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ObjectiveTo investigate the contamination status of Staphylococcus aureus in food and the presence of enterotoxin genes in Jiading District, Shanghai, and to provide a basis for the prevention and treatment of foodborne Staphylococcus aureus disease.MethodsFrom 2021 to 2023, 15 types of food were sampled for S. aureus testing, and the presence of five enterotoxin genes, including sea⁃see, was tested in the strains.ResultsOut of 705 food samples, 88 (12.48%) were positive for S. aureus. S. aureus was detected in 12 of the 15 food types, with the three food types with the highest positive rates being cold noodles (45.00%), raw poultry (26.25%), and vegetable salads (20.00%). The enterotoxin gene carriage rate was 32.95% in food strains. The carriage rates for sea, seb, and sec were 7.95%, 12.50%, and 14.77%, respectively. Neither sed nor see was detected. The detection rate of strains carrying two types of enterotoxin genes was 2.27%. The enterotoxin carriage rates in strains from vegetables, beverages, and raw meat were 57.14%, 40.00%, and 30.00%, respectively.ConclusionThe S. aureus detection rate in food in Jiading District is much higher than the national average. The enterotoxin gene carriage rates are high, with food strains carrying sea, seb, and sec, with sec being the most prevalent. There is a need to enhance monitoring of S. aureus and enterotoxins, especially in high-risk foods such as noodles, vegetables, and non-packaged beverages.

The status of coinfection during the national outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.5.2 or BF.7 in China in the winter of 2022, which is suspected to contribute substantially to the overloaded severe cases, needs to be investigated. We analyzed the coinfection status of 385 severe patients infected with the Omicron variant in Guangzhou using metagenomic sequencing. We found that 317 (82.3 %) patients were coinfected with at least one additional pathogen(s), including bacteria (58.7 %), fungi (27.1 %) and viruses (73.5 %). Pseudomonas aeruginosa (P. aeruginosa) (24.2 %), Staphylococcus aureus (S. aureus) (14.0 %), and Klebsiella pneumoniae (K. pneumonia) (13.4 %) ranked as the top three coinfected bacteria. Aspergillus fumigatus (A. fumigatus) (39.5 %), Pneumocystis jirovecii (P. jirovecii) (24.4 %) and Canidia albicans (C. albicans) (22.1 %) were the top three coinfected fungi. Epstein-Barr virus (EBV) (63.1 %), Human herpesvirus 7 (HHV-7) (34.8 %), and Herpes simplex virus 1 (HSV-1) (32.6 %) were the top three coinfected viruses. Of note, the detection of multiple coinfections of potential pathogenic bacteria, fungi, and viruses, despite lacking consistent patterns, highlighted a complicated synergistic contribution to disease severity. Our study presents the most comprehensive spectrum of bacterial, fungal, and viral coinfections in Omicron-associated severe coronavirus disease 2019 (COVID-19), implying that the coinfection of conditional pathogens might synergistically deteriorate the Omicron infection outcomes.

ObjectiveTo establish a non-targeted screening method for emerging contaminants in drinking water based on high-resolution mass spectrometry and apply it to actual water samples.MethodsA total of 9 drinking water samples collected from 3 reservoirs in Shanghai were purified and concentrated by HLB solid phase extraction column， then separated and analyzed by liquid chromatography high-resolution mass spectrometer and gas chromatography high⁃resolution mass spectrometer. The acquired data were analyzed by Thermo Tracefinder， Excel and other software combined with mzCloud and NIST databases. The methodology was verified with representative compound standards. Pesticide and perfluorinated compounds were taken as examples to analyze their pollution status.ResultsA non-targeted analysis strategy based on liquid chromatography and gas chromatography tandem high-resolution mass spectrometry was established. The pollution level of 20 kinds of pesticides and 4 kinds of perfluorinated compounds identified in 9 drinking water samples were higher in the Huangpu River than in the Yangtze River estuary.ConclusionThe established non-targeted screening method by high-resolution mass spectrometry can detect potential emerging contaminants in drinking water without relying on the standards， which provides a powerful technical means for water quality monitoring and risk assessment.

Mitochondria, crucial intracellular organelles, are central to energy metabolism, signal transduction, apoptosis, calcium homeostasis, and a myriad of other biological processes, making them a focal point in diverse research fields. The capacity to fluorescently label and visually track mitochondria is crucial for understanding their biological roles. We present mulberrin-Cy3, a novel small molecule fluorescent probe that selectively labels mitochondria in animal cells, including cancer cells, with relative ease. This protocol details the synthesis of mulberrin-Cy3 and its use for visualizing mitochondria in living cells. The synthesis is straightforward and time-efficient, and the labeling method is more accessible than traditional approaches, providing a cost-effective option for mitochondrial visualization at room temperature. The labeling is rapid, with effective labeling achieved within 5 min of incubation. The fluorescent signal is stable and brighter, offering a significant advantage over existing methods. Mulberrin-Cy3 represents a promising mitochondrial labeling compound, providing researchers with a novel experimental tool to explore the complex biological functions of mitochondria. This innovation has the potential to significantly advance our comprehension of mitochondrial dynamics and their role in cellular health and disease.

The early Cambrian period is a critical time in Earth’s history, marked by the second oxygenation of the atmosphere (known as the Neoproterozoic Oxidation Event) and the rapid evolution of animals, as well as the worldwide large-scale deposits. Polymetallic Ni-Mo deposits have been previously reported in the black rocks at the base of the Cambrian strata on the Yangtze Block, South China, while their genesis is still controversial, and their global occurrences are uncertain. Therefore, we conduct a comprehensive analysis of trace elements and rare earth elements in the black rocks at the base of the Yurtus Formation in the Sugaitblak section (SGT) in Aksu, Tarim Block, and then make a comprehensive study by combining our and other data from the Tarim and Yangtze Block. The distribution patterns of the trace and rare earth element data from our study and another two sections in the Aksu area have shown that the black rocks in the Tarim were strongly enriched in V, Sr, Mo, Ba, and U, moderately enriched in Zn, Cu, Cr, and Pb, and slightly depleted in Ni, and Co during the early Cambrian. These enriched elements could mainly originate from the waning hydrothermal fluids, and then precipitate and preserve in sediments under anoxic environment. The comparison of trace elements in the black rocks between the Tarim and the Yangtze Block suggests that the types of enriched metal elements in these two regions were different, and metal concentrations in the Yangtze Block were much higher than those in the Tarim by 1–3 orders of magnitude. This result indicates that there are no polymetallic Ni-Mo enrichment layers in the black rocks in the Tarim during the early Cambrian, and the widespread polymetallic Ni-Mo enrichments in South China have been more likely influenced by the local hydrothermal activity. It could be further speculated that the early Cambrian period may have been an active period for seafloor hydrothermal activity, with locally different chemical components in hydrothermal fluids. Abundant metal nutrients brought by these hydrothermal activities may have been a potential factor for the rapid evolution of life during this period.

ObjectiveTo establish a GC-MS method for the detection of 9 halogenated alkanes in toothpastes.MethodsVarious brands of toothpastes on the market were collected as the samples. Methanol was used as the extraction solution. After shaking， vortex and centrifugation， the supernatant was filtered by 0.45 μm microporous filter membrane. The content of 9 halogenated alkanes including chloromethane， dichloromethane， chloroform， 1，1，1-trichloroethane， carbon tetrachloride， 1，2-dichloroethane， 1，1，2-trichloroethane， 1，2-dibromoethane， 1，2，3-trichloropropane in the filtrate were determined by GC-MS.ResultsThe 9 halogenated alkanes showed a good linear relationship in the concentration range of 0.03‒1.00 μg·mL-1. The correlation coefficient was ≥0.998 7 with the detection limits of 0.015‒0.056 mg·kg-1. The spiked recoveries were 83.2%‒108.9% with the RSD of 2.0%‒9.8%. In the actual sample analysis， 1，1，2-trichloroethane and 1，2，3-trichloropropane were respectively detected in 6 toothpaste samples from 5 brands.ConclusionThe method is simple， sensitive， accurate， and has good spiked recoveries. It can be used for the determination of halogenated alkanes in toothpastes.

Kuwanon C is a unique flavonoid found in the mulberry family, characterized by two isopentenyl groups. While previous research has focused on various properties of kuwanon C, such as antioxidant, hypoglycemic, antimicrobial, food preservation, skin whitening, and nematode lifespan extension, little attention has been given to its potential role in oncological diseases. In this study, we investigate the antitumor effect of kuwanon C in cervical cancer cells and elucidate its specific mechanism of action. We assessed the antitumor effects of kuwanon C using various experimental techniques, including cell proliferation assay, wound healing assays, EdU 488 proliferation assay, mitochondrial membrane potential assay, ROS level assay, cell cycle, apoptosis analysis, and studies on kuwanon C target sites and molecular docking. The results revealed that kuwanon C significantly impacted the cell cycle progression of HeLa cells, disrupted their mitochondrial membrane potential, and induced a substantial increase in intracellular ROS levels. Moreover, kuwanon C exhibited notable anti-proliferative and pro-apoptotic effects on HeLa cells, surpassing the performance of commonly used antitumor drugs such as paclitaxel and cisplatin. Notably, kuwanon C demonstrated superior efficacy while also being more easily accessible compared to paclitaxel. Our study demonstrates that kuwanon C exerts potent antitumor effects by its interaction with the mitochondrial and endoplasmic reticulum membranes, induces a significant production of ROS, disrupts their normal structure, inhibits cell cycle progression, and stimulates apoptotic signaling pathways, ultimately resulting in the death of HeLa tumor cells. As an isopentenyl compound derived from Morus alba, kuwanon C holds great promise as a potential candidate for the development of effective antitumor drugs.

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks.

ObjectiveTo investigate the contamination status， serotype distribution and drug resistance of Salmonella in retail poultry in Jiading District， Shanghai.MethodsFour types of poultry （chicken， duck， goose and pigeon） have been sampled from commercial markets. Salmonella contamination has been isolated and identified using serotype analysis. Furthermore， resistance of isolated Salmonella strains towards 16 commonly used antibiotics has been determined.ResultsA total of 60 Salmonella strains were isolated from 80 poultry samples. The detection rates of Salmonella in pigeon， goose， duck and chicken were 86.67%， 81.82%， 72.73% and 59.38%， respectively. Contamination status has been categorized by storage conditions. Lowest detection rate （65.63%） has been noted in poultry samples under refrigeration storage. The majority serotypes of Salmonella have been revealed as Salmonella typhimurium， Salmonella enteritidis and Salmonella corvallis. Relatively higher drug resistance was discovered with tetracycline， nalidixic acid， ampicillin and chloramphenicol， with drug resistance rate of 60.00%， 58.33%， 50.00% and 48.33%， respectively. Low drug resistance was revealed with cefotaxime. In addition， these Salmonella strains were completely sensitive to imipenem. Significant difference in drug resistance was identified across the types of poultry or Salmonella serotypes. The 11.67% of Salmonella strains were non-resistant to any tested drugs. Multi-drug resistant strains accounted for 46.67% of isolated strains， which were resistant to 13 different antibiotics. Ampicillin-tetracycline or chloramphenirol-cefazolin drug resistance pattern suggested that the Salmonella strain was multi-drug resistant.ConclusionSalmonella contamination remains high in retail poultry in Jiading District， Shanghai. Drug resistance to antibiotics is increasing. Therefore， monitoring and control of Salmonella should be strengthened.

Fires play a significant role in ecosystems, exerting a profound influence on climate, vegetation, and geochemical cycles, while being reciprocally affected by these factors. The reconstruction of past fire events serves as a valuable window into understanding environmental changes over time. To investigate the history of ancient fires on the Eastern Coast of China, we conducted the first charcoal analysis on a loess profile of Shengshan Island (East China Sea). Along with other biological and geochemical proxies, we successfully reconstructed the ancient fire dynamics and elucidated their driving mechanisms in this region since the Late Pleistocene. Our initial findings revealed a peak in charcoal concentration during the 60-50 ka period, but after calibrating for sedimentation rate, the concentration significantly decreased. Fire activities remained weak during 50-30 ka, likely due to the scarcity of combustible materials. Between 30-12 ka, fires were frequent in the early period, while gradually diminishing during the later stage. Dry climate and dense vegetation likely attributed to frequent fires in early period, while some extreme events (e.g., sudden change in temperature) may have decreased the fire frequency in later period. The Holocene (began ~12 ka) evidenced the most frequent fire events as a high charcoal concentration was recorded, likely caused by human activities. After comparing our findings with other paleoecological records from surrounding areas, we confirmed the accuracy of our reconstruction of ancient fires. This reconstruction captures not only local shifts but also broader regional changes. Overall, our study highlights the importance of calibrating sedimentation rate in charcoal profiles, while also contributing to an enhanced understanding of environmental changes along the Eastern Coast of China since the Late Pleistocene.

ObjectiveTo investigate the prevalence of Staphylococcus aureus in a hospital in Jiading District， Shanghai， and to determine the enterotoxin gene profiles of Staphylococcus aureus isolated from patients and environment.MethodsSpecimens were collected from environment and patients from a hospital for Staphylococcus aureus isolation and identification. Furthermore， enterotoxin genes （SEA‒SEE） of Staphylococcus aureus were detected.ResultsA total of 54 Staphylococcus aureus strains were isolated from 780 hospital environmental specimens from 2018 to 2021， with a prevalence of 6.92%. In the armrests in the wards， patient’s pillows/quilts， and bedside cupboards， the prevalence was determined to be 20.00%， 20.00% and 16.67%， respectively. In contrast， in the computer’s mouse and keyboard and work clothes of physicians and nurses， the prevalence was 17.42% and 16.67%， respectively. Meanwhile， from 2018 to 2021， a total of 75 strains were collected from patients， of which 36.00%， 14.67% and 14.67% were from the departments of intensive care medicine （ICU）， neurosurgery and orthopedics， respectively. The prevalence of Staphylococcus aureus enterotoxin genes were 48.15% and 61.33% in the environment and patients， respectively. Furthermore， the prevalence of SEA， SEB， SEC， SED， and SEE genes were 14.67%， 41.33%， 9.33%， 1.33%， 1.33% in patients， respectively ， whereas 20.37%， 25.93%， 1.85 %， 1.85% and 0% in environmental specimens， respectively. In the environmental specimens isolated from comprehensive ICU， the prevalence of enterotoxin genes was 77.27%. In the patient's specimens， Staphylococcus aureus was mostly isolated in sputum. Additionally， the prevalence of enterotoxin genes was high in the patients of departments of respiratory medicine， ICU， and orthopedics.ConclusionPrevalence of Staphylococcus aureus remains moderate in the hospital environment. Enterotoxin genes of Staphylococcus aureus are common， with seb gene as the most common gene， followed by SEA gene. It warrants strengthening the disinfection and control of Staphylococcus aureus in the hospital， especially in the ICU.

Based on the mutual compensation of offshore wind energy and wave energy, a hybrid wind–wave power generation system can provide a highly cost-effective solution to the increasing demands for offshore power. To provide comprehensive guidance for future research, this study reviews the energy conversion and coupling technologies of existing hybrid Wind–wave power generation systems which have not been reported in previous publications. The working principles of various wind and wave energy conversion technologies are summarised in detail. In addition, existing energy coupling technologies are specifically classified and described. All aforementioned technologies are comprehensively compared and discussed. Technological gaps are highlighted, and future development forecasts are proposed. It is found that the integration of hydraulic wind turbines and oscillating wave energy converters is the most promising choice for hybrid wind–wave power extraction. DC and hydraulic coupling are expected to become mainstream energy coupling schemes in the future. Currently, the main technological gaps include short their operating life, low energy production, limited economic viability, and the scarcity of theoretical research and experimental tests. The field offers significant opportunities for expansion and innovation.

ObjectiveTo investigate the contamination status and drug resistance of Campylobacter spp. in poultry sold in Jiading District， Shanghai.MethodsFour types of poultry meats （chickens， ducks， geese and pigeons） were sampled from commercial markets， and potential Campylobacter spp. contamination was isolated and identified. Furthermore， resistance of isolated Campylobacter spp. to 15 commonly used antibiotics was tested.ResultsTotally 29 Campylobacter jejuni strains and 34 Campylobacter. coli were isolated from 236 commercial poultry samples. The most severe contamination of Campylobacter spp. was found in chicken samples， with a detection rate of 34.04%， while the lowest detection rate of Campylobacter spp. was found in duck （19.67%）. Contamination status was categorized with different storage conditions. The lowest detection rate of 6.67% was noted under frozen condition， while highest detection rate of 41.27% was noted under cold storage. Campylobacter jejuni was completely resistant to cefazolin， ciprofloxacin， nalidixic acid and tetracycline， and Campylobacter coli was completely resistant to cefazolin， cefoxitin and nalidixic acid； Campylobacter spp. showed the lowest resistance to imipenem. Multi-drug resistant strains accounted for 100.00% of the isolated strains. 96.83% of the strains were resistant to more than 5 drugs， with the highest number reaching 14 kinds of antibiotics.ConclusionThere is a significant difference in the contamination status and drug resistance of Campylobacter spp. isolated from four types of poultry meats sold in Jiading District， Shanghai， and the drug resistance is serious. It is strongly recommended that the use of antibiotics should be strictly controlled. Freezing can effectively reduce Campylobacter spp. pollution.

In recent years, deep learning-based hashing techniques have garnered significant attention within the academic community, owing to their impressive performance achievements. However, the current deep hashing method is limited by its use of neural networks cannot make good use of the important feature information of the image, thus limiting the retrieval accuracy. In this paper, we design a two-channel feature extraction network called the local and global feature fusion network (LGDH). Specifically, the features extracted by visual geometry group (VGG) network and con-volution-enhanced image transformer (CeiT) network are deeply orthogonal fusion of local and global features, which solves the problem of lack of semantic correlation in existing deep hash methods. To fully harness the insights gained from previous hash function learning, we employ long short-term memory (LSTM) decoder to proficiently shape and create a resilient public binary code space. Through detailed experiments, our proposed method has achieved the Sota level in three public datasets CIFAR10, NUS-WIDE and MIRFLICKR compared with multiple cutting-edge algorithms, which can prove the effectiveness of our pro-posed method.

Wrist pulse is one kind of biomedical signals, it is affected not only by the heart beatings, but also by the conditions of nerves, organs, muscles, skin, etc. Therefore, wrist pulse signals can reflect a person's physical state and it has been widely used in health status analysis. However, previous works mainly use traditional machine learning methods to analyze wrist pulse signal. Because wrist pulse signal is high-dimensional and complex, it is difficult for traditional machine learning methods to learn effective information from them. This study aims to explore the utilizing of deep learning methods on wrist pulse signal analysis. We propose a novel multi-kernel Convolutional Neural Network for wrist pulse signal classification. Our model can handle multiple kinds of input features and each of them will pass through a convolutional neural network that has three different sizes of convolution kernel to capture multi-scale information in different time steps. We compare our method with traditional machine learning methods on two tasks: Coronary Atherosclerotic Heart Disease Classification and Traditional Chinese Medicine Constitution yin deficiency and yang deficiency Classification. Besides, we also research the influence of different input features and different channels on wrist pulse signal analysis. The results show that our model significantly improves the performance on the two tasks, which proves the deep learning method is more suitable to deal with complex wrist pulse data.

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

ObjectiveTo study the relationship between the exposure to two kinds of phthalate esters （PAEs） ［Di-N-butyl phthalate，（DBP） and Di-（2-ethylhexyl）phthalate （DEHP）］ and estrogen homeostasis in pregnant women.MethodsIn 2021， we classified the Jiading District of Shanghai into five geographical areas， east， west， south， north and central. A total of 151 pregnant women from each area were selected for questionnaire survey， with random urine samples during first， second， and third trimesters collected. A DBP metabolite ［Mono-N-butyl phthalate （MBP）］ and two DEHP metabolites ［Mono（2-ethylhexyl） phthalate （MEHP）， Mono（2-ethyl5-oxohexyl） phthalate， （MEOHP）］ and three estrogens ［estrone （E1）， 17β -estradiol （E2）， and estriol （E3）］ in urine were determined by ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry. After a natural logarithmic transformation of PAEs metabolite levels and estrogen concentration， multivariable linear regression was used to control potential confounders and determine the relationship between PAEs metabolite levels and estrogen concentration.ResultsThe detection rates of three PAEs metabolites in urine of pregnant women were more than 98%. The median corrected concentrations of MBP， MEHP and MEOHP were 5.18， 0.59 and 4.23 mg·kg-1， respectively. During the whole pregnancy， MEOHP was positively correlated with E1 （β=0.450， 95%CI： 0.057‒0.844）， and MBP was positively correlated with E3 （β=0.250， 95%CI： 0.034‒0.465）. Stratified by trimesters， MBP was positively correlated with E3 in the first trimester （β=0.428， 95%CI： 0.103‒0.752）. MEOHP was positively correlated with E1 in the second trimester （β=0.734， 95%CI： 0.130‒0.752）， and had a possitive trend with E1 in the third trimester （β=0.744， 95%CI： -0.140‒1.629）. In addition， MEHP had a negative correlation with E1 in the second trimester （β=-0.498， 95%CI： -1.063‒0.066）. MEOHP had a positive correlation trend with E2 （β=0.628， 95%CI： -0.101‒1.356） in the third trimester.ConclusionPAEs exposure may interfere with estrogen homeostasis during pregnancy and differs by trimesters. Given the cross-sectional nature of this study， it warrants further study to validate the findings.

Abstract. Objective:. Shugan Hewei therapy (SHT) acts to soothe the liver and harmonize the stomach. It is a classical traditional Chinese medicine method widely used in China to treat chronic atrophic gastritis (CAG) due to liver qi invading the stomach. However, the clinical effects of SHT remain unclear. We aimed to evaluate the overall clinical effective rate and safety of SHT in treating CAG. Methods:. We used the Jadad scale and Cochrane Collaboration risk of bias tool to evaluate the methodological quality of studies investigating SHT. Eight medical databases were searched to identify relevant studies. After data extraction and quality evaluation, 27 randomized controlled trials, including 2,441 patients, were considered eligible for analysis. No serious heterogeneity or publication bias was observed across the included studies. We used Revman 5.3 statistical software to evaluate the general clinical effective rate and safety of SHT. Results:. The results showed that SHT was more effective (RR=1.25; 95% CI [1.20, 1.29]; P

ObjectiveIn order to optimize the shape of the floating buoy of a multi-degree-of-freedom wave energy converter to improve its energy conversion efficiency, a method for shape optimization based on a genetic algorithm (GA) is proposed. MethodsA B-spline surface is used to define the shape of the floating buoy, and the GA is applied to take all control points of the surface as individual variables in the iterative population. AQWA and WEC-Sim software are then used to establish a time-domain numerical model for each shape generated by the iteration, and the value of the objective function is calculated. The evolution curves of the objective function and optimized shape are then obtained. The effects of different schemes of shape definition and objective functions on the optimization results are studied and analyzed respectively.ResultsThe optimization method can significantly improve the energy conversion efficiency of a multi-degree-of-freedoms (DOFs) wave energy converter, and the shape definition scheme and objective function have a great impact on the final optimization results.ConclusionThe results can provide a feasible design method for the shape optimization of the floating buoy of a multi-DOFs wave energy converter.

ObjectiveTo investigate the contamination situation， serotype distribution and drug resistance of salmonella in poultry sold in Jiading District， Shanghai.MethodsBetween 2019 and 2020， four types of poultry meat （chicken， duck， goose and pigeon） were sampled from markets， and potential salmonella contamination was isolated and identified via serotyping. Furthermore， resistance detection of isolated salmonella to 13 commonly-used antibiotics was conducted.ResultsA total of 32 salmonella strains were isolated from 156 commercial poultry samples. No salmonella was identified in pigeon samples， while the most severe contamination was detected in goose samples， with a detection rate of 28.00%. Contamination situation was categorized with different storage conditions. The lowest detection rate of 12.90% was noted under cold storage， while the highest rate of 26.67% was detected under frozen condition. The major serotypes of salmonella were Salmonella typhimurium， Salmonella enteritidis and Salmonella Argonne. Relatively higher drug resistance was detected with nalidixic acid and tetracycline， with drug resistance rate of 59.38% and 40.63%， respectively. Low drug resistance was detected with cephalosporins， and no drug resistance was detected with imipenem. Significant difference in drug resistance was noted in the serotype of Salmonella isolated from meat of chicken， duck and goose. Three major salmonella strains had different drug resistance. 21.88% （7/32） of salmonella strains were non-resistant to any tested drugs. Multi-drug resistant strains accounted for 31.25% （10/32） of the isolated strains. At most， salmonella was resistant to 11 kinds of different antibiotics.ConclusionThere is a significant difference in the contamination situation and drug resistance of Salmonella isolated from four types of poultry meat sold in Jiading District， Shanghai. It is strongly recommended that different supervision strategies and management upon classification be implemented.

BackgroundChronic atrophic gastritis (CAG), premalignant lesions of gastric cancer (GC), greatly increases the risk of GC. Gastroscopy with tissue biopsy is the most commonly used technology for CAG diagnosis. However, due to the invasive nature, both ordinary gastroscope and painless gastroscope result in a certain degree of injury to the esophagus as well as inducing psychological pressure on patients. In addition, patients need fast for at least half a day and take laxatives.MethodsIn this study, fecal metabolites and microbiota profiles were detected by metabolomics and 16S rRNA V4-V5 region sequencing.ResultsAlteration of fecal metabolites and microbiota profiles was found in CAG patients, compared with healthy volunteers. To identify the most relevant features, 7 fecal metabolites and 4 microbiota were selected by random forest (RF), from A and B sample sets, respectively. Furthermore, we constructed support vector machines (SVM) classifification model using 7 fecal metabolites or 4 gut microbes, or 7 fecal metabolites with 4 gut microbes, respectively, on C sample set. The accuracy of classifification model was 0.714, 0.857, 0.857, respectively, and the AUC was 0.71, 0.88, 0.9, respectively. In C sample set, Spearman’s rank correlation analysis demonstrated heptadecanoic acid and pentadecanoic acid were signifificantly negatively correlated to Erysipelotrichaceae_UCG-003 and Haemophilus, respectively. We constructed SVM classifification model using 2 correlated fecal metabolites and 2 correlated gut microbes on C sample set. The accuracy of classification model was 0.857, and the AUC was 0.88.ConclusionTherefore, heptadecanoic acid and pentadecanoic acid, crosstalk with fecal-derived gut microbiota namely Erysipelotrichaceae_UCG-003 and Haemophilus, are potential non-invasive biomarkers for CAG diagnosis.

An event of mesoscale vortex with MCS occurred in Shandong region on 3 August 2015 was investigated and analyzed using the WRF mesoscale numerical model based on NCEP/NCAR analysis data and Doppler radar observation data. The study revealed the reason for the occurrence and development of the mesoscale vortex and its effect on the mesoscale convection system under the environment of the northeast cold vortex. The results are as follows. (1) The upper-level jet stream' s strong divergence in front of the trough of the synoptic-scale northeast cold vortex and the low-level instability energy release provided favorable condition for the lower mesoscale vortex and convection occurrence. The generation of the low-level mesoscale vortex promoted the occurrence of the MCS. (2) When the mesoscale vortex forms and develops, the positive vorticity of the lower layer is mainly affected by the horizontal divergence term and the vertical transport term, with the former having a greater effect. The positive vorticity of the middle layer is mainly affected by the vertical transport term. Due to positive contributions of the two terms, the positive vorticity increases, and the vortex develops.When the mesoscale vortex weakens, the horizontal divergence term in the middle and lower layers weakens and the vertical transport term becomes negative, leading to the decrease of positive vorticity generation and the increase of negative vorticity generation, and therefore the vortex decreases. (3) When the MCS develops, the increase of perturbation effective potential energy mainly comes from the latent heat release and the conversion from zonal mean effective potential energy to disturbance effective potential energy, and then the perturbation effective potential energy is transformed into the perturbation kinetic energy through the vertical motion. The increase of the perturbation kinetic energy promotes the development of convections. The mesoscale vortex not only provides energy for the severe convection, but also influences the evolution of the strong convection.

Abstract A point absorber wave energy converter (WEC) which converts wave energy into electrical energy with a multi‐axis power‐take‐off (PTO) system is considered here. Previous wave tank trials have proved the ability of the multi‐axis WEC to absorb wave power. In this study, the boundary element method (BEM) based software Ansys@ AQWATM was applied to model and analyze the power absorption performance of the multi‐axis WEC for a wider range of regular and irregular waves. To verify the numerical model, a laboratory‐scale physical model was manufactured and tested. Results show that the multi‐axis WEC can absorb more power from the incident regular wave power compared to single‐axis WECs and the efficiency reaches up to 45%. It is found that the wave frequency and incident angle significantly influence the amount of absorbed power from different motion modes and PTO axes. Then the numerical model is simulated at 12 nearshore locations in East and South China Sea. The results indicate that the multi‐axis WEC can absorb up to 5 kW and the efficiency can reach up to 29% at the most energetic site Shengshan. In addition, considerable differences of mean absorbed power efficiency can be found between sea states.

Insufficient intratumoral T-cell infiltration and lack of tumor-specific immune surveillance in tumor microenvironment (TME) hinder the progression of cancer immunotherapy. In this study, we explored a recombinant vaccinia virus encoding an EpCAM BiTE (VV-EpCAM BiTE) to modulate the immune suppressive microenvironment to enhance antitumor immunity in several solid tumors. VV-EpCAM BiTE effectively infected, replicated and lysed malignant cells. The EpCAM BiTE secreted from infected malignants effectively mediated the binding of EpCAM-positive tumor cells and CD3ϵ on T cells, which led to activation of naive T-cell and the release of cytokines, such as IFN-γ and IL-2. Intratumoral administration of VV-EpCAM BiTE significantly enhanced antitumor activity in malignancies with high other than with low EpCAM expression level. In addition, immune cell infiltration was significantly increased in TME upon VV-EpCAM BiTE treatment, CD8+ T cell exhaustion was reduced and T-cell-mediated immune activation was markedly enhanced. Taken together, VV-EpCAM BiTE sophistically combines the antitumor advantages of bispecific antibodies and oncolytic viruses, which provides preclinical evidence for the therapeutic potential of VV-EpCAM BiTE.

The development and utilization of urban underground space is an important way to solve the “great urban disease”. As one of the most important types of urban underground foundations, utility tunnels have become increasingly popular in municipal construction. The investigation of utility tunnels is a general task and three-dimensional laser scanning technology has played a significant role in surveying and data acquisition. However, three-dimensional laser scanning technology suffers from noise and occlusion in narrow congested utility tunnel spaces, and the acquired point clouds are imperfect; hence, errors and redundancies are introduced in the extracted geometric elements. The topology of reconstructed BIM objects cannot be ensured. Therefore, in this study, a hierarchical segmentation method for point clouds and a topology reconstruction method for building information model (BIM) objects in utility tunnels are proposed. The point cloud is segmented into facades, planes, and pipelines hierarchically. An improved mean-shift algorithm is proposed to extract wall line features and a local symmetry-based medial axis extraction algorithm is proposed to extract pipelines from point clouds. A topology reconstruction method that searches for the neighbor information of wall and pipeline centerlines and establishes collinear, perpendicular, and intersecting situations is used to reconstruct a topologically consistent 3D model of a utility tunnel. An experiment on the Guangzhou’s Nansha District dataset successfully reconstructed 24 BIM wall objects and 12 pipelines within the utility tunnel, verifying the efficiency of the method.

The pores and coarse lamellar Mg17Al12 that inevitably occur in the weld zone are the major challenge for laser-welded magnesium (Mg) alloys including AZ31B. In order to improve microstructure uniformity and eliminate welding defects, a new process assisted with combination of heat and cryogenic treatment was applied in this study. The results showed that after solution treatment, the number and size of precipitates decreased and the uniformity of the microstructure improved. After cryogenic treatment, the lamellar Mg17Al12 was cracked into particles, and the grain size was refined. After solution + cryogenic treatment, Al8Mn5 substituted the lamellar Mg17Al12. Through studying the changes in microhardness, precipitates, and microstructure under different treatments, it was found that the conversation of Mg17Al12 from lamellar state into particle-like state as well as the appearance of dispersed Al8Mn5 particles played a second-phase strengthening role in improving the mechanical properties of Mg alloy laser-welded joint, and the tensile strength (258.60 MPa) and elongation (10.90%) of the sample were 4.4% and 32.6% higher than those of the as-welded joint.

Abstract Background LncRNA DLGAP1-AS2 plays an oncogenic role in glioma, while its role in other cancers is unknown. This study aimed to study the role of DLGAP1-AS2 in non-small cell lung cancer (NSCLC). Methods Expression of DLGAP1-AS2 in NSCLC and paired non-tumor tissues from 64 NSCLC patients and the prognostic value of DLGAP1-AS2 for NSCLC were analyzed by performing a 5-year follow-up study. The interaction between DLGAP1-AS2 and miR-503 was confirmed by dual luciferase reporter assay, and their relationship was explored in NSCLC cells transfected with DLGAP1-AS2 expression vector or miR-503 mimic. The roles of DLGAP1-AS2 and miR-503 in regulating cyclin D1 expression were analyzed by RT-qPCR and Western blot. Cell proliferation was analyzed by CCK-8 assay. Results DLGAP1-AS2 was upregulated in NSCLC and predicted poor survival. Interaction between DLGAP1-AS2 and miR-503 was confirmed by dual luciferase activity assay. Overexpression experiments showed that DLGAP1-AS2 and miR-503 overexpression failed to significantly affect the expression of each other. Interestingly, DLGAP1-AS2 overexpression upregulated cyclin D1, a target of miR-503, increased cell proliferation and reduced the effects of miR-503 overexpression on cyclin D1 expression and cell proliferation. Conclusions DLGAP1-AS2 may regulate miR-503/cyclin D1 to promote cell proliferation in NSCLC.

Floating offshore wind turbines (FOWTs) are subject to intensive structural loads due to the extra degrees of freedom (DOF) of the floating platform, which may shorten the fatigue lifetime of critical wind turbine structures. Integrating wave energy converters (WECs) into FOWTs could potentially help improve both overall energy capture and platform dynamic responses and, thus, is expected to reduce the levelized cost of energy (LCOE). In this work, a novel hybrid wind-wave energy platform consisting of a semi-submersible FOWT and three heaving-type WECs is proposed, and the feasibility of reducing FOWT dynamic responses and fatigue loads by integrating heaving-type WECs with different PTO control schemes is investigated. More specifically, the aero-hydro-servo-elastic-mooring coupled numerical model is established, and a preliminary study is performed to evaluate the dynamic responses and power production of the hybrid platform under various environmental conditions. Particularly, the two kinds of PTO control strategies have been comparatively studied, which have shown that the active bang-bang control could effectively suppress the platform heave and pitch motion by up to 34.6 % and 17.1%, respectively. Moreover, the tower-base fatigue damage equivalent load (DEL) has been reduced by up to 11.21%, and the system power production could be increased by almost 6%. Therefore, it is shown that integrating heaving-type wave energy converters with bang-bang control is able to effectively reduce the dynamic responses and fatigue loads of semi-submersible FOWT while absorbing additional wave energy at the same time.

Abstract Background Cytochrome P450 (CYPs) participate in the mechanisms of cardiovascular disease. The purpose of this research was to evaluate the contributions of CYP24A1 variants to coronary heart disease (CHD) among the Chinese Han population. Methods This study included 505 CHD cases and 508 controls. Four variants of CYP24A1 (rs2762934, rs1570669, rs6068816 and rs2296241) were chosen and genotyped by the Agena MassARRAY system among the Chinese population. The linkage between CYP24A1 variants and CHD risk were assessed by logistic regression to compute the odds ratio (OR) and 95% confidence interval (CI). Then, multifactor dimensionality reduction (MDR) was applied to analyze the interactions of CYP24A1 variants. Results The results of this study showed that CYP24A1 rs6068816 significantly enhanced CHD risk in multiple genetic models (allele: P = 0.014; codominant: P = 0.015; dominant: P = 0.043; recessive: P = 0.040; additive: P = 0.013), whereas rs2296241 was likely to protect individuals from CHD (codominant: P = 0.019; recessive: P = 0.013; additive: P = 0.033). Stratification analysis revealed that CYP24A1 polymorphisms had strong relationships with CHD risk that were dependent on age, sex, Gensini grade and smoking status (P

In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.

Fault detection module is one of the most important components in modern industrial systems. In this paper, we propose a novel fault detection framework which makes use of both normal and faulty measurement signals at the same time. In this framework, the multivariate time series (MTS) pieces which are extracted from measurement signals in a time interval are used as the training and testing samples, and a K-nearest neighbour rule of MTS pieces is applied for fault detection. Moreover, a Mahalanobis distance based dynamic time warping method is used to measure the divergence among MTS pieces, and a one-class metric learning algorithm is proposed to learn the appropriate Mahalanobis distance. Experimental results on the Tennessee Eastman process demonstrate that the proposed method has improved fault detection performance compared with classical approaches on certain kinds of faults.

AbstractThe frontotemporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations that these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences (a) are semantically and syntactically combinable into phrase- and clause-level meanings, and (b) occur in an order licensed by the language’s grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network provided that the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N = 47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the blood oxygen level–dependent response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input, providing that composition can take place.

Background. To explore the application value of layered strain technique in non-ST elevation acute coronary syndrome (NSTE-ACS). Methods. 120 patients with suspected NSTE-ACS undergoing coronary angiography in our hospital from December 2018 to December 2019 were prospectively selected. According to the results of coronary angiography, the patients were divided into the significant CAD group and the nonsignificant CAD group. Echocardiography was performed 1-2 hours before invasive coronary angiography. The long axis and circumferential strain of the endocardium, myocardial layer, and epicardium were evaluated by the layered strain technique. The territorial longitudinal strain (TLS), the global longitudinal strain (GLS) of the three myocardial layers, and the global circumferential strain (GCS) were calculated based on the perfusion region of the three coronary arteries and the 17-segment model of the left ventricle. The primary endopoints were TLS and GCS of the three-layer myocardium. Results. Compared with the nonsignificant CAD patients, the TLS and GCS of three-layer myocardium in significant CAD patients were decreased, especially in the endocardium. The absolute values of TLS and GCS of the endocardium and epicardium in significant CAD patients were lower than those in nonsignificant CAD patients. This indicates a significant decrease in endocardial function. Receiver operating characteristic (ROC) curve analysis showed that endocardial TLS was superior to LVEF, Troponin I (TnI), and other strain parameters in evaluating the extent of coronary lesions. Conclusions. The layered strain technique of 2D-STE can evaluate the severity of coronary lesions in patients with NSTE-ACS, and for significant CAD patients, endocardial function is significantly more impaired than epicardial function.

BackgroundIn the wake of the severe impact of COVID-19 on the food security of the vulnerable groups in rural areas, the issue of how to achieve the Sustainable Development Goals 2 aims to "Zero Hunger" (SDG 2) and ensure the food safety of farmers has drawn unprecedented attention. Nutritional intake is generally used as an important indicator to reflect family food security. Under the background that Chinese farmers have gradually changed from the traditional diversified production mode to the specialized production of crops, the main purpose of this article is to explore what are the impact of crop specialization on farmers' nutritional intake? Could the specialization of crop production be taken as an important measure to ensure the food safety of farmers and achieve the SDG 2?MethodsBased on the micro-survey data from 866 farmer households in China, this paper using Seemingly Unrelated Regressions model, Group Regression model and Mediating Effect model to analyze the average and heterogeneous effects of crop specialization on the nutritional intake of farmers, as well as the mediating effect of income. In addition, robustness test and endogenous treatment were performed by using alternative explanatory variables and IV-2SLS method was used to estimate the results.ResultsAfter correcting for endogenous bias, crop specialization had a significant negative impact on energy intake and fat intake of farmers at the statistical level of 5% and 1% respectively, especially for farmers in mountainous areas. Household income played a mediating effect on the effect of crop specialization on farmers' energy and fat intake, and the proportion of the masking effect was 8.43% and 8.96% respectively. In addition, household financial capital and social capital have a significant positive impact on farmers' nutritional intake.ConclusionsCrop specialization cannot guarantee the food safety of farmers in terms of nutritional intake. However, when the development trend of crop specialization is irreversible, more attention should be paid to improving the level of various livelihood capital of farmers, especially those in mountainous areas, and to continuously increasing their income to ease and ultimately eliminate the negative impact of crop specialization on farmers' nutritional intake, which finally make everyone realize the SDG 2.

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies.

The rapid development of three-dimensional (3D) laser scanning technology has provided a new technical means for the geometric accuracy evaluation of subway stations. With high precision and high efficiency, laser scanning technology can present the construction site condition in a panoramic way, which is essential for achieving high precision and all-round geometric accuracy evaluation. However, when the survey coordinate system of the design building information modeling (BIM) predefined in the design stage is not applied during the laser scanning data acquisition or the BIM loses the survey coordinate system during the interaction, the objects will have different coordinate positions in the point cloud and BIM, which will limit the accuracy comparison between the two data sources. Meanwhile, the existing methods mainly focus on the above overground buildings, and the accuracy evaluation of underground structures mainly focuses on the overall deformation monitoring. So far, the existing methods do not constitute a hierarchical index system to assess the geometric accuracy of various objects in the subway station. This study proposes a method to evaluate the geometric accuracy of subway stations based on laser scanning technology. A coarse-to-fine coordinate registration from point cloud to the design BIM is used to unify coordinates in different reference systems; and geometric accuracy evaluation of different structures in subway stations is achieved by developing geometric accuracy evaluation indexes and technical systems. The method is applied to the geometric accuracy monitoring of the Hongqi Road subway station, and the experimental results verify the reliability of the method.

Abstract Fibroadenomas (FAs) are the most common fibroepithelial lesions and the most common benign tumors of the breast in women of reproductive age. Although MED12 mutations, an overwhelming majority of all mutations, and some other gene mutations have been found in FAs, the genomic landscapes of FAs are still not completely clear and the genomic mutation spectrums of FAs in Chinese population remains unknown. Here, by performing whole exome sequencing of 12 FAs and the corresponding normal breast tissues in Chinese Han population, we observed the somatic and germline landscapes of genetic alterations. We identified 16 recurrently mutated genes with 37 nonsynonymous or frameshift somatic mutations and 27 recurrent somatic copy number variants (CNVs). In these mutated genes, MED12 was the most common in FAs, harboring 6 nonsynonymous/frameshift somatic mutations and 1 CNV. In addition, 6 germline mutations of tumor susceptibility genes in 5 FAs were identified and the tumor mutational burden of the 5 FAs was significantly higher than the other 7 FAs without germline mutations. This study provides genomic mutation spectrums of FAs in Chinese population and expand the genetic spectrum of FAs.

Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.

The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.

Device-free localization (DFL), requiring no extra devices equipped with a target, is an important field of research on the Internet of Thing (IoT). Energy efficiency issue is essential for the development of the IoT, but seldom of the existing papers are focus on it. So we investigate this issue with quantized data of only several bits under the compressive sensing (CS) framework, which can both reduce the required wireless link number and the bit number in the DFL scheme. First, through exploiting the discrete property of CS theory, we calculate the discrete measurement probability bypass computing the complex or uncalculated measurement probability density function (pdf), which can well represent the measurement distribution characteristic. Second, we design a unique quantization scheme for each wireless link according to their measurement probability and build a novel DFL model by analyzing the quantization error. Third, a new DF-QVBI algorithm is proposed to recover the target location, which can make great use of the quantization error. Finally, numerical simulations show the superiority and robustness of the proposed method.

In the case of preheating and non-preheating, the rapid prototyping system of high-power fiber laser and electromagnetic induction heating equipment was used to fabricate 12CrNi2 alloy steel. The microstructure of the molded parts was observed under a scanning electron microscope, and the hardness test of different parts was conducted using the Victorinox hardness tester. The tensile properties in different directions were tested using the universal material testing machine. This study investigated the effect of preheating on the microstructure, hardness, and tensile properties of laser melting deposited 12CrNi2 alloy steel in different directions. The obtained results show that the microstructure of the single-channel molten pool without preheating is lath martensite, and the molten pool of block-shaped forming parts is tempered martensite and bainite mixed structure. No obvious structural difference between XOZ and YOZ sections is observed. However, the overall hardness of the YOZ cross section is larger than that of the XOZ cross section. Large-scale macroscopic crack defects appear in both sections, and the mechanical properties are poor. Under preheating conditions, bainite transformation occurs in the molten pool because of the decrease in the temperature gradient. The single-channel molten pool shows an excellent bainite structure. No tempered martensite transformation in the molten pool of block-shaped forming parts, mainly granular bainite, is observed. Cross-sectional hardness distribution is more uniform with preheating than without preheating. High tensile strength and low plasticity are detected in the tensile and overlap directions. The tensile strength is up to 1189 MPa, the yield strength is 951 MPa, and the elongation is only 2.8%. No obvious anisotropy in performance is observed. Preheating can reduce the temperature gradient in the molten pool, reduce the thermal stress, effectively control the crack defects, promote the homogenization of the microstructure, reduce the anisotropy of the microstructure and properties, and improve the mechanical properties of the alloy steel forming parts.

In order to minimize the downloading time of short-lived applications like Web browsing, Web application, and short video clips, the recently standardized HTTP/2 adopts stream multiplexing on one single TCP connection. However, aggregating all content objects within one single connection suffers from the head-of-line blocking issue. Quick UDP Internet connection (QUIC), by eliminating such an issue on the basis of UDP, is expected to further reduce the content downloading time. However, in mobile network environments, the single connection strategy still leads to a degraded and high-variant completion time due to the unexpected hindrance of congestion window growth caused by the common but uncertain fluctuations in round trip time and also random loss event at the air interface. To retain resilient congestion window against such network fluctuations, we propose an intelligent connection management scheme based on QUIC which not only employs adaptively multiple connections but also conducts a tailored state and congestion window synchronization between these parallel connections upon the detection of network fluctuation events. According to the performance evaluation results obtained from an LTE-A/Wi-Fi testing network, the proposed multiple QUIC scheme can effectively overcome the limitations of different congestion control algorithms (e.g., the loss-based new reno/CUBIC and the rate-based BBR), achieving substantial performance improvement in both median (up to 59.1%) and 95th completion time (up to 72.3%). The significance of this piece of work is to achieve highly robust short-lived content downloading performance against various uncertainties of network conditions as well as with different congestion control schemes.

Source localization is an important field of research and the received signal strength (RSS)based method is of particular interest. Further, by exploiting the sparsity of localization problem, the compressive sensing (CS) can be applied to considerably decrease the number of RSS measurements, especially in multi-source scenario. However, most existing CS-based localization methods usually neglect some practical issues. In particular, the sensor positions are assumed be known exactly, while in practice they may not be accurate. Additionally, a uniform Gaussian noise assumption is made that the noise variances of sensors are identical, but in practice the noise should be nonuniform. When these assumptions are violated, the localization performance will deteriorate dramatically. To address such issues, in this paper, we formulate the source localization based on superimposed RSS measurements as a sparse signal recovery problem. Moreover, by regarding the sensor positions as adjustable parameters, the inaccurate sensor positions can be refined through the adjustment of parameters. Following this idea, we develop a novel iterative algorithm for joint signal recovery and parameter optimization based on the variational expectation-maximization algorithm. Consequently, the sensor position uncertainty can be alleviated and thus the signal recovery performance will be improved greatly. Meanwhile, it is also capable of learning the variance of nonuniform noise. Extensive simulation results demonstrate the superiority of the proposed method.

This paper presents a constant-pressure hydraulic PTO system that can convert stored pressure energy into electrical energy at a stable speed through hydraulic motors and generators. A multi-chamber cylinder can be connected to the main power generation circuit by check valves, and the motor displacement can be controlled by a fuzzy controller to maintain the main power generation circuit under stable pressure. The hydraulic transformer can control the forces applied to the floater. The hydrodynamic parameters of the floater are calculated by AQWA, and the optimal PTO damping of the hydraulic system is analyzed as the target of transformer control. MATLAB/Simulink and AMESim are used to carry out the co-simulation. Three kinds of wave elevation time-series for the specific state are designed for the simulation. In the co-simulation, three approaches are carried out for the simulation including no control strategy, fuzzy control with a fixed transformer ratio, and fuzzy control with a variable transformer ratio. Under the fuzzy control with a fixed transformer ratio, the floater displacement and captured energy do not increase significantly, but the oil pressure fluctuation is very stable, which indicates that the fuzzy controller maintains the stability of the main power circuit. While under fuzzy control with a variable transformer ratio, the power generation is not larger than those under no control strategy or fuzzy control with a fixed transformer ratio, which proves that this hydraulic transformer concept is less efficient.

BackgroundPhotodynamic therapy (PDT) efficacy is determined in part by the concentration of photosensitizer (PS) at the treatment site. The blood-brain barrier (BBB) poses a significant limitation on the transport of PS into the post-operative resection region where brain tumors most often recur. Macrophages (Ma), as opposed to free or nanoparticle bound agents, are known to actively migrate to and around tumors, and can therefore be used as delivery vectors for both drugs and photosensitizers.MethodsMouse Ma (RAW264.7) and F98 rat glioma cells were used in all experiments along with the photosensitizer AlPcS2a. Mitomycin-treated Ma were loaded with photosensitizer (PS) and mixed with glioma cells, forming hybrid spheroids. F98 spheroids were incubated with supernatants derived from PS-loaded Ma (MaPS). Light treatment (PDT) was administered at various radiant exposures from a 670 nm diode laser. The growth of both types of spheroids was evaluated by measurement of spheroid volume after 14 days in culture.ResultsPDT on F98 cell spheroid cultures, mediated by either free or PS-released from Ma, demonstrated a significant growth inhibition with supernatants harvested after 4 and 24 h. A significant PDT-induced growth inhibition was demonstrated in the MaPS/F98 hybrid spheroid experiments.ConclusionSince the efficacy of PDT, mediated by either free or released photosensitizer was comparable, the uptake and released photosensitizer was not degraded. MaPS, incorporated in hybrid tumor spheroids also mediated effective PDT. These results indicate that Ma have potential as an effective vector for photosensitizer delivery to resected brain tumors.