Unveiling the NIMBY effect in construction and demolition waste landfilling: Factors, paths, and solutions.

The construction and operation of the construction and demolition waste (C&DW) landfills often encounter significant opposition from nearby residents, which is called the "not in my backyard" (NIMBY) effect. However, little is known about the formation mechanism of the NIMBY effect in C&DW landfilling, so this research was conducted for this purpose. First, the influencing factors leading to the NIMBY effect were determined based on a literature review and questionnaire survey. Then, the interrelationship and influencing path of critical factors were revealed using expert interviews and Interpretative Structural Modelling. The results shown that 12 factors from four levels (including residents, society, government, and enterprises) caused the NIMBY effect in C&DW landfilling. These factors formed a complex network comprising 18 influencing paths. Notably, policy and responding measures as pivotal bottom-level factors that trigger the NIMBY effect by indirectly impacting residents' rights awareness and shaping public perception towards C&DW landfill operation enterprises through directly affecting personal interest, cognitive bias, distrust, disposal technology, management level, opinion leader, and other intermediate factors, ultimately triggering the NIMBY effect. Moreover, strategies for mitigating or resolving the NIMBY effect were proposed, such as protecting personal reasonable interests, understanding the potential risks of C&DW landfills rationally, reporting the C&DW landfills from an objective perspective, improving policies and promoting public participation, and enhancing supervision of the C&DW landfills. The study added new knowledge to the current public's NIMBY effect in C&DW landfilling. Meanwhile, it also provided a reference for formulating C&DW landfilling policies and selecting landfill sites.

The rest-activity rhythm, genetic susceptibility and risk of type 2 diabetes: A prospective study in UK Biobank.

AIMS: This study aims to examine the association between the rest-activity rhythm (RAR) and the incidence of type 2 diabetes (T2D). MATERIALS AND METHODS: In total, 97 503 participants without diabetes in the UK Biobank cohort were recruited. Wearable accelerometry was used to monitor circadian behaviour. The parameters of RAR including inter-daily stability, intra-daily variability, relative amplitude (RA), most active continuous 10 h period (M10), and least active continuous 5 h period (L5) were calculated to evaluate the robustness and regularity of the RAR. The weighted polygenic risk score for T2D (T2D-PRS) was calculated. Cox proportion hazards models were used to evaluate the survival relationship and the joint and interaction effects of RAR parameters and T2D-PRS on the occurrence of T2D. RESULTS: During 692 257 person-years follow-ups, a total of 2434 participants were documented. After adjustment for potential confounders, compared with participants in the highest quartile of RA and M10, the participants in the lowest quartile had a greater risk of T2D (HRRA = 2.06, 95% CI: 1.76-2.41; HRM10 = 1.33, 95% CI: 1.19-1.49). Meanwhile, the highest quartile of L5 was related to a higher risk of T2D (HR = 1.78, 95% CI: 1.55-2.24). The joint analysis showed that the high T2D-PRS with the lowest quartile of RA and M10, or highest quartile of L5 jointly increased the risk of T2D (HRRA = 4.46, 95% CI: 3.36-6.42; HRM10 = 3.15, 95% CI: 2.29-4.32; HRL5 = 3.09, 95% CI: 2.40-3.99). No modification effects of T2D-PRS on the association between the RAR parameters and risk of T2D were observed (p > .05). CONCLUSION: The unbalanced RAR are associated with a greater risk of T2D, which are independent of known risk factors of T2D.

Long-term immune responses in patients with confirmed novel coronavirus disease-2019: a 9-month prospective cohort study in Shanghai, China.

BACKGROUND: The duration of antibodies against SARS-CoV-2 in Covid-19 patients remains uncertain. Longitudinal serological studies are needed to prevent disease and transmission of the virus. METHODS: In 2020, 414 blood samples were tested, obtained from 157 confirmed Covid-19 patients, in a prospective cohort study in Shanghai. RESULTS: The seropositive rate of IgM peaked at 40.5% (17/42) within 1 month after illness onset and then declined. The seropositive rate of IgG was 90.6% (58/64) after 2 months, remained above 85% from 2 to 9 months and was 90.9% (40/44) after 9 months. Generalized estimating equations models suggested that IgM (P < 0.001) but not IgG significantly decreased over time. Age ≥ 40 years (adjusted odds ratio [aOR] 4.531; 95% confidence interval [CI] 1.879-10.932), and cigarette smoking (aOR 0.344; 95% CI 0.124-0.951) were associated with IgG, and age ≥ 40 years (aOR 2.820; 95% CI 1.579-5.036) was associated with IgM. After seroconversion, over 90% and 75.1% of subjects were estimated to remain IgG-positive 220 and 254 days, respectively. Of 1420 self-reported symptoms questionnaires, only 5% reported symptoms 9 months after onset. CONCLUSIONS: In patients with a history of natural infection, anti-SARS-CoV-2 IgG is long-lived, being present for at least 9 months after illness onset. The long duration of natural immunity can mitigate and eliminate Covid-19 and the ongoing pandemic.

Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer.

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10-4, 1.8 × 10-4, 0.8 × 10-4, and 1.0 × 10-4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management.

From malaria elimination to post-elimination: a 10-year surveillance data study in Shanghai.

BACKGROUND: The aim of this study was to investigate and analyse the characteristics of malaria in Shanghai from 2010 to 2019 and to provide suggestions for areas with a similar elimination process in China in order to prompt development of strategies and interventions in the post-elimination stage. METHODS: This was a cross-sectional study exploring the malaria characteristics during 2010-2019 in Shanghai, China. Malaria data from the Infectious Diseases Information Reporting Management System (IDIRMS) between 2010 and 2012 and data from the Parasitic Diseases Information Reporting Management System (PDIRMS) between 2013 and 2019 were combined for analysis in this study. RESULTS: From 2010 to 2019, a total of 436 malaria cases were reported in Shanghai. Among them, 415 (95.18%) were imported from abroad, 19 (4.36%) were domestically acquired from other provinces, 1 (0.23%) case was caused by blood transfusion, and 1 (0.23%) had a long incubation. Only Plasmodium vivax was found in domestically indigenous cases; Plasmodium falciparum accounted for the largest proportion of imported cases. Domestically acquired cases were only reported in 2010-2011 and 88% occurred in June to September; no significant seasonal difference was observed for imported cases over the 10 years. No local transmission has occurred in Shanghai since 2012. The median interval from fever onset to diagnosis was 3 days. Between 2010 and 2019, among 308 foci, 33 were classified as potential transmission and dispersed in suburb areas (Minhang, Baoshan, Jiading, Pudong, Jinshan, Songjiang, Qingpu, Fengxian, and Chongming). Only Anopheles sinensis was present and the proportion of Anopheles sinensis in different species of mosquitoes under surveillance in Shanghai decreased from 2011 to 2019. CONCLUSIONS: Shanghai faces the challenge of malaria re-establishment caused by imported malaria in the post-elimination stage. Therefore, risk investigation and assessment should be carried out, and receptivity and susceptibility should be assessed for every point of focus. Training should be continued to strengthen facility staff capability, and multisectoral coordination and cooperation need to be conducted efficiently to maintain malaria elimination in Shanghai.

Fetal malnutrition is associated with impairment of endogenous melatonin synthesis in pineal via hypermethylation of promoters of protein kinase C alpha and cAMP response element-binding.

This study investigated whether and how fetal malnutrition would influence endogenous melatonin synthesis, and whether such effect of fetal malnutrition would transmit to the next generation. We enrolled 2466 participants and 1313 of their offspring. The urine 6-hydroxymelatonin sulfate and serum melatonin rhythm were measured. Methylation microarray detection and bioinformatics analysis were performed to identify hub methylated sites. Additionally, rat experiment was performed to elucidate mechanisms. The participants with fetal malnutrition had lower 6-hydroxymelatonin sulfate (16.59 ± 10.12 µg/24 hours vs 24.29 ± 11.99 µg/24 hours, P < .001) and arear under curve of melatonin rhythm (67.11 ± 8.16 pg/mL vs 77.11 ± 8.04 pg/mL, P < .001). We identified 961 differentially methylated sites, in which the hub methylated sites were locating on protein kinase C alpha (PRKCA) and cAMP response element-binding protein (CREB1) promoters, mediating the association of fetal malnutrition with impaired melatonin secretion. However, such effects were not observed in the offspring (all P > .05). Impaired histomorphology of pineal, decreased melatonin in serum, pineal, and pinealocyte were also found in the in vivo and in vitro experiments (P < .05 for the differences of the indicators). Hypermethylation of 10 CpG sites on the PRKCA promoter and 8 CpG sites on the CREB1 promoter were identified (all P < .05), which down-regulated PRKCA and CREB1 expressions, leading to decreased expression of AANAT, and then resulting in the impaired melatonin synthesis. Collectively, fetal malnutrition can impair melatonin synthesis through hypermethylation of PRKCA and CREB1 promoters, and such effects cannot be transmitted to the next generation.

Dibromoacetic acid exposure is associated with abnormal melatonin rhythm in rats via inhibition of p-CREB1-AANAT signalling pathway.

Dibromoacetic acid (DBA) is a by-product of disinfection in drinking water, which could cause many adverse effects in test animals. However, little research on its neurotoxicity has been conducted, and its mechanism has not been elucidated. In the present study, ninety Sprague-Dawley rats were administered DBA at doses of 0, 30, and 90 mg/kg body weight for 28 days via oral gavage. We found that DBA could induce obvious neurotoxicity in the pineal gland as indicated by histological changes and impaired rhythm of melatonin in pineal and serum. In the mechanism study, transcriptome data showed that DBA exposure could induce 732 differential expression genes. Besides, GO and KEGG analysis results indicated that these genes were enriched in circadian rhythms, among which CREB1 had the most significant fold change. And immunofluorescence staining (IF) and immunohistochemical staining (IHC) results showed that the number of amber-colored masculine neurons for the p-CREB1 in the 90 mg/kg group was markedly lower, and staining for the p-CREB1 was weaker. Moreover, the results of PCR and western blot showed that DBA exposure could down-regulate the expressions of CREB1 and p-CREB1, leading to the decreased expressions of gene and protein of arylalkylamine N-acetyltransferase (AANAT), and then resulting in the impaired melatonin synthesis in the pineal and serum. In conclusion, DBA exposure is associated with abnormal melatonin rhythm via inhibition of the p-CREB1-AANAT signalling pathway.

Surveillance of the 'bud event of norovirus-associated gastroenteritis' in schools: does it work in the prevention of norovirus infection outbreaks in Shanghai?

Outbreaks of norovirus-associated gastroenteritis have been reported in schools in recent decades in China. For early warning and response to infectious disease outbreaks, the Shanghai Infectious Diseases Bud Event Surveillance System (IDBESS) was established in 2016. Bud event is a term used for the early sign of a potential infectious disease outbreak in public settings when the first few cases appear. This study aimed to describe the epidemiological characteristics of Norovirus-associated gastroenteritis bud events from June 2016 to December 2017 and to understand factors influencing the severity of events. Data were extracted from the IDBESS, supplemented by field investigations and school absence surveillance. In total, 189 bud events of Norovirus-associated gastroenteritis were reported in schools and kindergartens, affecting 3827 individuals and 52.38% happened in primary schools. The attack rate of Norovirus-associated gastroenteritis was 3.82% on average in students in the affected schools. In each event, case numbers varied between 5 and 148, with a median of 16. The duration of bud events lasted for 2 days on average. School absence happened in 47.93% (1797/3749) of affected students and the average duration of absence was 3.07 days. It was found that a longer delay before reporting was associated with a longer-lasting duration of bud event (OR = 2.25, 95% CI: 1.65, 3.07). In conclusion, ascribed to the sensitive threshold for alerting and the timely field investigation, the surveillance of bud events of Norovirus-associated gastroenteritis is effective in the control of Norovirus infection among preschool children and students in Shanghai.

Epidemiological and clinical characteristics of 161 discharged cases with coronavirus disease 2019 in Shanghai, China.

BACKGROUND: In December 2019, the outbreak of coronavirus disease 2019 (COVID-19) began in Wuhan, China, and rapidly spread to other regions. We aimed to further describe the epidemiological and clinical characteristics of discharged COVID-19 cases and evaluate the public health interventions. METHODS: We collected epidemiological and clinical data of all discharged COVID-19 cases as of 17 February 2020 in Shanghai. The key epidemiological distributions were estimated and outcomes were also compared between patients whose illness were before 24 January and those whose illness were after 24 January. RESULTS: Of 161 discharged COVID-19 cases, the median age was 45 years, and 80 (49.7%) cases were male. All of the cases were categorized as clinical moderate type. The most common initial symptoms were fever (85.7%), cough (41.0%), fatigue (19.3%), muscle ache (17.4%), sputum production (14.9%), and there were six asymptomatic cases. 39 (24.2%) cases got infected in Shanghai, and three of them were second-generation cases of Shanghai native cases. The estimated median of the time from onset to first medical visit, admission, disease confirmation, and discharge for 161 cases was 1.0 day (95% CI, 0.6-1.2), 2.0 days (95% CI, 1.5-2.6), 5.2 days (95% CI, 4.6-5.7), 18.1 days (95% CI, 17.4-18.8), respectively. The estimated median of the time from admission to discharge was 14.0 days (95% CI, 13.3-14.6). The time from onset to first medical visit, admission and disease confirmation were all shortened after the Shanghai's first-level public health emergency response. In Cox regression model, the significant independent covariates for the duration of hospitalization were age, the time from onset to admission and the first-level public health emergency response. CONCLUSIONS: Local transmission had occurred in Shanghai in late January 2020. The estimated median of the time from onset to discharge of moderate COVID-19 was 18.1 days in Shanghai. Time intervals from onset to first medical visit, admission and disease confirmation were all shortened after the Shanghai's first-level public health emergency response. Age, the first-level public health emergency response and the time from onset to admission were the impact factors for the duration of hospitalization.

Three infection clusters related with potential pre-symptomatic transmission of coronavirus disease (COVID-19), Shanghai, China, January to February 2020.

We report three clusters related with potential pre-symptomatic transmission of coronavirus disease (COVID-19) between January and February 2020 in Shanghai, China. Investigators interviewed suspected COVID-19 cases to collect epidemiological information, including demographic characteristics, illness onset, hospital visits, close contacts, activities' trajectories between 14 days before illness onset and isolation, and exposure histories. Respiratory specimens of suspected cases were collected and tested for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) assay. The interval between the onset of illness in the primary case and the last contact of the secondary case with the primary case in our report was 1 to 7 days. In Cluster 1 (five cases), illness onset in the five secondary cases was 2 to 5 days after the last contact with the primary case. In Cluster 2 (five cases) and Cluster 3 (four cases), the illness onset in secondary cases occurred prior to or on the same day as the onset in the primary cases. The study provides empirical evidence for transmission of COVID-19 during the incubation period and indicates that pre-symptomatic person-to-person transmission can occur following sufficient exposure to confirmed COVID-19 cases. The potential pre-symptomatic person-to-person transmission puts forward higher requirements for prevention and control measures.

Uncovering episodic influence of oceans on extreme drought events in Northeast Brazil by ordinal partition network approaches.

Since 2012, the semiarid region of Northeast Brazil (NEB) has been experiencing a continuous dry condition imposing significant social impacts and economic losses. Characterizing the recent extreme drought events and uncovering the influence from the surrounding oceans remain to be big challenges. The physical mechanisms of extreme drought events in the NEB are due to varying interacting time scales from the surrounding tropical oceans (Pacific and Atlantic). From time series observations, we propose a three-step strategy to establish the episodic coupling directions on intraseasonal time scales from the ocean to the precipitation patterns in the NEB, focusing on the distinctive roles of the oceans during the recent extreme drought events of 2012-2013 and 2015-2016. Our algorithm involves the following: (i) computing drought period length from daily precipitation anomalies to capture extreme drought events; (ii) characterizing the episodic coupling delays from the surrounding oceans to the precipitation by applying the Kullback-Leibler divergence (KLD) of complexity measure, which is based on ordinal partition transition network representation of time series; and (iii) calculating the ratio of high temperature in the ocean during the extreme drought events with proper time lags that are identified by KLD measures. From the viewpoint of climatology, our analysis provides data-based evidence of showing significant influence from the North Atlantic in 2012-2013 to the NEB, but in 2015-2016, the Pacific played a dominant role than that of the Atlantic. The episodic intraseasonal time scale properties are potential for monitoring and forecasting droughts in the NEB in order to propose strategies for drought impacts reduction.

Epidemiology of human infections with avian influenza A(H7N9) virus in China.

BACKGROUND: The first identified cases of avian influenza A(H7N9) virus infection in humans occurred in China during February and March 2013. We analyzed data obtained from field investigations to describe the epidemiologic characteristics of H7N9 cases in China identified as of December 1, 2013. METHODS: Field investigations were conducted for each confirmed case of H7N9 virus infection. A patient was considered to have a confirmed case if the presence of the H7N9 virus was verified by means of real-time reverse-transcriptase-polymerase-chain-reaction assay (RT-PCR), viral isolation, or serologic testing. Information on demographic characteristics, exposure history, and illness timelines was obtained from patients with confirmed cases. Close contacts were monitored for 7 days for symptoms of illness. Throat swabs were obtained from contacts in whom symptoms developed and were tested for the presence of the H7N9 virus by means of real-time RT-PCR. RESULTS: Among 139 persons with confirmed H7N9 virus infection, the median age was 61 years (range, 2 to 91), 71% were male, and 73% were urban residents. Confirmed cases occurred in 12 areas of China. Nine persons were poultry workers, and of 131 persons with available data, 82% had a history of exposure to live animals, including chickens (82%). A total of 137 persons (99%) were hospitalized, 125 (90%) had pneumonia or respiratory failure, and 65 of 103 with available data (63%) were admitted to an intensive care unit. A total of 47 persons (34%) died in the hospital after a median duration of illness of 21 days, 88 were discharged from the hospital, and 2 remain hospitalized in critical condition; 2 patients were not admitted to a hospital. In four family clusters, human-to-human transmission of H7N9 virus could not be ruled out. Excluding secondary cases in clusters, 2675 close contacts of case patients completed the monitoring period; respiratory symptoms developed in 28 of them (1%); all tested negative for H7N9 virus. CONCLUSIONS: Most persons with confirmed H7N9 virus infection had severe lower respiratory tract illness, were epidemiologically unrelated, and had a history of recent exposure to poultry. However, limited, nonsustained human-to-human H7N9 virus transmission could not be ruled out in four families.

Two confirmed cases of severe fever with thrombocytopenia syndrome with pneumonia: implication for a family cluster in East China.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) was first reported in China in 2011. Human-to-human transmission of the virus occurred occasionally in family clusters. However, pneumonia as an onset syndrome was not common in most SFTS cases. Our aim is to report a family cluster of SFTS with clinical manifestation of pneumonia in Shanghai. METHODS: Epidemiologic investigations were conducted when a family cluster of severe fever with thrombocytopenia syndrome virus (SFTSV) infection was identified in Shanghai in June 2016. Samples were collected from two secondary cases and two close contacts with fever. SFTSV was detected by Real-Time reverse transcription polymerase chain reaction (RT-PCR). RESULTS: There were two confirmed STFS cases and one potential index case. The potential index case became ill on 21 May and died on 31 May. Case A had onset from 4 to 23 June and case B from 8 June to 25 June. All the three cases experienced pneumonia at the early stage of SFTSV infection. Three (3) out of thirty two (32) close contacts had symptoms of fever or cough but were detected STFSV negative by real-time RT-PCR. According to epidemiologic investigations, the potential index case had outdoor activities on a nearby hill. A tick bite could have been the reason for the SFTSV infection in the potential index case as ticks were found both in grassland or shrubs on the hill and also found on mice caught in her house. Both cases A and B had provided bedside care for the potential index case without any protection and had contacted with blood and other body fluids. CONCLUSION: It was a family cluster of SFTSV infection imported from Jiangsu province located in the east of China. We suggested to become alert to atypical SFTSV infected cases.

Epidemiology of norovirus infections among diarrhea outpatients in a diarrhea surveillance system in Shanghai, China: a cross-sectional study.

BACKGROUND: Norovirus is an important cause of gastroenteritis both in children and adults. In China, few studies have been conducted on adult populations. This study aimed to determine the contribution of norovirus to gastroenteritis, characterize the features of norovirus infections, compare them with other pathogens, and test the effectiveness of the surveillance system. METHODS: A citywide surveillance network on diarrhea patients was established. Samples were collected with intervals from both children and adults among diarrhea outpatients in hospitals and tested for viruses using rRT-PCR and for bacteria in CDCs. Patient information was acquired through interviews and recorded into a dedicated online system. The Pearson χ2 test, multivariate logistic regression models and discriminant models were fitted into its comparisons with the non-norovirus group and other pathogens. RESULTS: Norovirus was detected in 22.91% of sampled diarrhea patients. The seasonal distribution of norovirus infections was different from non-norovirus patients (p<0.001), with a half-year peak. Higher proportions of males (p=0.001, OR=1.303, 95% CI=1.110-1.529), local citizens (p<0.001) and officials/clerks (p=0.001, OR=1.348, 95% CI=1.124-1.618) were affected with norovirus when compared with non-norovirus patients. Diarrhea patients affected with norovirus featured nausea (p<0.001, OR=1.418, 95% CI=1.176-1.709) and vomiting (p<0.001, OR=1.969, 95% CI=1.618-2.398), while fewer manifested fever (p=0.046, OR=0.758, 95% CI=0.577-0.996) and abdominal pain (p=0.018, OR=0.815, 95% CI=0.689-0.965). Children were more vulnerable to rotavirus (p=0.008, OR=1.637, 95% CI=1.136-2.358) and bacteria (p=0.027, OR=1.511, 95% CI=1.053-2.169) than norovirus. There was a seasonal difference between the GI and GII genotypes (p<0.001). Officials or clerks were more easily affected with GI than GII (p=0.006, OR=1.888, 95% CI=1.205-2.958). CONCLUSIONS: This study was based on a citywide hospital-sentinel surveillance system with multiple enteric pathogens included. Norovirus was recognized as the most prevalent enteric pathogen in Shanghai. The seasonal peak was from October to April. Males had a higher prevalence than females. Local citizens and officials/clerks were more vulnerable to norovirus than other pathogens. Compared with rotavirus and bacteria, children were less frequently affected by norovirus. Nausea and vomiting were typical of norovirus, whereas fever and abdominal pain were uncommon symptoms of this pathogen. GI and GII infections were centered in different seasons. Officials and clerks were more easily affected by GI than GII.

Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces.

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.

Developing a regional scale construction and demolition waste landfill landslide risk rapid assessment approach.

In recent years, construction and demolition waste (CDW) landfills landslide accidents have occurred globally, with consequences varying due to surrounding environmental factors. Risk monitoring is crucial to mitigate these risks effectively. Existing studies mainly focus on improving risk assessment accuracy for individual landfills, lacking the ability to rapidly assess multiple landfills at a regional scale. This study proposes an innovative approach utilizing deep learning models to quickly locate suspected landfills and develop risk assessment models based on surrounding environmental factors. Shenzhen, China, with significant CDW disposal pressure, is chosen as the empirical research area. Empirical findings from this study include: (1) the identification of 52 suspected CDW landfills predominantly located at the administrative boundaries within Shenzhen, specifically in the Longgang, Guangming, and Bao'an districts; (2) landfills at the lower risk of landslides are typically found near the northern borders adjacent to cities like Huizhou and Dongguan; (3) landfills situated at the internal administrative junctions generally exhibit higher landslide risks; (4) about 70 % of these landfills are high-risk, mostly located in densely populated areas with substantial rainfall and complex topographies. This study advances landfill landslide risk assessments by integrating computer vision and environmental analysis, providing a robust method for governments to rapidly evaluate risks at CDW landfills regionally. The adaptable models can be customized for various urban and broadened to general landfills by adjusting specific indicators, enhancing environmental safety protocols and risk management strategies effectively.

An evaluation model for city-scale construction and demolition waste management effectiveness: A case study in China.

The growing generation of construction and demolition waste (CDW) has emerged as a prominent challenge on global environmental agendas. However, the effectiveness of CDW management (CDWM) strategies varies among cities. Existing literature predominantly evaluates the effectiveness of CDWM at the project level, offering a localized perspective that fails to capture a city's comprehensive CDWM profile. This localized focus has certain limitations. To fill this gap in city-scale evaluations, this study introduces a novel model for assessing CDWM effectiveness at the municipal level. An empirical investigation was conducted across 11 cities within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) to operationalize this model. The model defines five distinct levels of CDWM effectiveness. Findings indicate that Hong Kong consistently achieves the highest level (level I), while the majority of cities fall within levels III and IV. This pattern suggests that CDWM effectiveness in the GBA is moderately developed, with uneven progress in CDW management outcomes and supporting systems. Essentially, there is a lack of synchronous development of CDWM results and guarantee systems. The proposed evaluation model enriches existing CDWM research field and offers a framework that may inform future studies in other countries.

Soil-Transmitted Helminth Infections in Shanghai, China: Surveillance from 2014 to 2023.

Soil-transmitted helminthiasis remains a notable health problem in developing countries. In 1990 in Shanghai, a city in eastern China, 47% of the population was affected. Due to a series of comprehensive approaches, the prevalence decreased to 0.5% in 2009. We collected 10-year surveillance data to assess the epidemic situation of soil-transmitted helminth (STH) infections in Shanghai. Stool samples and questionnaires from participants were collected. The Kato-Katz technique was used to detect infections with Ascaris lumbricoides, hookworm (Ancylostoma duodenale and Necator americanus), and Trichuris trichiura. From 2014 to 2023, a total of 77,685 participants were screened for parasitic infections, and the overall prevalence of STH infections decreased from 0.1% to 0% in Shanghai. Of 77,685 participants, 25 (0.03%) were positive for intestinal helminths, with the most common parasite being A. lumbricoides (72.0%). Generally, elderly participants from rural areas with lower education levels were more likely to be infected with STHs. A total of 2,914 questionnaires were collected, and most respondents (72.6%) had good knowledge about the transmission routes of STHs. However, 12.3% of participants did not know the damage to health caused by STHs, and 19.3% had no idea of how to prevent infection. These data demonstrate the current state of STH infections in Shanghai. The results suggest that various comprehensive measures should be encouraged, continuously implemented, and strengthened accordingly so that STH elimination can be integrated into the Healthy China initiative by 2030.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases.

BACKGROUND: Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified. METHODS: We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period. RESULTS: We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%). CONCLUSIONS: Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-Based BCIs.

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.