Spatial Heterogeneity and Regional Clustering of Factors Influencing Chinese Adolescents’ Physical Fitness

There is often significant spatial heterogeneity in the factors influencing physical fitness in adolescents, yet less attention has been paid to this in established studies. Based on the 2018 Chinese National Student Physical Fitness Standard Test data, this study uses a multi-scale, geographically weighted regression (MGWR) model combined with a K-means clustering algorithm to construct a spatial regression model of the factors influencing adolescent physical fitness, and to investigate the degree of spatial variation in the physical fitness of Chinese adolescents from a socio-ecological perspective of health promotion. The following conclusions were drawn: the performance of the youth physical fitness regression model was significantly improved after taking spatial scale and heterogeneity into account. At the provincial scale, the non-farm output, average altitude, and precipitation of each region were strongly related to youth physical fitness, and each influencing factor generally showed a banded spatial heterogeneity pattern, which can be summarized into four types: N–S, E–W, NE–SW, and SE–NW. From the perspective of youth physical fitness, China can be divided into three regions of influence: the socio-economic-influenced region, mainly including the eastern region and some of the central provinces of China; the natural-environment-influenced region, which mainly includes the northwestern part of China and some provinces in the highland region; and the multi-factor joint-influenced region, which mainly includes the provinces in the central and northeastern regions of China. Finally, this study provides syndemic suggestions for physical fitness and health promotion for youths in each region

Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (ED) and the total wind energy (ET) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m2 to nearly 1250 kWh/m2. In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern

New Method of Modeling Daily Energy Consumption

At present, papers concerning energy consumption and forecasting are predominantly dedicated to various known techniques and their combinations. On the other hand, the research on load modeling and forecasting methodologies is quite limited. This paper presents a new approach concerning hourly energy consumption using a multivariate linear regression model. The proposed technique provides a way to accurately model day-to-day energy consumption using just a few selected variables. The number of data points required to describe a whole day’s consumption depends on the demanded precision, which is up to the user. This model is self-configurable and very fast. The applied model shows that four hours are sufficient to describe energy consumption during the remainder of a given day. We show that for about 84% of the data points, the relative error of the model is below 2.5%, and for all the data points the error does not exceed 7.5%. We obtained a mean relative uncertainty of 1.72% in the learning data set, and 1.69% and 1.82% in the two testing data sets, respectively. In addition, we conclude that the model can also detect days with unusual energy consumption

A Tracklet-before-Clustering Initialization Strategy Based on Hierarchical KLT Tracklet Association for Coherent Motion Filtering Enhancement

Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition

New Phenylspirodrimanes from the Sponge-Associated Fungus Stachybotrys chartarum MUT 3308

Two phenylspirodrimanes, never isolated before, stachybotrin J (1) and new stachybocin G (epi-stachybocin A) (2), along with the already reported stachybotrin I (3), stachybotrin H (4), stachybotrylactam (5), stachybotrylactam acetate (6), 2α-acetoxystachybotrylactam acetate (7), stachybotramide (8), chartarlactam B (9), and F1839-J (10) were isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308. Their structures were established based on extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses. Absolute configurations of the stereogenic centers of stachybotrin J (1), stachybocin G (2), and stachybotrin I (3), were determined by comparison of their experimental circular dichroism (CD) spectra with their time-dependent density functional theory (TD-DFT) circular dichroism (ECD) spectra. The putative structures of seventeen additional phenylspirodrimanes were proposed by analysis of their respective MS/MS spectra through a Feature-Based Molecular Networking approach. All the isolated compounds were evaluated for their cytotoxicity against five aggressive cancer cell lines (MP41, 786, 786R, CAL33, and CAL33RR), notably including two resistant human cancer cell lines (786R, CAL33RR), and compounds 5, 6, and 7 exhibited cytotoxicity with IC50 values in the range of 0.3−2.2 µM

Latent Trajectories and Risk Factors of Prenatal Stress, Anxiety, and Depression in Southwestern China—A Longitudinal Study

(1) Background: Few studies have explored the heterogeneity of trajectories of stress, anxiety, and depressive symptoms during pregnancy. This study aimed to explore the trajectory groups of stress, anxiety, and depressive symptoms in women during pregnancy and the risk factors associated with those groups. (2) Methods: Data came from pregnant women recruited from January to September 2018 in four hospitals in Chongqing Province, China. A structured questionnaire was given to pregnant women, which collected basic information, including personal, family, and social information. The growth mixture model was applied to identify potential trajectory groups, and multinomial logistic regression was applied to analyze factors of trajectory groups. (3) Results: We identified three stress trajectory groups, three anxiety trajectory groups, and four depression trajectory groups. Less developed regions, inadequate family care, and inadequate social support were associated with a high risk of stress; residence, use of potentially teratogenic drugs, owning pets, family care, and social support were strongly associated with the anxiety trajectory group; family care and social support were the most critical factors for the depression trajectory group. (4) Conclusions: The trajectories of prenatal stress, anxiety, and depressive symptoms are dynamic and heterogeneous. This study may provide some critical insights into the characteristics of women in the high-risk trajectory groups for early intervention to mitigate worsening symptoms

Opera 2015 Project: Accurate Measurement Equipment for Earthquake Electromagnetic Emissions and Radio Seismic Indicator

Electromagnetic emissions from earthquakes are known as precursors and are of considerable importance for the purpose of early alarms. The propagation of low-frequency waves is favored, and the range between tens of mHz to tens of Hz has been heavily investigated in the last thirty years. This work describes the self-financed Opera 2015 project that initially consisted of six monitoring stations over Italy, equipped with electric and magnetic field sensors, among others. Insight of the designed antennas and low-noise electronic amplifiers provides both characterization of performance (similar to the best commercial products) and the elements to replicate the design for our own independent studies. Measured signals through data acquisition systems were then processed for spectral analysis and are available on the Opera 2015 website. Data provided by other world-known research institutes have also been considered for comparison. The work provides examples of processing methods and results representation, identifying many exogenous noise contributions of natural or human-made origin. The study of the results occurred for some years and led us to think that reliable precursors are confined to a short area around the earthquake due to the significant attenuation and the effect of overlapping noise sources. To this aim, a magnitude-distance indicator was developed to classify the detectability of the EQ events observed during 2015 and compared this with some other known earthquake events documented in the scientific literature

Experimental Investigation on Interface Performance of UHPC-Strengthened NC Structure through Push-Out Tests

Strengthening concrete structures with ultra-high performance concrete (UHPC) can both improve the bearing capacity of the original normal concrete (NC) structure and prolong the service life of the structure due to the high strength and durability of UHPC. The key to the synergistic work of the UHPC-strengthened layer and the original NC structures lies in the reliable bonding of their interfaces. In this research study, the shear performance of the UHPC–NC interface was investigated by the direct shear (push-out test) test method. The effects of different interface preparation methods (smoothing, chiseling, and planting straight and hooked rebars) and different aspect ratios of planted rebars on the failure mode and shear performance of the pushed-out specimens were studied. Seven groups of push-out specimens were tested. The results show that the interface preparation method can significantly affect the failure mode of the UHPC–NC interface, which is specifically divided into interface failure, planted rebar pull-out, and NC shear failure. The critical aspect ratio for the pull-out or anchorage of planted rebars in UHPC is around 2. The interface shear strength of straight-planted rebar interface preparation is significantly improved compared with that of the chiseled and smoothened interfaces, and as the embedding length of the planted rebar becomes longer, it first increases greatly and then tends to be stable when the rebar planted in UHPC is fully anchored. The shear stiffness of UHPC–NC increases with the increase of the aspect ratio of planted rebars. A design recommendation based on the experimental results is proposed. This research study supplements the theoretical basis of the interface design of UHPC-strengthened NC structures

PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs

Hundreds of processor cores or modules are integrated into a single chip. The traditional bus or crossbar is challenged by bandwidth, scalability, and silicon area, and cannot meet the requirements of high end applications. Network-on-chip (NoC) has become a very promising interconnection structure because of its good scalability, predictable interconnect length and delay, high bandwidth, and reusability. However, the most available packet routing NoC may not be the perfect solution for high-end heterogeneous multi-core real-time systems-on-chip (SoC) because of the excessive latency and cache cost overhead. Moreover, circuit switching is limited by the scale, connectivity flexibility, and excessive overhead of fully connected systems. To solve the above problems and to meet the need for low latency, high throughput, and flexibility, this paper proposes PCCNoC (Packet Connected Circuit NoC), a low-latency and low-overhead NoC based on both packet switching (setting-up circuit) and circuit switching (data transmission on circuit), which offers flexible routing and zero overhead of data transmission latency, making it suitable for high-end heterogeneous multi-core real-time SoC at various system scales. Compared with typically available packet switched NoC, our PCCoC sees 242% improved performance and 97% latency reduction while keeping the silicon cost relatively low

A Comprehensive Analysis of Immune Response in Patients with Non-Muscle-Invasive Bladder Cancer

Background. Bladder carcinoma has elevated morbimortality due to its high recurrence and progression in localized disease. A better understanding of the role of the tumor microenvironment in carcinogenesis and response to treatment is needed. Methods. Peripheral blood and samples of urothelial bladder cancer and adjacent healthy urothelial tissue were collected from 41 patients and stratified in low- and high-grade urothelial bladder cancer, excluding muscular infiltration or carcinoma in situ. Mononuclear cells were isolated and labeled for flow cytometry analysis with antibodies aimed at identifying specific subpopulations within T lymphocytes, myeloid cells and NK cells. Results. In peripheral blood and tumor samples, we detected different percentages of CD4+ and CD8+ lymphocytes, monocyte and myeloid-derived suppressor cells, as well as differential expression of activation- and exhaustion-related markers. Conversely, only a significant increase in bladder total monocytes was found when comparing bladder and tumor samples. Interestingly, we identified specific markers differentially expressed in the peripheral blood of patients with different outcomes. Conclusion. The analysis of host immune response in patients with NMIBC may help to identify specific markers that allow optimizing therapy and patient follow-up. Further investigation is needed to establish a strong predictive model