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UVA Assisted 4-Thiothymidine for Cancer Treatment
This article reviews the developments of 4-thiothymidine analogues, assisted with UVA light, as a novel cancer therapy. First, the key points on synthetic chemistry, photochemistry and cellular toxicity of 4-thiothymidine are summarized. As the chemical structure of 4-thiothymidine is very similar to that of its parent thymidine, thus 4-thiothymidine can be readily incorporated into cellular DNA, and with the help of thymidine kinase, much more preferably into cancerous DNA. Unlike thymidine, 4-thiothymdine can absorb strongly in UVA (longer wavelengths of UV) light. Thus UVA-assisted 4-thiothymidine offers an effective cancer treatment. Some underlying mechanisms of action by 4-thiothymidine/UVA and compares this cancer approach with the commonly used photodynamic therapy are discussed. The various interactions between 4-thiothymidine with human serum albumin are introduced. Finally, a short conclusion on the past efforts and a brief prospect for future work in this exciting research field are given
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model the charge prediction task and the relevant article extraction task in a
unified framework. The experimental results show that, besides providing legal
basis, the relevant articles can also clearly improve the charge prediction
results, and our full model can effectively predict appropriate charges for
cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201
Anger or Fear? Effects of Discrete Emotions on Deviant Security Behavior
Deterrence theory has received considerable attention in recent years. However, scholars have begun to call for research beyond the deterrence approach on security behaviors, and argue that the theory of emotion should not be omitted from information systems security decision making [15, 81]. In this research, we examine and distinguish effects of anger and fear on perceived costs of sanctions and deviant security behavior. A research model is developed based on deterrence theory and cognitive appraisal theory of emotion. We propose to design a scenario of introducing a new security monitoring system, to analyze the interplays of anger, fear, perceived certainty, perceived severity of sanctions and deviant security behavior. The results will have important implications for comprehensively understanding employees’ deviant security behavior
A survey of deep learning approaches for WiFi-based indoor positioning
One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments
WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time
The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research
An analysis of the properties and the performance of WiFi RTT for indoor positioning in non-line-of-sight environments
Indoor positioning system based on WiFi Round-Trip Time (RTT) measurement is believed to deliver sub-metre level accuracy with trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environments re-mains a research challenge.To this end, this paper investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present a large-scale real-world dataset containing both RTT and received signal strength (RSS) signal measures with correct ground-truth labels.Our results indicated that RTT fingerprinting system delivered an accuracy below 0.75 m which was 98% better than RSS fingerprinting and 166% better than RTT trilateration, which failed to deliver sub-metre accuracy as claimed
A Multi-Scale Feature Selection Framework for WiFi Access Points Line-of-sight Identification
A dynamic model switching algorithm for WiFi fingerprinting indoor positioning
In 2023, there are various WiFi technologies and algorithms for an indoor positioning system. However, each technology and algorithm comes with their own strengths and weaknesses that may not universally benefit all building locations. Therefore, we propose a novel algorithm to dynamically switch to the most optimal positioning model at any given location, by utilising a Machine Learning based weighted model selection algorithm, with WiFi RSS and RTT signal measures as the input features. We evaluated our algorithm in three real-world indoor scenarios to demonstrate an improvement of up to 1.8 metres, compared to standard WiFi fingerprinting algorithm
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