Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

Therapeutic and Radiosensitizing Effects of Armillaridin on Human Esophageal Cancer Cells

Background. Armillaridin (AM) is isolated from Armillaria mellea. We examined the anticancer activity and radiosensitizing effect on human esophageal cancer cells. Methods. Human squamous cell carcinoma (CE81T/VGH and TE-2) and adenocarcinoma (BE-3 and SKGT-4) cell lines were cultured. The MTT assay was used for cell viability. The cell cycle was analyzed using propidium iodide staining. Mitochondrial transmembrane potential was measured by DiOC6(3) staining. The colony formation assay was performed for estimation of the radiation surviving fraction. Human CE81T/VGH xenografts were established for evaluation of therapeutic activity in vivo. Results. AM inhibited the viability of four human esophageal cancer cell lines with an estimated concentration of 50% inhibition (IC50) which was 3.4–6.9 μM. AM induced a hypoploid cell population and morphological alterations typical of apoptosis in cells. This apoptosis induction was accompanied by a reduction of mitochondrial transmembrane potential. AM accumulated cell cycle at G2/M phase and enhanced the radiosensitivity in CE81T/VGH cells. In vivo, AM inhibited the growth of CE81T/VGH xenografts without significant impact on body weight and white blood cell counts. Conclusion. Armillaridin could inhibit growth and enhance radiosensitivity of human esophageal cancer cells. There might be potential to integrate AM with radiotherapy for esophageal cancer treatment

Investigating the Possibility of Intervertebral Disc Regeneration Induced by Granulocyte Colony Stimulating Factor-Stimulated Stem Cells in Rats

Intervertebral disc (IVD) degeneration is a multifactorial process that is influenced by contributions from genetic predisposition, the aging phenomenon, lifestyle conditions, biomechanical loading and activities, and other health factors (such as diabetes). Attempts to decelerate disc degeneration using various techniques have been reported. However, to date, there has been no proven technique effective for broad clinical application. Granulocyte colony-stimulating factor (GCSF) is a growth factor cytokine that has been shown to enhance the availability of circulating hematopoietic stem cells to the brain and heart as well as their capacity for mobilization of mesenchymal bone marrow stem cells. GCSF also exerts significant increases in circulating neutrophils as well as potent anti-inflammatory effects. In our study, we hypothesize that GCSF can induce bone marrow stem cells differentiation and mobilization to regenerate the degenerated IVD. We found that GCSF had no contribution in disc regeneration or maintenance; however, there were cell proliferation within end plates. The effects of GCSF treatment on end plates might deserve further investigation

Using Hidden Markov Model for Stock Day Trade Forecasting

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. In this thesis, we will make a new use of the HMM and apply it on day trading stock forecast. However, the HMM is based on probability and statistics theory. In a statistics framework, the HMM is a composition of two stochastic processes, a Hidden Markov chain, which accounts for temporal variability, and an observable process, which accounts for spectral variability. The combination contains uncertainly status just likes the stock walk trace. Therefore, the HMM and the stock walk trace have the same idea by coincidence. In this thesis, we will try to learn the stock syntax; just like how the HMM model was used in speech recognition in different languages, and the take the next step ahead in price prediction. Additionally, the stock market is the reflection of the economy. The stock trace is impacted by many factors such as policy, psychology, microeconomics, economics, and capital, etc. There, in this thesis, the TAIFEX Taiwan index futures (TX) and day trade are used to avoid all the uncertainty factors. After the all experiments, it is proven that the HMM is better than the benchmark methodRandom Walk method and the Investment Trust & Consulting Association method- Modified Trading method. Moreover, the result is very conspicuous by the statistics testing of significance

Exploring Digital Nudging on Customer Emotions and Attitudes: Implications for Socially Responsible Consumption Behavior in the Cosmetics Field

Trending in online shopping, businesses frequently employ digital nudging to stimulate consumer purchases. These mechanisms include monetary incentives, such as providing discount information; messenger effect, harnessing the power of opinion leaders to guide consumer purchasing decisions. However, past research has predominantly focused on whether consumer behavior changes, while omitting the emotional and attitudinal factors that drive these changes. In today\u27s society, there is a strong focus on appearance, leading to the widespread use of beauty products. Regrettably, these products pose a certain environmental threat, sparking concerns about social responsibility. Therefore, this study is grounded in social contagion theory and aims to investigate the positive or negative emotions and attitudes that consumers may develop under the influence of digital nudging. Additionally, we will explore how these emotions and attitudes affect subsequent socially responsible consumer behavior. We plan to combine quantitative and qualitative research methods to gain a deeper understanding of the fundamental reasons shaping consumer behavior, which may be related to product characteristics or marketing strategies. This research will aid future businesses in making more informed choices when crafting marketing plans