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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. [ABSTRACT FROM AUTHOR]

Background: The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models—a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models. Methods: We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index. Results: The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia. Conclusion: Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings. [ABSTRACT FROM AUTHOR]

This article presents an innovative study to enhance the accuracy of lubrication oil evaporation measurement from the oil film through thermal and sensitivity analyses. The study employs a novel experimental configuration to examine the influence of temperature variations and oil film thickness on oil evaporation. The oil film's temperature is investigated comprehensively, applying experimental and theoretical approaches through different conditions. In order to enhance the accuracy of oil film temperature estimation, the study suggests introducing a correction factor accounting for the thermal properties of vapor during the multiphase liquid evaporation process. The findings indicate that small changes in the thickness of the oil film have no discernible effect on its temperature. In contrast, the evaporation characteristics of the oil film are notably influenced by the properties of its liquid and vapor phases. Incorporating the proposed correction factor (0.84) enhances the accuracy of the oil film temperature estimation equation. The findings presented in this study hold significant value for improving the accuracy of modeling considerations for the optimal configuration of piston rings in internal combustion engines. [ABSTRACT FROM AUTHOR]

Coherent personal narratives employed for self-understanding (self function) and social proximity (social function) are theorized to inform life and the sense of self with meaning. Yet, it has not been tested whether this rather individualistic notion of narrative identity holds true across cultures. Therefore, this study investigates potential cultural differences in the extent to which narrative identity of Emirati and U.S.-American female undergraduates reflect and relate to meaning in life and the sense of self. Results showed that the U.S.-American narratives were more thematically coherent and served the self and social functions more than the Emirati narratives. Thematic coherence and self function related significantly to meaning in life in the Emirati subsample, but surprisingly not in the U.S.-American subsample. In both samples, the social function related to self-concept clarity and the sense of coherence. These results further our understanding on cultural differences in constructing meaning in life and the sense of self through narrative identity. [ABSTRACT FROM AUTHOR]

The current research utilizes a low voltage scanning electron microscopy (LV-SEM) with an electron beam along with low loading energy of lower than 2.2 KeV to minimize damage and specimen surface charging. The NovaSEM, is used as an efficient tool in the current study due to the high resolution information it can gather as images and its high magnification nanometers. Polyamide 12 (PA12), as a polymer matrix, and Cloisite 30B (C30B) nanoclay, as a filling material, were the materials tested in this study. From the results obtained, CBS was found to be a significant and valuable tool for certain complex tasks when studying and analyzing polymer/clay interfaces. CBS in conjunction with beam deceleration in a LV-SEM was used to map the C30B clay distribution on PA12 particles and within PA12-nanoclay nanocomposites manufactured from the latter's clay distribution particles within the polymer particles' surfaces. This SEM experimentation has demonstrated that using the clay's air plasma design preceding the composite preparation resulted in removing the large clay assemblages. The plasma treatment has improved the interfacial adhesion and dispersion in the nanoclay/PA12 composite, resulting in similar maximum stress values which were both higher than the pure PA12. Thus, the mechanical tests exhibited performance enhancement for the resulting composites defined in the present work, and the enhancement of this method can be identified via SEM imaging. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Conventional minimum spanning tree (MST) algorithms typically start by creating a distance matrix of the n(n-1)/2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$n(n-1)/2$$\end{document} pairs of data points, leading to a time complexity of O(n2)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$O(n^2)$$\end{document}. This initial step poses a computational bottleneck. To overcome this limitation, we present a novel method that constructs an initial random k-neighbor graph and optimizes this graph by employing a crawling technique to efficiently approximate the k Nearest Neighbors (kNN) graph. This crawling approach allows us to approximate the closest neighbors of each node. Subsequently, the approximate kNN graph is utilized to build an initial approximate MST and iteratively refine it by the same crawling process. Using this approach, an approximate MST can be obtained for a data set of size n with empirical cost around O(n1.07)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$O(n^{1.07})$$\end{document} and a minimal O(n) memory consumption. In contrast to spatial tree-based approaches, the presented method also scales well to high dimensional data. We have shown that the proposed approach achieves such a level of performance with only a marginal accuracy reduction between 0.5% and 6%. These qualities make it an excellent candidate for approximate MST calculation for high-dimensional, large data sets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a major public health issue due to high morbidity and mortality. Different screening programs were implemented to reduce its burden.Objectives: To estimate the prevalence of CRC screening uptake using fecal immunochemical test (FIT) or guaiac fecal occult blood testing (gFOBT) in Emirati nationals. Other objectives were to measure the incidence of CRC in the screened population, to measure the outcomes of follow-up screening colonoscopy after positive FIT/gFOBT and to identify the causes of not performing follow-up screening colonoscopy after positive FIT/gFOBT.Methodology: Adult Emirati nationals aged 40-75 years who visited Ambulatory healthcare services clinics, Abu Dhabi in 2015-2016 were included in the study. The electronic medical records of the eligible individuals were reviewed retrospectively. The prevalence of CRC screening was measured among the eligible population using the FIT/gFOBT. The IBM SPSS Statistics program, version 21.0.0, was used for analysis.Result: 45,147 unique individuals were eligible for screening, and only 23.5% were screened using FIT/gFOBT. Of the screened individuals, 13.5% had positive FIT/ gFOBT, and 30.5% of those underwent follow-up screening colonoscopy. CRC was diagnosed in 11 individuals. Colonic polyp were found in 30.5% of individuals who had undergone a follow-up colonoscopy. Collectively 933 individuals did not undergo follow-up screening colonoscopy after having a positive FIT/gFOBT, and about 36.3% had collected the result and referred to a gastroenterologist but did not attend the appointment.Conclusion: CRC screening uptake using FIT/gFOBT is low among the adult Emirati nationals. [ABSTRACT FROM AUTHOR]

Date palm (Phoenix dactylifera L.) is the oldest known tree grown in the world’s arid regions. It has been cultivated for centuries because of its enormous cultural, agricultural, and environmental benefits. Recently date palm groves have been declined drastically due to anthropogenic, abiotic and, biotic stresses. Attaining sustainable farming and optimum crop production requires frequent monitoring of the physiological status of date palms. This demands rapid and non-destructive estimation of chlorophyll concentration in date palm leaflets overtime. In this study, four date palm cultivars, exhibiting distinct concentrations of chlorophyll, were used to assess the potential of using SPAD502+ and CCM-200 portable chlorophyll meters to estimate the concentration of chlorophyll in date palm leaflets. Regression analyses were performed to model the relationship between the absolute concentration of chlorophyll measured in vitro and the optic indices of the two portable chlorophyll meters (SPAD and CCI). The results revealed that polynomial and power prediction models, which demonstrated remarkably close fits to each another, are the best functions to parameterize the relationships. The calibration models developed in this study were very strong and recorded high coefficient of determinations along with low relative errors for both the cultivar-specific fits (R2 ≥ 0.89; Error % ≤ 18.3) as well as the generic species-specific fits (R² ≥ 0.822; Error % ≤ 25.0). The results confirmed that both SPAD502+ and CCM-200 are equally effective tools for rapid and nondestructive estimation of chlorophyll concentration in date palm leaflets. [ABSTRACT FROM AUTHOR]

This study addresses the underexplored area of radiation interaction properties of antibiotics and their implications for stability, efficacy, and structural integrity. Focusing on a range of antibiotics classified by the United Arab Emirates National AMR Surveillance system, the research evaluates key metrics such as attenuation coefficients, effective electron density, exposure buildup factor, and photon transmission factors. Utilizing advanced MCNPX Monte Carlo simulation, we observed varied interaction patterns among different antibiotics, with significant findings on their photon interaction characteristics. The study highlights the potential molecular changes in antibiotics due to radiation exposure, emphasizing the importance of such research in fields from medical radiography to space exploration. Results indicate that radiation exposure can significantly affect antibiotic properties, underscoring the need for careful evaluation of drugs in radiation-rich environments to ensure their effectiveness and safety. • Explored radiation interactions in some antibiotics for medical implications. • Measured antibiotics' attenuation properties across a wide-energy spectrum. • Employed MCNPX simulations for in-depth energy deposition analysis. • Fluoroquinolones show minimal radiation attenuation behaviours. • Antibiotics with high energy deposition may undergo significant molecular changes. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the criminal behavior on the social media platforms and to classify the gathered data effectively as negative, positive, or neutral in order to identify a suspect. In this study, data was collected from two platforms, Twitter and Facebook, resulting in the creation of two datasets. The following findings have been pointed out from this study: Initially, VADER twitter sentimental analysis showed that out of 5000 tweets 50.8% people shared a neutral opinion, 39.2% shared negative opinion and only 9.9% showed positive opinion. Secondly, on Facebook, the majority of people showed a neutral response which is 55.6%, 38.9% shared positive response and only 5.6% shared negative opinion. Thirdly, the score of sentiments and engagement in every post affects the intensities of sentiments. [ABSTRACT FROM AUTHOR]

This research aimed at discovering the polite and impolite expressions regarding the approbation, modesty, agreement and sympathy maxims of Leech (1983) in the conversations in the Fourth Crescent English Course for Yemen taught in Yemeni Secondary Schools. It also aimed at interpreting them and how the violations of those maxims of Leech happen in the contexts of politeness principle in the conversation Textbook 4. It is a descriptive qualitative and analytical, in which the information was collected from the related literature, previous studies and the Crescent English textbook 4. This research used the content analysis of the fourth Crescent English Course for Yemen as the sources of data. The most important findings of this research revealed that the agreement maxim and approbation maxim of Leech's politeness principle are highly used, while the modesty and sympathy are rarely used in the textbook 4. The identifiable Leech maxims are 13 approbation data, 4 modesty data, 20 agreement data and 4 sympathy data employed in the conversations of the Crescent Textbook 4. The violations of maxims are found in one data of the approbation maxim and in 7 data of the agreement maxims in the textbook, while the violations of the modesty and sympathy maxims are not found in the textbook. [ABSTRACT FROM AUTHOR]

A patient of tetrology of Fallot with complete atresia of the pulmonary outflow tract with ventriculocoronary connections is presented.MDCT imaging revealed left coronary sinus, with a large fistula draining into the free wall of hypoplastic right ventricular cavity with tortuous channel arising from right ventricular outflow, and communicating with proximal limb of the fistula forming a complete loop suggesting a right ventricle–to – left coronary sinus sinusoid. [ABSTRACT FROM AUTHOR]

This work considers the non-convex finite-sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly scaled and/or ill-conditioned, and a primary goal of this work is to introduce methods that alleviate this issue. Thus, here we include a preconditioner based on Hutchinson's approach to approximating the diagonal of the Hessian and couple it with several gradient-based methods to give new 'scaled' algorithms: Scaled SARAH and Scaled L-SVRG. Theoretical complexity guarantees under smoothness assumptions are presented. We prove linear convergence when both smoothness and the PL-condition are assumed. Our adaptively scaled methods use approximate partial second-order curvature information and, therefore, can better mitigate the impact of badly scaled problems. This improved practical performance is demonstrated in the numerical experiments also presented in this work. [ABSTRACT FROM AUTHOR]

The distance and duration of human spaceflight missions is set to markedly increase over the coming decade as we prepare to send astronauts to Mars. However, the health impact of long-term exposure to cosmic radiation and microgravity is not fully understood. In order to identify the molecular mechanisms underpinning the effects of space travel on human health, we must develop the capacity to monitor changes in gene expression and DNA integrity in space. Here, we report successful implementation of three molecular biology procedures on board the International Space Station (ISS) using a miniaturized thermal cycler system and C. elegans as a model organism: first, DNA extraction–the initial step for any type of DNA analysis; second, reverse transcription of RNA to generate complementary DNA (cDNA); and third, the subsequent semi-quantitative PCR amplification of cDNA to analyze gene expression changes in space. These molecular procedures represent a significant expansion of the budding molecular biology capabilities of the ISS and will permit more complex analyses of space-induced genetic changes during spaceflight missions aboard the ISS and beyond. [ABSTRACT FROM AUTHOR]

Polyamide 12 (PA12) powder was exposed for up to 3 h to low pressure air plasma treatment (LP‐PT) and several minutes by two different atmospheric pressure plasma jets (APPJ) i.e., kINPen (K‐APPJ) and Hairline (H‐APPJ). The chemical and physical changes resulting from LP‐PT were observed by a combination of Scanning Electron Microscopy (SEM), Hot Stage Microscopy (HSM) and Fourier transform infrared spectroscopy (FTIR), which demonstrated significant changes between the plasma treated and untreated PA12 powders. PA12 exposed to LP‐PT showed an increase in wettability, was relatively porous, and possessed a higher density, which resulted from the surface functionalization and materials removal during the plasma exposure. However, it showed poor melt behavior under heating conditions typical for Laser Sintering. In contrast, brief PJ treatments demonstrated similar changes in porosity, but crucially, retained the favorable melt characteristics of PA12 powder. [ABSTRACT FROM AUTHOR]

Highlights • A comprehensive study of plant decomposition effects on distributed optimization-based control was performed. • Different levels of communication and cooperation between local controllers were considered. • Different dynamic optimization platforms were employed. • Different levels of system uncertainty were considered. • Closed-loop performance and computational efficiency were evaluated over a wide operating range. Abstract A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated through tracking outputs in different tracking zones corresponding to desired operating conditions. Different levels of communication and cooperation between local controllers, levels of system uncertainty, and dynamic optimization platforms are considered. The results are analyzed to determine the most suitable method for decomposition of the studied integrated process. [ABSTRACT FROM AUTHOR]

The tendency of nanoparticles (NPs) to form large aggregates has been a major limitation to their widespread applications where utilizing monodisperse and stable suspension of NPs is essential. The aggregation of NPs becomes more challenging when there is less affinity between the dispersed phase (NPs) and the continuous phase (solvent), such as, dispersion of hydrophilic metal oxide NPs into a nonpolar (organic) solvent. The objective of this study is to systematically investigate the synergistic effects of eight dispersion parameters on the size and stability of indium tin oxide (ITO) NPs in heptane. The matrix of experimentation was designed using an L 18 Taguchi method. The analysis of variance (ANOVA) of the experimental results revealed that the most significant factors on the size and stability of NPs were the mass of ITO NPs and the volume of the dispersing agent. Taguchi signal-to-noise (SN) ratio analysis was used to determine the optimal factor levels for the preparation of well-dispersed and stable NP suspensions. Confirmation tests were carried out at the suggested levels of the ANOVA predictive model, and highly stable ITO NPs in heptane with the size distribution of 43.0–68.3 nm were obtained. The results of the present parametric study can be used for a broad range of applications where effective stabilization of metal oxide NPs in organic solvents is desired. [ABSTRACT FROM AUTHOR]

There is an impetus to decarbonize transportation sector and mitigate climate change. This study examines the effect of adopting hydrogen (H 2) as a fuel for Dubai Buses at different penetration scales on carbon dioxide (CO 2) emissions reduction. A H 2 supply-chain system dynamics model is developed to conduct life cycle cost and environmental analysis and evaluate the efficacy of different carbon prices and subsidies. Gray, green and mixed H 2 production scenarios were considered. The results show that gray hydrogen reduces 7.1 million tons of CO 2 , which is half of green hydrogen buses. Replacing diesel fleet at end of lifetime with mixed hydrogen bus fleet was the optimal approach to promote green hydrogen at pump reaching $4/kg in a decade. This gradual transition reduces 62% of the well-to-wheel CO 2 emissions of the new bus fleet and creates mass for economies of scale as carbon prices and subsidies cannot promote green hydrogen alone. • Subsidies alone cannot commercialize green hydrogen as a fuel for public transport. • Trade-offs between cost and CO 2 reductions using green, gray and mixed hydrogen. • Mixed hydrogen in Dubai buses create a scale to lower cost and emissions. • Integrated approach from government, policy makers, and industries generate demand. • At large scale and in the long term, full green hydrogen transport is economical. [ABSTRACT FROM AUTHOR]

Salinity stress has detrimental effects on various aspects of plant development. However, our understanding of strategies to mitigate these effects in crop plants remains limited. Recent research has shed light on the potential of sodium acetate as a mitigating component against salinity stress in several plant species. Here, we show the role of acetate sodium in counteracting the adverse effects on oat (Avena sativa) plants subjected to NaCl-induced salinity stress, including its impact on plant morphology, photosynthetic parameters, and gene expression related to photosynthesis and antioxidant capacity, ultimately leading to osmoprotection. The five-week experiment involved subjecting oat plants to four different conditions: water, salt (NaCl), sodium acetate, and a combination of salt and sodium acetate. The presence of NaCl significantly inhibited plant growth and root elongation, disrupted chlorophylls and carotenoids content, impaired chlorophyll fluorescence, and down-regulated genes associated with the plant antioxidant defense system. Furthermore, our findings reveal that when stressed plants were treated with sodium acetate, it partially reversed these adverse effects across all analyzed parameters. This reversal was particularly evident in the increased content of proline, thereby ensuring osmoprotection for oat plants, even under stressful conditions. These results provide compelling evidence regarding the positive impact of sodium acetate on various plant development parameters, with a particular focus on the enhancement of photosynthetic activity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper addresses the impact of decomposition on the closed-loop performance and computational efficiency of model predictive control (MPC) of nonlinear process networks. Distributed MPC structures with different communication strategies are designed for regulation of an integrated reactor-separator process. Different system decompositions are also considered, including decompositions into local controllers with minimum interactions obtained via community detection methods. The closed-loop performance and computational effort of the different MPC designs are analyzed. Through such a comprehensive comparison, tradeoffs between performance and computation effort, and the importance of systematic choice of the system decomposition, are documented. [ABSTRACT FROM AUTHOR]

This study was designed toinvestigate the efficacy of adenovirus vector-mediated brain-derived neurotrophic factor (BDNF) ex vivo gene transfer to human umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs) in a rat sciatic nerve crush injury model. BDNF protein and mRNA expression after infection was checked through an enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qRT-PCR). Male Sprague-Dawley rats (200–250 g, 6 weeks old) were distributed into threegroups (n = 20 each): the control group, UCB-MSC group, and BDNF-adenovirus infected UCB-MSC (BDNF-Ad + UCB-MSC) group. UCB-MSCs (1 × 10 6 cells/10 μl/rat) or BDNF-Ad + UCB-MSCs (1 × 10 6 cells/10 μl/rat)were transplantedinto the rats at the crush site immediately after sciatic nerve injury. Cell tracking was done with PKH26-labeled UCB-MSCs and BDNF-Ad + UCB-MSCs (1 × 10 6 cells/10 μl/rat). The rats were monitored for 4 weeks post-surgery. Results showed that expression of BDNF at both the protein and mRNA levels was higher inthe BDNF-Ad + UCB-MSC group compared to theUCB-MSC group in vitro.Moreover, BDNF mRNA expression was higher in both UCB-MSC group and BDNF-Ad+ UCB-MSC group compared tothe control group, and BDNF mRNA expression in theBDNF-Ad + UCB-MSC group was higher than inboth other groups 5 days after surgeryin vivo. Labeled neurons in the dorsal root ganglia (DRG), axon counts, axon density, and sciatic function index were significantly increased in the UCB-MSC and BDNF-Ad+ UCB-MSCgroupscompared to the controlgroup four weeksaftercell transplantation. Importantly,the BDNF-Ad + UCB-MSCgroup exhibited more peripheral nerve regeneration than the other two groups.Our results indicate thatboth UCB-MSCs and BDNF-Ad + UCB-MSCscan improve rat sciatic nerve regeneration, with BDNF-Ad + UCB-MSCsshowing a greater effectthan UCB-MSCs. [ABSTRACT FROM AUTHOR]

This study assesses the need for a future climate change protocol with the aim to protect the environment and reduce global warming trends. Such protocol shall provide a framework for developed nations, such as the United States, as well as developing nations, such as China, in order to further improve both their economy and their standards of living. Considering the elaboration of the future emission reduction framework, a comprehensive understanding of the current and future energy consumption portfolios is provided. The United States and China were used as illustrative examples and the existing emission reduction policies as basis. As a result, it was found that there is an urgent need to formulate such protocol. [ABSTRACT FROM PUBLISHER]

The increasing global demand for petroleum-based fuels, mainly driven by the economic growth in emerging markets imposes significant challenges in terms of energy supply and environmental mitigation strategies. This work introduces an approach for the design and decision making of primary energy source, production, storage, and distribution networks for hydrogen supply in regions (or countries) under emission constraints. The problem was mathematically represented using a source-sink system approach to determine the most suitable hydrogen supply chain (HSC) network. The optimization problem was formulated as a Mixed Integer Linear Programming (MILP) model using GAMS ® modeling system. The optimization objective consists of the minimization of the total network cost, both in terms of capital and operating expenditures, subject to: supply, demand, mass conservation, technical performance, economic, and environmental constraints. The model was used to plan the future hydrogen supply chain network for Germany in the year 2030 under emission constraints. The optimization results show that the model is a valuable tool for planning the optimal hydrogen supply chain network of a particular region or country. [ABSTRACT FROM AUTHOR]

Cogeneration of water and power in integrated cogeneration production plants is a common practice in the Middle East and North Africa (MENA) countries. There are several combinations of water desalination and power technologies which give significant adverse environmental impact. Renewable and alternative energy technologies have been recently proposed as alternative power production paths in the water and power sector. In this study, we examine the optimal capacity expansion of water and power infrastructure over an extended planning horizon. A generic mixed integer linear programming model is developed to assist in the decision making process on: (1) optimal installation of cogeneration expansion capacities; (2) optimal installation of renewable and alternative power plants; (3) optimal operation of the integrated water and power supply chain over large geographical areas. Furthermore, the model considers the installation of carbon capture methods in fossil-based power plants. A case study will be presented to illustrate the mathematical programming application for the Emirate of Abu Dhabi (AD) in the United Arab Emirates (UAE). The case study is solved reflecting different scenarios: base case scenario, integration of renewable and alternative technologies scenario, and CO 2 reduction targets scenario. The results show that increased carbon tax values up to 150 $/ton-CO 2 gives a maximum 3% cost increase for the supply chain net present value. The installation of carbon capture methods is not an economical solution due to its high operation energy requirements in the order of 370 kW h per ton of captured CO 2 . Thus, the Cplex solver in GAMS chooses optimal solutions without installation of carbon capture processes. In addition, higher degree of alternative and renewable energy technologies penetration within the energy mix reduces the overall net present value of the network, and the carbon emissions by 40%, and 12%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a new step-wise sequential solution strategy for the synthesis of heat integrated water networks (HIWNs) comprising of two mathematical models. The first model minimizes water and energy costs. The optimal freshwater and water flow rates within the HIWN are determined from this model. Using these optimal flow rates, different configurations of HIWN are evaluated for the purpose of heat integration. The second model is the stage-wise heat exchanger network (Yee and Grossmann, 1990), which is solved for each of the evaluated network configuration in a sequential manner. Using this proposed strategy, a set of locally optimal HIWNs are produced and the best one is chosen based on the minimum total annual cost (TAC). The proposed sequential strategy is applied to small and medium sized examples in the literature. The results show that the proposed new sequential solution strategy can be successfully applied to small and medium sized HIWN problems. [ABSTRACT FROM AUTHOR]

In this paper, neural algorithms, including the multi-layered perceptron (MLP) differential approximator, generalized hybrid power series, discrete Hopfield neural network, and the hybrid numerical, are used for constructing models that incorporate a priori knowledge in the form of differential equations for dynamic engineering processes. The properties of these approaches are discussed and compared to each other in terms of efficiency and accuracy. The presented algorithms have a number of advantages over other traditional mesh-based methods such as reduction of the computational cost, speed up of the execution time, and data integration with the a priori knowledge. Furthermore, the presented techniques are applicable when the differential equations governing a system or dynamic engineering process are not fully understood. The proposed algorithms learn to compute the unknown or free parameters of the equation from observations of the process behavior, hence a more precise theoretical description of the process is obtained. Additionally, there will be no need to solve the differential equation each time the free parameters change. The parallel nature of the approaches outlined in this paper make them attractive for parallel implementation in dynamic engineering processes. [ABSTRACT FROM AUTHOR]

Recent regulations regarding water quality motivated research in reverse osmosis (RO) technology for water desalination especially under boron restrictions. Boron control in the permeate products by reverse osmosis technology comprises several mitigation options which impose capital and operation costs. In our study, we investigate the design of RO system under the design concept of split partial second pass reverse osmosis (SPSPRO) in order to allow extraction of permeate streams along the RO pressure vessels. The design approach is based on superstructure optimization which allows the identification of optimal process layout and optimal operation conditions of the process units. pH adjustment of the network streams is also included in the model to control boron species distribution during the separation process. A mixed integer nonlinear program (MINLP) model is subsequently formulated based on the superstructure representation. The mathematical programming formulation is demonstrated on a case study to show the application of the mathematical programming model. The results show lower treatment cost through the proposed mathematical programming model. [ABSTRACT FROM AUTHOR]

In this study, a multiperiod model that considers uncertainty in the gas feedstock fuel price is developed for the optimal design of electric power systems. The optimization problem was formulated as a multiperiod stochastic programming model using the GAMS® modeling system. Previous studies have analyzed the United Arab Emirates’ (UAE) power infrastructure either using a deterministic point of view or simulation tools (e.g., MESSAGE and MARKAL). These previous research has demonstrated that natural gas will remain playing a significant role as key feedstock fuel in the UAE’s power sector. However, the present work is designed to be the first to consider different supply options for the natural gas feedstock (i.e., domestic, pipeline imports, and LNG imports) and electricity imports in the UAE power sector. Moreover, the natural gas supply and electricity import options are considered to be decision variables in the problem’s formulation. Additionally, the considered case studies assumed a realistically existing power infrastructure for the UAE, whereas previous works considered the planning of the UAE power infrastructure as a Greenfield project. Also, to the authors’ knowledge this is the first work to consider a robust optimization model for planning the UAE power infrastructure under uncertainty in the long term horizon. The model was used to study the planning of the power plant infrastructure in the UAE between 2015 and 2040 under uncertainty in the natural gas price. The optimization results show that the model is a valuable tool for planning the optimal power plant infrastructure of the country, reducing levelized electricity costs, and mitigating social and environmental damages. [ABSTRACT FROM AUTHOR]

A Mixed Integer Linear Programming (MILP) formulation is presented for the optimal design of the United Arab Emirates’ (UAE) power system. The model was formulated in the General Algebraic Modeling System (GAMS), which is a mathematical modeling language for programming and optimization. Previous studies have either focused on the estimation of the UAE’s energy demands or the simulation of the operation of power technologies to plan future electricity supply. However, these studies have used international simulation tools such as “MARKAL” and “MESSAGE”; whereas the present work presents an optimization model. The proposed design optimization model can be used to estimate the most suitable combination of power plants under CO 2 emission and alternative energy targets, carbon tax, and social benefits of air emissions avoidance. Although the proposed model was used to estimate the future power infrastructure in the UAE, the model includes several standard power technologies; thus, it can be extended to other countries. The proposed optimization model was verified using historical data of the UAE power sector operation in the year 2011. Likewise, the proposed model was used to study the 2020 UAE power sector operations under three scenarios: domestic vs. international natural gas prices (considering different carbon tax levels), social benefits of using low emission power technologies (e.g., renewable and nuclear), and CO 2 emission constraints. The results show that the optimization model is a practical tool for designing the UAE power infrastructure, evaluating future production technologies and scenarios, and identifying key parameters affecting the UAE power sector. [ABSTRACT FROM AUTHOR]

As hydrogen demand in the refineriesis increasing, hydrogen managementstrategies are of great interest for the refiners. Hydrogen managementdeals with the optimal distribution, routing, and flow allocationof hydrogen gas within a refinery linking the hydrogen producing andconsuming units. This is also known as the hydrogen network synthesisor design. Due to uncertainty in the crude feedstock or final productspecification in a refinery, there exists variations or uncertaintiesin the operating conditions (parameters) required for hydrogen networkdesign. Hence there is a need to incorporate these uncertainties whiledesigning hydrogen networks. In this paper, we address the issue ofoptimal hydrogen network design under uncertainty or uncertain operatingparameters. For this, we develop a nonconvex multiscenario Mixed IntegerNonlinear Programming (MINLP) model which is the deterministic equivalentof mathematical two stage stochastic model. The purpose is to developa network design that is optimal for different operating conditionsof a refinery constituted by multiple scenarios of operation. Thesuperstructure for this hydrogen network design under uncertaintyincludes hydrogen reuse, recycle, and purification options. It alsohas hydrogen gas stream conditioning equipment, like compressors andvalves, to cater to the nonisobaric conditions and heaters and coolersto deal with the nonisothermal conditions. The size of this multiscenariotwo stage mathematical model builds with increase in the number ofscenarios. For this, standard optimization solvers may not be ableto guarantee global optimality within reasonable computational time.We propose a specific solution strategy to solve this mathematicalmodel to global optimality by convexifying the nonlinear terms. Thisstrategy is found to exhibit exceptional computational time savingscompared to the conventional branch and bound based global optimizationapproaches. Two case studies are solved to illustrate the applicabilityof the proposed model and solution strategy. [ABSTRACT FROM AUTHOR]

This study presents the retrofit of water supply chain problem through multi period mixed integer linear program (MILP) model. The major strategic decisions include the determination of optimal new facility locations and capacity expansions of water desalination supply chain infrastructure assets which consists of water desalination plants, pipelines, and storage tanks, over a long time planning horizon. Other strategic decisions deal with the optimal selection of desalination technologies for existing and new desalination plants. In addition, the model provides decisions to define the pipeline network configuration for water transportation among several sites. The operation decisions are modelled to optimize the water production from desalination plants, energy consumption, brine disposal, as well as CO 2 emissions. In addition, water transportation through the pipeline network and water storage at every site are optimized to satisfy water demand at every time period, and to minimize the net present value of the supply chain network. Finally, the proposed approach is solved for a case study to illustrate the application of the proposed mathematical programming model. The results show economic and environmental benefits if one considers full site integration and water production coordination in the supply chain network through the planning time horizon. [ABSTRACT FROM AUTHOR]

Abstract: Osmotic pressure is a separation barrier during the reverse osmosis (RO) desalination operation. Hydraulic pressure must be inserted on the membrane surface to overcome the osmotic pressure and to facilitate the desalination process. Pressure retarded osmosis (PRO) is a renewable source for power production. Power generation through PRO is achieved through salinity gradient generated across an asymmetric membrane. In this study, we seek an optimal integrated system of RO and PRO for seawater desalination and power generation. A mixed integer nonlinear programming (MINLP) model is developed to find optimal osmosis processes arrangement with auxiliary equipment, and their optimal operation conditions for seawater desalination and power generation. Several case studies were analyzed to show the applicability of the proposed model. In general, the results show that the operation of RO can be a feasible source of salinity gradient for power generation by PRO technology while producing desalinated water streams. The process layouts show arrangement of RO and PRO stages with recycled streams in order to increase the salinity gradient in the PRO stages. This is an undesirable condition for the RO operation and it is suggested from our results that high performance PRO membrane with high water permeation and salt rejection may eliminate this condition. [Copyright &y& Elsevier]

This study demonstratesa novel approach to examine the wettabilitytendency on calcium carbonate using zeta potential measurements. Differentcrude oil samples and asphaltenic solutions were studied and wettabilityprofiles of limestone for these fluids were obtained using the zetapotential technique. In this study, suitable models (using Mathematica8 and MS Excel) have been examined in order to fit the obtained datato the corresponding wettability profiles. A logistic model and acycloid model were considered to represent the wettability profilesof limestone for crude oil and asphaltenic solutions, respectively.The R-squared value for the logistic function fitwas found to be 0.99 with a standard deviation of 0.61 and a relativeerror of 1 × 10–3%. For the cycloid model,the best fit to the wettability profile for asphaltenic solutions,with different concentrations of 0.625, 1.25, and 2.5 wt %, were foundto have an R-squared value ranging 0.86–0.98with a maximum relative error of 1.33%. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a simulation model for an energy hub consisting of natural gas (NG) turbines as the main sources of energy (including both electricity and heat) and two renewable energy sources-wind turbines (WTs) and photovoltaic (PV) solar cells. The hub also includes water electrolyzers for hydrogen production. The hydrogen serves as an energy storage medium that can be used in some transportation applications, or it can be mixed with the NG feed stream to improve the emission profile of the gas-turbine unit. The capacity of the designed hub is meant to simulate and replace the coal-fired Nanticoke Generating Station with a NG-fired power plant. Therefore, the aim of this work is to develop a simulated model that combines different energy generation technologies, which are evaluated in terms of the total energy produced, the cost per kWh of energy generated, and the amount of emissions produced. The proposed model investigates the benefits, both economic and environmental, the technological barriers, and the challenges of energy hubs by developing several scenarios. The simulation of these scenarios was done using General Algebraic Modeling System (GAMS®). Although the software is strongly known for its optimization capability, the mixed complementary problems solver makes it a strong tool for solving equilibrium problems. Excess energy produced during off-peak demand by WTs and PV solar cells was used to feed the electrolyzer to produce H2 and O2. The proposed approach shows that a significant reduction in energy cost and greenhouse gas emissions were achieved, in addition to the increased overall efficiency of the energy hub. Out of the examined three scenarios, Scenario C appeared to be the most feasible option for a combination of renewable and non-renewable technologies as it did not only produce hydrogen, but also provided electricity at relatively lower prices. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Vitrification of articular cartilage (AC) could enhance tissue availability but requires high concentrations of cyroprotective agents (CPAs). This study investigated relative injuries caused by commonly used CPAs. We hypothesized that the in situ chondrocyte dose–injury relationships of five commonly used CPAs are nonlinear and that relative injuries could be determined by comparing cell death after exposure at increasing concentrations. Human AC samples were used from four patients undergoing total knee arthroplasty surgery. Seventy μm slices were exposed in a stepwise protocol to increasing concentrations of 5 CPAs (max=8M); dimethyl sulfoxide (Me2SO), glycerol (Gly), propylene glycol (PG), ethylene glycol (EG), and formamide (FM). Chondrocyte viability was determined by membrane integrity stains. Statistical analysis included t-tests and nonlinear least squares estimation methods. The dose–injury to chondrocytes relationships for all CPAs were found to be nonlinear (sigmoidal best fit). For the particular loading protocol in this study, the data identified the following CPA concentrations at which chondrocyte recoveries statistically deviated significantly from the control recovery; 1M for Gly, 4M for FM and PG, 6M for Me2SO, and 7M for EG. Comparison of individual means demonstrated that Gly exposure resulted in the lowest recovery, followed by PG, and then Me2SO, FM and EG in no specific order. The information from this study provides an order of damage to human chondrocytes in situ of commonly used CPAs for vitrification of AC and identifies threshold CPA concentrations for a stepwise loading protocol at which chondrocyte recovery is significantly decreased. In general, Gly and PG were the most damaging while DMSO and EG were among the least damaging. [Copyright &y& Elsevier]

Abstract: The hybrid use of generalized similarity analysis (GSA) with intelligent computing tools such as neural networks and fuzzy logic, provide accurate and fast numerical simulation for distributed dynamic systems. The GSA combines dimensional, inspectional, and order-of-magnitude methods to derive the complete set of a minimum number of high-level designable variables. Thus, the input variable space is reduced, and this in turn reduces the number of input vectors needed for model development. The generated concise fuzzy logic neural network leads to a shorter running time, and a greater accuracy. This approach is multidisciplinary and is demonstrated for three different applications: injectivity in an oil reservoir, oil reservoir characterization, and a driven distributed transmission line with its load. [Copyright &y& Elsevier]

Fresh water consumption and wastewater management is a mandatory task to conserve water resources and to reduce wastewater discharge from chemical production processes. Such objectives have been addressed in many industrial sectors which consume large amounts of fresh water. Possibilities for reducing wastewater volumes by regeneration and recycling routes in the pulp and paper industry are analyzed. During pulp preparation and paper making processes by the Kraft pulping method a large amount of water is required to deliver the finished paper product. Reverse osmosis (RO) is applied for the analyses as an interception separation technology to reduce salt concentrations in wastewater streams for recycling purposes. The RO network synthesis problem is formulated as mixed integer nonlinear programming model which is solved using the general algebraic modeling system. A preliminary cost estimate indicates economic incentives by installation of RO units to avoid wastewater discharge and generate relatively clean water streams for inter-plant usage. [ABSTRACT FROM AUTHOR]

Abstract: The ‘Hydrogen Economy’ is a proposed system where hydrogen is produced from carbon dioxide free energy sources and is used as an alternative fuel for transportation. The utilization of hydrogen to power fuel cell vehicles (FCVs) can significantly decrease air pollutants and greenhouse gases emission from the transportation sector. In order to build the future hydrogen economy, there must be a significant development in the hydrogen infrastructure, and huge investments will be needed for the development of hydrogen production, storage, and distribution technologies. This paper focuses on the analysis of hydrogen demand from hydrogen FCVs in Ontario, Canada, and the related cost of hydrogen. Three potential hydrogen demand scenarios over a long period of time were projected to estimate hydrogen FCVs market penetration, and the costs associated with the hydrogen production, storage and distribution were also calculated. A sensitivity analysis was implemented to investigate the uncertainties of some parameters on the design of the future hydrogen infrastructure. It was found that the cost of hydrogen is very sensitive to electricity price, but other factors such as water price, energy efficiency of electrolysis, and plant life have insignificant impact on the total cost of hydrogen produced. [Copyright &y& Elsevier]

Abstract: Background: Vitrification is a method of cryopreservation by which cells and tissues can be preserved at low temperatures using cryoprotective agents (CPAs) at high concentrations (typically ⩾6.0M) to limit the harmful effects of ice crystals that can form during cooling processes. However, at these concentrations CPAs are significantly cytotoxic and an understanding of their toxicity characteristics and interactions is important. Therefore, single-CPA and multiple-CPA solutions were evaluated for their direct and indirect toxicities on chondrocytes. Methods: Chondrocytes were isolated from human articular cartilage samples and exposed to various single-CPA and multiple-CPA solutions of five common CPAs (dimethyl sulfoxide (DMSO), ethylene glycol (EG), propylene glycol (PG), glycerol (Gy) and formamide (Fm)) at both 6.0 and 8.1M concentrations at 0°C for 30min. Chondrocyte survival was determined using a fluorescent cell membrane integrity assay. The data obtained was statistically analyzed and regression coefficients were used to represent the indirect toxicity effect which a specific combination of CPAs exerted on the final solution’s toxicity. Results: Multiple-CPA solutions were significantly less toxic than single-CPA solutions (P <0.01). The indirect toxicity effects between CPAs were quantifiable using regression analysis. Cell survival rates of approximately 40% were obtained with the four-CPA combination solution DMSO–EG–Gy–Fm. In the multiple-CPA combinations, PG demonstrated the greatest degree of toxicity and its presence within a combination solution negated any benefits of using multiple lower concentration CPAs. Conclusions: Multiple-CPA solutions are less cytotoxic than single-CPA solutions of the same total concentration. PG was the most toxic CPA when used in combinations. The highest chondrocyte survival rates were obtained with the 6.0M DMSO–EG–Gy–Fm combination solution. [Copyright &y& Elsevier]

Abstract: The design of a future hydrogen supply chain (HSC) network is challenging due to the: (1) involvement of many echelons in the supply chain network, (2) high level of interactions between the supply chain components and sub-systems, and (3) uncertainty in hydrogen demand. Most of the early attempts to design the future HSC failed to incorporate all these challenges in a single generic optimization framework using mathematical modeling approach. Building on our previous multiperiod MILP model, the model presented in this paper is expanded to take into account uncertainty arising from long-term variation in hydrogen demand using a scenario-based approach. The model also adds another echelon: fueling stations and local distribution of hydrogen. Our results show that the future HSC network is somewhat similar to the existing petroleum infrastructure in terms of production, distribution, and storage. In both situations, the most feasible solution is centralized production plants with truck and rail delivery and small-to-large storage facilities. The main difference is that the future hydrogen supply has the benefits of using distributed forecourt production of hydrogen at local fueling stations via several production technologies. Finally, the performance of the studied models was evaluated using sensitivity and risk analyses. [Copyright &y& Elsevier]

Abstract: Introducing hydrogen as the fuel of the future necessitates a comprehensive, widespread supply chain network that is capable of producing, distributing, storing, and dispensing hydrogen to end users. Most of the early attempts to design and model the future hydrogen supply chain (HSC) were either limited to examining an individual component of the supply chain or focused on a predetermined hydrogen pathway. In these studies, a simulation-based approach has commonly been adopted rather than using a mathematical programming-based approach. The work presented here is an extension of an early attempt to design and operate a deterministic, steady-state HSC network using a mathematical modelling approach. In this paper, however, the model is developed to consider the availability of energy sources (i.e. raw materials) and their logistics, as well as the variation of hydrogen demand over a long-term planning horizon leading to phased infrastructure development. The proposed model is formulated as a mixed-integer linear programming (MILP) and solved via a commercial software tool, GAMS. The results show that the optimal design of the future HSC network of Great Britain (GB) starts with small-size plant together with using the hydrogen currently produced by chemical processing plants. As demand grows, more plants of different sizes should be built to meet the demand. The hydrogen produced will be transported using liquid hydrogen trucks and stored in different sizes of storage facilities. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Dapagliflozin (DAPA), a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is well-recognized for its therapeutic benefits in type 2 diabetes (T2D) and cardiovascular diseases. In this comprehensive in vitro study, we investigated DAPA's effects on cardiomyocytes, aortic endothelial cells (AECs), and stem cell-derived beta cells (SC-β), focusing on its impact on hypertrophy, inflammation, and cellular stress. Our results demonstrate that DAPA effectively attenuates isoproterenol (ISO)-induced hypertrophy in cardiomyocytes, reducing cell size and improving cellular structure. Mechanistically, DAPA mitigates reactive oxygen species (ROS) production and inflammation by activating the AKT pathway, which influences downstream markers of fibrosis, hypertrophy, and inflammation. Additionally, DAPA's modulation of SGLT2, the Na+/H + exchanger 1 (NHE1), and glucose transporter (GLUT 1) type 1 highlights its critical role in maintaining cellular ion balance and glucose metabolism, providing insights into its cardioprotective mechanisms. In aortic endothelial cells (AECs), DAPA exhibited notable anti-inflammatory properties by restoring AKT and phosphoinositide 3-kinase (PI3K) expression, enhancing mitogen-activated protein kinase (MAPK) activation, and downregulating inflammatory cytokines at both the gene and protein levels. Furthermore, DAPA alleviated tumor necrosis factor (TNFα)-induced inflammation and stress responses while enhancing endothelial nitric oxide synthase (eNOS) expression, suggesting its potential to preserve vascular function and improve endothelial health. Investigating SC-β cells, we found that DAPA enhances insulin functionality without altering cell identity, indicating potential benefits for diabetes management. DAPA also upregulated MAFA, PI3K, and NRF2 expression, positively influencing β-cell function and stress response. Additionally, it attenuated NLRP3 activation in inflammation and reduced NHE1 and glucose-regulated protein GRP78 expression, offering novel insights into its anti-inflammatory and stress-modulating effects. Overall, our findings elucidate the multifaceted therapeutic potential of DAPA across various cellular models, emphasizing its role in mitigating hypertrophy, inflammation, and cellular stress through the activation of the AKT pathway and other signaling cascades. These mechanisms may not only contribute to enhanced cardiac and endothelial function but also underscore DAPA's potential to address metabolic dysregulation in T2D. Key message: DAPA effectively attenuates ISO-induced cardiomyocyte hypertrophy by reducing cell size and improving cellular structure. DAPA exhibits anti-inflammatory properties in AECs by restoring AKT and PI3K expression, upregulating MAPK activation, and downregulating inflammatory gene expression. DAPA enhances insulin functionality in SC-β cells without altering cell identity, suggesting potential benefits in diabetes management. DAPA's modulation of SGLT2, NHE1, and GLUT1 expression in cardiomyocytes underscores its role in cellular ion balance and glucose metabolism, contributing to its cardioprotective mechanisms. DAPA alleviates TNFα-induced inflammation and stress responses in AECs, while enhancing eNOS expression, indicating its potential to preserve vascular function. DAPA attenuates NLRP3 activation and reduces NHE1 and GRP78 expression in SC-β cells, offering novel insights into its anti-inflammatory and stress-modulating effects. [ABSTRACT FROM AUTHOR]

• A framework for integrating renewable energy in the process industry is introduced. • The multi-energy hub is used in the framework development. • A refinery-wide case study was used to illustrate the framework. • Costs and carbon emissions resulting from different configurations of energy generation technologies were determined. With growing research interest in renewable energy generation and storage technologies, a need arises for a framework integrating renewable energy within the process industry. In this study, the multi-energy hub approach was used to develop a model to achieve economic gains and carbon emissions reduction. Furthermore, a case study on a refinery was carried out to investigate the applicability of the proposed model. Lowest carbon emissions were realized when utilizing wind coupled with concentrated solar power technologies for electricity and heat generation, respectively. This configuration, for the particular refinery case study, mitigated about 9.8 ktonnes carbon dioxide emissions at an additional annual cost of about $88,000, as compared to the assumed case of utilizing grid energy. Whilst considering energy storage, a further reduction of 1.94 ktonnes of carbon emissions can be attained by employing 9.8 MWh of thermal energy storage, at an added annual cost of $275,000. Moreover, different scenarios were investigated to study the impact of schemes such as carbon cap-and-trade and carbon capture and storage on economic costs and carbon dioxide emissions. Under the carbon cap-and-trade scenario, the lowest annual cost while minimizing annual carbon dioxide emissions was realized when a high carbon credit value of $ 0.00014 gCO 2 −1 was utilized. A Pareto front was generated, outlining the optimal cost and carbon emissions when employing different configurations and storage technologies. [ABSTRACT FROM AUTHOR]