Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of “just-in-time” injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption

Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs

Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods

A Description of the Development and Architecture of an SMS-Based System for Dealing With Depression

AbstractDepression is one of the leading mental health disorders in the world. With an exponential rate of growth the disease will soon surpass the ability for health care professionals to monitor and treat individuals with the disease. This paper describes a software system that continuously monitors an individual's emotional state through SMS and responds to the individual with supportive text messages. The development of the queries and responses is described along with the functioning hardware and software for the system

Identification of heavy metal ions from aqueous environment through gold, Silver and Copper Nanoparticles: An excellent colorimetric approach

Heavy metal pollution has become a severe threat to human health and the environment for many years. Their extensive release can severely damage the environment and promote the generation of many harmful diseases of public health concerns. These toxic heavy metals can cause many health problems such as brain damage, kidney failure, immune system disorder, muscle weakness, paralysis of the limbs, cardio complaint, nervous system. For many years, researchers focus on developing specific reliable analytical methods for the determination of heavy metal ions and preventing their acute toxicity to a significant extent. The modern researchers intended to utilize efficient and discerning materials, e.g. nanomaterials, especially the metal nanoparticles to detect heavy metal ions from different real sources rapidly. The metal nanoparticles have been broadly utilized as a sensing material for the colorimetric detection of toxic metal ions. The metal nanoparticles such as Gold (Au), Silver (Ag), and Copper (Cu) exhibited localized plasmon surface resonance (LPSR) properties which adds an outstanding contribution to the colorimetric sensing field. Though, the stability of metal nanoparticles was major issue to be exploited colorimetric sensing of heavy emtal ions, but from last decade different capping and stabilizing agents such as amino acids, vitmains, acids and ploymers were used to functionalize the metal surface of metal nanoparticles. These capping agents prevent the agglomeration of nanoparticles and make them more active for prolong period of time. This review covers a comprehensive work carried out for colorimetric detection of heavy metals based on metal nanoparticles from the year 2014 to onwards. © 202

Sensitive and selective electrochemical detection of bisphenol A based on SBA-15 like Cu-PMO modified glassy carbon electrode

This work reports the electrochemical detection of bisphenol A (BPA) using a novel and sensitive electrochemical sensor based on the Cu functionalized SBA-15 like periodic mesoporous organosilica-ionic liquid composite modified glassy carbon electrode (Cu@TU-PMO/IL/GCE). The structural morphology of Cu@TU-PMO is characterized by X-ray powder diffraction (XRD), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), Field emission scanning electron microscopy (FESEM), and Brunauer-Emmett-Teller (BET). The catalytic activity of the modified electrode toward oxidation of BPA was interrogated with cyclic voltammetry (CV) and differential pulse voltammetry (DPV) in phosphate buffer solution (pH 7.0) using the fabricated sensor. The electrochemical detection of the analyte was carried out at a neutral pH and the scan rate studies revealed that the sensor was stable. Under the optimal conditions, a linear range from 5.0 nM to 2.0 mu M and 4.0 to 500 mu M for detecting BPA was observed with a detection limit of 1.5 nM (S/N = 3). The sensor was applied to detect BPA in tap and seawater samples, and the accuracy of the results was validated by high-performance l

A review of polymeric membranes and processes for potable water reuse

Conventional water resources in many regions are insufficient to meet the water needs of growing populations, thus reuse is gaining acceptance as a method of water supply augmentation. Recent advancements in membrane technology have allowed for the reclamation of municipal wastewater for the production of drinking water, i.e., potable reuse. Although public perception can be a challenge, potable reuse is often the least energy-intensive method of providing additional drinking water to water stressed regions. A variety of membranes have been developed that can remove water contaminants ranging from particles and pathogens to dissolved organic compounds and salts. Typically, potable reuse treatment plants use polymeric membranes for microfiltration or ultrafiltration in conjunction with reverse osmosis and, in some cases, nanofiltration. Membrane properties, including pore size, wettability, surface charge, roughness, thermal resistance, chemical stability, permeability, thickness and mechanical strength, vary between membranes and applications. Advancements in membrane technology including new membrane materials, coatings, and manufacturing methods, as well as emerging membrane processes such as membrane bioreactors, electrodialysis, and forward osmosis have been developed to improve selectivity, energy consumption, fouling resistance, and/or capital cost. The purpose of this review is to provide a comprehensive summary of the role of polymeric membranes and process components in the treatment of wastewater to potable water quality and to highlight recent advancements and needs in separation processes. Beyond membranes themselves, this review covers the background and history of potable reuse, and commonly used potable reuse process chains, pretreatment steps, and advanced oxidation processes. Key trends in membrane technology include novel configurations, materials, and fouling prevention techniques. Challenges still facing membrane-based potable reuse applications, including chemical and biological contaminant removal, membrane fouling, and public perception, are highlighted as areas in need of further research and development. Keywords: Potable reuse; Polymeric membranes; Reverse osmosis; Filtration; Fouling; Revie

Echoenvironmental survey on fisheries site selection in the Guilan, Mazandaran and Golestan province

The lack of sustainable management programs has resulted in the drastic decline of sturgeons stocks in the Caspian Sea in the past two decades. Legal catch quotas for all Caspian littoral states has dropped from 28500 tons in 1985 to 460 tons in 2007 while caviar production in the Caspian Sea during the same period decreased from 3000 tons to 70 tons. Caviar production in the I.R. of Iran dropped from 305 tons in 1985 to about 11 tons in 2007. The "Strategic and applied research planning for sturgeon management and conservation" was developed with the collaboration of academicians from universities, researchers, experts, fishery authorities and representatives of the executive government to maintain sustainable development and rational management of sturgeon stocks in the Caspian Sea. The preparation and development of this planning was carried out through 121 sessions (2100 person hours) A comprehensive report (375 pg) was prepared which was divided into five volumes; Volume I comprises an introduction, objectives, strategies and planning, present status of production and caviar harvest, catch and export quotas, revenue generated and release of sturgeon fingerlings, Volume II comprises analysis of the problem by constructing a problem tree with 8 broad categories to analyze 344 problems and an objective tree which is the hierarchic flowchart of objectives with 9 broad categories with 241 items, Volume III comprises a review and analysis of previous and ongoing research (414 projects) on different aspects of sturgeon during the past 40 years, Volume IV comprises prioritizing research objectives outlined by the objective tree and finally Volume V which prioritizes primary objectives for 14 executive and 10 research programs. On the basis of the evaluation of the present status and for the sustainable development and rational management of sturgeon stocks a strategic and applied research planning program was proposed and developed within the framework of three primary objectives; 1) Management and sustainable use of Caspian Sea resources (5 programs), 2) Rehabilitation and restoration of stocks (5 programs), 3) Development of aquaculture (4 programs). The applied research program for each proposed executive program was outlined in 10 programs, 42 comprehensive plans and 222 projects. The primary objective on management and sustainable use of Caspian Sea resources includes 5 programs, 14 comprehensive plans and 63 projects, while the primary objective on rehabilitation and restoration of stocks includes 4 programs, 16 comprehensive plans and 87 projects. The primary objective to address sturgeon aquaculture includes 1 program, 12 comprehensive plans and 72 projects. The impacts and outcome of each of these programs was determined and presented. It is evident that if the present situation persists, the catch figures for adult sturgeon specimens and caviar production in Iran will reach zero in 2021. But if concerted efforts are taken and the proposed strategic and applied research planning program is executed (commencing from 2009) we can put a halt to these declining trends. By producing sturgeon fingerlings to restore population abundances, by conserving and protecting them in the Caspian Sea and by conducting applied research we can produce 206.4 tons of caviar by the year 2033. Apart from harvesting caviar from the Caspian Sea we can also annually produce 3000 tons sturgeon meat and 60 tons farmed caviar starting from the year 2023. The total budget proposed for implementation of this program for a period of 14 years (2008-2022) is USD 2483 million. If this budget is allocated in time and the proposed strategic program is properly and totally implemented, we can not only save many sturgeon populations from extinction but also ensure job opportunities for 6000 fisherman, provide 1480 new job opportunities and annually produce 266 tons of caviar which will generate USD 4957 million

New and Existing Roadway Inventory Data Acquisition Methods

A number of agencies collect roadway inventory data using the traditional manual method. Representing an advancement in roadway inventory data collection, mobile mapping systems use state-of-the-art imaging, georeference, and software technologies to collect data and are emerging as an alternative to the manual method. To gain an in-depth understanding of which method is more accurate and economical for an inventory job, this study compares the two data collection methods. Four experiments examine descriptive inventory data collected by the two methods, considering data accuracy in different roadway environments, type of inventory element, and data collection time. Because there are mobile mapping systems with different technological characteristics, the four experiments utilize four different mobile mapping systems to cover the spectrum of various systems available for data collection. Statistical analysis shows that the accuracy of descriptive inventory data depends on the method of collection and that the manual method provides slightly more accurate data. Furthermore, the roadway environment and the type of inventory element measured affect data accuracy. Compared with the manual method, the mobile mapping systems required less time during field operations but more time during office processing. This research suggests that transportation agencies interested in adopting mobile mapping systems for data collection might not see significant improvements in descriptive inventory data accuracy. However, the use of mobile mapping systems for inventory data collection provides other benefits