Big data analytics and mining for effective visualization and trends forecasting of crime data.

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process

Integrated Moist‐Thermoelectric Generator for Efficient Waste Steam Energy Utilization

Abstract Industrial waste steam is one of the major sources of global energy losses. Therefore, the collection and conversion of waste steam energy into electricity have aroused great interest. Here, a “two‐in‐one” strategy is reported that combines thermoelectric and moist‐electric generation mechanisms for a highly efficient flexible moist‐thermoelectric generator (MTEG). The spontaneous adsorption of water molecules and heat in the polyelectrolyte membrane induces the fast dissociation and diffusion of Na+ and H+, resulting in the high electricity generation. Thus, the assembled flexible MTEG generates power with a high open‐circuit voltage (Voc) of 1.81 V (effective area = 1cm2) and a power density of up to 4.75±0.4 µW cm−2. With efficient integration, a 12‐unit MTEG can produce a Voc of 15.97 V, which is superior to most known TEGs and MEGs. The integrated and flexible MTEGs reported herein provide new insights for harvesting energy from industrial waste steam

Chitosan and β-Cyclodextrin-epichlorohydrin Polymer Composite Film as a Plant Healthcare Material for Carbendazim-Controlled Release to Protect Rape against Sclerotinia sclerotiorum (Lib.) de Bary

The influence of β-cyclodextrin-epichlorohydrin (β-CD-EP) polymers on the improvement of the solubility and antifungal activity of carbendazim has been investigated. Meanwhile, the potential of the chitosan and β-CD-EP composite film used as a plant healthcare material for carbendazim-controlled release to protect rape against Sclerotinia sclerotiorum (Lib.) de Bary has been evaluated. β-CD-EP-1 and 2 (β-CD content, 750 mg/g and 440 mg/g, respectively) were found to significantly improve the solubility of the guest molecule carbendazim (17.9 and 18.5 times, respectively) and the 1:1 stoichiometry of the host-guest was confirmed by the Job’s plot. A slight synergism was observed for the β-CD-EP/carbendazim complex against S. sclerotiorum (Lib.) de Bary, indicating an enhancement to the bioavailability of carbendazim. The in vitro release studies revealed that β-CD-EP polymers could efficiently modulate carbendazim release behaviors, such as the release retard and rate. The in vivo efficacy experiments demonstrated that the β-CD-EP/carbendazim and chitosan composite film could significantly prolong the effective duration of carbendazim at a concentration of 100 μg/mL compared with spraying carbendazim at 500 μg/mL. Thereby, a highly useful and strategic concept in plant disease control by a plant healthcare material—the chitosan and polymeric β-CD-EP composite film—is provided, which could also serve as a concept for related plant diseases

Attention-Embedded Triple-Fusion Branch CNN for Hyperspectral Image Classification

Hyperspectral imaging (HSI) is widely used in various fields owing to its rich spectral information. Nonetheless, the high dimensionality of HSI and the limited number of labeled data remain significant obstacles to HSI classification technology. To alleviate the above problems, we propose an attention-embedded triple-branch fusion convolutional neural network (AETF-Net) for an HSI classification. The network consists of a spectral attention branch, a spatial attention branch, and a multi-attention fusion branch (MAFB). The spectral branch introduces the cross-channel attention to alleviate the band redundancy problem in high dimensions, while the spatial branch preserves the location information of features and eliminates interfering image elements by a bi-directional spatial attention module. These pre-extracted spectral and spatial attention features are then embedded into a novel MAFB with large kernel decomposition technique. The proposed AETF-Net achieves multi-attention features reuse and extracts more representative and discriminative features. Experimental results on three well-known datasets demonstrate the superiority of the method AETF-Net

Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs

Figure skating scoring is challenging because it requires judging players’ technical moves as well as coordination with the background music. Most learning-based methods struggle for two reasons: 1) each move in figure skating changes quickly, hence simply applying traditional frame sampling will lose a lot of valuable information, especially in 3 to 5 minutes lasting videos; 2) prior methods rarely considered the critical audio-visual relationship in their models. Due to these reasons, we introduce a novel architecture, named Skating-Mixer. It extends the MLP framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we collected a high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8 types of programs with 7 different rating metrics, overtaking other datasets in both quantity and diversity. Experiments show the proposed method achieves SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to the recent competitions in Beijing 2022 Winter Olympic Games, proving our method has strong applicability

Aerodynamic stability of vehicle passing through a bridge tower at high speed under crosswind conditions with different road adhesion coefficients

In a complex environment, large bridges will present different adhesion coefficient, often accompanied by large crosswind, which makes the flow field around the vehicle complex and changeable. It is easy to cause unnecessary lane change and sideslip of the vehicle, which will affect the driving stability and lead to serious traffic accidents. Therefore, this paper studies the aerodynamic stability of cars passing through the bridge tower at high speed under different road adhesion and crosswind conditions. The detached-eddy simulation (DES) model was employed and the reliability of the DES model was verified by wind tunnel tests. An overlapping mesh technique was adopted to realize the motion of the vehicle. A multibody dynamic (MBD) model of the vehicle was established, and its robustness was verified. A two-way coupling model was then established based on the aerodynamic and MBD models. Subsequently, the aerodynamic characteristics and dynamic response of the vehicle passing through the bridge tower at a high speed were compared and analyzed using one-way and two-way coupling methods, with road adhesion coefficients of 1.0, 0.6, and 0.4 under crosswind conditions. The results show that the aerodynamic characteristics of the vehicle passing the bridge tower at a low adhesion coefficient under two-way coupling change evidently, and the trajectory and body attitude of the vehicle change significantly. As the adhesion coefficient of the road surface decreases, the vehicle passes through the bridge tower with a large lateral displacement and yaw angle. The maximum lateral force of −1406 N and the maximum yaw moment of 803 N∙m are generated when the car passes through the bridge tower under the two-way coupling with the adhesion coefficient of 0.4. Under two-way coupling, the lateral displacement and yaw angle caused by the bridge deck with an adhesion coefficient of 0.4 are 0.265 m and 0.0205 rad, respectively, which is larger than those of the bridge deck with adhesion coefficients of 1.0 and 0.6. Because the coupling effect of aerodynamic and vehicle motion is not considered in the one-way coupling, the one-way and two-way coupling simulation results differ significantly. The results indicate that it is necessary to use a two-way coupling method to study the aerodynamic stability of vehicles passing through a bridge tower at a high speed under crosswind conditions

IL-32 Promotes the Radiosensitivity of Esophageal Squamous Cell Carcinoma Cell through STAT3 Pathway

Objective. This study is set out to determine the relationship between IL-32 and radiosensitivity of esophageal squamous cell carcinoma (ESCC). Methods. Western blot was adopted for measuring IL-32 expression in Eca-109 and TE-10 cells. Eca-109 and TE-10 cells with interference or overexpression of IL-32 were treated with the presence or absence of X-ray irradiation. Then, the use of CCK8 assay was to detect proliferation ability, and effects of IL-32 expression on radiosensitivity of ESCC were tested by colony formation assay. The cell apoptosis was detected using flow cytometry. STAT3 and p-STAT expression, and apoptotic protein Bax were detected by western blot. Results. Colony formation assay and CCK8 assay showed that compared with the NC group without treatment, the growth of the ESCC cells, that is Eca-109 and TE-10, was significantly inhibited in the OE+IR group with highly expressed IL-32 and irradiation. In flow cytometry analysis, in Eca-109 and TE-10 cells, highly expressed IL-32 combined with irradiation significantly increased apoptosis compared with the control group. Highly expressed IL-32 has a synergistic effect with irradiation, inhibiting STAT3 and p-STAT3 expression and increasing apoptotic protein Bax expression. Conclusion. IL-32 can improve the radiosensitivity of ESCC cells by inhibiting the STAT3 pathway. Therefore, IL-32 can be used as a new therapeutic target to provide a new attempt for radiotherapy of ESCC