The Origins of Islam in the Arabian context

This thesis investigates the origins of Islam, and their relations with the Arabian context, with the help of two types of materials - the Qur'an and Muslim tradition, and the archeological finds. First, I analyze the external and internal situations of pre-Islamic Arabia. Then, I discuss the conditions of Mecca and pre-Islamic Arabian polytheism, and their roles in the emergence of Islam. After that, I examine various monotheistic elements in Arabia that may form the origins of Islam, as well as the origins of God's names. Finally, I focus on the condition of Yathrib and the relation between the old monotheisms (Judaism and Christianity) and Islam. Mecca and Yathrib were two crucial places for the emergence of Islam. Due to the differences of their milieus, the early Muslims were persecuted by polytheists in Mecca, while Yathrib (later Medina) became an arena for polemics with the old monotheisms according to the Qur'an and Muslim tradition. Based on the simple Abrahamism that was once popular in northwest Arabian peripheries, Muhammad's new proposition of Abraham's religion and the Hanifiya may have played a key role in the emergence of Islam, producing a connection between his new monotheism and pre-Islamic Arabian history. The establishment of the Ka'ba in a central position in Islam, signalled the independence of early Islam from the old monotheisms. Later, the Arab Muslim conquest of Mesopotamia and the cultural and religious integration that followed added to the final shape of Islam. All these elements contributed to the origins of Islam.Master i The Religious Roots of EuropeMAHF-RREURRE34

Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas

The purpose of this paper was to develop a real-time alarm monitoring system that can detect the fatigue driving state through wireless communication. The drivers’ electroencephalogram (EEG) signals were recorded from occipital electrodes. Seven EEG rhythms with different frequency bands as gamma, hbeta, beta, sigma, alpha, theta and delta waves were extracted. They were simultaneously assessed using relative operating characteristic (ROC) curves and grey relational analysis to select one as the fatigue feature. The research results showed that the performance of theta wave was the best one. Therefore, theta wave was used as fatigue feature in the following alarm device. The real-time alarm monitoring system based on the result has been developed, once the threshold was settled by using the data of the first ten minutes driving period. The developed system can detect driver fatigue and give alarm to indicate the onset of fatigue automatically

Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal

Aiming at the problem that some traditional high voltage circuit breaker fault diagnosis methods were over-dependent on subjective experience, the accuracy was not very high and the generalization ability was poor, a fault diagnosis method for energy storage mechanism of high voltage circuit breaker, which based on Convolutional Neural Network (CNN) characteristic matrix constructed by sound-vibration signal ,was proposed. In this paper, firstly, the morphological filtering was used for background noise cancellation of sound signal, and the time scale alignment method based on kurtosis and envelope similarity were proposed to ensure the synchronism of the sound-vibration signal. Secondly, the Pearson correlation coefficient was used to construct two-dimensional image characteristic matrix for the expanded sound-vibration signal. Finally, the characteristic matrix was trained by utilizing CNN. Local Response Normalization (LRN) and core function decorrelation were utilized to improve the structure of CNN model, which reduced the bad impact of large data fluctuation of energy storage process on the diagnostic accuracy of circuit breaker energy storage mechanism. Compared with the traditional method, the proposed method has obvious advantages, whose total accurate rate up to 98.2 % and generalization performance is excellent

Contrastive Masked Autoencoders for Self-Supervised Video Hashing

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabeled videos. Typically, state-of-the-art SSVH methods consider these two points in a two-stage training pipeline, where they firstly train an auxiliary network by instance-wise mask-and-predict tasks and secondly train a hashing model to preserve the pseudo-neighborhood structure transferred from the auxiliary network. This consecutive training strategy is inflexible and also unnecessary. In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames. Particularly, we find that a higher masking ratio helps video understanding. Besides, we fully exploit the similarity relationship between videos by maximizing agreement between two augmented views of a video, which contributes to more discriminative and robust hash codes. Extensive experiments on three large-scale video datasets (i.e., FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art results. Code is available at https://github.com/huangmozhi9527/ConMH.Comment: This work is accepted by the AAAI 2023. 9 pages, 6 figures, 6 table

AdaCompress: Adaptive Compression for Online Computer Vision Services

With the growth of computer vision based applications and services, an explosive amount of images have been uploaded to cloud servers which host such computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the {\em de facto} compression and encapsulation method before one uploads the images, due to its wide adaptation. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model, e.g., the standard quality level of JPEG leads to 50\% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models including InceptionNet, ResNet, etc. Knowing this, designing a better JPEG configuration for online computer vision services is still extremely challenging: 1) Cloud-based computer vision models are usually a black box to end-users; thus it is difficult to design JPEG configuration without knowing their model structures. 2) JPEG configuration has to change when different users use it. In this paper, we propose a reinforcement learning based JPEG configuration framework. In particular, we design an agent that adaptively chooses the compression level according to the input image's features and backend deep learning models. Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size. In our real-world evaluation on Amazon Rekognition, Face++ and Baidu Vision, our approach can reduce the size of images by 1/2 -- 1/3 while the overall classification accuracy only decreases slightly.Comment: ACM Multimedi

PolyMetformin combines carrier and anticancer activities for in vivo siRNA delivery

Metformin, a widely implemented anti-diabetic drug, exhibits potent anticancer efficacies. Herein a polymeric construction of Metformin, PolyMetformin (PolyMet) is successfully synthesized through conjugation of linear polyethylenimine (PEI) with dicyandiamide. The delocalization of cationic charges in the biguanide groups of PolyMet reduces the toxicity of PEI both in vitro and in vivo. Furthermore, the polycationic properties of PolyMet permits capture of siRNA into a core-membrane structured lipid-polycation-hyaluronic acid (LPH) nanoparticle for systemic gene delivery. Advances herein permit LPH-PolyMet nanoparticles to facilitate VEGF siRNA delivery for VEGF knockdown in a human lung cancer xenograft, leading to enhanced tumour suppressive efficacy. Even in the absence of RNAi, LPH-PolyMet nanoparticles act similarly to Metformin and induce antitumour efficacy through activation of the AMPK and inhibition of the mTOR. In essence, PolyMet successfully combines the intrinsic anticancer efficacy of Metformin with the capacity to carry siRNA to enhance the therapeutic activity of an anticancer gene therapy

Effects of transglutaminase pre-crosslinking on salt-induced gelation of soy protein isolate emulsion

peer-reviewedThe salt-induced gelation behavior of soy protein isolate (SPI) emulsions was markedly influenced by microbial transglutaminase (TGase) pre-crosslinking. Rheological data showed that when SPI emulsions were incubated with TGase at low concentrations (1 and 3 U/g protein) at 50 °C for 30 min prior to gelation, no change in storage modulus (G′), but enhanced resistance to deformation of the gels was observed. Extensive crosslinking by TGase (5 U/g protein) resulted in severe decreases in gel firmness and fracture properties (yielding stress and strain), likely due to the impairment of hydrophobic bonds and the formation of coarse networks. The water-holding capacity of the gels was significantly enhanced by increased concentrations of TGase. Interactive force analysis indicated that non-covalent interactions and disulfide bonds are the primary forces involved in CaSO4-induced SPI emulsion gel, but TGase treatment may limit hydrophobic interactions within the gel network. These results are of great potential value for the application of TGase in the food industry

Effect of Football Shoe Collar Type on Ankle Biomechanics and Dynamic Stability During Anterior and Lateral Single-Leg Jump Landings

In this study, we investigated the effects of football shoes with different collar heights on ankle biomechanics and dynamic postural stability. Fifteen healthy college football players performed anterior and lateral single-leg jump landings when wearing high collar, elastic collar, or low collar football shoes. The kinematics of lower limbs and ground reaction forces were collected by simultaneously using a stereo-photogrammetric system with markers (Vicon) and a force plate (Kistler). During the anterior single-leg jump landing, a high collar shoe resulted in a significantly smaller ankle dorsiflexion range of motion (ROM), compared to both elastic (p = 0.031, dz = 0.511) and low collar (p = 0.043, dz = 0.446) types, while also presenting lower total ankle sagittal ROM, compared to the low collar type (p = 0.023, dz = 0.756). Ankle joint stiffness was significantly greater for the high collar, compared to the elastic collar (p = 0.003, dz = 0.629) and low collar (p = 0.030, dz = 1.040). Medial-lateral stability was significantly improved with the high collar, compared to the low collar (p = 0.001, dz = 1.232). During the lateral single-leg jump landing, ankle inversion ROM (p = 0.028, dz = 0.615) and total ankle frontal ROM (p = 0.019, dz = 0.873) were significantly smaller for the high collar, compared to the elastic collar. The high collar also resulted in a significantly smaller total ankle sagittal ROM, compared to the low collar (p = 0.001, dz = 0.634). Therefore, the high collar shoe should be effective in decreasing the amount of ROM and increasing the dynamic stability, leading to high ankle joint stiffness due to differences in design and material characteristics of the collar types