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Abstract Nanoribbons (NRs) of atomic layer transition metal dichalcogenides (TMDs) can boost the rapidly emerging field of quantum materials owing to their width-dependent phases and electronic properties. However, the controllable downscaling of width by direct growth and the underlying mechanism remain elusive. Here, we demonstrate the vapor-liquid-solid growth of single crystal of single layer NRs of a series of TMDs (MeX2: Me = Mo, W; X = S, Se) under chalcogen vapor atmosphere, seeded by pre-deposited and respective transition metal-alloyed nanoparticles that also control the NR width. We find linear dependence of growth rate on supersaturation, known as a criterion for continues growth mechanism, which decreases with decreasing of NR width driven by the Gibbs-Thomson effect. The NRs show width-dependent photoluminescence and strain-induced quantum emission signatures with up to ≈ 90% purity of single photons. We propose the path and underlying mechanism for width-controllable growth of TMD NRs for applications in quantum optoelectronics.

Abstract Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS2. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

Abstract Arteriviruses infect a variety of mammalian hosts, but the receptors used by these viruses to enter cells are poorly understood. We identified the neonatal Fc receptor (FcRn) as an important pro-viral host factor via comparative genome-wide CRISPR-knockout screens with multiple arteriviruses. Using a panel of cell lines and divergent arteriviruses, we demonstrate that FcRn is required for the entry step of arterivirus infection and serves as a molecular barrier to arterivirus cross-species infection. We also show that FcRn synergizes with another known arterivirus entry factor, CD163, to mediate arterivirus entry. Overexpression of FcRn and CD163 sensitizes non-permissive cells to infection and enables the culture of fastidious arteriviruses. Treatment of multiple cell lines with a pre-clinical anti-FcRn monoclonal antibody blocked infection and rescued cells from arterivirus-induced death. Altogether, this study identifies FcRn as a novel pan-arterivirus receptor, with implications for arterivirus emergence, cross-species infection, and host-directed pan-arterivirus countermeasure development.

Abstract Transition metal carbides have been adopted in energy storage, conversion, and extreme environment applications. Advancements in their 2D counterparts, known as MXenes, enable the design of unique structures at the ~1 nm thickness scale. Alkali cations have been essential in MXenes manufacturing processing, storage, and applications, however, exact interactions of these cations with MXenes are not fully understood. In this study, using Ti3C2T x , Mo2TiC2T x , and Mo2Ti2C3T x MXenes, we present how transition metal vacancy sites are occupied by alkali cations, and their effect on MXene structure stabilization to control MXene’s phase transition. We examine this behavior using in situ high-temperature x-ray diffraction and scanning transmission electron microscopy, ex situ techniques such as atomic-layer resolution secondary ion mass spectrometry, and density functional theory simulations. In MXenes, this represents an advance in fundamentals of cation interactions on their 2D basal planes for MXenes stabilization and applications. Broadly, this study demonstrates a potential new tool for ideal phase-property relationships of ceramics at the atomic scale.

Abstract Recently Belle II reported the first measurement of B + → K + + invisible(inv), which is 2.7σ above the standard model (SM) prediction. If confirmed, this calls for new physics beyond SM. In the SM, the invisible particles are neutrino-anti-neutrino pairs. There are more possibilities when going beyond the SM. In this work, we focus on decays to dark matter (DM) and show that the B → K + inv excess from Belle II and DM relic density can be simultaneously explained in a simple extension of the SM. The model introduces a real scalar singlet ϕ acting as a DM candidate, and two heavy vector-like quarks Q, D with the same quantum numbers as the SM left-handed quark doublet and right-handed down-type quark singlet, respectively. All these new particles are odd under a ℤ2 symmetry while the SM particles are even. The model can successfully explain the Belle II anomaly and DM relic density for TeV-scale heavy quarks with hierarchical Yukawa couplings involving b and s quarks. At the same time, it can easily satisfy other flavour physics constraints. Direct detection searches utilizing the Migdal effect constrain some of the parameter space.

Mitigating climate change requires a portfolio of strategies and the use of carbon dioxide removal techniques or negative emissions technologies (NETs) will be necessary to achieve this goal. However, the high implementation costs of advanced NETs lead to expensive carbon credits, hindering their broad acceptance and use. One potential solution involves governmental support through subsidies, aiming to boost the availability of NET-derived carbon credits. This research uses a graphical technique based on an extension of pinch analysis to identify the ideal subsidy level for carbon dioxide removal, taking into account factors such as carbon pricing, supply, and demand. The proposed approach modifies the limiting composite curve (LCC) methodology to accurately determine the optimal subsidy and establish the baseline amount of subsidized carbon dioxide removal needed. The approach enables the convenient and efficient construction of the LCC using a composite table algorithm. To illustrate the proposed methodology, two case studies composed of different NETs and demand sectors are investigated. The results show the most advantageous subsidy levels for these technologies, providing valuable insights to guide policymakers and investors in their decarbonization efforts. This work contributes to the development of effective governance and investment strategies by optimizing NET subsidy allocation. Such optimization is crucial for facilitating the widespread implementation of these technologies, which are in-line with the global efforts to mitigate climate change.

ABSTRACTDespite its potential benefits, lutein availability can be limited due to inadequate dietary intake of fruits, vegetables, and seaweed. Additionally, lutein faces challenges related to gastrointestinal absorption due to its physicochemical characteristics. To tackle these issues, fortifying staple foods with lutein is proposed, but its low solubility and susceptibility to oxidative degradation present hurdles. Nanoencapsulation technology emerges as a promising solution for improving lutein stability during food processing, storage, and absorption. However, lutein’s stability remains a challenge, with susceptibility to degradation by temperature, UV-light, and oxygen exposure. Food processing involving high temperatures can cause degradation, while specific UV-wavelengths lead to cleavage of lutein molecules. This critical viewpoint carries substantial scientific significance by urging researchers globally to prioritize clinical investigations and the advancement of plant-based lutein nanoencapsulation in basic dietary items. Clinical studies focusing on lutein-plant-based nanoencapsulation approaches are essential to improve its stability and bioaccessibility, ultimately contributing to better vision health.

Negative emission technologies (NETs) are now considered essential for achieving net-zero emissions by mid-century. These technologies work by removing carbon dioxide from the atmosphere and storing it in various mediums, such as biomass, soil, deep underground, or the ocean. Rather than relying on a single large-scale NET, portfolios offer a way to manage the risks and sustainability issues associated with these technologies. Optimizing the deployment of NET portfolios is an emerging area of research. One challenge in this optimization is considering the permanence of carbon dioxide removal (CDR) for different technologies, as some NETs are more susceptible to reversal (e.g., forests are at risk of fires) than others (e.g., geological storage has a lower risk of reversal). CDR permanence is crucial in NET portfolio optimization because it affects the actual CDR potential of the portfolio over time. Currently, there is a lack of studies considering the permanence of NETs in portfolios. One approach to address this is by using time-evaluated costs, where the various NETs have different costs of permanent removal (CPR) depending on the considered planning horizon or time of permanence. This work aims to bridge the research gap by applying the concept of CPR in optimizing NET portfolios. Two mixed-integer linear programming models are used to optimize a NET portfolio under CPR, resource, budget, and capacity constraints, subject to target CDR. Different times of permanence (from 25 to 1,000 y) are investigated. The results show varying NET portfolios depending on the time of permanence considered. This work contributes to the analysis of decarbonization portfolios for decision-making to mitigate climate change.

Objectives We conducted an implementation science mental health treatment study in western Kenya, testing strategies for scale up of evidence-based mental health services for common adult disorders using a non-specialist workforce, integrated with existing primary care (Sequential Multiple, Assignment Randomized Trial of non-specialist-delivered psychotherapy (Interpersonal Psychotherapy) and/or medication (fluoxetine) for major depression and post-traumatic stress disorder (PTSD) (SMART DAPPER)). Because study launch coincided with the COVID-19 pandemic, participants were allowed to attend treatment visits via mHealth (audio-only mobile phone) or in-person. We conducted a secondary data analysis of the parent study to evaluate preference for mHealth or in-person treatment among our study participants, including rationale for choosing in-person or mHealth treatment modality, and comparison of baseline demographic and clinical characteristics.Design, setting, participants and interventions Participants were public sector primary care patients at Kisumu County Hospital in western Kenya with major depression and/or PTSD and were individually randomised to non-specialist delivery of evidence-based psychotherapy or medication (n=2162).Outcomes Treatment modality preference and rationale were ascertained before randomised assignment to treatment arm (psychotherapy or medication). The parent SMART DAPPER study baseline assessment included core demographic (age, gender, relationship status, income, clinic transport time and cost) and clinical data (eg, depression and PTSD symptoms, trauma exposures, medical comorbidities and history of mental healthcare). Given that this evaluation of mHealth treatment preference sought to identify the demographic and clinical characteristics of participants who chose in-person or mHealth treatment modality, we included most SMART DAPPER core measurement domains (not all subcategories).Results 649 (30.3%) SMART DAPPER participants preferred treatment via mHealth, rather than in person. The most cited rationales for choosing mHealth were affordability (18.5%) (eg, no transportation cost) and convenience (12.9%). On multivariate analysis, compared with those who preferred in-person treatment, participants who chose mHealth were younger and had higher constraints on receiving in-person treatment, including transport time 1.004 (1.00, 1.007) and finances 0.757 (0.612, 0.936). Higher PTSD symptoms 0.527 (0.395, 0.702) and higher disability 0.741 (0.559, 0.982) were associated with preference for in-person treatment.Conclusions To our knowledge, this is the first study of public sector mental healthcare delivered by non-specialists via mHealth for major depression and/or PTSD in Sub-Saharan Africa. Our finding that mHealth treatment is preferred by approximately one-third of participants, particularly younger individuals with barriers to in-person care, may inform future mHealth research to (1) address knowledge gaps in mental health service implementation and (2) improve mental healthcare access to evidence-based treatment.Trial registration number NCT03466346.

Background: A bioactive fraction of Cinnamomum burmanii and Lagerstroemia speciosa, DLBS3233, has recentlybeen used for type-2-diabetes treatment due to its favorable effect on insulin sensitivity. The insulin resistance leadingto metabolic syndrome is closely linked to hyperandrogenemia in polycystic ovary syndrome (PCOS). This studyevaluated the metabolic and reproductive efficacy and safety of DLBS3233 in insulin-resistant PCOS women.Materials and Methods: This was a 2-arm, randomized, double-blind, controlled, noninferiority clinical study overa 6-month therapy with DLBS3233 100-mg daily in comparison to metformin-XR 750 mg twice daily, involving 124PCOS women with insulin resistance. The primary efficacy endpoint was the improvement of Homeostasis ModelAssessment-Insulin Resistance (HOMA-IR). Secondary endpoints were improvements in other metabolic and reproductiveparameters. Safety endpoints were based on blood pressure, heart rate, electrocardiogram findings, liver andrenal function, and adverse events.Results: After 6 months, HOMA-IR improvement in DLBS3233-treated group (-1.03 ± 0.50) and metformin-XR (-1.19± 0.50) were comparable, with a between-group difference fell within the pre-set non-inferiority margin (0.16; 95% confidenceinterval (CI): -1.24, 1.56; P=0.3168). The HOMA-IR in both groups were significantly improved from baseline.On all secondary endpoints, both groups showed comparable effects. Markedly fewer adverse events occurred in theDLBS3233 treated group than in the Metformin-XR-treated group and most were mild clinically and had been resolvedby the end of the study.Conclusion: Treatment with DLBS3233 100-mg daily in PCOS women demonstrated comparable efficacy to metformin-XR 750-mg twice daily in improving insulin resistance. However, the non-inferiority of DLBS3233 to metformin-XR remains inconclusive. DLBS3233 was more tolerable than metformin-XR (registration number: NCT01733459).

Abstract Background The accessibility of pharmacies has been associated with overall health and wellbeing. Past studies have suggested that low income and racial minority communities are underserved by pharmacies. However, the literature is inconsistent in finding links between area-level income or racial and ethnic composition and access to pharmacies. Here we aim to assess area-level spatial access to pharmacies across New York State (NYS), hypothesizing that Census Tracts with higher poverty rates and higher percentages of Black and Hispanic residents would have lower spatial access. Methods The population weighted mean shortest road network distance (PWMSD) to a pharmacy in 2018 was calculated for each Census Tract in NYS. This statistic was calculated from the shortest road network distance to a pharmacy from the centroid of each Census block within a tract, with the mean across census blocks weighted by the population of the census block. Cross-sectional analyses were conducted to assess links between Tract-level socio demographic characteristics and Tract-level PWMSD to a pharmacy. Results Overall the mean PWMSD to a pharmacy across Census tracts in NYS was 2.07 Km (SD = 3.35, median 0.85 Km). Shorter PWMSD to a pharmacy were associated with higher Tract-level % poverty, % Black/African American (AA) residents, and % Hispanic/Latino residents and with lower Tract-level % of residents with a college degree. Compared to tracts in the lowest quartile of % Black/AA residents, tracts in the highest quartile had a 70.7% (95% CI 68.3–72.9%) shorter PWMSD to a pharmacy. Similarly, tracts in the highest quartile of % poverty had a 61.3% (95% CI 58.0-64.4%) shorter PWMSD to a pharmacy than tracts in the lowest quartile. Conclusion The analyses show that tracts in NYS with higher racial and ethnic minority populations and higher poverty rates have higher spatial access to pharmacies.

PURPOSEBreast cancer is the leading type of cancer diagnosed and the second leading cause of cancer-related death in Ghana. Mammography and ultrasound have proven benefits in the early detection of breast cancer. This study evaluates mammography, breast ultrasound, and radiology work force availability throughout Ghana.METHODSA survey was administered to all hospitals in Ghana from November 2020 to October 2021. Mammography, breast ultrasound services, and the number of radiologists were assessed. For mammography, the number performed per month, cost incurred by the patient, where images were read, and how long it took to receive reports were also assessed. Health Facilities Regulatory Authority records on diagnostic centers were obtained to identify additional in-country breast imaging services.RESULTSThree hundred and twenty-eight of 346 hospitals participated in the survey (95%). Only 21 hospitals reported on-site mammography. One hospital reported performing >100 mammographies per month. The average cost to the patient ranged from 100 to 500 Cedis ($17-87 US dollars [USD]), although three hospitals performed mammography at no cost. An additional 10 mammography machines were identified at diagnostic centers throughout the country, with 41.3% of the female population living within 1 hour of mammography services. There were 135 hospital-based breast ultrasound services identified with 69.5% of the female population living within 1 hour of these services. There were an additional 190 ultrasound machines at diagnostic centers. There were 96 in-country radiologists identified.CONCLUSIONAlthough there is limited availability and utilization of mammography in Ghana, there is more readily available ultrasonography. A focus on increasing breast cancer early diagnostic capabilities with breast ultrasound should be prioritized in addition to further expansion of the radiology workforce.

Abstract MXene is investigated as an electrode material for different energy storage systems due to layered structures and metal‐like electrical conductivity. Experimental results show MXenes possess excellent cycling performance as anode materials, especially at large current densities. However, the reversible capacity is relatively low, which is a significant barrier to meeting the demands of industrial applications. This work synthesizes N‐doped graphene‐like carbon (NGC) intercalated Ti3C2Tx (NGC‐Ti3C2Tx) van der Waals heterostructure by an in situ method. The as‐prepared NGC‐Ti3C2Tx van der Waals heterostructure is employed as sodium‐ion and lithium‐ion battery electrodes. For sodium‐ion batteries, a reversible specific capacity of 305 mAh g−1 is achieved at a specific current of 20 mA g−1, 2.3 times higher than that of Ti3C2Tx. For lithium‐ion batteries, a reversible capacity of 400 mAh g−1 at a specific current of 20 mA g−1 is 1.5 times higher than that of Ti3C2Tx. Both sodium‐ion and lithium‐ion batteries made from NGC‐Ti3C2Tx shows high cycling stability. The theoretical calculations also verify the remarkable improvement in battery capacity within the NGC‐Ti3C2O2 system, attributed to the additional adsorption of working ions at the edge states of NGC. This work offers an innovative way to synthesize a new van der Waals heterostructure and provides a new route to improve the electrochemical performance significantly.

Background: Pectoralis major muscle/myocutaneous flaps (PMMFs) are commonly used in reconstructive surgery, but may result in shoulder disability on the donor side. A systematic review evaluating this morbidity could be beneficial for guiding patients and providers considering this procedure. Methods: In October 2022, a systematic review of studies evaluating quantitative/qualitative measures of functional morbidity after PMMF was conducted. The results were categorized into PMMF's effect on range of motion (ROM), strength, and ability to complete shoulder-related activities/quality of life. Results: Eleven studies were included for analysis, which analyzed standard PMMF and two PMMF variants that spared portions of the muscle. Three of five studies demonstrated reduced shoulder ROM for standard PMMF versus controls lasting at least 4 months after head and neck reconstruction. Two of five studies, including two prospective studies demonstrated reduced shoulder strength for standard PMMF versus controls lasting at least 3 months after surgery. Five of nine studies found significant impairment in the ability to conduct shoulder-related activities, including work, up to one year postoperatively for standard PMMF versus controls. Muscle-sparing PMMF variants exhibited more promising outcomes in some categories. Conclusion: Standard PMMF results in prolonged reductions in shoulder ROM and strength, which may impair patients in shoulder-related activities. Other reconstructive options should be considered in patients who frequently participate in such activities. For patients requiring PMMF, muscle-sparing PMMF variants should be considered as alternatives to the standard PMMF.

Abstract We provide an exhaustive classification of three-family DFSZ axion models that have no cosmological domain wall problem. This result is obtained by making the Peccei-Quinn symmetry flavour dependent in certain specific ways, thus reinforcing a possible connection between the strong CP problem and the flavour puzzle. Known DFSZ flavour variants such as the top-specific model emerge as special cases. Key features of the phenomenology of these models are briefly discussed.

Energy security is essential to ensure that energy is always available, affordable, and socially acceptable. To realize these aspects, this paper presents a design and business model for installing and operating a local energy community DC microgrid that allows individual households to power electrical loads for their homes and the whole community. Interested households can purchase solar PV units at a discounted price through a discount by the hosting local government unit (LGU). A percentage of the energy harnessed will be transferred through the microgrid to electrify public infrastructures such as streetlights, which covers the discount given. To optimize the setup so that both parties see the cost-saving potential of this new concept, a multi-objective linear programming (MOLP) model was developed to minimize the total cost by the participating households and the LGU. The optimization model was applied to a sample case study involving a community in the Philippines. Sensitivity analysis was also performed to identify the change in the objectives’ values when the case study’s inputs were changed. With the proper inputs to the model, the developed MOLP model successfully provided the optimal design and setup of the DC microgrid where both households and LGU have a lower lifetime electricity cost than relying on the main grid alone or the typical standalone solar energy setup.

BackgroundScalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.MethodsWe used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.ResultsPenalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions—intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.ConclusionIn some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.

Peperomia pellucida, a tropical plant belonging to the Piperaceae family, has long been recognized for its potential medicinal properties, including anti-inflammatory, analgesic, and antimicrobial effects. This study investigates the efficacy of Peperomia pellucida nanoemulsion (PPNE) as a novel therapeutic approach for improving burn wound healing in Wistar rats. The nanoemulsion was formulated using the high-energy ultrasonication method, optimized for droplet size, polydispersity index, and stability. A 30 % burn wound was induced on the dorsum of 60 Wistar rats, which were then divided into three groups: control (untreated), silver sulfadiazine (SSD) treated, and PPNE treated. Inflammatory cytokine levels (TNF-α, IL-6, and IL-1) were measured using enzyme-linked immunosorbent assay (ELISA). Results showed that PPNE treatment significantly accelerated wound closure rates compared to both control and SSD-treated groups, with complete wound closure achieved by day 10 in the PPNE-treated group. Histopathological examination revealed improved re-epithelialization, granulation tissue formation, and collagen deposition in the PPNE group compared to the control and SSD groups. Additionally, PPNE treatment significantly reduced inflammatory cytokine levels and enhanced antioxidant enzyme activities, suggesting a reduction in inflammation and oxidative stress in the treated wounds. In conclusion, this study demonstrates the potential of P. pellucida nanoemulsion as a promising and effective therapeutic approach for improving burn wound healing. The observed enhanced wound healing properties may be attributed to the combined anti-inflammatory, antioxidant, and antimicrobial effects of the PPNE, warranting further investigation and potential clinical application.

Abstract Background Cervical cancer is the most common cancer and the leading cause of cancer-related death in Gambian women. The Gambian Ministry of Health is striving to improve access to screening, diagnostic, and treatment services for cervical cancer, but comprehensive data on currently available services is limited making it challenging to appropriately prioritize the ideal next steps for expanding care. This study aims to describe the current services available for the prevention, screening, and treatment of cervical cancer in The Gambia and provide suggestions for expanding geographic access to care. Methods A survey aimed at assessing the availability of key cervical cancer-related services was developed and then administered in person by research assistants to all secondary and tertiary health facilities (HFs) in The Gambia. ArcGIS Pro Software and 2020 LandScan population density raster were used to visualize and quantify geographic access to care. Survey results were compared with published targets outlined by the Gambian Ministry of Health in the “Strategic Plan for the Prevention and Control of Cervical Cancer in The Gambia: 2016–2020.” Results One hundred and two HFs were surveyed including 12 hospitals, 3 major health centers, 56 minor health centers, and 31 medical centers/clinics. Seventy-eight of these HFs provided some form of cervical cancer-related service. HPV vaccination was available in all health regions. Two-thirds of the population lived within 10 km of a HF that offered screening for cervical cancer and half lived within 10 km of a HF that offered treatment for precancerous lesions. Ten HFs offered hysterectomy, but nine were located in the same region. Two HFs offered limited chemotherapy. Radiotherapy was not available. If all major health centers and hospitals started offering visual inspection with acetic acid and cryotherapy, 86.1% of the population would live within 25 km of a HF with both services. Conclusions Geographic access to cervical cancer screening, and precancer treatment is relatively widespread across The Gambia, but targeted expansion in line with the country’s “Strategic Plan” would improve access for central and eastern Gambia. The availability of treatment services for invasive cancer is limited, and establishing radiotherapy in the country should continue to be prioritized.

Abstract The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.

Purpose – Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts. Design/methodology/approach – Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction. Findings – The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction. Research limitations/implications – The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force. Practical implications – These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology. Social implications – Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions. Originality/value – Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Abstract Background Upon arrival, the prevalence of overweight and obesity is lower in new immigrants than their native counterparts in the U.S. With longer residency in the U.S., these differences converge over time, followed by higher prevalence among immigrants than native U.S. residents. Results from the Live Well project in the Greater Boston area demonstrate the viability of utilizing a culturally adapted, community-based participatory research (CBPR) approach to reduce weight gain among newly immigrated mother–child dyads. Methods Haitian, Latina, and Brazilian mother–child dyads (n = 390), new to the U.S. (fewer than 10 years) were enrolled in a one- to two-year long CBPR lifestyle intervention that targeted dietary and physical activity behaviors. Attendance was recorded to establish dose. Demographics, anthropometrics, and relevant covariates were collected from participants at baseline, 6, 12, 18, and 24 months. Body Mass Index (BMI) was calculated using objectively measured height and weight. Linear mixed regression models were used to assess change in BMI and BMI z-score of mothers and children respectively. Results At baseline, nearly 75% of mothers and 50% of children were either overweight or obese (BMI ≥ 25.0 and BMI z-score ≥ 85th percentile, respectively). Only 20% of mothers attended all 12 intervention sessions in year 1. Using intent-to-treat analyses, no significant time, intervention, or time × intervention effects were observed for weight change of mothers or children at follow-up. Mothers in the highest quantile (those who attended all 12 intervention sessions) had significant reductions in BMI at 18 months (1.76 units lower, 95%CI: -3.14, -0.37) and 24 months (2.61 units, 95%CI -3.92, -1.29) compared to mothers in the lower quantiles, including those with no exposure. Such dose effects on BMI z-scores were not noted for children. Conclusions Findings from Live Well demonstrate the viability of utilizing a CBPR approach to address overweight and obesity among immigrant mothers. Given the higher-than-expected prevalence of overweight and obesity among mother–child dyads by ~ 6 years of U.S. residency, and lower maternal participation rates in the intervention, additional research is necessary to identify the optimal intervention length, retention strategies, and approach to jointly support healthy maternal and child weight.

Abstract The attainable region interpretation of the thermodynamic principles has indicated that carbon dioxide (CO2) can be either hydrogenated directly to form dimethyl ether (DME) or gasoline. The process that converts CO2 to DME is more thermodynamically favourable at lower temperature. A certain thermodynamic temperature range (25 to 300 °C) is suggested for the conversion of CO2 to DME via a methanol intermediate pathway without addition of work. Optimal synthesis routes derived from P-graph's mutual exclusion solver were compared with reactions reported in literature and showed great correlation. The reactions collectively possess Gibbs free energy of less than zero, and negative enthalpy of reaction. With P-graph attainable region technique, the case studies have demonstrated that the synthesis of DME and gasoline using CO2 hydrogenation via methanol intermediate and carbon monoxide intermediate from Fischer–Tropsch synthesis is feasible with no work and heat requirement. Both case studies have demonstrated visual advantage of P-graph and data-driven applications. The benefit of integrating the P-graph framework with machine learning model like decision tree classifier was also demonstrated in the second case study as it solves topological optimisation problems without scaling constraints.

Abstract This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support. The paper does, however, present less-discussed benefits with new concepts such as ‘poly-digital’, where many different apps/features (e.g. a sleep app, mood logging app and a mindfulness app, etc.) can each address different factors of wellbeing, perhaps resulting in an aggregation of marginal gains. Another benefit is that digital mental health offers the ability to collect high-resolution real-world client data and provide client monitoring outside of therapy sessions. These data can be collected using digital phenotyping and ecological momentary assessment techniques (i.e. repeated mood or scale measures via an app). This allows digital mental health tools and real-world data to inform therapists and enrich face-to-face sessions. This can be referred to as blended care/adjunctive therapy where service users can engage in ‘channel switching’ between digital and non-digital (face-to-face) interventions providing a more integrated service. This digital integration can be referred to as a kind of ‘digital glue’ that helps join up the in-person sessions with the real world. The paper presents the challenges, for example, the majority of mental health apps are maybe of inadequate quality and there is a lack of user retention. There are also ethical challenges, for example, with the perceived ‘over-promotion’ of screen-time and the perceived reduction in care when replacing humans with ‘computers’, and the trap of ‘technological solutionism’ whereby technology can be naively presumed to solve all problems. Finally, we argue for the need to take an evidence-based, systems thinking and co-production approach in the form of stakeholder-centred design when developing digital mental health services based on technologies. The main contribution of this paper is the integration of ideas from many different disciplines as well as the framework for blended care using ‘channel switching’ to showcase how digital data and technology can enrich physical services. Another contribution is the emergence of ‘poly-digital’ and a discussion on the challenges of digital mental health, specifically ‘digital ethics’.

PURPOSEBreast cancer is the most frequent cancer and second most common cause of cancer-related death in Ghana. Early detection and access to diagnostic services are vital for early treatment initiation and improved survival. This study characterizes the geographic access to hospital-based breast cancer diagnostic services in Ghana as a framework for expansion.METHODSA cross-sectional hospital-based survey was completed in Ghana from November 2020 to October 2021. Early diagnostic services, as defined by the National Comprehensive Cancer Network (NCCN) Framework for Resource Stratification, was assessed at each hospital. Services were characterized as available >80% of the time in the previous year, 80% of the time, with 43% of the population living within 45 km. Ten of the 12 met full NCCN Core criteria, and none met full NCCN Enhanced criteria. An additional 12 hospitals were identified that provide the majority of NCCN Basic services but lack select services necessary to meet this criterion. Expansion of services in these hospitals could result in an additional 20% of the population having access to NCCN Basic-level early diagnostic services within 45 km.CONCLUSIONHospital-based services for breast cancer early diagnosis in Ghana are available but sparse. Many hospitals offer fragmented aspects of care, but only a limited number of hospitals offer the full NCCN Basic or Core level of care. Understanding current availability and geographical distribution of services provides a framework for potential targeted expansion of services.

Several large-scale, pragmatic clinical trials on opioid use disorder (OUD) have been completed in the National Drug Abuse Treatment Clinical Trials Network (CTN). However, the resulting data have not been harmonized between the studies to compare the patient characteristics. This paper provides lessons learned from a large-scale harmonization process that are critical for all biomedical researchers collecting new data and those tasked with combining datasets. We harmonized data from multiple domains from CTN-0027 (N = 1269), which compared methadone and buprenorphine at federally licensed methadone treatment programs; CTN-0030 (N = 653), which recruited patients who used predominantly prescription opioids and were treated with buprenorphine; and CTN-0051 (N = 570), which compared buprenorphine and extended-release naltrexone (XR-NTX) and recruited from inpatient treatment facilities. Patient-level data were harmonized and a total of 23 database tables, with meticulous documentation, covering more than 110 variables, along with three tables with "meta-data" about the study design and treatment arms, were created. Domains included: social and demographic characteristics, medical and psychiatric history, self-reported drug use details and urine drug screening results, withdrawal, and treatment drug details. Here, we summarize the numerous issues with the organization and fidelity of the publicly available data which were noted and resolved, and present results on patient characteristics across the three trials and the harmonized domains, respectively. A systematic harmonization of OUD clinical trial data can be accomplished, despite heterogeneous data coding and classification procedures, by standardizing commonly assessed characteristics. Similar methods, embracing database normalization and/or "tidy" data, should be used for future datasets in other substance use disorder clinical trials.

SMART DAPPER is an implementation science study responding to mental health treatment gaps for depression and trauma-related disorders in Sub-Saharan Africa (SSA). We report on patient experiences in a study using a Sequential, Multiple Assignment Randomized Trial (SMART) design to test first and second line non-specialist treatment using psychotherapy (Interpersonal Psychotherapy [IPT] or medication (fluoxetine [FLX]), integrated within public sector primary care in western Kenya. An embedded qualitative study conducted in-depth interviews (n = 17) and three (n = 3) focus group discussions with participants (May to October 2021). Audio-recorded interviews were transcribed and translated into English; we deductively and inductively analyzed transcripts guided by grounded theoretical approaches and content analysis. We drew on the health belief model and socio-ecological framework to present findings, including perceived severity (motivations for taking part in the intervention), impacts of the intervention at the individual, interpersonal, and community and health systems levels as well as barriers and facilitators. Participants discussed family and marital conflict, loss of a child, loss of income or a job, and traumatic events such as a death or illness. Impacts at the individual level included reduced headaches, improved appetite and weight management, increased energy, improved sleep, better self-efficacy, and improved concentration, which was reported to lead to increased economic opportunities. At the interpersonal level, participants noted a reduction in conflict, better conflict management and resolution, increased harmony with family and community members, and improved relationships with their partners and children. Perceived challenges included balancing the intervention with livelihoods, preference for traditional medicines, actual or anticipated side effects with medication (FLX), mental health stigma, major life events, and perceived inadequate counseling and challenges with providers. The findings demonstrate the potential of the SMART DAPPER intervention for depression and trauma-related disorder treatments and underscore the challenges and barriers that must be addressed when scaling similar interventions. Trial registration: ClinicalTrials.gov identifier: NCT03466346.

IntroductionBreast cancer poses a significant health challenge in Sub-Saharan Africa, particularly in Ghana, where late-stage diagnoses and limited healthcare access contribute to elevated mortality rates. This study focuses on the crucial role of pathology and laboratory medical (PALM) services in the timely diagnosis of breast cancer within Ghana.MethodsA cross-sectional survey of hospitals was completed from November 2020 to October 2021, with 94.8% of identified in-country hospitals participating. Pathology service-related parameters assessed included whether pathology was available for the diagnosis of breast cancer on-site or via external referral, the number of pathology personnel, additional breast cancer diagnostic capabilities including estrogen and progesterone and/or HER2 testing, and the time from biopsy to patients receiving their results. Geospatial mapping was used to identify areas of limited access.ResultsOf the 328 participating hospitals, 136 (41%) reported breast cancer pathology services, with only 6 having on-site capabilities. Pathology personnel, comprising 15 consultants and 15 specialists, were concentrated in major referral centers, particularly in Greater Accra and Kumasi. An assessment of referral patterns suggested that 75% of the population reside within an hour of breast cancer pathology services. Among the 136 hospitals with access to breast cancer pathology, only a limited number reported that results included ER/PR (38%) and HER2 testing (33%).ConclusionGhana has been able to ensure significant pathology service availability through robust referral pathways with centralized labs. Despite this, difficulties persist with the majority of pathology results not including hormone receptor testing which is important in providing tumor specific treatment.

Dinosauria debuted on Earth's stage in the aftermath of the Permo-Triassic Mass Extinction Event, and survived two other Triassic extinction intervals to eventually dominate terrestrial ecosystems. More than 231 million years ago, in the Upper Triassic Ischigualasto Formation of west-central Argentina, dinosaurs were just getting warmed up. At this time, dinosaurs represented a minor fraction of ecosystem diversity. Members of other tetrapod clades, including synapsids and pseudosuchians, shared convergently evolved features related to locomotion, feeding, respiration, and metabolism and could have risen to later dominance. However, it was Dinosauria that radiated in the later Mesozoic most significantly in terms of body size, diversity, and global distribution. Elevated growth rates are one of the adaptations that set later Mesozoic dinosaurs apart, particularly from their contemporary crocodilian and mammalian compatriots. When did the elevated growth rates of dinosaurs first evolve? How did the growth strategies of the earliest known dinosaurs compare with those of other tetrapods in their ecosystems? We studied femoral bone histology of an array of early dinosaurs alongside that of non-dinosaurian contemporaries from the Ischigualasto Formation in order to test whether the oldest known dinosaurs exhibited novel growth strategies. Our results indicate that the Ischigualasto vertebrate fauna collectively exhibits relatively high growth rates. Dinosaurs are among the fastest growing taxa in the sample, but they occupied this niche alongside crocodylomorphs, archosauriformes, and large-bodied pseudosuchians. Interestingly, these dinosaurs grew at least as quickly, but more continuously than sauropodomorph and theropod dinosaurs of the later Mesozoic. These data suggest that, while elevated growth rates were ancestral for Dinosauria and likely played a significant role in dinosaurs' ascent within Mesozoic ecosystems, they did not set them apart from their contemporaries.

Impurities compounds in any pharmaceutical product or drug substance are inevitable from a chemistry point of view. The quality and safety of a pharmaceutical product are also significantly affected by these impurities content; therefore, impurities need to be identified and characterized through the use of appropriate analytical methods. Pramipexole is a nonergot dopamine agonist used to treat various Parkinson’s disease symptoms. Two unknown impurities were detected from a pramipexole dihydrochloride solid dosage form. These impurities were identified and characterized using ultra-performance liquid chromatography coupled with high-resolution mass spectroscopy (UPLC-HRMS). These impurities were found to be enriched when mannitol existed in the formulation. The structure and mechanism involved in the existence of the impurities were proposed. Furthermore, observation of the binding affinity potential risk of these impurities to the pramipexole receptor has also been demonstrated through molecular docking and molecular dynamics simulation study. The binding energy result showed that pramipexole interaction with dopamine receptors D2 and D3 was higher than pramipexole mannose adduct and pramipexole ribose adduct.

Abstract While hexagonal boron nitride (hBN) has been widely used as a buffer or encapsulation layer for emerging electronic devices, interest in utilizing it for large‐area chemical barrier coating has somewhat faded. A chemical vapor deposition process is reported here for the conformal growth of hBN on large surfaces of various alloys and steels, regardless of their complex shapes. In contrast to the previously reported very limited protection by hBN against corrosion and oxidation, protection of steels against 10% HCl and oxidation resistance at 850 °C in air is demonstrated. Furthermore, an order of magnitude reduction in the friction coefficient of the hBN coated steels is shown. The growth mechanism is revealed in experiments on thin metal films, where the tunable growth of single‐crystal hBN with a selected number of layers is demonstrated. The key distinction of the process is the use of N2 gas, which gets activated exclusively on the catalyst's surface and eliminates adverse gas‐phase reactions. This rate‐limiting step allowed independent control of activated nitrogen along with boron coming from a solid source (like elemental boron). Using abundant and benign precursors, this approach can be readily adopted for large‐scale hBN synthesis in applications where cost, production volume, and process safety are essential.

Abstract Background Lumbrokinase derived from earthworms, Lumbricus rubellus is known to have fibrinolytic enzymes that have potential as therapeutic drugs due to its ability to dissolve fibrin. The current study is aimed to purify the Lumbrokinase from L. rubellus and identify its protein component. Methods Water extract of local earthworm Lumbricus rubellus revealed several proteins. Therefore, to identify its protein component, purification through HiPrep DEAE fast flow and proteomic analysis were conducted prior to identifications. A combination of two-dimension gel electrophoresis (2DE) and electrospray ionization mass spectrometry analysis was used to identify the purified fractions. Results The purified fractions contain five protein bands, namely F25-1, F25-2, F85-1, F85-2, and F85-3, which displayed strong fibrinogenolytic activity. F25 fractions showed fibrinogenolytic activity of 974.85 U/mg, while F85 fractions showed higher activity of 1,484.11 U/mg. Fractions F85-1, F85-2, and F85-3 showed molecular weights of 42.6 kDa, 27.03 kDa, and 14 kDa, respectively and were identified as Lumbrokinase iso-enzymes. Conclusion This preliminary study indicates that the F25 and F85 fractions are similar to published fibrinolytic protease-1 and lumbrokinase, respectively, in terms of their amino acid sequence.

COVID-19 is an emerging disease that attacks the respiratory and systemic systems. Various treatments have been used for COVID-19, but no antiviral agents seemed to be efficacious. Many medicinal plants are commonly used for viral infections in Indonesia, including the guajava leaf. The study aimed to determine the effects of Psidium guajava extract supplementation on inflammation markers of asymptomatic and mild COVID-19 patients. The conversion time of PCR results was also evaluated. This study was an experimental, single-blinded, randomized clinical trial (ClinicalTrials.gov: NCT04810728) comparing the efficacy of P. guajava extract at the dose of 1000 mg/8h on top of standard treatment with the standard treatment only for asymptomatic and mild COVID-19 subjects. The primary endpoints were neutrophil and lymphocyte percentages as well as the neutrophil/lymphocyte ratio (NLR) on day 7 of the treatment. The secondary endpoints were high-sensitivity c-reactive protein (hs-CRP) level, PCR-based conversion time, and recovery rate at weeks 2 and 4. A total of 90 subjects were enrolled, and there were 40 subjects in the P. guajava (experimental) group and 41 subjects in the control group who completed the research. On day 7, significantly lower percentage of neutrophil (52.4% vs 58.9%, p = 0.002), higher lymphocyte percentage (35.5%, vs 29.7%, p = 0.002), and lower NLR (1.5 vs 2.1, p = 0.001) were demonstrated in the experimental versus control group. The PCR-based conversion time was shorter (14 days vs 16 days, p

The marine environment provides a rich source of distinct creatures containing potentially revolutionary bioactive chemicals. One of these organisms is Caulerpa racemosa, a type of green algae known as green seaweed, seagrapes, or green caviar. This organism stands out because it has great promise for use in medicine, especially in the study of cancer. Through the utilization of computational modeling (in silico) and cellular laboratory experiments (in vitro), the chemical components included in the green seaweed C. racemosa were effectively analyzed, uncovering its capability to treat non-small cell lung cancer (NSCLC). The study specifically emphasized blocking SRC, STAT3, PIK3CA, MAPK1, EGFR, and JAK1 using molecular docking and in vitro. These proteins play a crucial role in the EGFR Tyrosine Kinase Inhibitor Resistance pathway in NSCLC. The chemical Caulersin (C2) included in C. racemosa extract (CRE) has been identified as a potent and effective agent in fighting against non-small cell lung cancer (NSCLC), both in silico and in vitro. CRE and C2 showed a level of inhibition similar to that of osimertinib (positive control/NSCLC drug).

Climate change is one of the major environmental problems that humanity is facing. Studies suggest the use of negative emission technologies (NETs) to substantially decrease atmospheric CO2 concentration. Biochar can be applied to the soil for long-term carbon sequestration and simultaneous increase in soil fertility. However, the change in biochar properties after field application and exposure has not yet been thoroughly investigated, necessitating the need to determine if biochar can maintain its desirable properties with aging. Rough set-based machine learning (RSML) can be used to generate a data-driven model to predict the effect of different attributes on % change in C content of biochar. Four rules were accepted that relate the effect of feedstock type, pyrolysis temperature, aging method, and aging duration to % change in C content of biochar. Rules 1 and 2 cover 27 % and 14 % of the training set with 100 % accuracy, while Rules 3 and 4 cover 14 % and 10 % of the training set with 66.67 % accuracy. The performance of the rules was assessed via a k-fold (k=10) cross validation method and checked for mechanistic plausibility. The findings of the study can help maximize the potential benefit of biochar in climate mitigation.

Negative emission technologies (NETs) support climate change mitigation by capturing carbon dioxide from the atmosphere for storage in a separate environmental compartment. NETs have multi-footprints that may negatively affect the environment and society if these technologies are implemented on large scales. The solution is implementing multiple technologies in NET portfolios at smaller scales for sustainability and risk reduction. However, computing optimal NET portfolios are challenged by uncertainties in the availability of resources that are difficult to predict precisely. This work implements a two-step approach to evaluating the robustness of NET portfolios. The first step is mathematical modeling to generate optimal and suboptimal solutions. The second step is subjecting the solutions to Monte Carlo simulation to evaluate the tradeoff between their cost and robustness against uncertain resource availability. The two-step approach is demonstrated in a case study on NET portfolios. Results show the existence of suboptimal solutions with higher costs but are more robust compared to the optimal solution. The two-step approach identifies the solutions that will perform well under uncertainty, thus supporting climate change mitigation decision analysis.

Environment, social, and governance (ESG) considerations have become a necessity for businesses. A company’s environmental and community initiatives have been found to greatly influence customer perception. This is even more critical for industries that are difficult to decarbonize like the aviation industry. There has been little investigation on the role of ESG strategies on company financial performance which can dictate the sustainability of initiative implementation. This work uses ESG performance indicators to develop a rule-based model for predicting company financial performance as measured by return on assets (ROA). Results suggest that the most critical attributes of the ESG framework are Innovation, Workforce, Human Rights, Product Responsibility, Shareholders, and the aggregate ESG score. The best–performing model correctly predicts 15 out of 28 of the validation data (53.57 %). A rule of interest is that which states IF (Human Rights = Average) THEN (ROA = Average). It had the highest coverage for both training and validation data with a certainty of 61 %, and a prediction accuracy of 71.4 %, highlighting the importance of Human Rights on firm value.

In silico toxicology protocols are meant to support computationally-based assessments using principles that ensure that results can be generated, recorded, communicated, archived, and then evaluated in a uniform, consistent, and reproducible manner. We investigated the availability of in silico models to predict the carcinogenic potential of pregabalin using the ten key characteristics of carcinogens as a framework for organizing mechanistic studies. Pregabalin is a single-species carcinogen producing only one type of tumor, hemangiosarcomas in mice via a nongenotoxic mechanism. The overall goal of this exercise is to test the ability of in silico models to predict nongenotoxic carcinogenicity with pregabalin as a case study. The established mode of action (MOA) of pregabalin is triggered by tissue hypoxia, leading to oxidative stress (KC5), chronic inflammation (KC6), and increased cell proliferation (KC10) of endothelial cells. Of these KCs, in silico models are available only for selected endpoints in KC5, limiting the usefulness of computational tools in prediction of pregabalin carcinogenicity. KC1 (electrophilicity), KC2 (genotoxicity), and KC8 (receptor-mediated effects), for which predictive in silico models exist, do not play a role in this mode of action. Confidence in the overall assessments is considered to be medium to high for KCs 1, 2, 5, 6, 7 (immune system effects), 8, and 10 (cell proliferation), largely due to the high-quality experimental data. In order to move away from dependence on animal data, development of reliable in silico models for prediction of oxidative stress, chronic inflammation, immunosuppression, and cell proliferation will be critical for the ability to predict nongenotoxic compound carcinogenicity.

Rationale & Objective: Heart failure (HF) is an important cause of morbidity and mortality among individuals with chronic kidney disease (CKD). A large body of evidence from preclinical and clinical studies implicates excess levels of fibroblast growth factor 23 (FGF23) in HF pathogenesis in CKD. It remains unclear whether the relationship between elevated FGF23 levels and HF risk among individuals with CKD varies by HF subtype. Study Design: Prospective cohort study. Settings & Participants: A total of 3,502 participants were selected in the Chronic Renal Insufficiency Cohort study. Exposure: Baseline plasma FGF23. Outcomes: Incident HF by subtype and total rate of HF hospitalization. HF was categorized as HF with preserved ejection fraction (HFpEF, ejection fraction [EF] ≥ 50%), HF with reduced EF (HFrEF, EF

The demand for renewable alternatives to natural gas as a fuel for industries and residential consumers has been growing fast. Bio-natural gas is expected to play an important role in green energy transition and climate change. Large amounts of high organic load wastewater are discharged from coking, pharmaceutical, papermaking, printing and dyeing, petrochemical, and food processing industries. Bio-natural gas could be produced from high organic load wastewater using anaerobic digestion or membrane bio-reactor and biogas upgrading technologies. This study performs a comprehensive analysis of bio-natural gas produced from high organic load wastewater (BNGWW) in China. The status quo is compared with the potential use of this alternative, coupled with a techno-economic analysis of production technologies, energy policies, and opportunities and challenges. A model is proposed to estimate the provincial BNGWW demand in China from 2016 to 2020 and the decomposed demand across 42 sectors from 2016 to 2019. Factors influencing BNGWW potential are analyzed under the changes of time, region, and sectors. The result shows that BNGWW demand is affected mainly by natural gas prices, industrial scale, and chemical oxygen demand of organic wastewater. Some provinces in East China, Central and Southern China, and Xinjiang have high BNGWW potential. The agricultural and food processing industry has the highest production potential. Challenges and barriers to the development of BNGWW have also been highlighted.

Negative Emissions Technologies (NETs) are now necessary solutions for climate change mitigation. NET portfolios that recommend a technology mix of individual NETs at smaller scales are preferred to ease their implementation's environmental and social impact. Resource constraints using the Planetary Boundaries as a framework can be used to ensure that the multi-footprints of NETs are within the planet’s safe operating space. However, for regional-scale applications, the uncertain resource constraints may highly depend on local conditions and the decisionmaker. There is a lack of studies investigating the effect of uncertain resource constraints on NET portfolios. This work addresses the research gap by applying a global sensitivity analysis using a space-filling Design of Experiments (DOE) strategy. First, a fuzzy, multi-period non-linear mathematical programming model is formulated. Computer experiments using a Latin hypercube space-filling DOE and regression analysis are then performed to investigate the effects of the varying resource constraints on the optimum NET portfolio. This approach enables the investigation of interactions and higher order effects of the parameters on the responses, the total negative emission potential and net present value of the portfolio, and the generation of response surface graphs, which are not available using traditional sensitivity analysis. The approach is demonstrated using a case study in the Association of Southeast Asian Nations region.

Carbon dioxide removal (CDR) will be needed to offset residual greenhouse gas (GHG) emissions and achieve carbon neutrality. Enhanced weathering (EW) is a promising CDR technique based on the acceleration of naturally occurring reactions between alkaline minerals with carbonic acid in rainwater. The reactive minerals are pulverized and then applied at a calibrated rate to terrestrial sites; the weathering reaction results in carbon sequestration as bicarbonate ions in the runoff water. EW can be deployed via carbon management networks (CMNs) of sources (mineral-crushing plants) and sinks (application sites). However, current CMN optimization models fail to account for the presence of multiple players (i.e., government and industry) with conflicting objectives. Bilevel optimization models can be used to account for these conflicts via leader-follower games. In this work, a P-graph approach to the optimization of EW-based CMNs is developed. The government is assumed to act as the leader seeking to minimize external costs to the public by specifying acceptable transport routes for mineral powder; the industry is assumed to act as the follower seeking to maximize its CDR earnings by minimizing its costs subject to the transport network topology constraints. Note that the government imposes the latter constraints in anticipation of the industry’s intent to maximize revenues. The model is implemented as a Python code and demonstrated with an illustrative case study. Results show that by following the Stackelberg solution, cost of transportation may be reduced by at least 5 % and the risk of death by 79 %.

Large industrial facilities offer attractive targets to malicious attacks by terrorists. These attacks can trigger cascading failures, capitalizing on the high levels of integration among the components of highly optimized industrial systems. Any defensive measure will generally be resource-constrained and may be difficult to conceal perfectly. In this work, a novel hybrid Stackelberg-Leontief model is developed for planning optimal defensive measures in industrial sites against targeted attacks. The core of the model represents the industrial site with a physical input-output model, which is embedded in a leader-follower game. The defender acts as the leader, who selects defensive measures to implement from a suite of options. Each selected defensive measure incurs a fixed cost regardless of whether an attack occurs or not; however, the defensive measure also mitigates the economic damage caused by any attack. The attacker acts as the follower, who selects an attack strategy based on a set of options with damage coefficients; the attacker is also resource-constrained and needs to optimize the attack while considering the defensive measures put in place. The defender anticipates the attacker’s strategic behavior in developing the defense strategy, resulting in a Stackelberg game formulated as a bilevel mixed integer linear program. The model is illustrated with a didactic case study. In both scenarios considered, the Stackelberg strategy is for the defender to protect the RO module with the attacker targeting the CHP and RO module (Scenario 1) or the CHP, Chiller, and RO module (Scenario 2). This results in the lowest possible mitigation cost (100 USD/h) and a 26 – 27 % increase in the leader’s objective relative to a no defense strategy.

Bio-natural Gas (BNG) from livestock manure (BNGLM) is a potential alternative energy source that can yield significant resource and environmental benefits. Energy potentials and carbon intensity differences in livestock manure utilization may exist in provincial areas. The spatial and temporal distribution characteristics for the BNG potentials are analyzed. A life-cycle carbon emission accounting model for carbon intensity was proposed. The results show that with the increase of scale and intensification of animal husbandry, the BNG potential is increasing. The potentials vary greatly among provinces. The annual carbon emission reduction potential from BNGLM reached 1.25 Gt. The proposed energy potential, its spatial characteristics, and carbon emission reduction measurement are expected to provide better support for enhancing specific regional plans for waste manure utilization and development.

Trading of carbon credits will become prevalent in response to global decarbonization efforts. There will be companies specializing in the sale of credits generated internally (e.g., using negative emissions technologies) or purchased from external sources (e.g., generated from energy conservation projects). Process Integration methodology can be adapted to provide vital decision support for such activities. In this work, an extension of Pinch Analysis (PA) is developed for planning the sale of carbon credits under temporal constraints. Projects and facilities that generate carbon credits are treated as sources, while buyers of the credits are treated as demands. It is assumed that carbon credits need to be generated before they are sold, and time is used in a manner analogous to temperature in conventional PA to impose directionality constraints when transferring credits from sources to sinks. The flow of carbon credits from each source and into each demand is assumed to occur at a fixed rate between the well-defined start and end times. Targeting viable sales is done using the standard graphical Composite Curve approach. The methodology is demonstrated with a series of illustrative case studies.

The development of Bio-Natural Gas (BNG) is of great strategic significance in alleviating the shortage of fossil natural gas and fostering low-carbon energy transition. This work shows the economic and environmental analysis of the straw-based BNG supply chain using a Mixed Integer Linear Programming (MILP) model. Considering a sustainable supply chain framework, including straw collection, transportation, pre-treatment and product, and technologies (e.g., Anaerobic Digestion and Gasification), the optimal alternative is determined. Different straw harvesting rates and carbon emission reduction targets are designed to evaluate the cost and carbon emissions of each routine. The proposed model is applied to a country case study in China to illustrate the optimization of a regional straw-based BNG supply chain. The results show that the minimal cost ranges from 6.02 to 8.55 RMB/m3, and the emission reduction potential is from 9.82 to 12.74 kg/m³. The use of straw-based BNG can generate environmental benefits and estimate costs.

We analyse the potential of the proposed μ+μ+ and μ+e− collider μTRISTAN to complement the searches for charged-lepton flavour-violation (CLFV) that can be carried out by Belle II. μTRISTAN offers the possibility of directly producing and studying new resonances that could mediate CLFV for a certain range of masses. In addition, we find that it can produce competitive bounds to those from Belle II for cases where the new resonance lies beyond direct reach. We illustrate these points with three Z3 “lepton triality” models, where we also find an example that can only be probed by μTRISTAN. These three models feature doubly-charged scalars, denoted k1,2,3 respectively, that induce both CLFV and flavour-conserving processes. Tree-level k1 exchange induces the CLFV scattering process μ+e−→e+τ−, while k2 interactions induce μ+μ+→τ+e+, μ+e−→τ+μ− and make a non-SM contribution to the flavour-conserving scattering μ+μ+→μ+μ+. The k3 model has a non-SM contribution to the flavour-conserving process μ+e−→μ+e−. Other scattering processes involving k1, k2 or k3 are not relevant for μTRISTAN and outside the scope of our analysis. We quantify the sensitivity of μTRISTAN for each of these processes. For the k1 and k2 cases we compare the μTRISTAN reach to the expected sensitivity of Belle II to the crossing symmetry related CLFV τ decays.

Neighborhoods have been identified as important determinants of health-related outcomes, but limited research has assessed the influence of neighborhood context along the cancer continuum. This study used census tract-level data from the United States Census Bureau and Centers for Disease Control and Prevention to characterize Miami-Dade County census tracts (n = 492) into social vulnerability clusters and assess their associated breast, cervical, and colorectal cancer screening participation rates. We identified disparities by social vulnerability cluster in cancer screening participation rates. Further investigation of geographic disparities in social vulnerability and cancer screening participation could inform equity-focused cancer control efforts.

The separation of nanoparticles from oxide hosts by exsolution forms the basis for catalytic and energy-related applications. Here, the authors elucidate the multistep mechanism of exsolution at perovskite surfaces by combining real-time electron microscopy and computational methods.