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Abstract Rivers and road systems play crucial roles in urban development and in shaping the vitality of cities. The present study focuses on the spatiotemporal development patterns of canal cities. Taking Suzhou as an example, employing various spatial measurement methods combined with multidimensional spatiotemporal data, it clearly demonstrates the urban morphological patterns and societal characteristics related to the road and river network systems at different stages of urban development. Utilizing data extracted from historical and modern maps of Suzhou and employing the YOLO (You Only Look Once) algorithm, this study establishes a spatiotemporal dataset for Suzhou's ancient city, a representative Chinese canal city. An optimized space syntax algorithm for public service facilities, utilizing Python library like Geopandas, Numpy, is proposed to analyze the river and road system's impact on historical development and urban morphology. With multiple spatial econometric models applied to maps from various periods, this research explores the following findings: (1) By controlling rivers as a variable, it quantitatively confirms that rivers were crucial components of urban systems in ancient cities, playing a significant role in urban development and activities, though gradually overlooked in modern times. (2) Public service facilities of different types demonstrate patterns of Accessibility and Integration within the river-road system across various periods. (3) Changes in the distribution of public service facilities correspond to phased shifts in the center of Suzhou’s ancient city, mutually validating the authenticity of realistic paintings, historical maps, and the methodologies used in this study. This study emphasizes a broader historical and spatiotemporal perspective, augmented by quantitative research methods. It investigates the distribution and developmental characteristics of various types of public service facilities across different periods. The research aim is to understand the impact of river and road systems on urban development, as well as the morphological and social changes in historical cities that arise from urban activities.

Abstract Urbanization has led to homogenizing heritage site landscapes, and the protective measures have become disconnected from public needs. Additionally, the complex and diverse overall characteristics of heritage sites and the uneven distribution of values across different areas are related to the lack of landscape experience assessment. The “subjective + objective” cognitive evaluation and visual perception framework that adopts the Scenic Beauty Estimation Procedure-Semantic Differential (SBE–SD) method and eye-tracking analysis can compensate for the limitations of a single evaluation method by integrating quantitative and qualitative analysis. This research takes the Yi’an Fortress in Zhangpu County, Fujian Province, as the object and examines the visual experiences of different areas and types of landscapes within the Yi’an Fortress. The findings reveal several key insights: (1) Significant differences were found in the landscape experiences of different areas within the heritage site. The visual experience score of the core building area of Yi’an Fortress is (1.01) > the heritage entrance area (0.897) > the residential area (0.841) > the natural ecological area (0.784), indicating that the natural ecological area should be the focus of future protection and development efforts, with a particular emphasis on enhancing the ‘landscape aesthetic’ and ‘landscape cultural’ aspects. (2) The landscape experience scores can be used to understand the reasons for the differences in participants’ experiences of different landscapes. The architectural heritage landscape of the Yi’an Fortress scored highest in the experience evaluation due to its superior performance in terms of ‘landscape form’ factors. The cultural decorative landscape scored next, while the garden greening landscape scored the lowest, due to their poorer performance in terms of ‘landscape form’ and ‘landscape aesthetics’. (3) The eye-tracking data was consistent with the results of the subjective evaluation, validating the “subjective + objective” cognitive evaluation and visual perception framework, employing the SBE–SD method and eye-tracking analysis is a scientific and effective method for assessing the visual experience of heritage landscapes. These results provide a scientific basis for the heritage planners and managers of the Yi’an fortress to improve the landscape environment, better meet public needs, and preserve the unique character of this vital cultural resource. Furthermore, this study offers a new research method and approach for the protection of other heritage landscapes.

Abstract Rapid urbanization has reformed the peripheries of China’s main cities during the last four decades. The rural areas have been constantly developed through infrastructure development in recent days which introduces a newly semi-urban region referred to as Desakota. The purpose of this research is to discover potential influencing factors of housing construction technology in desakotas of China which needs to be assessed for the implementation of Desakotas forming globally. In this study, a total of six key primary factors and twenty key secondary factors have been shortlisted, which were surveyed by 120 respondents. After the statistical analysis, it is seen that one primary factor, “Architectural and engineering mechanical factors” has been ranked top. Moreover, “Cost, financial and economic evaluation and Analysis of the impact of the natural environment on buildings” both ranked top among twenty secondary factors. This study adds to the body of knowledge by revealing the primary contributing elements for the first time that need to be taken into consideration in the rural and town communities of desakotas in China. The study results could help stakeholders, construction firms, academicians, researchers, and the government to focus their efforts and resources on the significant changes in the Desakotas in preparation for rapid urban development.

China is rich in mineral resources, and problems of goaf formed in the process of resource exploitation are serious obstacle to the development of China's economic, so it is of great significance for the assessment and management of goafs. This paper introduces emerging dung beetle optimizer (DBO) algorithm and establishes DBO-BP (back-propagation) model, at the same time, it is compared with a series of heuristic algorithms coupled with BP neural network models: PSO (particle swarm optimization) - BP model, WOA (whale optimization algorithm) - BP model, and SSA (sparrow search algorithm) - BP model. Then they are applied to evaluate the hazard of goafs, the result shows that the DBO-BP model gets the highest train set accuracy, which is at least 2.7 % higher than other models, while the DBO-BP model obtains the highest test set accuracy, meanwhile its effectiveness and stability have also been proven. Finally we apply the established DBO-BP model to evaluate the hazard of the tungsten mine goaf of Yaogangshan in Hunan Province, and its excellent practicability was confirmed. This paper may provide a reference for the solution of nonlinear engineering problems.

Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s–895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point (579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results.

Human-induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) represent a promising and renewable cell source for therapeutic applications. A systematic evaluation of the immunological properties and engraftment potential of iMSCs generated from urine-derived iPSCs is lacking, which has impeded their broader application. In this study, we differentiated urine-derived iPSCs into iMSCs and assessed their fundamental MSC characteristics, immunogenicity, immunomodulatory capacity and in vivo engraftment. Compared to umbilical cord-derived MSCs (UCMSCs), iMSCs demonstrated an enhanced proliferative capacity, a higher level of regenerative gene expression, and lower immunogenicity, strengthening resistance to apoptosis induced by allogeneic peripheral blood mononuclear cells (PBMCs) and the NK-92 cell line. In addition, iMSCs exhibited a diminished ability to inhibit T cell proliferation and activation compared with UCMSCs. Transcriptomic analyses further revealed the decreased expression of immune regulatory factors in iMSCs. After transfusion into mouse models, iMSCs engrafted in the lungs, liver, and spleen and exhibited the ability to migrate to tumor tissues. Our results indicated that iMSCs generated from urine-derived iPSCs have a significant replicative capacity, low immunogenicity and unique immunomodulatory properties, and hence offer obvious advantages in immune privilege and allogenic therapeutic application prospects.

In large-scale dairy farming, the use of high-temperature-fermented dairy manure bedding instead of rice husk-based bedding and other commercial types of bedding is widely favored. Strip-stacking aerobic fermentation is the main production method of dairy manure bedding, but it has problems including unstable fermentation and the secondary breeding of pathogens. In this work, a multi-probe, integrated, online monitoring system for temperature and relative humidity was used for fermentation process optimization. The effects of the temporal and spatial distribution of fermentation temperature and relative humidity on the nutrient content curve and the moisture and ash content of manure bedding materials were systematically studied. The effect of the fermentation process on the retention rate of effective bedding materials (cellulose, hemicellulose, and lignin) was analyzed. The experiments proved that high-quality bedding material can be obtained through reasonable stacking fermentation. The fabricated bedding material has a total dry base content consisting of cellulose, hemicellulose, and lignin of 78%, an ash content of 6%, and a nutrient content of 17%. The obtained bedding material was produced to increase the bed rest rate and continuously inhibit the bedding bacteria content, keeping it at a low level for 5 days. This study proves that temperature and humidity monitoring can guide the optimization of the strip-stacking fermentation process of dairy manure and that it can be applied to large-scale farms to improve fermentation parameters.

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments.

BackgroundDrowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. ObjectiveWe aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. MethodsUnintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. ResultsA total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and –9.5% to the overall effects, respectively. ConclusionsThis study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

With spherical Fe-Mn-CuOx/γ-Al2O3 catalyst as the packing of RPB (Rotating Packed Bed), the apparent reaction kinetics of phenol degradation by high gravity intensified heterogeneous catalytic ozonation was investigated under different operating conditions (high gravity factor, pH value, liquid flow rate, temperature). The reaction rate constants in BR (Bubbling Reactor) were compared, and the apparent reaction kinetics empirical models were established. The research results showed that the direct reaction rate constant of ozone and phenol (kPhOH/O3/RPB) in the high gravity field ranges in 8.17×104~8.80×105 L·mol-1·s-1, and the indirect reaction rate constant of ·OH and phenol (kPhOH/·OH/RPB) varies in the range of 1.54×1010~1.78×1010 L·mol-1·s-1, which are higher than these for BR (kPhOH/O3/BR=1.64×104 L·mol-1·s-1, kPhOH/·OH/BR=1.05×1010 L·mol-1·s-1). The activation energy of the indirect reaction (0.53 kJ/mol) is significantly lower than that of direct reaction (34.07 kJ/mol). By linearly fitting the apparent reaction rate constants under various reaction conditions, the apparent kinetic empirical models of direct and indirect reactions were obtained. The models show that among the influencing factors, β has the greatest influence on the apparent reaction rate.

BackgroundGlobal warming may increase the frequency of compound hot extreme (CHE).However, there is still a lack of studies assessing the associations between CHE and preterm birth (PTB), and the underlying biological mechanisms remain unclear.ObjectiveTo estimate the association of exposure to CHE during pregnancy with PTB, and to explore the roles of inflammatory, endothelial dysfunction, and oxidative stress in the association between CHE and PTB.MethodsAll participants were selected from the Prenatal Environments and Offspring Health (PEOH), a prospective birth cohort conducted in Guangzhou. In this study, a total of 2449 participants who gave birth from May to October in 2014 to 2017 were enrolled, and among them blood samples were collected from 311 preterm (n=43) and full-term (n=268) pregnant women at the time of delivery. A hot day/night was identified as a day when the daily maximum temperature/minimum temperature was higher than its 90th percentile in the study period, and a CHE was defined as having both a hot night and a following hot day. The meteorological data were obtained from the China Meteorological Data Sharing Service System. Anusplin was used to assess the daily maximum temperature, daily minimum temperature, and relative humidity of the participant residence. Enzyme-linked immunosorbent assay (ELISA) was used to measure C reactive protein (CRP), endothelin-1 (ET-1), and malondialdehyde (MDA) levels in maternal serum, and their results were transformed by natural logarithm. A distributed lag nonlinear model was used to investigate the associations of exposures to hot day, hot night, and CHE during pregnancy with PTB at different lag days, and a logistic regression model was used to investigate the associations of CRP, ET-1, and MDA with PTB.ResultsThe incidence rate of PTB was 6.2% in all selected participants. Compared with the non-hot day, the RRs (95%CIs) of CHE in lag 3, 7, and 14 days on PTB were 1.43 (1.12-1.84), 1.24 (1.08-1.43), and 1.17 (1.05-1.30), respectively, and the cumulative effects (% difference) (95%CI) of CHE in lag 14 days on maternal serum CRP, ET-1, and MDA were 0.33% (−0.45%-1.12%), 0.59% (0.11%-1.07%), and 0.57% (0.09%-1.05%), respectively. Compared with the Q1 (lowest quartile) for CRP, ET-1 and MDA, the RRs (95%CIs) of Q4 (highest quartile) for PTB were 1.27 (0.50-3.22), 1.51 (0.61-3.72), and 2.07(0.81-5.27), respectively.ConclusionMaternal exposure to CHE during pregnancy might be associated with an increased risk of PTB. Prenatal exposure to CHE is positively associated with maternal serum CRP, ET-1, and MDA, and the three biochemical indicators are also positively associated with PTB. However, the above conclusions still need further confirmation.

BackgroundIt is projected that the frequency, density, and duration of compound hot extreme may increase in the 21st century in the context of global warming.ObjectiveTo explore the association between compound hot extreme and blood pressure, and identify sensitive populations.MethodsThis was a cross-sectional study. The study subjects were from six Guangdong Province Chronic Disease and Nutrition Surveys during 2002 through 2015. A questionnaire was administered to the participants with questions about demographic information, drinking and smoking status, and measurements on their height, weight, and blood pressure were also collected. We chose the data of May, September, and October to explore the association between compound hot extreme and blood pressure. Compound hot extreme means a hot day with a proceeding hot night. Daily meteorological data were obtained from China Meteorological Data Service Centre. We employed inverse distance weighting to interpolate the temperature and relative humidity values for each participant. A distributed lag non-linear model was used to estimate the association between compound hot extreme and blood pressure. Stratified analyses by sex, age, area, body mass index (BMI), smoking status, and drinking status were also performed to identify sensitive populations. A sensitivity analysis was conducted by adjusting the degrees of freedom for lag spline and removing relative humidity.ResultA total of 10967 participants without history of hypertension were included in this study. The average systolic blood pressure (SBP) was 120.8 mmHg and the average diastolic blood pressure (DBP) was 74.5 mmHg. The proportion of participants who experienced hot day, hot night, or compound hot extreme were 9.34%, 17.95% and 2.90%, respectively. Compared to hot day, hot night and compound hot extreme were related with decreased blood pressure, and the effect of compound hot extreme was stronger: the changes and 95%CI for SBP was −6.2 (−10.3-−2.1) mmHg, and for DBP was −2.7 (−5.2-−0.2) mmHg. Compound hot extreme induced decreased SBP among male, population ≥ 65 years, and those whose BMI < 24 kg·m-2, and their ORs (95%CIs) were −6.2 (−10.7-−1.6). −19.1 (−33.0-−5.1), and −6.7 (−11.8~−1.6) mmHg, respectively, and also decreased DBP among population ≥ 65 years, and its OR (95%CI) was −8.4 (−15.6-−1.1) mmHg. During compound hot extremes, participants living in rural areas showed decreased SBP and DBP, and the ORs (95%CIs) were −10.5 (−16.6-−4.5) and −4.4 (−7.7-−1.1) mmHg respectively, while those living in urban areas showed increased SBP, and the OR (95%CI) was 9.7 (2.9-16.5) mmHg. A significant decrease in blood pressure [OR (95%CI)] was also found in non-smokers [DBP, −3.7 (−6.6-−0.8) mmHg] and non-drinkers [SBP, −4.8 (−9.4-−0.2) mmHg; DBP, −3.4 (−6.0-−0.9) mmHg].ConclusionCompound hot extreme is negatively associated with SBP, and being male, aged 65 years and over, and having BMI < 24 kg·m−2 may be more sensitive to compound hot extreme.

BackgroundUnder the background of global climate change, temperature has increased dramatically. Most studies about association between temperature and human health are conducted in low-altitude areas, but rarely focus on plateau areas.ObjectiveTo examine the association between temperature and non-accidental mortality risk in Tibet Plateau, China and to identify vulnerable populations for formulating targeted policies of climate change adaptation.MethodsThe mortality data, meteorological data, and pollutant data of Tibet area between 2013 to 2019 were collected. Based on time-stratified case-crossover design, conditional logistic regression models were used to analyze the exposure-response relationship between temperature and cause-specific mortality, which was linearized to obtain excess risk for 1 ℃ change; attributable fraction was calculated for assessing burden attributable to temperature; and stratified analyses were further conducted by gender, age (＜65 years old, ≥65 years old), and causes of death (cardiovascular diseases, cerebrovascular diseases, and respiratory diseases). Sensitivity analyses were conducted by adjusting model parameters and variables.ResultsA total of 26 045 non-accidental deaths were collected in Tibet during 2013 and 2019, and the P50 of temperature was 5.0 ℃. The non-accidental mortality risk increased as temperature become colder. A 1 ℃ decrease in temperature was associated with a 2.01% (95%CI: 0.94%-3.07%) increase in total non-accidental mortality, while the association changed to 2.05% (95%CI: 0.62%-3.47%) for male and 1.96% (95%CI: 0.34%-3.56%) for female, both of statistial significance; 1.45% (95%CI: −0.10%-2.98%) for the people

Background: Although many studies have reported the mortality effect of temperature, there were few studies on the mortality risk of humidity, let alone the joint effect of temperature and humidity. This study aimed to investigate the joint and interaction effect of high temperature and relative humidity on mortality in China, which will deepen understanding the health risk of mixture climate exposure. Methods: The mortality and meteorological data were collected from 353 locations in China (2013–2017 in Jilin, Hunan, Guangdong and Yunnan provinces, 2009–2017 in Zhejiang province, and 2006–2011 in other Provinces). We defined location-specific daily mean temperature ≥ 75th percentile of distribution as high temperature, while minimum mortality relative humidity as the threshold of high relative humidity. A time-series model with a distributed lag non-linear model was first employed to estimate the location-specific associations between humid-hot events and mortality, then we conducted meta-analysis to pool the mortality effect of humid-hot events. Finally, an additive interaction model was used to examine the interactive effect between high temperature and relative humidity. Results: The excess rate (ER) of non-accidental mortality attributed to dry-hot events was 10.18% (95% confidence interval (CI): 8.93%, 11.45%), which was higher than that of wet-hot events (ER = 3.21%, 95% CI: 0.59%, 5.89%). The attributable fraction (AF) of mortality attributed to dry-hot events was 10.00% (95% CI: 9.50%, 10.72%) with higher burden for females, older people, central China, cardiovascular diseases and urban city. While for wet-hot events, AF was much lower (3.31%, 95% CI: 2.60%, 4.30%). We also found that high temperature and low relative humidity had synergistic additive interaction on mortality risk. Conclusion: Dry-hot events may have a higher risk of mortality than wet-hot events, and the joint effect of high temperature and low relative humidity may be greater than the sum of their individual effects.

Abstract Relation extraction between entity pairs is an increasingly critical area in natural language processing. Recently, the pre‐trained bidirectional encoder representation from transformer (BERT) performs excellently on the text classification or sequence labelling tasks. Here, the high‐level syntactic features that consider the dependency between each word and the target entities into the pre‐trained language models are incorporated. Our model also utilizes the intermediate layers of BERT to acquire different levels of semantic information and designs multi‐granularity features for final relation classification. Our model offers a momentous improvement over the published methods for the relation extraction on the widely used data sets.

Objective To quickly and automatically evaluate the risk of hazardous chemical substances in food quickly and automatically. Methods Based on the Spring MVC framework, a quantitative assessment model of food chemicals was integrated and a visualization of the assessment result was provided. Results A set of food safety risk assessment software chemical hazard based on multi-source data fusion electronic. Conclusion The e-evaluation system described in this article had enabled the process and automation of a quantitative assessment model for the risk of food chemicals. It had also enabled the quantitative assessment model for food chemicals to be widely used in the business, thereby improving the efficiency of assessment.

Objective To achieve a high degree of process-oriented and automated decision-making on food safety risk assessment and data fusion among various business units. Methods Establish a temporal model library covering method such as dietary exposure assessment, hazard factor assessment, and spatio-temporal clustering detection, which can automatically connect to the basic data of each link of food traceability to obtain corresponding data, and calculate the risk assessment result through the selected risk assessment model construct a risk assessment matrix. Based on extract-transform-load (ETL) technology and data analysis algorithm implemented by R language, the basic data warehouse, risk assessment model base and risk decision support system were integrated. Results The establishment of a food safety risk assessment decision-making system would effectively resolve the problems of time-consuming and labor-intensive traditional data assessment and data cleaning difficulties. Based on the electronization of the original risk assessment process, the model input, calculation, and output would be integrated, and multi-year historical monitoring would be integrated to quickly customize the risk assessment research scenarios for harmful factors in common food categories. Conclusion This platform help improve the work efficiency of relevant business personnel, and promote data exchange and collaborative sharing between business units.

Lumbar disc herniation is a common disorder in adults that is accompanied by lower back and radicular pain. A 32-year-old man visited our clinic with 1-week history of persistent lower back pain and weakness in his right big toe. Magnetic resonance imaging (MRI) of his lumbar spine revealed herniated discs at L3/L4, L5/S1 and L4/L5, where a right-sided intraspinal mass lesion deep to the L4 vertebral body was causing compression of the nerve root. The patient underwent conservative treatment and reported no symptoms referrable to his back or leg 4 months later. Follow-up MRI showed no herniation of the nucleus pulposus at the L4/L5 level or lesion deep to the vertebral body of L4, whereas no changes had occurred to the status of the herniated L3/L4 and L5/S1 discs. The present case and a literature review show that a sequestered lumbar disc herniation can regress within a relatively short timeframe without surgery. The authors emphasise the utility of conservative therapy for patients who do not have a definitive surgical indication.

Pseudomonas aeruginosa is a common opportunistic pathogen that causes acute nosocomial necrotizing pneumonia and is the predominant source of chronic lung infections in patients with the genetic disorder cystic fibrosis. Early diagnosis in infected patients and monitoring P. aeruginosa contamination is therefore of great importance in controlling disease spread and development with timely drugs intervention treatment and cut off infection source. Traditional culture-biochemical methods are time consuming and highly dependent on technicians and expensive instruments. To address these challenges, the present study aimed to develop a rapid, sensitive, and specific, on-site detection method for P. aeruginosa based on recombinase polymerase amplification (RPA) combined with lateral flow strip (LFS) technology. The experimental process included screening and modification of primer and probe sets targeting the unique virulence gene elastase B (lasB); specificity detection in 29 strains of P. aeruginosa and 23 closely-related pathogenic bacteria; sensitivity measurements with gradient-diluted P. aeruginosa genomic DNA and probit regression analysis; and clinical application evaluation using 574 patients samples and calculating coincidence rate and kappa index value in comparison with the culture-biochemical method. The P. aeruginosa RPA-LFS assay could complete the amplification process at 37°C constant temperature within 30 min and results could be visualized by the naked eye within 10 min on LFS. The assay displayed high sensitivity with a limit of detection of 3.05 CFU/reaction. It also demonstrated high specificity by showing no cross reaction with other pathogenic bacteria, and rapidness by being completed in less than an hour. Furthermore, when used with clinical samples, the assay had a coincidence rate of 98.26% with the culture-biochemical method and a kappa index value of 0.9433. These data indicate that the RPA-LFS assay represents a major improvement for P. aeruginosa detection, especially in resource-limited areas.

Drowning is a major public health problem and a leading cause of death in children living in developing countries. We seek better machine learning (ML) algorithms to provide a novel risk-assessment insight on non-fatal drowning prediction. The data on non-fatal drowning were collected in Qingyuan city, Guangdong Province, China. We developed four ML models to predict the non-fatal drowning risk, including a logistic regression model (LR), random forest model (RF), support vector machine model (SVM), and stacking-based model, on three primary learners (LR, RF, SVM). The area under the curve (AUC), F1 value, accuracy, sensitivity, and specificity were calculated to evaluate the predictive ability of the different learning algorithms. This study included a total of 8390 children. Of those, 12.07% (1013) had experienced non-fatal drowning. We found the following risk factors are closely associated with the risk of non-fatal drowning: the frequency of swimming in open water, distance between the school and the surrounding open waters, swimming skills, personality (introvert) and relationality with family members. Compared to the other three base models, the stacking generalization model achieved a superior performance in the non-fatal drowning dataset (AUC = 0.741, sensitivity = 0.625, F1 value = 0.359, accuracy = 0.739 and specificity = 0.754). This study indicates that applying stacking ensemble algorithms in the non-fatal drowning dataset may outperform other ML models.

The urban street has evolved into an important indicator reflecting citizens’ living standard today, and pedestrian walking activity in the streets has been proved to be a major facilitator of public health. Uncertainties, however, exist in the factors affecting pedestrian walking behavior and walking experience in streets. Especially, the factors affecting pedestrian walking behavior and walking experience in the historical and cultural streets. For the study of their main influencing factors, Hefang Street business block and Gongchen Bridge life block in Hangzhou are selected here as the study objects. Both non-participatory and participatory research methods are adopted to collect pedestrian information and observe pedestrians’ ambiguous behavior, specific behavior, and stopping behavior. According to the study result, walking preference, walking time, environmental characteristics, and land-use mix (LUM) significantly impact pedestrian walking motivation. The type differences between Gongchen Bridge life block and Hefang Street business block leads to the difference in pedestrians’ behaviors and their stopping time in business. Meanwhile, gender differences bring pedestrians’ significant differences in walking motivation. Pedestrian walking preference and walking time are positively correlated with walking motivation in both streets. Environmental characteristics and LUM have also been proved to be important influencing factors of pedestrians’ walking motivation. In this article, design and planning strategies are proposed for streets of different types in an attempt to provide reference for the revitalization and utilization of cultural heritage streets.

In recent years, increasingly prominent energy and environmental problems have pushed for higher requirements for buildings’ energy saving. According to the conventional energy-saving design method, the cooperative operation between architects, structural and equipment engineers and other professionals cannot run smoothly, so the energy-saving and emission reduction efficiency of the whole building cannot be improved effectively. The integrated design process (IDP) is a systematic method, which is applied in the scheme design stage and according to which the multi-level design factors of cities and buildings are considered comprehensively. It provides a concrete path of multi-specialty collaborative operation for the building’s climate responsive design. In this article, the development, operation process, software platform, evaluation and decision-making methods of the IDP are reviewed in a comprehensive manner. Finally, the prospect of IDP applied to the climate responsive design of buildings is analyzed, and some suggestions for future development are put forward. The IDP framework proposed in the research can provide a reference method for architectural climate responsive design practice and help formulate the future policy of energy-saving design.

Aeromonas sobria, a common conditional pathogenic bacteria, is widely distributed in the environment and causes gastroenteritis in humans or septicemia in fish. Of all Aeromonas species, A. sobria is the most frequently isolated from human infections especially in immunocompromised subjects. Innate immunity is the first protection system of organism to resist non-specific pathogens invasion; however, the immune response process of hosts against A. sobria infection re\mains unexplored. The present study established an A. sobria infection model using primary mouse peritoneal macrophages (PMφs). The adherence and cytotoxicity of A. sobria on PMφs were determined by May-Grünwald Giemsa staining and LDH release measurement. Pro-inflammatory cytokine expression levels were measured using qPCR, western blotting, and ELISA methods. We also investigated the levels of ASC oligomerization and determined the roles of active caspase-1 in IL-1β secretion through inhibition assays and explored the activated pattern recognition receptors through immunofluorescence. We further elucidated the roles of activated inflammasome in regulating the host’s inflammatory response through inhibition combined with ELISA assays. Our results showed that A. sobria induced lytic cell death and LDH release, whereas it had no adhesive properties on PMφs. A. sobria triggered various proinflammatory cytokine transcription level upregulation, and IL-1β occupied the highest levels. The pro-IL-1β protein expression levels increased in a dose-dependent manner with MOI ranging from 1 to 100. This process was regulated by ASC-dependent inflammasome, which cleavage pro-IL-1β into active IL-1β p17 with activated caspase-1 p20. Meanwhile, the expression levels of NLRP3 receptor significantly increased, location analysis revealed puncta-like surrounding nuclear, and inhibition of NLRP3 inflammasome downregulated caspase-1 activation and IL-1β secretion. Blocking of NLRP3 inflammasome activation through K+ efflux and cathepsin B or caspase approaches downregulated A. sobria–induced proinflammatory cytokine production. Overall, these data indicated that A. sobria induced proinflammatory cytokine production in PMφs through activating NLRP3 inflammasome signaling pathways.

Abstract Background Yaks living in the high-altitude hypoxic environment of Tibetan plateau (3600 m) have special gut microbes. However, it is still little research on yak probiotics until now. Therefore, the purpose of our study was to evaluate the growth promoting effect, antioxidant capability, immune effect, and anti-inflammatory ability of Bacillus subtilis and Bacillus velezensis isolated from Tibetan yaks in mice model. Results The results showed that the isolated strains supplementation not only improve the growth performance but also increased the length of villus in the small intestine and intestinal digestive enzyme activity. Importantly, we observed that the T-AOC, SOD, and GSH-PX levels were increased and the MDA content was reduced in probiotic-treated mice, which implied that probiotics supplementation can ameliorate the antioxidative activity of mice. The levels of AST and ALT correlated with the hepatic injury were reduced and the levels of AKP, TP, GLB, ALB, Ca, and P were markedly higher than those in the control group. Additionally, mice treated with probiotics exhibited higher serum IgG, IgM and IgA, which can reflect the immune status to some extent. At the same time, the major pro-inflammatory factor TNF-α, IL-6, and IL-8 were down-regulated and the anti-inflammatory factor IL-10 was up-regulated compared with the control groups. Conclusions In conclusion, these results demonstrated that Bacillus subtilis and Bacillus velezensis supplementation can increase overall growth performance and ameliorate the blood parameters related to inflammation and immunity of mice.

Blur kernel (BK) estimation is the crucial technique to guarantee the success of blind image deblurring. In this paper, we propose a multi-regularization-constrained method to estimate an accurate BK from a single motion-blurred image. First, in order to generate sharp and reliable intermediate latent results, we propose a model which combines the spatial scale, ${L} _{0}$ norm, and the dark channel prior. Second, in order to preserve the continuity and the sparsity, and to remove the flaw in the BK, a dual-constrained regularization model, which combines the ${L} _{0}$ -regularized intensity prior and the ${L} _{2}$ -regularized gradient prior, is proposed for accurate BK estimation. The proposed model can not only preserve the continuity and the sparsity of the BK very well but also can remove the flaw thoroughly. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with the state-of-the-art methods demonstrate that our method estimates more accurate BKs and obtains higher quality deblurring images in terms of both subjective vision and quantitative metrics.

Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification. However, it cannot meet the time and memory requirements for processing large-scale data. We have reinvestigated the classification accuracy and running efficiency of the algorithm for solving SMLR problems using the Alternating Direction Method of Multipliers (ADMM), which is called fast SMLR (FSMLR) algorithm in this paper. By reformulating the optimization problem of FSMLR, we transform the serial convex optimization problem to the distributed convex optimization problem, i.e., global consensus problem and sharing problem. Based on the distributed optimization problem, we propose two distribute parallel SMLR algorithms, sample partitioning-based distributed SMLR (SP-SMLR), and feature partitioning-based distributed SMLR (FP-SMLR), for a large-scale sample and large-scale feature datasets in big data scenario, respectively. The experimental results show that the FSMLR algorithm has higher accuracy than the original SMLR algorithm. The big data experiments show that our distributed parallel SMLR algorithms can scale for massive samples and large-scale features, with high precision. In a word, our proposed serial and distribute SMLR algorithms outperform the state-of-the-art algorithms.

Logistic regression as a classic classification algorithm has limitations that can only be applied to linearly separable data. For linearly indivisible data, we use a kernel trick to map it to a higher dimensional space, making it easier to separate and structure in this space. However, with the increasing scale of data, the use of kernel trick is becoming more and more restricted. When the data reaches a certain scale, the cost of storage and computing kernel matrix is very expensive. To mitigate the problem of kernel matrix overload, we employ the low-rank approximate kernel matrix to speed up the solution of kernel logistic regression (KLR) and propose a framework for quickly solving the KLR algorithm. We use a fast iterative algorithm similar to the sequential minimal optimization (SMO) algorithm to solve the dual problem in the KLR. In addition, in this framework, the low-rank approximation is combined with gradient descent and Newton iteration algorithm, respectively. The low-rank approximation is used to reduce the redundant information in the data, which not only speeds up the solution of the KLR but also improves the accuracy of classification. Finally, the extensive experiments show that the KLR optimization algorithms based on our proposed framework outperform the state-of-the-art algorithms.

Multinomial logistic regression (MLR) has been widely used in the field of face recognition, text classification, and so on. However, the standard multinomial logistic regression has not yet stressed the problem of data redundancy. That is to say, in multi-class classification, there are many similar features among different classes, which will cause the corresponding classes not being correctly classified. As data redundancy is a common phenomenon in many fields, in response to this phenomenon, this paper proposes a maximal uncorrelated MLR (MUMLR) classification model to solve the problem of data redundancy in multi-class classification. The main idea is to reduce the weight of similar features and try to keep more discriminative information in the data by adding an uncorrelated regularization. In addition, we use the Cauchy-Buniakowsky-Schwarz inequation to scale the original objective function into the convex function and solve it by the Adam optimization method. Its main advantages are as follows: for data with more redundant information, the classification effect of the proposed algorithm is better than the state-of-the-art algorithms. In addition, we prove that the regularization we proposed can also be applied to neural networks and has achieved good results.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the stable-state matching underdamped stochastic resonance (SMUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

In order to study the performance evaluation of spatial governance of village and town business communities, this paper establishes a five-dimensional system: industrial upgrading (I), environmental renovation (E), life quality (L), humanistic characteristics (H), and governance of society (G) based on the PSR (pressure–state–response) model. In the study, twelve village and town business communities of different types in the Anji County of China are selected as empirical cases; field research and information interviews are conducted in each sample community; the constructed project libraries and constructed ledgers in the past three years are adopted. The GIS 10.2 software is used to draw a policy fund distribution map for spatial governance, compare and study the core characteristics of financial investment for spatial governance in each sample community, and reveal how important the application of the evaluation system is for its performance analysis. The “precise strategies” and “driving foundations” of the spatial governance of village and town business communities are summarized.

There are as many as 25 kinds of minerals (including non-ferrous metals, ferrous metals, rare and dispersed elements, precious metals, non-metallic and energy minerals) enriched in uranium-polymetallic fertile beds in black rock series, which is therefore widely attracting scholars all over the world. However, there is still great controversy in terms of the metallogenic mechanism in such beds. The black rock series have been systematically sampled from the Baizhuyu deposit in northwestern Hunan Province, China based on field geological and radioactivity surveys. Major and trace elements as well as rare earth elements (REE) of uranium-polymetallic phosphorite and its wall rocks were analyzed. Furthermore, carbon and oxygen isotopes, Sm-Nd isotopes, and mineralogy of the Baizhuyu deposit were studied. The results show that dolomite is a normal marine sediment, while and uranium-polymetallic elements were pre-enriched in phosphorites and black carbonaceous argillaceous shales and slates that formed from marine sedimentation and submarine exhalative sedimentation. Hydrothermal reworking to uranium-polymetallic phosphorites is significant as a result of submarine exhalative sedimentation. The research results of this paper can support a better understanding of metallogenesis and the future exploration of uranium-polymetallic phosphorite in the Lower Cambrian Niutitang Formation in the study area.

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters such as the depth and width of different rooms and their window-to-wall ratios etc., to establish a climate responsive optimization design process with indoor lighting environment comfort, with heating and cooling demand as the objective functions. Meanwhile, based on Monte Carlo simulation data, ANN (Artificial Neural Network) is used to establish a prediction model to analyze the sensitivity of interior design parameters under different typical cities’ climatic conditions. The study results show that the recommended values for the total width and total depth of indoor units under the climatic conditions of each city are both approximately 14.97 m and 7.88 m. Among them, under the climatic conditions of Harbin and Shenzhen, the design parameters of residential interiors can take the recommended value of UDI optimal or nZEB optimal. While the recommended values of window-to-wall ratios for the north bedroom, master bedroom and living room in Shanghai residential interiors are 0.26, 0.32 and 0.33, respectively. The recommended value of the window-to-wall ratio of the master bedroom in Kunming residences is 0.36, and that of the remaining rooms is between 0.15 and 0.18. The recommended values of window-to-wall ratios for the master bedroom and living room in Beijing residences are 0.41 and 0.59, respectively, and that for the remaining rooms are 0.15. The multi-objective optimization process based on parametric performance simulation used in the study can effectively assist architects in making energy-saving design decisions in the preliminary stage, allowing architects to have a case to follow in the actual design operation process.

Building energy waste has become one of the major challenges confronting the world today, so specifications and targets for building energy efficiency have been put forward in countries around the world in recent years. The schematic design stage matters a lot for building energy efficiency, while most architects nowadays are less likely to make energy efficiency design decisions in this stage due to the lack of necessary means and methods for analysis. An integrated multi-objective multivariate framework for optimization analysis is proposed for the schematic design stage in the paper. Here, the design parameters of the building morphology and the design parameters of the building envelope are integrated for analysis, and an integrated performance prediction model is established for low-rise and medium-rise residential buildings. Then, a comparison of the performance indicators of low-rise and medium-rise residential buildings under five typical urban climatic conditions is carried out, and the change patterns of the lighting environment, thermal environment, building energy demand, and life cycle cost of residential buildings in each city under different morphological parameters and design parameters of the building envelope are summarized. Specific analysis methods and practical tools are provided in the study for architectural design to ensure thermal comfort, lighting comfort, low energy consumption, and low life-cycle cost requirement, and this design method can inspire and guide the climate adaptation analysis and design process of low-rise and medium-rise residential buildings in China, improve architects’ perception of energy-saving design principles of low-rise and medium-rise residential buildings on the ontological level, as well as provide them with a method to follow and a case to follow in the actual design process.

Summary: Background: Coronary heart disease is rapidly increasing in developing countries, but access to cardiac rehabilitation and secondary prevention remains low. In this study, we aimed to assess the effectiveness of a smartphone-based cardiac rehabilitation and secondary prevention programme delivered via the social media platform WeChat (SMART-CR/SP). Methods: In this parallel-group, single-blind, randomised controlled trial, we recruited patients aged 18 years or older with coronary heart disease who had received percutaneous coronary interventions from a large tertiary hospital in Shanghai, China. Participants were randomly assigned (1:1) by block randomisation to either a 2-month intensive programme followed by a 4-month step-down phase of SMART-CR/SP or to usual care. In the SMART-CR/SP group, participants received comprehensive cardiac rehabilitation and secondary prevention via WeChat. The usual care group received standard outpatient cardiology follow-up but without formal cardiac rehabilitation and secondary prevention. Assessments were done at baseline, 2 months, 6 months, and 12 months. The primary outcome was change in functional capacity from baseline, measured by 6-min walk distance, at 2 months and 6 months. Analysis was by intention to treat. Research personnel involved in assessments were blinded to group allocation. Adverse-event analysis was based on percentage of patients who discontinued the study owing to adverse events. SMART-CR/SP programme-related safety issues were also recorded. This study was registered with the Chinese Clinical Trial Registry, number ChiCTR-INR-16009598. Findings: Between Nov 17, 2016, and March 18, 2017, 312 patients (mean age 60·5 years [SD 9·2]), of whom 58 (19%) were female and 254 (81%) were male, were recruited and subsequently randomly assigned to SMART-CR/SP (n=156) or usual care (n=156). The improvement in 6-min walk distance at 2 months was significantly greater in the SMART-CR/SP group (from 489·2 m [99·4] at baseline to 539·1 m [68·0]) than in the control group (from 485·0 m [93·5] at baseline to 517·8 m [74.6]), with an adjusted mean difference of 20·64 m (95% CI 7·50–33·77; p=0·034). This improvement was maintained at 6 months (mean 6-min walk distance 543·4 m [67·5] in the SMART-CR/SP group vs 523·5 m [60·2] in the control group), with a mean between-group difference of 22·29 m (8·19–36·38; p=0·027). No adverse events or SMART-CR/SP programme-related safety issues were reported by participants during the study. Interpretation: SMART-CR/SP was found to be a cardiac rehabilitation and secondary prevention service model with high efficacy and accessibility and to be easy to use. These results justify the implementation of similar models of care on a broader scale. Funding: Curtin University.

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

The redeposition of pristine phosphorite plays an important role in phosphorus accumulation, which created reworked phosphorite extensively on the continental shelf. This paper, using geochemical analysis combined with data from petrology and diagenesis, focuses on the reconstruction of the formation processes of the Late Cretaceous Thaniyat phosphorite deposition in northwestern Saudi Arabia, which is a part of the famous large Neo-Tethys Ocean’s phosphorite deposit. The results of our study illustrate that the phosphorites represent the reworked products from the north, close to the edge of the Neo-Tethys Ocean’s shelf, where upwelling had accreted the pristine phosphorite. The reworked phosphatic grains were redeposited near the shore in sandstone, forming sandy phosphorite and on a carbonate platform and creating calcareous phosphorite. The microscale sedimentological and geochemical information hosted in the eroded phosphorite grains indicates that the source sediment, pristine phosphorite, occurred under a fluctuating geophysical condition and in a relatively limited geochemical environment. They were physically crushed and transported landward and deposited under oxic conditions, forming the Thaniyat phosphorites. Early diagenesis in the Thaniyat phosphorite was evidenced by recrystallization of the phosphate minerals, geochemical depletion, and C and O isotope excursion.

This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) as the objective functions for optimization. The design variables concern building orientation, envelope components, and window types, etc. The optimal solution is provided from two different perspectives of the public sector (energy saving optimal) and private households (cost-optimal) respectively. By comparing the optimization results with the performance indicators of the reference buildings in various cities, the outcome can give the precious indications to rebuild the U.S. residential buildings with a view to energy-efficiency and cost optimality depending on the location.

Autophagy is a highly conserved and regulated process in eukaryotic cells and remodels cytoplasm, recovers essential nutrients, and disposes of unwanted cytoplasmic components. Autophagy-related gene (ATG) 8, identified in Heortia vitessoides Moore, which is an oligophagous pest of Aquilaria sinensis (Lour.), was characterized (HvATG8). Multiple sequence alignment showed that HvATG8 possesses highly conserved domain structures. Stage- and tissue-specific expressions indicated that HvATG8 is highly expressed in prepupal, pupal, and adult stages and in the midgut of larvae and abdomen of adults. Lack of function of HvATG8 by RNA interference resulted in a significant decrease in survival rate and an increase in abnormal or nonviable phenotypes in H. vitessoides. Transition rate from larval to pupal stages was 33.0% and from pupal to adult stages was 15.0% after injection. Reduction of ATG8 expression reduced survival of H. vitessoides. Therefore, HvATG8 possibly plays a key role in normal growth stage of H. vitessoides. HvATG8 suppression downregulates HvATG3 expression, suggesting that the two genes are interconnected. Further, HvATG8 expression increased by 20-hydroxyecdysone treatment, starvation, and extreme temperature exposure. Starvation also altered expression of other ATGs in H. vitessoide. This study may be used to guide research on molecular mechanisms of autophagy in insects.

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.

Since the weak fault characteristics of mechanical equipment are often difficult to extract in strong background noise, stochastic resonance (SR) is widely used to extract the weak fault characteristics, which is able to utilize the noise to amplify weak fault characteristics. Although classical bistable stochastic resonance (CBSR) can enhance the weak characteristics by adjusting the parameters of potential model, when potential barrier height is adjusted potential well width is also changed and vice versa. The simultaneous change of both potential well width and barrier height is difficult to obtain a suitable potential model for better weak fault characteristic extraction and further fault diagnosis of machinery. For this reason, the output signal-to-noise ratio (SNR) of CBSR is greatly reduced, and the corresponding enhancement ability of weak fault characteristics is limited. In order to avoid the shortcomings, a new SR method is proposed to extract weak fault characteristics and further diagnose the faults of rotating machinery, where the classical bistable potential is replaced with a bistable confining potential to get the optimal SR. The bistable confining potential model not only has the characteristics of the classical bistable potential model but also has the ability to adjust the potential width, barrier height, and wall steepness independently. Simulated data are used to demonstrate the proposed new SR method. The results indicate that the weak fault characteristics can be effectively extracted from simulated signals with heavy noise. Experiments on the bearings and planetary gearboxes demonstrate that the proposed SR method can correctly diagnose the faults of rotating machinery and moreover has higher spectrum peak and better recognition degree compared with the CBSR method.

Juvenile hormone diol kinase (JHDK) is a critical enzyme involved in juvenile hormone degradation in insects. In this study, HvJHDK in the Heortia vitessoides Moore (Lepidoptera: Crambidae) transcriptional library was cloned. Stage-specific expression patterns of HvJHDK, HvJHEH, and HvJHE as well as juvenile hormone titers were determined. The three tested enzymes participated in juvenile hormone degradation. Moreover, juvenile hormone titers peaked after larval−larval molts, consistent with a role for juvenile hormone in inhibition of metamorphosis. HvJHDK was subsequently suppressed using RNA interference (RNAi) to reveal its functions. Different concentrations of dsJHDK elicited the optimal interference efficiency at different life stages of H. vitessoides. Suppression of HvJHDK decreased HvJHDK content and increased the juvenile hormone titer, thereby resulting in reduced triglyceride content, sharply declined survival rate, clearly lethal phenotypes, and extended larval growth. Moreover, suppression of HvJHDK upregulated HvJHEH and HvJHE expression levels, suggesting that there is feedback regulation in the juvenile hormone metabolic pathway. Taken together, our findings provide molecular references for the selection of novel insecticidal targets.

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.