Usability and comfort in Canadian offices: Interview of 170 university employees

Increasing building automation to improve energy efficiency introduces a risk of reducing occupants' perceived control and overall comfort. To this end, this paper presents a field study that used contextual techniques to explore the relationship between occupants' perceived control and comfort, as well as their preferences for building automation. A total of 170 occupants in 23 Canadian university campus buildings were interviewed in their offices using semi-structured interviews. All interviews entailed verbally administering a survey while photographs were systematically used to identify the context of occupants' interactions with building controls. Findings revealed that occupants' perception of comfort was moderately correlated to their perception of control over their indoor environment. Occupants also showed an overwhelming preference for more control opportunities in their offices (e.g. operable windows and dimmable lighting controls). Conducting interviews in offices yielded many interesting anecdotes and enabled the researcher to identify contextual issues related to building controls' accessibility, which may have been unnoticed otherwise. The findings of this research contribute to a broader debate within the research community about the appropriate level of building automation to optimize energy efficiency and occupant comfort

Key Performance Indicators Detection Based Data Mining

One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety

Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

Eyes on the Goal! Exploring Interactive Artistic Real-Time Energy Interfaces for Target-Specific Actions in the Built Environment

Current research is focused on sensing and modeling occupant behavior to predict it and automate building controls. Another line of research recommends influencing the behavior of occupants through feedback mechanisms and engagement. Yet, most of the work has focused on pushing occupants to reduce energy consumption over a long time and does not explore the potential to guide users to take specific actions promptly. The study examines a new interface mechanism that aims to solicit immediate and predefined actions from occupants. Building on seminal research in the field, the study uses art visualization to reinterpret social feedback. We test this approach in an immersive interaction space where participants react to artistic visuals to attain predefined settings for three indoor devices. In the 197 interactions recorded, participants’ overall actions conformed with the predefined goals. The participants were able to reach all or some of the targets in more than 80%, within an average of less than 30 seconds. We also see that complementing the visuals with textual hints improved the interaction in terms of engagement and accuracy. We conclude that ambient, abstract, and artistic real-time goal-driven feedback is effective in influencing immediate actions. We recommend that guiding occupants didactically has a strong potential for advancing building controls

Eco-Nudging: Interactive Digital Design to Solicit Immediate Energy Actions in The Built Space

In the built space, building occupants, their behaviours and control actions are research areas that have gained a lot of attention. This is well justified since energy behaviours can result in differences of up to 25% in building energy consumption. Previous research recommends exploring ways to influence occupants' energy behaviour – through eco-feedback and by directly engaging occupants with building controls. Very little attention has been given to the role digital art and design can play in soliciting and changing human energy-related actions and behaviours in the built space. This paper proposes a new process that combines eco-feedback, gamification, and ecological digital art to trigger occupants to take immediate and precise control actions in the built space. We design, deploy and test this by creating an immersive human-building-interaction apparatus, which we place in a month-long exhibition. This experimental interface was informed by a novel vision for engagement-based human-building interactions deeply rooted in aesthetics, digital art and design. It also uses digital art to mediate between the occupants and energy-performance of spaces by redefining their relationship with and perception of energy – moving from metrics and quantities understanding to one that is art and emotion-based. The analysis reveals that this new type of human-engagement-based interactive building-control mechanism can add a significant layer of influence on energy-related actions – without revoking the individuals' ability to control their environment. It also highlights digital design and art's power in guiding actions and interactions with the built space

Effectiveness of using WiFi technologies to detect and predict building occupancy

This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies

Polycyclic Aromatic Hydrocarbons in Grilled Meats from Restaurants

Polycyclic aromatic hydrocarbons are a group of lipophilic compounds that can be generated during the preparation of food items at elevated temperatures. They are regarded as potentially genotoxic and carcinogenic to human beings, related to increased incidence of breast and colorectal cancers, oxidative DNA injury, and bad effects on children neuro-differentiation. Thus, they are considered a public health concerns. A total of thirty samples of grilled beef steak, beef kofta and chicken (ten each) were collected from different restaurants. The samples were extracted by magnesium sulfate and sodium acetate in acetonitrile then purified in magnesium sulfate, primary, secondary amine and silica gel, and finally measured by gas chromatography-mass spectrometry (GC-MS). Benzo[a]pyrene was recorded with the highest average level (3.63µg/kg) in grilled kofta samples, but it was not detected in chicken samples. On the other hand, PAH4, PAH8 and ƩPAHs content were more abundant in grilled beef steak (5.32, 9.97 and 56.91µg/kg). Meanwhile, they recorded the least concentrations of grilled chicken from different restaurants. Furthermore, benzo[a]pyrene exceeded the permissible limits of the European Commission and Egyptian National Food Safety Authority in grilled kofta samples; further studies are needed to investigate the limits of exposure to these harmful compounds from meats and other food items

Advanced simulation methods for occupant-centric building design

Performance quantification through simulation has been particularly advantageous to building design, as it can be applied to non-existent buildings in the design process, allows for testing design variants under identical conditions, and demands much less resources as compared to physical measurements. Consequently, use of building simulation in the design process has evolved to – for example – establish and verify design performance, screen and optimize design parameters, and study robustness and adaptability in adverse conditions. In this context, the present chapter investigates how the state-of-the-art simulation-aided design procedures can contribute to realize occupant-centric design objectives. To this end, the chapter, first, discusses the ways in which simulation-aided design methods can represent occupants and capture their interactions with buildings’ environmental control systems. Subsequently, a number of key simulation-aided design methods and objectives are explored with a focus on the role of occupants. Finally, a carefully described prototypical building model serves to demonstrate and test the introduced occupant-centric simulation-aided design procedures

Core Microbial Functional Activities in Ocean Environments Revealed by Global Metagenomic Profiling Analyses

Metagenomics-based functional profiling analysis is an effective means of gaining deeper insight into the composition of marine microbial populations and developing a better understanding of the interplay between the functional genome content of microbial communities and abiotic factors. Here we present a comprehensive analysis of 24 datasets covering surface and depth-related environments at 11 sites around the world's oceans. The complete datasets comprises approximately 12 million sequences, totaling 5,358 Mb. Based on profiling patterns of Clusters of Orthologous Groups (COGs) of proteins, a core set of reference photic and aphotic depth-related COGs, and a collection of COGs that are associated with extreme oxygen limitation were defined. Their inferred functions were utilized as indicators to characterize the distribution of light- and oxygen-related biological activities in marine environments. The results reveal that, while light level in the water column is a major determinant of phenotypic adaptation in marine microorganisms, oxygen concentration in the aphotic zone has a significant impact only in extremely hypoxic waters. Phylogenetic profiling of the reference photic/aphotic gene sets revealed a greater variety of source organisms in the aphotic zone, although the majority of individual photic and aphotic depth-related COGs are assigned to the same taxa across the different sites. This increase in phylogenetic and functional diversity of the core aphotic related COGs most probably reflects selection for the utilization of a broad range of alternate energy sources in the absence of light.This work was supported by King Abdullah University for Science and Technology Global Collaborative Partners (GCR) program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript