete are discussed, as well as the potential application of the “gray box” approach as a generic methodology in life cycle assessment.

Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n = 49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4 ± 1.9 ms vs. 12.8 ± 2.1 ms during active hours; n = 47, analysis of variance (ANOVA) q = 0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58 ± 0.2 vs. 0.45 ± 0.12, respectively, adj. p = 0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.

The use of mosquito repellents is an efficient way to prevent mosquito-borne diseases. Despite the accumulation of information about repellents, there remains the challenge of the lack of understanding of their mechanism of action. There is also a need for systematic methods for discovering new alternatives that mitigate the drawbacks of repellents currently in use. To address these research gaps, a computer-aided molecular design (CAMD) framework is developed for the optimal molecular design of mosquito repellents. In this framework, the mosquito repelling attribute of molecules are predicted using a data-driven hyperbox-based machine learning approach in the absence of a mechanistic prediction model. The best set of rules is selected from plausible alternative models developed. For the prediction of important physical properties, a group contribution-based method using reliable models is implemented. Subsequently, the CAMD formulation is developed as a mixed-integer linear programming model to obtain structures with minimum viscosity. Results show that of the structures generated, the hyperbox classifier correctly predicted the repelling ability of all molecules found to be known repellents in literature. The molecules not found in the databases provide key insights on where experimental research to develop new repellents should be targeted. Thus, this newly developed framework can be applied as a systematic technique to screen and narrow down the search space for candidate mosquito repellent molecules before final experimental verification.

Rationale & Objective: Having a usual source of care increases use of preventive services and is associated with improved survival in the general population. We evaluated this association in adults with chronic kidney disease (CKD). Study Design: Prospective, observational cohort study. Setting & Participants: Adults with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study. Predictor: Usual source of care was self-reported as: 1) clinic, 2) emergency department (ED)/urgent care, 3) other. Outcomes: Primary outcomes included incident end-stage kidney disease (ESKD), atherosclerotic events (myocardial infarction, stroke, or peripheral artery disease), incident heart failure, hospitalization events, and all-cause death. Analytical Approach: Multivariable regression analyses to evaluate the association between usual source of care (ED/urgent care vs clinic) and primary outcomes. Results: Among 3,140 participants, mean age was 65 years, 44% female, 45% non-Hispanic White, 43% non-Hispanic Black, and 9% Hispanic, mean estimated glomerular filtration rate 50 mL/min/1.73 m2. Approximately 90% identified clinic as usual source of care, 9% ED/urgent care, and 1% other. ED/urgent care reflected a more vulnerable population given lower baseline socioeconomic status, higher comorbid condition burden, and poorer blood pressure and glycemic control. Over a median follow-up time of 3.6 years, there were 181 incident end-stage kidney disease events, 264 atherosclerotic events, 263 incident heart failure events, 288 deaths, and 7,957 hospitalizations. Compared to clinic as usual source of care, ED/urgent care was associated with higher risk for all-cause death (HR, 1.53; 95% CI, 1.05-2.23) and hospitalizations (RR, 1.41; 95% CI, 1.32-1.51). Limitations: Cannot be generalized to all patients with CKD. Causal relationships cannot be established. Conclusions: In this large, diverse cohort of adults with moderate-to-severe CKD, those identifying ED/urgent care as usual source of care were at increased risk for death and hospitalizations. These findings highlight the need to develop strategies to improve health care access for this high-risk population.

Purpose – This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies. Design/methodology/approach – Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices. Findings – The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy. Originality/value – This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.

Introduction: Subclinical changes to cardiac structure and function detected with echocardiography precede the development of clinical heart failure (HF) in persons with chronic kidney disease (CKD). Circulating cardiac biomarkers may reflect these pathophysiological changes. This study investigated associations between established biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP] and high-sensitivity troponin T [hsTnT]) and novel biomarkers (growth differentiation factor 15 [GDF-15], galectin-3 [Gal-3], and soluble ST-2 [sST-2]), using echocardiographic measurements in persons with CKD. Methods: In cross-sectional analyses among 2101 participants with mild to moderate CKD in the Chronic Renal Insufficiency Cohort (CRIC), biomarker levels measured at baseline were evaluated with echocardiographic measurements 1 year later. These included left ventricular mass index (LVMI), left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), and left atrial diameter (LAD). Multivariable linear regression analyses tested associations of each biomarker with echocardiographic measurements, adjusting for covariates. Results: GDF-15 was significantly associated with higher LVMI (1.0 g/m2.7; 95% CI, 0.4–1.7), LVESV (0.4 ml/m2.7; 95% CI, 0.0–0.7), and LVEDV (0.6 ml/m2.7; 95% CI, 0.1–1.1), but not with LVEF or LAD. These findings were not significant when adjusting for NT-proBNP and hsTnT. Gal-3 and sST-2 had no significant associations. Higher levels of NT-proBNP and hsTnT were associated with all echocardiographic measurements. Conclusion: In patients with CKD, the novel biomarker GDF-15, a marker of inflammation and tissue injury, and clinical biomarkers NT-proBNP and hsTnT, were associated with echocardiographic measurements of subclinical cardiovascular disease. Collectively, these biomarkers may highlight biological pathways that contribute to the development of clinical HF.

Abstract We study the phenomenology of a hypercharge-zero SU (2) triplet scalar whose existence is motivated by two-step electroweak symmetry-breaking. We consider both the possibility that the triplets are stable and contribute to the dark matter density, or that they decay via mixing with the standard model Higgs boson. The former is constrained by disappearing charged track searches at the LHC and by dark matter direct detection experiments, while the latter is constrained by existing multilepton collider searches. We find that a two-step electroweak phase transition involving a stable triplet with a negative quadratic term is ruled out by direct detection searches, while an unstable triplet with a mass less than 230 GeV is excluded at 95% confidence level.

La Comisión Lancet de Hipertensión determinó que una medida clave para responder a la carga mundial que representa la hipertensión arterial era mejorar la calidad de las mediciones de la presión arterial, mediante la utilización de dispositivos cuya exactitud haya sido validada. En la actualidad existen 3000 dispositivos comercializados, pero muchos no tienen datos publicados sobre pruebas de exactitud conformes a las normas científicas establecidas. La falta de regulación o su ineficiencia, que permiten la autorización de dispositivos para uso comercial sin una validación oficial, posibilitan este problema. Además, han surgido tecnologías nuevas de medición de la presión arterial (por ejemplo, los sensores sin brazalete) sobre las cuales no existe unanimidad en la comunidad científica con respecto a las normas de exactitud de la medición. En conjunto, estos aspectos contribuyen a la disponibilidad generalizada de tensiómetros de consultorio o domiciliarios que ofrecen una exactitud limitada o incierta, que llevan a diagnósticos, manejo y farmacoterapia inapropiados de la hipertensión a escala mundial. Los problemas más importantes relacionados con la exactitud de los dispositivos de medición de la presión arterial se pueden resolver mediante el requisito regulatorio de una validación independiente obligatoria de los dispositivos, en consonancia con la norma ISO universalmente aceptada. Esta es una recomendación básica y constituye una necesidad internacional acuciante. Otras recomendaciones clave son la elaboración de normas de validación específicas para las tecnologías nuevas de medición de la presión arterial y la publicación en línea de listas de los dispositivos nuevos exactos que están a la disposición de los usuarios y los profesionales de salud. Las recomendaciones están en consonancia con las políticas de la Organización Mundial de la Salud sobre los dispositivos médicos y la atención universal de la salud. El cumplimiento de las recomendaciones aumentará la disponibilidad mundial de dispositivos de medición de la presión arterial que sean exactos y tendrá como efecto un mejor diagnóstico y tratamiento, reduciendo así la carga mundial de la hipertensión.

Abstract We present the first detailed phenomenological analysis of a radiative Majorana neutrino mass model constructed from opening up a ∆L = 2 mass-dimension-11 effective operator constructed out of standard model fields. While three such operators are generated, only one dominates neutrino mass generation, namely O 47 = L C ¯ L Q C ¯ L Q ¯ Q C HH , $$ {O}_{47}=\overline{L^C}L\overline{Q^C}L\overline{Q}{Q}^C HH, $$ where L denotes lepton doublet, Q quark doublet and H Higgs doublet. The under- lying renormalisable theory contains the scalars S 1 ∼ 3 ¯ 1 1 / 3 $$ {S}_1\sim \left(\overline{3},1,1/3\right) $$ coupling as a diquark, S 3 ∼ 3 ¯ 3 1 / 3 $$ {S}_3\sim \left(\overline{3},3,1/3\right) $$ coupling as a leptoquark, and Φ3 ∼ (3, 3, 2/3), which has no Yukawa couplings but does couple to S 1 and S 3 in addition to the gauge fields. Neutrino masses and mixings are generated at two-loop order. A feature of this model that is different from many other radiative models is the lack of proportionality to any quark and charged- lepton masses of the neutrino mass matrix. One consequence is that the scale of new physics can be as high as 107 TeV, despite the operator having a high mass dimension. This raises the prospect that ∆L = 2 effective operators at even higher mass dimensions may, when opened up, produce phenomenologically-viable radiative neutrino mass models. The parameter space of the model is explored through benchmark slices that are subject to experimental constraints from charged lepton flavour-violating decays, rare meson de- cays and neutral-meson mixing. The acceptable parameter space can accommodate the anomalies in R K (∗) and the anomalous magnetic moment of the muon.

IntroductionRepresentation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream tasks. It was hypothesised that wave segmentation would be a useful form of electrocardiogram (ECG) representation learning. In addition to reducing labelled data requirements, segmentation masks may provide a mechanism for explainable AI. This study details the development and evaluation of a Wave Segmentation Pretraining (WaSP) application.Materials and MethodsPretraining: A non-AI-based ECG signal and image simulator was developed to generate ECGs and wave segmentation masks. U-Net models were trained to segment waves from synthetic ECGs. Dataset: The raw sample files from the PTB-XL dataset were downloaded. Each ECG was also plotted into an image. Fine-tuning and evaluation: A hold-out approach was used with a 60:20:20 training/validation/test set split. The encoder portions of the U-Net models were fine-tuned to classify PTB-XL ECGs for two tasks: sinus rhythm (SR) vs atrial fibrillation (AF), and myocardial infarction (MI) vs normal ECGs. The fine-tuning was repeated without pretraining. Results were compared. Explainable AI: an example pipeline combining AI-derived segmentation masks and a rule-based AF detector was developed and evaluated.ResultsWaSP consistently improved model performance on downstream tasks for both ECG signals and images. The difference between non-pretrained models and models pretrained for wave segmentation was particularly marked for ECG image analysis. A selection of segmentation masks are shown. An AF detection algorithm comprising both AI and rule-based components performed less well than end-to-end AI models but its outputs are proposed to be highly explainable. An example output is shown.ConclusionWaSP using synthetic data and labels allows AI models to learn useful features for downstream ECG analysis with real-world data. Segmentation masks provide an intermediate output that may facilitate confidence calibration in the context of end-to-end AI. It is possible to combine AI-derived segmentation masks and rule-based diagnostic classifiers for explainable ECG analysis.

High-entropy alloys (HEAs) are materials which leverage the entropy of mixing to motivate the formation of single-phase solid solutions, even of immiscible elements. While these materials are well-recognized for their application to structural engineering, there is increasing interest in the use of HEAs for functional applications such as memory storage and energy devices. The current work investigates the HEA Al1.3CoCrCuFeNi, which has been previously shown to be single-phase at high temperatures, but undergoes phase separation at lower temperatures, transforming the structural and the functional properties. This phase separation is investigated at high temperatures with in-situ small angle neutron scattering (SANS) and scanning transmission electron microscopy (EDS). These techniques show that increasing the temperature up to 800°C, the microstructure of the HEA adiabatically disorders and abruptly homogenizes near 700°C, which is consistent with spinodal decomposition. Overall, the microstructural evolution proceeds mainly by the atomistic redistribution of the constituent elements within simple crystal lattices, producing coherent phase mixtures.

Abstract Research on engineered materials that integrate different 2D crystals has largely focused on two prototypical heterostructures: Vertical van der Waals stacks and lateral heterostructures of covalently stitched monolayers. Extending lateral integration to few layer or even multilayer van der Waals crystals could enable architectures that combine the superior light absorption and photonic properties of thicker crystals with close proximity to interfaces and efficient carrier separation within the layers, potentially benefiting applications such as photovoltaics. Here, the realization of multilayer heterstructures of the van der Waals semiconductors SnS and GeS with lateral interfaces spanning up to several hundred individual layers is demonstrated. Structural and chemical imaging identifies {110} interfaces that are perpendicular to the (001) layer plane and are laterally localized and sharp on a 10 nm scale across the entire thickness. Cathodoluminescence spectroscopy provides evidence for a facile transfer of electron‐hole pairs across the lateral interfaces, indicating covalent stitching with high electronic quality and a low density of recombination centers.

Hepatitis B virus (HBV) infects human and causes chronic liver infection, leading to liver cirrhosis and hepatocellular carcinoma. HBV X (Hbx) protein is known to interact with tumor suppressor protein p53 and block its translocation into the nucleus. This study outlines the overproduction of Hbx protein from HBV subgenotype B3 in Escherichia coli BL21(DE3), including its purification and refolding. The gene encoding Hbx was first codon-optimized and inserted into pET16b. The recombinant plasmid was then transformed into E. coli BL21(DE3) as an expression host. Optimization of Hbx expression was performed with variation of IPTG concentration and overproduction temperature. The results showed that Hbx protein was optimally induced by 0.075 mM IPTG and overproduction of Hbx at 17, 25, and 37°C exhibited no difference in protein level and location. The optimal refolding of Hbx was obtained using 0.1 M arginine prior to elution from Nickel column using 100 mM imidazole and 0.25 M arginine. Hbx migrates differently in SDS-PAGE reducing and non-reducing, while the melting curve pattern in TSA analysis changed after the refolding step. Essentially, this purified Hbx protein could potentially be used for interaction study with p53 and the inhibitor candidate of the protein.

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meeting the net-zero emissions target by the year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. For the more conservative Scenario 1, in which CCS is only available in later years, and other NETs are assumed not to be available, all coal plants were replaced with biomass. Meanwhile, only 38% of natural gas plants are CCS retrofitted. The remaining natural gas plants are replaced with biogas. For the more aggressive Scenario 2, which includes all mitigation technologies, once again, all coal plants undergo fuel substitution. However, close to half of the natural gas plants are CCS retrofitted. The results demonstrated the potential of fuel substitutions for low-carbon alternatives in existing coal and natural gas power plants. Additionally, once NETs are mature and are available for commercial deployment, their deployment is crucial in aiding CO2 removal in minimal investment costs scenarios. However, the results indicate that the deployment of energy-producing NETs (EP-NETs), e.g., biochar and biomass with CCS, are far more beneficial in CO2 removal versus energy-consuming NETs (EC-NETs), e.g., enhanced weathering. The newly developed open-source software demonstrates the importance of determining the optimal deployment of mitigation technologies in meeting climate change targets for each period, as well as driving the achievement of net-zero emissions by mid-century.

The artificial urinary sphincter (AUS) is an effective treatment option for incontinence due to intrinsic sphincteric deficiency in the context of neurogenic lower urinary tract dysfunction, or stress urinary incontinence following radical prostatectomy. A subset of AUS devices develops infection and requires explant. We sought to characterize biofilm composition of the AUS device to inform prevention and treatment strategies. Indwelling AUS devices were swabbed for biofilm at surgical removal or revision. Samples and controls were subjected to next-generation sequencing and metabolomics. Biofilm formation of microbial strains isolated from AUS devices was reconstituted in a bioreactor mimicking subcutaneous tissue with a medical device present. Mean patient age was 73 (SD 10.2). All eighteen artificial urinary sphincter devices harbored microbial biofilms. Central genera in the overall microbe–metabolite interaction network were Staphylococcus (2620 metabolites), Escherichia/Shigella (2101), and Methylobacterium-Methylorubrum (674). An rpoB mutation associated with rifampin resistance was detected in 8 of 15 (53%) biofilms. Staphylococcus warneri formed greater biofilm on polyurethane than on any other material type (p < 0.01). The results of this investigation, wherein we comprehensively characterized the composition of AUS device biofilms, provide the framework for future identification and rational development of inhibitors and preventive strategies against device-associated infection.

Selection of the best green technology for a given application is a multi-criterion decision analysis problem. The tradeoffs that need to be made among conflicting attributes of the alternatives are inherently subjective and require the use of interactive decision analysis tools. In this work, a machine-learning based methodology is developed for ranking green technologies based on multiple criteria. First, an expert generates a training data set by ranking a subset of the alternative technologies using his/her tacit knowledge and preferences. Then, a machine learning procedure known as logical analysis of data (LAD) is used to detect patterns in these rankings. These patterns are approximations of the mental rules used by the expert to compare and rank the competing alternatives; after validation, they can be used to rank other technologies in the same class as those in the training data. This novel methodology is illustrated here for the case of ranking alternative energy storage technologies for stationary applications. This technique provides a rapid means of eliciting expert knowledge for ranking alternatives based on multiple criteria.

Climate change mitigation can be achieved through the large-scale deployment of different carbon management technologies in industry. Governments will also play an important role in creating regulatory environments that incentivize low-carbon investments by self-interested private firms. In game theory, a class of problems known as Stackelberg (or leader-follower) games can be used to model such government-industry interactions. In a Stackelberg game, government is represented as an upper-level decision-maker (leader) and industry acts as the lower-level decision-maker (follower). The government’s problem is to set regulations and incentives so that industry’s rational profit-maximizing reaction aligns closely with the government objectives; the latter is assumed to be directed towards environmental protection on behalf of the general public. In this work, a P-graph based approach to solving a special class of Stackelberg game is developed. The leader is assumed to have binary variables for technology options, while the follower controls binary and continuous variables within the process networks formed by the available options (i.e., determined by the leader). The algorithm is illustrated on a case study involving the deployment of different negative emissions technologies (NETs). The combined use of biochar (BC) and direct air capture (DAC) is found to give near-optimal land and water footprints for the leader when the follower optimizes for cost.

This work provides an overview of water resources' roles in the development of renewable energy-fueled electricity generation. It shows that the specific water consumptions of electricity from renewable sources vary significantly due to the setting of the system boundary, data availability, and data quality. Hydroelectricity has a relatively more significant water consumption compared with other renewable energy sources due to surface evaporation. The current studies mainly cover the water consumption of the operational stages of renewable energies. The unit water consumption of hydropower ranges from 0.2 to 245 L/kWh, for solar PV ranges from zero to 0.11 L/kWh, wind power from 0 to 0.64 L/kWh, and geothermal from 2.5 to 6.8 L/kWh. Future studies should put effort to provide a unified and comparable database of renewable energy-related water consumption and water pollutant generation. An implication is that renewable energy generation planning should account for local natural conditions, particularly water availability.

Biochar application to soil is a promising carbon sequestration and soil amendment strategy due to its ability to store carbon for multiple centuries in soil and improve the fertility of the receiving agricultural lands. Other potential benefit includes co-production of renewable energy supply in gaseous (biogas) or liquid (bio-oil) form. Biochar can possess a range of properties that can alter the quality characteristics of the receiving soil such as pH, bioavailable nutrients, and cation exchange capacity. Different types of soil require suitable alkalinity and nutrient levels to increase crop productivity. It is difficult to estimate the precise characteristics of any potential application site since soil conditions (e.g., pH, moisture, baseline microbial profile, etc.) may vary across the whole area. Biochar application can also widely affect soil CH4 and N2O fluxes, which in turn makes it difficult to predict the exact amount of net CO2 sequestration. It is necessary to apply appropriate methodologies to achieve the full sustainable potential of biochar in climate change mitigation and soil amendment. In this study, a fuzzy optimization model is developed to guide the implementation of industrial-scale biochar-based carbon management networks (BCMNs) in consideration of system uncertainties. The application of the model is illustrated via a case study which attains a degree of satisfaction value of 69.5 % (? = 0.695) with a corresponding carbon sequestration (CS) potential amounting to 600 kt.

Bioenergy parks are typically composed of separate biomass-processing plants that have agreed to collaborate via material and energy product synergies to achieve more sustainable operations. The inability of one or more component plants to provide the agreed demand for a product will cause cascading disruptions in the entire network. The inoperability in a bioenergy plant can be caused by a reduction in available raw materials, internal equipment malfunctions, or delays in transporting goods within the supply chain. Despite the disruption, bioenergy parks can continue their operation by optimizing the network's allocation of the material streams. In this work, a P-graph method is developed to optimally adjust operations of a bioenergy park under capacity disruptions. This model considers the effects of deviating from the baseline demand by introducing a penalty rate. This penalty is a financial cost to be deducted from the bioenergy park's optimal economic potential as a result of not being able to meet contractual obligations. A bioenergy park case study is chosen to demonstrate the proposed methodology. Results show that there is about a three-fold further decrease in the optimal profit when penalty rates are applied in the model.

Cities play a significant role on the fate of materials in economy-wide flows. Their sustainability is integral to the future of resource utilization. Circular city economies are recommended for the future of sustainable cities. Shifting towards a circular economy is challenging as urban metabolism is intertwined with city characteristics. The effectiveness of action plans is vital towards the shift through insight derivation of past city-level data. Rough set-based model can draw insights from city-level data in interpretable form, facilitating communication between analysts and city planners. This approach generates if-then rules based on the city-level data. This work generated 14 if-then rules from data of 100 cities using rough set theory. The model identified 5 relevant city characteristics from the 13 characteristics available from the data. The relevant characteristics are demographic, education, ease of doing business, income inequality, and tourism. The metric on waste management for cities was selected as the decision attribute of the model as it is the most relevant to circularity. The model attained a classification accuracy of 94 %. Specific if-then rules achieved coverage as high as 64 %, allowing ease of analysis. The rules suggest the level of development as the delineation between waste management system performances. The findings highlight the relevance of future studies on circular economy of developing countries. Results of this work provide insights on critical features that occur in more sustainable cities, which can be used to plan future circular city economies.

Sectoral contributions of greenhouse gas (GHG) emissions are at the heart of the climate debate. Carbon drawdown solutions from different sectors (e.g. energy, land, urban infrastructure, and industrial systems) will play significant roles in mitigating climate change in the coming decades. This work identifies the feasible carbon drawdown solutions to support GHG emissions reduction paths globally. These solutions should meet both economic constraints and carbon emissions targets. This work develops a novel approach that integrates Carbon Emission Pinch Analysis (CEPA), the Best-Worst Method (BWM), and Cost-Benefit Analysis (CBA). For each sector, BWM is used to evaluate the performance of reduction solutions and identify their priority weights. Then, CBA is used to rank the options further. Finally, CEPA combines with the Marginal Emission Reduction Cost Curve to find the final mix to meet carbon reduction targets and investment costs. The results prioritize global carbon drawdown potentials for the power (186.4 Gt CO2-eq), construction (97.9 Gt CO2-eq), industry (23.8 Gt CO2-eq), transportation (20.2 Gt CO2-eq), food, agriculture, and land use (2.5 Gt CO2-eq) sectors. Based on these results, this work presents an optimal path for carbon drawdown.

Reducing the embodied carbon footprint caused by construction projects is being pushed by many countries. One potential solution is the large-scale use of low-carbon building materials like geopolymers (GPs). GPs result from the chemical activation of aluminosilicate materials using an alkaline liquid, resulting in an inorganic polymeric network; it can also allow repurposing of the waste or by-products of industries. The sustainability of this new type of material is assessed here using Life Cycle Assessment (LCA). This study quantifies the cradle-to-gate environmental impacts of a localized geopolymer process in the Philippines and contrasts the impacts of Ordinary Portland Cement (OPC). The Life Cycle Inventory (LCI) analysis was performed using OpenLCA software and Life Cycle Impact Assessment (LCIA) using IPCC 2013 methodology. LCA results show that GP concrete with an RHA-based activator has a similar Global Warming Potential (GWP) as GP made using a commercial activator. The main contributors to the impacts are the production of the alkali activators, which indicates that the electricity generation mix has a significant influence on the environmental sustainability of GP.

Alternative low-carbon cementitious binders such as geopolymers are rapidly garnering scientific interest to replace Ordinary Portland Cement (OPC). Industrial waste by-products such as coal fly ash (CFA) and ground granulated blast furnace slag (GGBS) are usually utilized for this material. Microcapsules can also be added to geopolymers to enhance its self-healing capability as it has potential environmental benefits aside from cost savings. This study thus focuses on the cradle-to-gate LCA of geopolymer concrete containing microcapsules for self-healing. Two different microcapsules were considered: Urea-formaldehyde (UF)/Dicyclopentadiene (DCPD) and Poly(urea-urethane) (PUU)/Alkali-activator (AA). Three different geopolymer precursors were studied: CFA, GGBS and CFA/GGBS. OpenLCA software was used for the calculations and analysis, and the inventory data were obtained from Ecoinvent and OzLCI2019 supplemented by literature data. An impact assessment was carried out using CML 2001. Self-healing geopolymer concrete is better in terms of lower global warming potential but performs worse in other impact categories than conventional OPC concrete. Of the geopolymer concrete assessed, GGBS concrete has the lowest impacts. On the other hand, of the two self-healing microcapsules, UF/DCPD is better for the environment. The primary factor for this difference is the solvent used in microcapsule synthesis. From this result, it has been shown that self-healing GGBS concrete containing UF/DCPD microcapsules has the lowest impacts of the self-healing concrete assessed.

Despite being recognized as a key component towards reducing global GHG emissions, many challenges remain throughout the hydrogen value chain. Hydrogen storage is one critical aspect, since proper storage is essential for its wide scale deployment. One storage option is the chemisorption of hydrogen in metals and intermetallic alloys (i.e., metal hydrides). This approach can potentially address issues on safety, infrastructure, and cost which currently hinder the transition toward a hydrogen economy. The only suitable method for determining appropriate hydrogen storage materials is through experimentation which is resource intensive. There are many parameters that can affect the hydrogen storage capacity of a material; databases on previously investigated materials can be used to narrow down future investigation to the most promising candidates. Machine learning (ML) techniques can be employed to determine how different properties predict for hydrogen storage capacity. ML can use previously compiled data to generate a classification model that can be utilized for determining and predicting a material's viability for hydrogen storage. This paper uses the hyperbox ML technique to generate interpretable decision rules to predict if a metal hydride is a good candidate for hydrogen storage. A case study which specifically focuses on complex and magnesium hydrides is used to demonstrate this approach. The generated decision model had a false positive rate of 22.0 % and false negative rate of 36.8 %.

Park-wide water management is an important way to conserve water resources. However, the enhancement of inter-plant connection will weaken the system stability and strengthen the cascade effect of disaster losses. In this work, a dynamic inoperability input-output model is applied to park-wide water network to predict the impact of disturbances. The inoperability is expressed as the degree of water loss. The dependence of different water networks on freshwater is presented. This work analyzes the impact of freshwater supply disturbance on the water volume. By simulating the time-varying trajectory of inoperability, the indicators (i.e., resistance to disturbance, adaptability, and recoverability) are determined to evaluate the vulnerability of the water network in each scenario. The results show that under the same freshwater disturbance, water networks that are less dependent on freshwater are more robust but weaker in recoverability. In the case of freshwater disturbance, the possibility of disruptions in the participating plants of the without integrated optimization water network is about 1.5 times that of the integrated optimization water network.

Carbon capture and storage (CCS) is an important technology that can contribute to the reduction of greenhouse gas emissions. It involves the capture of CO2 from point sources such as power plants and subsequent storage in secure geological reservoirs. However, capture incurs parasitic power loss; thus, compensatory power from clean sources such as renewables will be needed to make up for the power losses. The conventional capture process is designed for steady-state operation, but flexible capture is possible to offset the intermittency of renewable energy. Systematic planning for robust CCS systems is needed to incorporate flexible mechanisms in CO2 capture. In this study, a mixed integer linear program (MILP) is developed to robust CCS retrofit subject to operational adjustments for multiple periods or scenarios. Retrofit decisions include options for flexible and non-flexible capture, accounting for trade-offs between the two options. Operational adjustments pertain to decisions to switch off the flexible capture plants to compensate for depressed renewable energy supply. A case study is presented to demonstrate the optimization model. From the case study, flexible mechanism can provide a more robust planning, where low availabilities of renewable energy can contribute up to 18% of the power demand.

Cities are expected to play a major role in managing climate change in the coming decades. The actual environmental performance of urban centres is difficult to predict due to the complex interplay of technologies and infrastructure with social, economic, and political factors. Machine learning (ML) techniques can be used to detect patterns in high-level city data to determine factors that influence favourable climate performance. In this work, rough set-based ML (RSML) is used to identify such patterns in the Sustainable Cities Index (SCI), which ranks 100 of the world’s major urban centres based on three broad criteria that cover social, environmental, and economic dimensions. These main criteria are further broken down into 18 detailed criteria that are used to calculate the aggregate SCI scores of the listed cities. Two of the environmental criteria measure energy intensity and greenhouse gas (GHG) emissions. RSML is used to generate interpretable rule-based (if/then) models that predict energy utilisation and GHG emissions performance of cities based on the other criteria in the database. Attribute reduction techniques are used to identify a set of 7 non-redundant criteria for energy use and 9 non-redundant criteria for GHG emissions; 6 criteria are common to these two sets. Then, RSML is used to generate rule-based models. A 10-rule model is determined for energy intensity, while an 11-rule model is found for GHG emissions. Both models were reduced further by eliminating rules with weak generalisation capability. A key insight from the rule-based models is that social, environmental, and economic attributes are associated with energy intensity and GHG emissions due to indirect effects.

Various energy storage technologies are currently available to address the problem of renewable energy intermittency. Due to wide selection of energy storage technologies with contrasting characteristics, strengths, and weaknesses, selection of the most appropriate technology for a specific type of application is a daunting task. In this study, the multiple attribute decision-making method VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) is used as a systematic approach for ranking available energy storage technologies. The competing technologies are evaluated based on energy density, power rating, discharge time, cycle efficiency, lifetime, and specific cost. Data for these various criteria are based on reports in the literature and are processed using the VIKOR algorithm. This study demonstrates that the lithium-ion battery is the best technology for energy storage of renewable energy with assessment index, Q = 0.16; it is followed closely by the sodium-sulfur battery, with Q = 0.17. The result of this study shows that VIKOR can be used to select the best energy storage technology for power generation and guide decision-makers on the most suitable technology for enabling the transition to a zero-carbon energy systems.

P-graph models of economic networks based on the Leontief input-output framework have been demonstrated in previous work. However, such models are subject to the limiting assumption of fixed technical coefficients that reflect the average state of technology currently in use. This limitation restricts the use of such models to incremental changes; more radical shifts in economic structure cannot be adequately represented. In this paper, a modified P-graph model of economic networks that allows partial substitution of inputs is developed. This modified formulation can represent major structural shifts, such as deep decarbonization or increased circularity, that are needed for improving the sustainability of modern economies. The new model is illustrated using a hypothetical case study. Results show that economic systems which allow for partial substitution are more resilient when disruptions occur in certain sectors of the economy.