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Despite the vast economic impact of real estate markets worldwide, research on real estate brokerage markets remains limited. Specifically, there are few studies that provide a systematic, integrated, and replicable analytical methodology to analyze and benchmark a given real estate brokerage market. To this end, this paper introduces a data analytics methodology for analyzing real estate brokerage markets, integrating various statistical and analytical methods to extract insights from market data, supporting real estate investment decisions. The applicability of the methodology is demonstrated with a case study analyzing data from the top 50 real estate brokerage firms in Dubai, UAE. As shown in the case study, applying this methodology to brokerage market data enables the visual benchmarking of firms, identification of similarities between them, profiling and comparison of clusters of firms, and exploration of the impacts of various categorical and numerical attributes on performance. A notable finding for the Dubai real estate brokerage market is that it takes a minimum of 700 days for a brokerage firm to mature and advance to the next level of business success.

Hydroxyapatite (HAp) polymer composites have gained significant attention due to their applications in bone regeneration and tooth implants. This review examines the synthesis, properties, and applications of Hap, highlighting various manufacturing methods, including wet, dry, hydrothermal, and sol–gel processes. The properties of HAp are influenced by precursor materials and are commonly obtained from natural calcium-rich sources like eggshells, seashells, and fish scales. Composite materials, such as cellulose–hydroxyapatite and gelatin–hydroxyapatite, exhibit promising strength and biocompatibility for bone and tissue replacement. Metallic implants and scaffolds enhance stability, including well-known titanium-based and stainless steel-based implants and ceramic body implants. Biopolymers, like chitosan and alginate, combined with Hap, offer chemical stability and strength for tissue engineering. Collagen, fibrin, and gelatin play crucial roles in mimicking natural bone composition. Various synthesis methods like sol–gel, hydrothermal, and solution casting produce HAp crystals, with potential applications in bone repair and regeneration. Additionally, the use of biowaste materials, like eggshells and snails or seashells, not only supports sustainable HAp production but also reduces environmental impact. This review emphasizes the significance of understanding the properties of calcium–phosphate (Ca-P) compounds and processing methods for scaffold generation, highlighting novel characteristics and mechanisms of biomaterials in bone healing. Comparative studies of these methods in specific applications underscore the versatility and potential of HAp composites in biomedical engineering. Overall, HAp composites offer promising solutions for improving patient outcomes in bone replacement and tissue engineering and advancing medical practices.

With the growing global population, abiotic factors have emerged as a formidable threat to agricultural food production. If left unaddressed, these stress factors might reduce food yields by up to 25% by 2050. Plants utilize natural mechanisms, such as reactive oxygen species scavenging, to mitigate the adverse impacts of abiotic stressors. Diverse plants exhibit unique adaptations to abiotic stresses, which are regulated by phytohormones at various levels. Brassinosteroids (BRs) play a crucial role in controlling essential physiological processes in plants, including seed germination, xylem differentiation, and reproduction. The BR cascade serves as the mechanism through which plants respond to environmental stimuli, including drought and extreme temperatures. Despite two decades of research, the complex signaling of BRs under different stress conditions is still being elucidated. Manipulating BR signaling, biosynthesis, or perception holds promise for enhancing crop resilience. This review explores the role of BRs in signaling cascades and summarizes their substantial contribution to plants’ ability to withstand abiotic stresses.

One of the main reasons for air pollution is industrial plants releasing huge amounts of air pollutants in the form of gas emissions. The different chemical pollutants and their corresponding levels present in these emissions, and their proximity to the industrial source, have serious effects on the nearby ecosystems. Some of the industrial nuisances include noise, smoke, dirt, dust, odor and noxious gases, which have to be minimized (if possible, eliminated), especially if the location is desired to be used as a community site. When choosing locations at which to build either new industrial plants or new community sites, software can be used to assess both the short-term and long-term concentration profiles of the various detrimental air pollutants. In this study, the AERMOD model was used to find an optimal location to build a new plant in Toledo, Ohio, USA. Simulations were performed to study the pollutant emissions and their dispersion patterns for four different geographic locations situated away from an existing plant in this region. The AERMOD model, along with the IRAP-h View model, which is approved by the US Environmental Protection Agency (EPA), has been successfully used to assess the fate and transport of pollutants from the proposed new industrial plants. The hazard quotients from the analysis of the results for these four different geographic locations were assessed. The highest total non-cancer hazard indices of 18.7 and 13.2 were obtained for fisher adult and fisher child, respectively, in one of the four locations. The acute inhalation quotient risk was less than the target hazard index of 0.25 for all the four locations. With respect to the concentrations of several chemicals of potential concern (COPC), such as soil, produce, beef, chicken, milk and pork, the fourth location (farthest east) recorded the minimum range values compared to the other three locations.

In the context of global advancements, the imperative of a sustainable energy supply looms large. Biomass, an adaptable and renewable resource, has garnered attention for its potential contributions, although economic uncertainties persist due to the intricate web of processing pathways. In response, the biorefinery concept emerges as a structured strategy to optimize the processing of microalgae and municipal solid waste (MSW), capitalizing on their multifaceted potential to yield diverse end-products. This review underscores the critical significance of a cohesive biorefinery paradigm that unites the processing of microalgae and MSW, unveiling their capacity to generate a spectrum of high-value products. The utilization of mixed-integer linear programming paves the way for an optimal biorefinery model that navigates through complex decisions. Challenges encompass the array of diverse feedstocks and the preliminary nature of data availability. The overarching goal of this research is to discern optimal pathways for the conversion of MSW and microalgae into energy and valuable products, with a focus on enhancing waste utilization and augmenting the energy supply. In the broader landscape, this comprehensive review advances strategies for sustainable energy generation and waste management, invigorating innovative approaches to shape future progress. By illuminating pathways towards maximizing the potential of biomass resources, this review contributes to the ongoing discourse on sustainable energy and waste utilization.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a ‘Summary of findings’ table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Cybersecurity procedures and policies are prevalent countermeasures for protecting organizations from cybercrimes and security incidents. Without considering human behaviors, implementing these countermeasures will remain useless. Cybersecurity behavior has gained much attention in recent years. However, a systematic review that provides extensive insights into cybersecurity behavior through different technologies and services and covers various directions in large-scale research remains lacking. Therefore, this study retrieved and analyzed 2210 articles published on cybersecurity behavior. The retrieved articles were then thoroughly examined to meet the inclusion and exclusion criteria, in which 39 studies published between 2012 and 2021 were ultimately picked for further in-depth analysis. The main findings showed that the protection motivation theory (PMT) dominated the list of theories and models examining cybersecurity behavior. Cybersecurity behavior and intention behavior counted for the highest purpose for most studies, with fewer studies focusing on cybersecurity awareness and compliance behavior. Most examined studies were conducted in individualistic contexts with limited exposure to collectivistic societies. A total of 56% of the analyzed studies focused on the organizational level, indicating that the individual level is still in its infancy stage. To address the research gaps in cybersecurity behavior at the individual level, this review proposes a number of research agendas that can be considered in future research. This review is believed to improve our understanding by revealing the full potential of cybersecurity behavior and opening the door for further research opportunities.

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO2 concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs.

Oocyte activation, a fundamental event during mammalian fertilisation, is initiated by concerted intracellular patterns of calcium (Ca2+) release, termed Ca2+ oscillations, predominantly driven by testis-specific phospholipase C zeta (PLCζ). Ca2+ exerts a pivotal role in not just regulating oocyte activation and driving fertilisation, but also in influencing the quality of embryogenesis. In humans, a failure of Ca2+ release, or defects in related mechanisms, have been reported to result in infertility. Furthermore, mutations in the PLCζ gene and abnormalities in sperm PLCζ protein and RNA, have been strongly associated with forms of male infertility where oocyte activation is deficient. Concurrently, specific patterns and profiles of PLCζ in human sperm have been linked to parameters of semen quality, suggesting the potential for PLCζ as a powerful target for both therapeutics and diagnostics of human fertility. However, further to PLCζ and given the strong role played by Ca2+ in fertilisation, targets down- and up-stream of this process may also present a significantly similar level of promise. Herein, we systematically summarise recent advancements and controversies in the field to update expanding clinical associations between Ca2+-release, PLCζ, oocyte activation and human fertility. We discuss how such associations may potentially underlie defective embryogenesis and recurrent implantation failure following fertility treatments, alongside potential diagnostic and therapeutic avenues presented by oocyte activation for the diagnosis and treatment of human infertility.

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods.

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by modeling different resources—RESs, energy storages, EVs, and distributed generations. In addition, a method based on bi-directional long short-term memory networks is investigated for forecasting various stochastic parameters, wind speed, electricity price, load demand, and EVs’ behavior. The results of this study show the superiority of BLSTM methods for modeling these parameters with an error of 1.47% in comparison with real data. Furthermore, to show the performance of BLSTMs, its results are compared with other benchmark methods such as shallow neural networks, support vector machines, and long short-term memory networks.

Electric vehicles (EV) have the potential to significantly reduce carbon emissions. Yet, the current electric vehicle charging infrastructure utilizes electricity generated from non-renewable sources. In this study, the rooftop area of structures is analyzed to assess electricity that can be generated through solar- and wind-based technologies. Consequently, planning an electric vehicle charging infrastructure that is powered through ‘clean’ energy sources is presented. We developed an optimal modeling framework for the consideration of Renewable Energy Technologies (RET) along with EV infrastructure. After examining the level of technology, a MATLAB image segmentation technique was used to assess the available rooftop area. In this study, two competitive objectives including the economic cost of the system and CO2 emissions are considered. Three scenarios are examined to assess the potential of RET to meet the EV demand along with the Abu Dhabi city one while considering the life-cycle emission of RET and EV systems. When meeting only EV demand through Renewable Energy Technologies (RET), about 187 ktonnes CO2 was reduced annually. On the other hand, the best economic option was still to utilize grid-connected electricity, yielding about 2.24 Mt CO2 annually. In the scenario of meeting both 10% EV demand and all Abu Dhabi city electricity demand using RE, wind-based technology is only able to meet around 3%. Analysis carried out by studying EV penetration demonstrated the preference of using level 2 AC home chargers compared to other ones. When the EV penetration exceeds 25%, preference was observed for level 2 (AC public 3ϕ) chargers.

The separation of solid particles from gas-solid process streams is an important unit operation in many chemical processes. Out of the many different types of separation equipment that are used for gas-solid separation, cyclone separators are widely used for their operational flexibility, efficiency and capital cost. This study focused on the design of an optimal configuration for several cyclones used in a fertilizer plant. The granulation step in the fertilizer plant leads to using different size cyclones and a different number of cyclones in series or parallel or a mix of both arrangements. A Mixed Integer Nonlinear Programming (MINLP) model is formulated to find the best cyclone arrangement with the optimal number of cyclones and dimensions from several combinations of 1D3D and 2D2D cyclones arranged in parallel-series for a high volume and heavy loading of solid particles. The objective function was to minimize the total cost, including the operating cost and the capital cost. The results indicated that a maximum of 90% efficiency is achieved with a parallel-series arrangement of 1D3D and 2D2D cyclones to be an optimal configuration for the maximum reduction in pollution level.

Security is one of the major issues in Wireless Sensor Networks (WSNs), as poor security disrupts the entire network and can have a significant effect on data transmission. WSNs need safe data transmission at a high rate while maintaining data integrity. By modifying the Z-MAC protocol and merging it with IHOP and elliptic-curve encryption techniques, the present research produced a novel protocol that enables safe data transfer. Additionally, the paper examined the IHOP technique for secure data transfer based on the Z-MAC protocol, which offers a simple and efficient key generation mechanism based on a hierarchical key management architecture. Additionally, the Z-MAC protocol offered low contention, high throughput, reduced latency, low power consumption, and increased efficiency. One of the most major applications of the Secure Z-MAC protocol may be the Vehicle Area Network, which would help in increasing highway automobile traffic while simultaneously enhancing individual safety and minimizing accidents.

Terrestrial gamma ray flashes (TGF) are intense and prompt bursts of X- and gamma-rays of up to 100 MeV of energy. Typically associated with thunderstorm activity, TGFs are produced by bremsstrahlung effects of electrons accelerated in strong electric fields generated by lightning. TGFs can be effectively targeted by gamma detectors with enhanced time stamping capabilities onboard of satellites operating at near-Earth low obits (LEO). Light-1 is a miniature satellite, a 3U CubeSat designed to detect, monitor and study terrestrial gamma ray flashes in low Earth orbit. The two payload detectors are composed of a photomultiplier tube and silicon photomultipliers. The two detectors are mounted at two ends of the CubeSat and the proposed orientation of the CubeSat will ensure maximum TGF detection probability. To allow an increased frequency of data downlink, Khalifa University has collaborated with NanoAvionics Corp, and hence Light-1 has access to three ground stations situated across the map, Abu Dhabi in United Arab Emirates, Vilnius in Lithuania, and Aalborg in Denmark. The satellite expected to launch in late-2021 is currently in its assembly and integration phase. This paper describes mission, concept, objectives, success criteria, design, analysis, status, and the future plans of Light-1 satellite.

Oral mucosa and salivary gland are composed of complex and dynamic networks of extracellular matrix, multiple cell types, vasculature, and various biochemical agents. Two-dimensional (2D) cell culture is commonly used in testing new drugs and experimental therapies. However, 2D cell culture cannot fully replicate the architecture, physiological, and pathological microenvironment of living human oral mucosa and salivary glands. Recent microengineering techniques offer state of the science cell culture models that can recapitulate human organ structures and functions. This narrative review describes emerging in vitro models of oral and salivary gland tissue such as 3D cell culture models, spheroid and organoid models, tissue-on-a-chip, and functional decellularized scaffolds. Clinical applications of these models are also discussed in this review.

Background and Objectives: The aim of this study was to evaluate the effects of the chewing exerciser (CE) on the functional recovery of the masticatory muscles after orthognathic surgery. Material and Methods: This randomized clinical trial was conducted in patients undergoing bimaxillary orthognathic surgery including bilateral sagittal split ramus osteotomy. Postoperative physiotherapy (PT) was performed for 3 weeks starting 3 weeks after the surgery. The patients were randomly divided into two groups: control (Con) (conventional PT) group and CE group (use of CE in addition to conventional PT). The masticatory function was evaluated based on three standards: bite force (BF), amount of mouth opening (MO), and surface electromyography (sEMG) of the anterior temporal muscle (TA), masseter muscle (MM), sternocleidomastoid muscle, and anterior belly of digastric muscle before, 3 weeks (before PT) and 6 weeks after the surgery (after PT). Results: Finally, 22 subjects participated in this study: 10 patients for Con group and 12 patients for CE group. In both groups, the BF, which was reduced significantly after the surgery, recovered after the PT similar to that before the surgery. In both groups, the MO was also significantly reduced by the surgery. However, it did not recover as much, as it was before the surgery after applying the PT. There was no difference in BF and MO between the two groups. All muscles did not show significant changes in sEMG by surgery and PT at both resting and clenching states. Conclusion: Applying CE as a PT after orthognathic surgery did not cause any harmful side effects. In both groups, the weakened muscle activity after orthognathic surgery (OGS) was adequately restored 6 weeks after the surgery. However, CE did not offer a statistically significant benefit to the masticatory function in the recovery process after OGS.