iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Kinetic and degradation efficiency of trichloroethylene (TCE) via photochemical process from contaminated water

Trichloroethylene (TCE) is a common pollutant in groundwater and one of the priority pollutants listed by the U.S. EPA. With regard to concentration ranges in previous studies exceeding environmental levels by far with millimolar concentrations of TCE, this study deals with the degradation of TCE at micromolar concentrations by UV/H2O2. The degradation rate of TCE at different dilute solution levels, 30, 300 and 3000 g L-1 (0.22, 2.28 and 22.83 micromolar) at different initial pHs was examined. In addition, samples were taken from four contaminated wells to measure the degradation rate of TCE. It was shown that thedegradation rate of TCE increased due to the reduction of initial concentration in both aqueous solution and groundwater samples. The TCE degradation constants in groundwater samples increased by a factor of 2.05, while the initial concentration reduced from 1345.7 to 97.7 μg1 L-1. By increasing the molar ratios of H2O2 to TCE from 13 to 129, caused the degradation rates to increase in aqueous solutions. No harmful byproducts such as aloacetic acids (HAAs) were detected at these low levels of initial concentration of TCE during process. This study confirmed that application of UV/H2O2 process could be an effective method in treating contaminated groundwater by TCE at low concentrations

Health impact assessment of air pollution in Shiraz, Iran : a two-part study

We aimed to assess health-impacts of short-term exposure to the air pollutants including PM10, SO2, and NO2 in Shiraz, Iran in a two-part study from 2008 to 2010. In part I, local relative risks (RRs) and baseline incidences (BIs) were calculate using generalized additive models. In part II, we estimated the number of excess hospitalizations (NEHs) due to cardiovascular diseases (CDs), respiratory diseases (RDs), respiratory diseases in elderly group (RDsE-people older than 65 years old), and chronic obstructive pulmonary diseases (COPDs) as a result of exposure to air pollutants using AirQ model, which is proposed approach for air pollution health impact assessment by World Health Organization. In part I, exposure to increase in daily mean concentration of PM10 was associated with hospitalizations due to RDs with a RR of 1.0049 [95% confidence interval (CI), 1.0004 to 1.0110]. In addition, exposure to increase in daily mean concentration of SO2 and NO2 were associated with hospitalizations due to RDsE and COPDs with RRs of 1.0540 [95% CI, 1.0050 to 1.1200], 1.0950 [95% CI, 1.0700 to 1.1100], 1.0280 [95% CI, 1.0110 to 1.0450] and 1.0360 [95% CI, 1.0210 to 1.0510] per 10 μg/m3 rise of these pollutants, respectively. In part II, the maximum NEHs due to CDs because of exposure to PM10 were in 2009-1489 excess cases (ECs). The maximum NEHs due to RDs because of exposure to PM10 were in 2009-1163 ECs. Meanwhile, the maximum NEHs due to RDsE and COPDs because of exposure to SO2 were in 2008, which are 520 and 900 ECs, respectively. In conclusion, elevated morbidity risks were found from acute exposure to air pollutants

Clustering approach based on feature weighting for recommendation system in movie domain

The advancement of the Internet has brought us into a world that represents a huge amount of information items such as movies, web pages, etc. with fluctuating quality. As a result of this massive world of items, people get confused and the question “Which one should I select?” arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including contentbased and collaborative techniques which are the most commonly used approaches in recommendation systems. This dissertation introduces a new recommendation model, a feature weighting technique to cluster the user for recommendation top-n movies to avoid new user cold start and scalability problem. The distinctive point of this study lies in the methodology used to cluster the user and the methodology which is utilized to recommend movies to new users. The model makes it possible for the new users to define a weight for every feature of movie based on its importance to the new user in scale of one (with an increment of 0.1). By using these weights, it finds nearest cluster of users to the new user and suggests him the top-n movies (with the highest rate and most frequency) which are reviewed by users that are in the targeted cluster. Rating and Movie dataset were are used during this study. Firstly, purity and entropy are applied to evaluate the clusters and then precision, recall and F1 metrics are used to assess the recommendation system. Eventually, the results of accuracy testing of proposed model are compared with two traditional models (OPENMORE and Movie Magician Hybrid) and based on the evaluation the level of preciseness of the proposed model is more better than Movie Magician Hybrid but worse than OPENMORE

Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry

Over the years, AI has been utilized as a powerful tool to address complex challenges in the AEC industry. In the current AI practices, machines predominantly plan, manage, control, and optimize work without appropriately considering human-related input and preferences. However, architects, engineers, managers, clients, and other decision makers should consider their input into their work to better generate their desired ideas, prototypes, and solutions. In addition, significant decisions in the AEC industry are mainly reliant on the heuristic processes where assumptions are developed from past experience. However, the current level of AI is not able to properly deal with such human information and experience. This fact especially in large projects can result in a failure to properly utilize the full benefits of AI. Thus, human-centered AI is an essential need to help the machines understand and utilize human input for amplifying human abilities and reflecting realistic conceptions in the AEC industry. This paper presents the major aspects and applications of human-centered AI in the AEC industry and discusses the anticipated benefits and challenges of this technology. Human-centered AI, mainly via natural language processing and machine reading comprehension, can understand and learn from human interests, preferences, languages, and behaviors for providing human-centered environments, systems, and approaches that satisfy human interests and preferences. As the major benefits, human-centered AI is expected to result in architectural processing optimization, design and engineering capability enhancement, data driven project management, collaboration improvement, and safety enhancement. Personalization of human-centered AI and training its systems are considered as the major challenges in developing this technology in the AEC industry. In addition, AEC-specific guidelines and statements should be regulated in developing human-centered AI utilized in hazardous areas. Human-centered AI is anticipated to provide the highest level of human control in the current fast-growing automation of the AEC industry

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

How Do Students Behave in a Gamified Course?—A Ten-Year Study

Gamified learning aims to motivate students using game elements. Although gamification can enhance students’ enjoyment and engagement, it is unclear how different students behave in and interact with gamified contexts. To this end, we analyze how different students interact with a gamified course. We devised such an experimental course on Multimedia Content Production (MCP), and ran it for ten years. At each year, we modified it after students’ feedback from the previous year. We determined student groups applying clustering techniques to learner performance data, independently analyzed the resulting clusters in terms of behavior, engagement, performance, and also compared those pairwise. Our analysis identified four different student groups (profiles/clusters) according to their performance and interactions with the course across all years. We found out that the best performing students were those that had significantly more interactions with course materials and consistently ranked highest. In addition, we found that performance indicators for students of all groups became stable within the first month after course start, allowing final grades to be predicted with high accuracy by then. Furthermore, all were deadline driven and became mainly active at the end of the semesters (indicating a lack of self-regulation skills). Moreover, we did not find any specific relation between students’ groups and gaming profiles (Brainhex categories). Finally, we propose practical implications and guidelines for designing compelling gamified learning experiences

A Pilot Study of RO16 Discoloration and Mineralization in Textile Effluents Using the Nanophotocatalytic Process

The nanophotocatalytic process using nano-structured semiconductors is one of the techniques used for the destructive oxidation of organic compounds such as dyes. The photocatalytic oxidation of Reactive Orange 16 aqueous solution, applied in the textile industry, was assessed by UV ray irradiation in the presence of TiO2 nanoparticles. It was found that the photons required for the process were completely absorbed when TiO2 concentration reached 0.4 g/L. Degradation of paint decreased with increasing TiO2 concentration. It is suggested that at very high concentrations, the active points on ions are covered and the number of radicals like ˙OH will, therefore, decrease on the surface of catalysts. Another explanation for this state of affairs is that UV screening may have the same function. The negative action of anions may be explained by the reaction of positive cavities accomplished by hydroxyl radicals with anions. This reaction can be described as corrosive for ˙OH and hVB+, which can prolong the process of color removal. The TiO2 in an acidic environment has a positive charge (p