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The livestock sector has complex relationships with the three fundamental pillars of sustainability, i.e., environmental, economic, and social. Devising a livestock farming strategy by considering the different sustainability pillars is essential. Although several decision support systems (DSSs) are available for the livestock sector, these DSSs differ in the way they address sustainability. This work emphasizes the importance of a holistic approach to sustainable livestock management rather than only targeting individual sustainability dimensions. We, therefore, propose an initial assessment framework to evaluate the capacity of livestock DSSs in targeting the different sustainability pillars. In line with this, we present a conceptual basis for deriving assessment criteria and indicators. We then use the proposed assessment framework to assess existing openly available livestock DSSs. We observe that the main focus of the existing and openly available livestock-related DSSs is on the indicators from environmental pillars, and only a few of them accommodate economic aspects. No openly available DSS includes social and governance-related points. More importantly, none of these DSSs can handle data streams from Internet of Things (IoT) devices and, hence, they miss on the superiority that advanced modelling techniques can provide. With these observations, we draft an extensive set of guidelines for future livestock-related DSSs to holistically target sustainability.

Factories use many manufacturing processes that consume a lot of energy and highly contribute to greenhouse gas emissions. The introduction of the concept of Industrial Internet in USA and Industry 4.0 in Europe offers many opportunities to reduce energy consumption in these factories. Introducing and utilizing smart techniques for the applications pertinent to manufacturing processes within the Industry 4.0 domain can offer many benefits for reducing energy consumption in smart factories. This paper investigates and discusses these opportunities and benefits. This paper also discusses the roles of Industry 4.0 technologies in enabling these opportunities. Consequently, introducing these capabilities will help significantly reduce both production costs and greenhouse gas emissions. This paper then provides a benefit analysis that shows the advantages of such leverage. In addition, this paper offers an enabling architecture and its components that include a cyber-physical system manufacturing services' layer, a fog manufacturing services' layer, a cloud manufacturing services' layer, and a blockchain-based service-oriented middleware to support such opportunities.

There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

Smart cities are becoming a reality. Various aspects of modern cities are being automated and integrated with information and communication technologies to achieve higher functionality, optimized resources utilization, and management, and improved quality of life for the residents. Smart cities rely heavily on utilizing various software, hardware, and communication technologies to improve the operations in areas, such as healthcare, transportation, energy, education, logistics, and many others, while reducing costs and resources consumption. One of the promising technologies to support such efforts is the Cloud of Things (CoT). CoT provides a platform for linking the cyber parts of a smart city that are executed on the cloud with the physical parts of the smart city, including residents, vehicles, power grids, buildings, water networks, hospitals, and other resources. Another useful technology is Fog Computing, which extends the traditional Cloud Computing paradigm to the edge of the network to enable localized and real-time support for operating-enhanced smart city services. However, proper integration and efficient utilization of CoT and Fog Computing is not an easy task. This paper discusses how the service-oriented middleware (SOM) approach can help resolve some of the challenges of developing and operating smart city services using CoT and Fog Computing. We propose an SOM called SmartCityWare for effective integration and utilization of CoT and Fog Computing. SmartCityWare abstracts services and components involved in smart city applications as services accessible through the service-oriented model. This enhances integration and allows for flexible inclusion and utilization of the various services needed in a smart city application. In addition, we discuss the implementation and experimental issues of SmartCityWare and demonstrate its use through examples of smart city applications.

Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficulty of linking a failing test to a faulty or underperforming component. Furthermore, a failing test can also be linked to a wrongly configured performance test. In this paper, we present Building Metadata Performance Testing (BuMPeT), a method that addresses this shortcoming by using building metadata models to extend performance testing with fault detection and diagnostics (FDD) capabilities. We present four different procedures that apply BuMPeT to different data sources and components. We have applied the proposed method to a case study building, located in Denmark, to test its capacity and benefits. Additionally, we use two real case scenarios to showcase examples of failing performance tests in the building, as well as discovery of causes of underperformance. Finally, to examine the limits to the benefits of the applied procedure, a detailed elaboration of a hypothetical scenario is presented. Our findings demonstrate that the method has potential and it can serve to increase the energy efficiency of a wide range of buildings.

Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.