ANGELAH: A Framework for Assisting Elders At Home

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

Multi-Layered Clustering for Power Consumption Profiling in Smart Grids

Open access publicationSmart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms (e.g., centralized clustering) are used for profiling individual’s power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is expensive high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared to the centralized clustering approach

Effects of two different management systems on hormonal, behavioral, and semen quality in male dromedary camels

Effects of two different management systems on male dromedary camel hormones, behaviors, and semen parameters were documented. Camels (n=6) were tested under two management systems: (i) housed in single boxes with 1-h freedom (H23); (ii) exposed to females for 17 h (from 3.30 p.m. to 8.30 a.m.) and then housed (ConExF). Blood was collected every morning; camel behavior was recorded twice a day: (i) from 7:00 to 8:00 a.m. to determine the short effects; (ii) from 2:00 to 3:00 p.m. to determine the long effects. Each camel underwent a female parade and semen collection thrice a week; sexual behavior, libido, and semen parameters were assessed. Testosterone and cortisol concentrations were higher in ConExF than H23. Compared to the H23 group, ConExF group spent more time walking, standing tripods, and looking outside their pen/box but they spent less time eating, ruminating, resting, standing, and showing stereotypical behaviors. In the morning, ConExF group spent more time walking, ruminating, and showing typical sexual behaviors compared to themselves during afternoon time and the H23 group. However, in the afternoon time, ConExF camels put more time their heads outside the box through the window and showed higher frequencies of stereotypies, probably due to a higher level of frustration. While the sexual behavioral score was higher and ejaculates showed a higher fraction of milky white and white-colored semen in ConExF than H23 group, their libido was similar. Overall, 17 h of exposure led to an increase in testosterone and cortisol levels, enhancing sexual behavior and semen color, but leading to frustration

Coverage and Energy Analysis of Mobile Sensor Nodes in Obstructed Noisy Indoor Environment: A Voronoi Approach

The rapid deployment of wireless sensor network (WSN) poses the challenge of finding optimal locations for the network nodes, especially so in (i) unknown and (ii) obstacle-rich environments. This paper addresses this challenge with BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi algorithm. In line with the scenario challenges, BISON nodes are restricted to (i) locally sensed as well as (ii) noisy information on the basis of which they move, avoid obstacles and connect with neighboring nodes. Performance is measured as (i) the percentage of area covered, (ii) the total distance traveled by the nodes, (iii) the cumulative energy consumption and (iv) the uniformity of nodes distribution. Obstacle constellations and noise levels are studied systematically and a collision-free recovery strategy for failing nodes is proposed. Results obtained from extensive simulations show the algorithm outperforming previously reported approaches in both, convergence speed, as well as deployment cost.Comment: 17 pages, 24 figures, 1 tabl

Effect of diet supplementation on growth and reproduction in camels under arid range conditions

Eighteen pregnant dromedary females (Camelus dromedarius) were used to determine the effect of concentrate supplement on growth and reproductive performances in peri-partum period. The females were divided into supplemented (n = 9; S) and unsupplemented (n = 9; C) experimental groups. All animals grazed, with one mature male, 7 to 8 hours per day on salty pasture rangelands. During night, they were kept in pen, where each female of group S received 4 kg per day of concentrate supplement during the last 3 months of gestation and 5 kg per day during the first 3 months post-partum. During the last 90 days of gestation daily body weight gain (DBG) was at least tenfold more important in group S than in group C (775 g vs. 72 g respectively). Supplementation affected birth weight of offspring (30.3 kg vs. 23.4 kg) and its DBG (806 g vs. 430 g) in group S and group C respectively. During the post-partum period, females in group S gained in weight (116 g per day) whereas females in group C lost more than 200 g per day. The mean post-partum interval to the first heat and the percentage of females in heat were 29.5 day and 44.4/ vs. 41.2 day and 71.4/ for the C and S groups, respectively. We conclude that under range conditions, dietary supplementation of dromedary during late pregnancy stage and post-partum period improves productive and reproductive parameters

Detecting indicators of cognitive impairment via Graph Convolutional Networks

While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia

Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure

The loss or compromise of any safety critical industrial infrastructure can seriously impact the confidentiality, integrity, or delivery of essential services. Research has shown that such threats often come from malicious insiders. To this end, survey- and electrocardiogram-based approaches were suggested to identify these insiders; however, these approaches cannot effectively detect or predict any malicious insiders. Recently, electroencephalograms (EEGs) have been suggested as a potential alternative to detect these potential threats. Threat detection using EEG would be highly reliable as it overcomes the limitations of the previous methods. This study proposes a proof of concept for a system wherein a model trained using a deep learning algorithm is employed to evaluate EEG signals to detect insider threats; this algorithm can classify different mental states based on four category risk matrices. In particular, it analyses brainwave signals using long short-term memory (LSTM) designed to remember previous mental states of each insider and compare them with the current brain state for associated risk-level classification. To evaluate the performance of the proposed system, we perform a comparative analysis using logistic regression (LR)—a predictive analysis used to describe the relationship between one dependent binary variable and one or more independent variables—on the same dataset. The experiment results suggest that LSTM can achieve a classification accuracy of more than 80% compared to LR, which yields a classification accuracy of approximately 51%

Boron-Based Inhibitors of the NLRP3 Inflammasome.

NLRP3 is a receptor important for host responses to infection, yet is also known to contribute to devastating diseases such as Alzheimer's disease, diabetes, atherosclerosis, and others, making inhibitors for NLRP3 sought after. One of the inhibitors currently in use is 2-aminoethoxy diphenylborinate (2APB). Unfortunately, in addition to inhibiting NLRP3, 2APB also displays non-selective effects on cellular Ca2+ homeostasis. Here, we use 2APB as a chemical scaffold to build a series of inhibitors, the NBC series, which inhibit the NLRP3 inflammasome in vitro and in vivo without affecting Ca2+ homeostasis. The core chemical insight of this work is that the oxazaborine ring is a critical feature of the NBC series, and the main biological insight the use of NBC inhibitors led to was that NLRP3 inflammasome activation was independent of Ca2+. The NBC compounds represent useful tools to dissect NLRP3 function, and may lead to oxazaborine ring-containing therapeutics

Artificial immune systems

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years