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Abstract Bioenergy with carbon capture and storage (BECCS) is a key negative emission technology for climate mitigation. Some countries have made no commitment to carbon neutrality but are viewed as potential BECCS candidates (hereafter, non-CN countries). Here we analyze contributions of these countries to global climate mitigation with respect to BECCS using an Earth system model with explicit representations of bioenergy crops. Switchgrass cultivation in these non-CN countries can further remove atmospheric CO2 by 9.1 ± 2.8 and 19.9 ± 5.2 PgC in the low-warming and overshot scenarios, resulting in an extra biogeochemical cooling effect of 0.01 ± 0.04 to 0.02 ± 0.06 °C. This cooling is largely counterbalanced by the biophysical warming, but the net effect is still an extra cooling. The non-CN countries play a more important role in the low-warming scenario than in the overshoot scenario, despite the inequality of temperature change among countries. Our study highlights the importance of a global system for climate mitigation.

Abstract In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.

Because the volatile content of isoamyl alcohol increases sharply on the seventh day of wheat mildew infection, isoamyl alcohol can be used as an early biomarker of wheat mildew infection. Currently, only a few sensors for isoamyl alcohol detection have been reported, and these sensors still suffer from low sensitivity and poor moisture resistance. Herein, the isoamyl alcohol sensitivity of 5 at% Er@LaFeO3 (ELFO) was enhanced by loading Ag nanoparticles on the surface of the ELFO microspheres, while the optimal operating temperature was reduced. The moisture resistance of Ag/ELFO was improved by the incorporation of g-C3N4 nanosheets (NSs) on the surface of Ag/ELFO through electrostatic self-assembly. Given the requirements for practical applications in grain granaries, the sensing behavior of a Ag/ELFO-based sensor incorporating g-C3N4 NSs at 20% relative humidity (RH) was systematically studied, and the sensor demonstrated excellent repeatability, long-term stability, and superior selectivity (791 at 50 ppm) for isoamyl alcohol with a low limit of detection (LOD = 75 ppb). Furthermore, the practical results obtained for wheat at different mildew stages further confirmed the potential of the g-C3N4/Ag/ELFO-based sensor for monitoring the early mildew stage of wheat. This work may offer guidance for enhancing the moisture resistance of gas-sensitive materials through the strategy of employing composite nanomaterials.

Accurate medical image segmentation is critical for various clinical applications, and convolutional neural networks (CNNs) have demonstrated promising results in this field. The performance of CNN models for segmenting specific organs or lesion areas from medical images heavily depends on the feature extraction ability of the backbone network. In this study, we aim to explore the deep features of the backbone network and propose a novel network that can accurately capture multi-scale image features for medical image segmentation. To achieve this goal, we built upon the widely used U-Net framework and evaluated the feature extraction performance of different backbone networks for medical images. Then, we introduced a novel backbone network called ResX block, which utilizes rectangular and dilated convolutions to capture multi-scale features.To validate our conclusions, we conducted experiments on four benchmark datasets, including Lits2017, 3Dircadb, LIDC, and LCTSC. Our results demonstrate that the proposed ResX block outperforms mainstream feature extraction blocks in terms of accuracy and robustness. Our study confirms the importance of accurate multi-scale feature extraction for improving the performance of CNNs in medical image segmentation. Furthermore, we have verified the potential of rectangular and dilated convolutions for capturing multi-scale features in medical images. Finally, we proposed a novel backbone network, the ResX block, which can be seamlessly integrated into any CNN used for medical image segmentation. Our study provides valuable insights for developing more accurate and efficient CNN models for medical image analysis.

Abstract There is a potential for transmission of sexually transmitted infections (STIs) within sexual minority women (SMW) in China. However, research specifically focused on STIs among SMW in China is severely limited. This study aims to evaluate the prevalence of STIs and identify associated risk factors among SMW in Beijing, China. This study comprised a baseline assessment followed by a follow-up evaluation. Consistent questionnaire interviews and STI tests were administered during both stages. Participants were recruited online in Beijing between 2020 and 2021 and factors associated with STIs were analyzed using logistic and Cox regression models. The baseline included 219 SMW, and 58.9% (129/219) of these individuals participated in the follow-up. During the baseline assessment, 4.1% (9/219) tested positive for chlamydia infection, while 5.0% (11/219) were HSV-2 seropositive. At the follow-up, the incidence of HSV-2 was 3.7 cases per 100 person-years. Notably, engaging in sexual activity with men and having an increased number of sexual partners were both identified as factors associated with a higher risk of STIs. The findings suggest that SMW in Beijing may face a significant risk of contracting STIs. As a preventive measure, there should be a concerted effort to promote STI testing within the SMW community.

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

Abstract It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

BackgroundTo date, non-occupational postexposure prophylaxis (PEP) has been widely accepted as a safe and effective intervention for HIV in many countries, yet it remains an underutilized prevention strategy in China. Evidence indicated a high demand for PEP among Chinese men who have sex with men, but the uptake and access to PEP service remain limited. In an era of rapid development of web-based technology, online medical platforms in China hold great promise in facilitating PEP provision and delivery by addressing problems such as accessibility, convenience, privacy protection, and antidiscrimination by integrating online and offline resources. However, there is a paucity of data concerning the uptake and outcomes of online PEP in China. ObjectiveThe aim of this study is to explore online PEP service provision and understand PEP uptake and outcome through a web-based cross-sectional study. MethodsFrom January 2020 to June 2021, we conducted a retrospective web-based survey among those seeking online PEP services via the internet medical platform “HeHealth” using a structured questionnaire. Participants were surveyed on sociodemographic characteristics, sexual and drug-related behaviors, history of preexposure prophylaxis (PrEP) usage, and PEP uptake. Statistical analysis included descriptive analysis, chi-square test, and multivariable logistic regression. P values

Male homosexual behavior dominates the mode of HIV infection among students in China. This study aims to identify factors associated with HIV risk behaviors among male students who have sex with men (student MSM) aged 15 to 19 in China. The online sampling used the standard respondent-driven sampling (RDS) method, with recruitment support from community-based organizations (CBOs) in Tianjin and Guangzhou. Multivariate logistic regression models were performed to identify factors associated with multiple HIV risk behaviors. A total of 449 subjects were recruited in this study, including 206 in Tianjin and 243 in Guangzhou. The presence of multiple HIV risk behaviors among student MSM, in many cases, was found to positively correlate with parental or friends’ awareness of their same-sex sexual behavior or sexual orientation, with some geographical differences. The prevalence of HIV risk behaviors among student MSM aged 15 to 19 was high and associated with the disclosure of their sexual orientation or their sex with males to parents and friends. These findings highlight the importance of addressing social support in HIV prevention among student MSM when delivering HIV-related education or services.

Abstract Background HIV epidemic among men who have sex with men (MSM) remains a major public health concern in China. Despite a growing body of research on transgender women worldwide, little is known about Chinese transgender women within MSM. We sought to estimate HIV incidence and distinguish risk factors of HIV acquisition among them from that among cisgener (non-transgender) MSM (cis-MSM). Methods We conducted an open cohort study among Chinese MSM, including those who were identified as transgender in Shanghai and Tianjin. Participants were initially recruited by local community-based organizations from January to June, 2016, and were followed up approximately every 6 months until June 2018. At each visit, a structured questionnaire was used to gather information on demographics, sexual risk behaviors, and HIV status. HIV incidence was calculated as the number of seroconversions divided by total number of person-years of follow-up among HIV-negatives at baseline. Risk factors of HIV acquisition were assessed by univariate and multivariate Cox regression models with time-dependent variables. Results A total of 1056 participants contributed 1260.53 person-years (PYs) of follow-up, 33 HIV seroconversions occurred during the follow-up period, yielding an estimated HIV incidence of 2.62 (95% CI 1.80–3.68) per 100 PYs. HIV incidence among transgender women was 4.42 per 100 PYs, which was significantly higher than that of 1.35 per 100 PYs among cis-MSM, demonstrating a threefold higher odds of HIV infection than cis-MSM. For transgender women, those lived locally ≤ 2 years (adjusted hazard ratio [aHR] = 1.76, 95% CI 1.13–2.76) and unprotected anal sex last time (aHR = 4.22, 95% CI 1.82–9.79) were more likely to acquire HIV. For cis-MSM, factors associated with HIV acquisition were frequency of anal sex ≥ 3 times in past one month (aHR = 4.19, 95% CI 1.06–16.47) and unprotected anal sex last time (aHR = 5.33, 95% CI 1.52–18.73). Conclusions Compared to cis-MSM, transgender women were at higher risk of HIV acquisition, highlighting an urgent need of tailored prevention. Future HIV program should consider to include them to ensure that this population in China are not left behind. Graphical abstract

Abstract Biochar has been proposed as a promising negative CO2 emission technology to mitigate future climate change with the additional benefit of increasing agricultural production. However, the spatial responses of soil organic carbon (SOC) to biochar addition in cropland are still uncertain, and the economic feasibility of large‐scale biochar implementation remains unclear. Here, we analyzed the response of SOC to biochar addition using 389 paired field measurements. The results show that biochar addition significantly increased SOC by 45.8% on average with large regional variations. Using a random forest model trained with soil, climate, biotic, biochar, and management factors, we found that the response of SOC to biochar addition was mainly dependent on biochar application rates, initial SOC, edaphic (e.g., pH), and climatic (e.g., mean annual precipitation) variables. Combined with the predicted SOC changes to biochar addition on the global cropland, we assessed the revenue of the biochar system based on the current and potential pyrolysis plants in the world using the life‐cycle analysis. Net revenue of the currently existing 144 pyrolysis plants increases with larger plant capacity and higher carbon price. Potential revenue of building new plants is high in regions like America and Europe but low in regions with infertile soil, low crop residues availability, and inconvenient transportation. The global CO2 removal of biochar application is 6.6 Tg CO2e (CO2 equivalent) year−1 with a net revenue of $ 177 million dollars at a carbon price of $ 50 t−1 CO2 for current pyrolysis plants with a biomass‐processing capacity of 20,000 t year−1. Our study provides a full economic assessment of idealized biochar addition scenarios and identifies the locations with maximal potential revenues with new pyrolysis plants.

BackgroundHIV self-testing (HIVST) holds great promise for expanding HIV testing. Nonetheless, large-scale data on HIVST behavior are scant. Millions of HIVST kits are sold through e-commerce platforms each year. ObjectiveThis study aims to analyze the spatiotemporal distribution of the HIVST kit–purchasing population (HIVSTKPP) in China. MethodsDeidentified transaction data were retrieved from a leading e-commerce platform in China. A joinpoint regression model was used to examine annual trends of the HIVSTKPP rates by calculating average annual percentage change. Bayesian spatiotemporal analysis was performed to locate hot spots with HIVSTKPP rates. Spatial autocorrelation analysis and space-time cluster analysis were conducted to identify clusters of HIVSTKPP. High-high clusters of HIVSTKPP can be identified by spatial autocorrelation analysis, and high-high clusters indicate that a region and its surrounding region jointly had a higher-than-average HIVSTKPP rate. Spatial regression analysis was used to elucidate the association between the number of HIV testing facilities, urbanization ratio (the proportion of urban population in the total population), and gross domestic product per capita and the HIVSTKPP. ResultsBetween January 1, 2016, and December 31, 2019, a total of 2.18 million anonymous persons in China placed 4.15 million orders and purchased 4.51 million HIVST kits on the web. In each of these 4 years, the observed monthly size of the HIVSTKPP peaked in December, the month of World AIDS Day. HIVSTKPP rates per 100,000 population significantly increased from 20.62 in 2016 to 64.82 in 2019 (average annual percentage change=48.2%; P

In signal processing of the growing semaphore, a microwave photonic filter (MPF) is capable of dealing with high-frequency signals, offering the advantages of high bandwidth, easy tuning, and more. This paper presents an efficient tunable microwave photonic filter that features a wideband tuning capability and narrow-band filtering effect based on the lithium niobate on insulator (LNOI) material platform. A multi-mode waveguide race-track type microring resonator has been designed, along with a thermal electrode that utilizes the thermo-optical effect of lithium niobate to adjust the microring resonator. The packaged device has been tested, with a wideband tunable range of 4.7~38.2 GHz achieved. This allows for cross-band continuous tuning across the C-band to Ka-band range. When supplied with 29.1 mW of electric power, the thermal tuning efficiency reaches 9.2 pm/mW, enabling high-frequency tuning of up to 38.2 GHz. The filter possesses a high resolution, exhibiting a 3 dB bandwidth of 662 MHz.

Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

BackgroundSince the introduction of HIV self-testing by UNAIDS in 2014, the practice has been extensively implemented around the world. HIV self-testing (HIVST) was developed in China around 2015, and the online purchase of HIVST kits through e-commerce platforms has since become the most important delivery method for self-testing, with advantages such as user-friendliness, speed, and better privacy protection. ObjectiveUnderstanding the spatiotemporal characteristics of online HIVST kit purchasing behavior and identifying potential impacting factors will help promote the HIV self-testing strategy. MethodsThe online retail data of HIVST kits from the 2 largest e-commerce platforms in China from 2015 to 2017 were collected for this study. The Bayesian spatiotemporal hierarchical model was used to investigate the spatiotemporal characteristics of online purchased HIVST kits. Ordinary least squares regression was used to identify potential factors associated with online purchase, including GDP per capita, population density, road density, HIV screening laboratory density, and newly diagnosed HIV/AIDS cases per 100,000 persons. The q statistics calculated by Geodetector were used to determine the interactive effect of every 2 factors on the online purchase. ResultsThe online purchase of HIVST kits increased rapidly in China from 2015 to 2017, with annual peak sales in May and December. Five economically superior regions in China, Pearl River Delta, Yangtze River Delta, Chengdu and surrounding areas, Beijing and Tianjin areas, and Shandong Peninsula, showed a comparatively higher spatial preference for online purchased HIVST kits. The GDP per capita (P

Abstract Background HIV-transmitted drug resistance (TDR) is found in antiretroviral therapy (ART)-naïve populations infected with HIV-1 with TDR mutations and is important for guiding future first- and second-line ART regimens. We investigated TDR and its effect on CD4 count in ART-naïve youths from the China-Myanmar border near the Golden Triangle to better understand TDR and effectively guide ART. Methods From 2009 to 2017, 10,832 HIV-1 infected individuals were newly reported along the Dehong border of China, 573 ART-naïve youths (16 ~ 25 y) were enrolled. CD4 counts were obtained from whole blood samples. HIV pol gene sequences were amplified from RNA extracted from plasma. The Stanford REGA program and jpHMM recombination prediction tool were used to determine genotypes. TDR mutations (TDRMs) were analyzed using the Stanford Calibrated Population Resistance tool. Results The most common infection route was heterosexuals (70.51%), followed by people who inject drugs (PWID, 19.20%) and men who have sex with men (MSM) (8.90%). The distribution of HIV genotypes mainly included the unique recombinant form (URF) (44.08%), 38.68% were CRFs, 13.24% were subtype C and 4.04% were subtype B. The prevalence of TDR increased significantly from 2009 to 2017 (3.48 to 9.48%) in ART-naïve youths (4.00 to 13.16% in Burmese subjects, 3.33 to 5.93% in Chinese subjects), and the resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside and nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 3.49, 2.62, and 0.52%, respectively. Most (94.40%, n = 34) of HIV-1-infected patients with TDRM had mutation that conferred resistance to a single drug class. The most common mutations Y181I/C and K103N, were found in 7 and 9 youths, respectively. The mean CD4 count was significantly lower among individuals with TDRMs (373/mm3 vs. 496/mm3, p = 0.013). Conclusions The increase in the prevalence of HIV-1 TDR increase and a low CD4 count of patients with TDRMs in the China-Myanmar border suggests the need for considering drug resistance before initiating ART in HIV recombination hotspots.

BackgroundsLittle was known about the impact of HCV prior infection on HIV transmission and acquisition. We aimed to explore whether HCV prior infection and its interactions with unprotected sex affected HIV acquisition.MethodsThis study was conducted among HIV heterosexual serodiscordant couples whose index cases were receiving treatment during 2008–2014 in Zhumadian. At baseline, we collected information on demographics and medical history of ART use, CD4 count, and HIV viral load for index partners, and also HIV and HCV status for non-index partners. For each year's visit, we followed up on sexual behaviors among couples in the recent year and HIV seroconversion of non-index partners. Analyses of the Cox model and synergistic interaction were performed.ResultsWe identified 81 HIV seroconversions over 18,370.39 person-years, with the overall HIV seroconversion rate of 0.44 per 100 person-years. Couples, whose index cases were aged 50 years and above, had a baseline viral load >400 copies per ml and no AIDS-defining illness, and newly-initiated ART in the study period had a higher risk of HIV seroconversion. Unprotected sex and HCV prior infection showed a synergistic association with HIV acquisition risk (RERI = 3.65, SI = 0.48, AP = 2.24).ConclusionUnprotected sex and HCV infection were independent risk factors associated with HIV acquisition. The coexistence of them might have a synergistic effect on the risk which needs further research.

Positive energy districts (PEDs) are urban areas which seek to take an integral approach to climate neutrality by including technological, spatial, regulatory, financial, legal, social, and economic perspectives. It is still a new concept and approach for many stakeholders. ChatGPT, a generative pre-trained transformer, is an advanced artificial intelligence (AI) chatbot based on a complex network structure and trained by the company OpenAI. It has the potential for the fast learning of PED. This paper reports a trial test in which ChatGPT is used to provide written formulations of PEDs within three frameworks: challenge, impact, and communication and dissemination. The results are compared with the formulations derived from over 80 PED experts who took part in a two-day workshop discussing many aspects of PED research and development. The proposed methodology involves querying ChatGPT with specific questions and recording its responses. Subsequently, expert opinions on the same questions are provided to ChatGPT, aiming to elicit a comparison between the two sources of information. This approach enables an evaluation of ChatGPT’s answers in relation to the insights shared by domain experts. By juxtaposing the outputs, a comprehensive assessment can be made regarding the reliability, accuracy, and alignment of ChatGPT’s responses with expert viewpoints. It is found that ChatGPT can be a useful tool for the rapid formulation of basic information about PEDs that could be used for its wider dissemination amongst the general public. The model is also noted as having a number of limitations, such as providing pre-set single answers, a sensitivity to the phrasing of questions, a tendency to repeat non-important (or general) information, and an inability to assess inputs negatively or provide diverse answers to context-based questions. Its answers were not always based on up-to-date information. Other limitations and some of the ethical–social issues related to the use of ChatGPT are also discussed. This study not only validated the possibility of using ChatGPT to rapid study PEDs but also trained ChatGPT by feeding back the experts’ discussion into the tool. It is recommended that ChatGPT can be involved in real-time PED meetings or workshops so that it can be trained both iteratively and dynamically.

Mogroside V is a bioactive ingredient extracted from the natural food Siraitia grosvenorii which possesses functions that stimulate lung humidification and cough relief activities, but its underlying mechanisms were rarely studied. To estimate its potential protective effect on ovalbumin (OVA)-induced pulmonary inflammation and understand its system-wide mechanism, integrated omics was applied in this study. Mogroside V effectively reduced the levels of IgE, TNF-α, and IL-5 in OVA-induced mice. The results of RNA-seq and data-independent acquisition proteomics approach revealed that 944 genes and 341 proteins were differentially expressed in the normal control group (NC) and ovalbumin-induced control group (OC) and 449 genes and 259 proteins were differentially expressed between the OC and the group treated with 50 mg/kg mogroside V (MV). After a combined analysis of the transcriptome and the proteome, 93 major pathways were screened, and we discovered that mogroside V exerts an anti-inflammation effect in the lung via NF-κB and JAK-STAT, both of which are among the signaling pathways mentioned above. In addition, we found that the key regulatory molecules (Igha, Ighg1, NF-κB, Jak1, and Stat1) in the two pathways were activated in inflammation and inhibited by mogroside V. Thus, mogroside V may be the main bioactivity component in S. grosvenorii that exerts lung humidification and cough relief effects.

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV’s trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits.

ABSTRACTUsing normalization of CD4 counts as the main evaluation parameter of complete immune restoration for HIV-1 patients under antiretroviral therapy (ART) might be not enough. A comprehensive evaluation system more accurately reflecting immune restoration are urgently needed. Totally, 91,805 HIV-1 patients from 17 tertiary hospitals in China during 2005–2018 were included in this study. Immune restoration and mortality were assessed. Patients initiated ART with baseline CD4 counts

A digital twin is regarded as a potential solution to optimize positive energy districts (PED). This paper presents a compact review about digital twins for PED from aspects of concepts, working principles, tools/platforms, and applications, in order to address the issues of both how a digital PED twin is made and what tools can be used for a digital PED twin. Four key components of digital PED twin are identified, i.e., a virtual model, sensor network integration, data analytics, and a stakeholder layer. Very few available tools now have full functions for digital PED twin, while most tools either have a focus on industrial applications or are designed for data collection, communication and visualization based on building information models (BIM) or geographical information system (GIS). Several observations gained from successful application are that current digital PED twins can be categorized into three tiers: (1) an enhanced version of BIM model only, (2) semantic platforms for data flow, and (3) big data analysis and feedback operation. Further challenges and opportunities are found in areas of data analysis and semantic interoperability, business models, data security, and management. The outcome of the review is expected to provide useful information for further development of digital PED twins and optimizing its sustainability.

Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlänge, Sweden, owned by the municipality. The aim was to present a digital spatial map of both electricity use and district heating demand in the spatial–temporal dimension. A toolkit for top-down data processing and analysis was considered based on the energy performance database of municipality-owned buildings. The data were initially cleaned, transformed and geocoded using custom scripts and an application program interface (API) for OpenStreetMap and Google Maps. The dataset consisted of 228 and 105 geocoded addresses for, respectively, electricity and district heating monthly consumption for the year 2018. A number of extra parameters were manually incorporated to this data, i.e., the total floor area, the building year of construction and occupancy ratio. The electricity use and heating demand in the building samples were about 24.47 kWh/m2 and 268.78 kWh/m2, respectively, for which great potential for saving heating energy was observed. Compared to the electricity use, the district heating showed a more homogenous pattern following the changes of the seasons. The digital mapping revealed a spatial representation of identifiable hotspots for electricity uses in high-occupancy/density areas and for district heating needs in districts with buildings mostly constructed before 1980. These results provide a comprehensive means of understanding the existing energy distributions for stakeholders and energy advisors. They also facilitate strategy geared towards future energy planning in the city, such as energy benchmarking policies.

In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment.

Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

Regarding the location of a facility, the presumption in the widely used p-median model is that the customer opts for the shortest route to the nearest facility. However, this assumption is problematic on free markets since the customer is presumed to gravitate to a facility by the distance to and the attractiveness of it. The recently introduced gravity p-median model offers an extension to the p-median model that account for this. The model is therefore potentially interesting, although it has not yet been implemented and tested empirically. In this paper, we have implemented the model in an empirical problem of locating vehicle inspections, locksmiths, and retail stores of vehicle spare-parts for the purpose of investigating its superiority to the p-median model. We found, however, the gravity p-median model to be of limited use for the problem of locating facilities as it either gives solutions similar to the p-median model, or it gives unstable solutions due to a non-concave objective function.