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The purpose of this study is to synthesize and characterize the mesoporous nanostructure of Hydroxyapatite (HA), evaluate the impact of the addition of sulphate ions (SO₄2-) on the microstructure properties of HA, and assess the ability of the prepared materials to be used as a carrier for Bovine serum albumin (BSA) protein. This study successfully synthesized 0.07 mole of SO₄2- substituted HA using the reflux microwave-assisted wet precipitation method and fully characterized it using modern techniques. In particular, XRD, XPS, Raman, FESEM, and BET techniques were used to explore the microstructure properties of materials. The results shows that the addition of SO₄2- ions revealed increasing the lattice parameters and surface area of HA, and the particles shape was changed from semi-spherical to rod like shape. Adsorption and desorption of protein molecules were evaluated using BSA protein for 24h at 37°C. BSA loading capacity and release behaviours were significantly improved with the incorporation of SO₄2- ions. The SO₄-HA materials hold a promising future for being used as a carrier for protein molecules.

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

BackgroundDespite the widespread institution of modern massive transfusion protocols with balanced blood product ratios, survival for patients with traumatic hemorrhage receiving ultramassive transfusion (UMT) (defined as ≥20 U of packed red blood cells [RBCs]) in 24 hours) remains low and resource consumption remains high. Therefore, we aimed to identify factors associated with mortality in trauma patients receiving UMT in the modern resuscitation era.MethodsAn Eastern Association for the Surgery of Trauma multicenter retrospective study of 461 trauma patients from 17 trauma centers who received ≥20 U of RBCs in 24 hours was performed (2014-2019). Multivariable logistic regression and Classification and Regression Tree analysis were used to identify clinical characteristics associated with mortality.ResultsThe 461 patients were young (median age, 35 years), male (82%), severely injured (median Injury Severity Score, 33), in shock (median shock index, 1.2; base excess, -9), and transfused a median of 29 U of RBCs, 22 U of fresh frozen plasma (FFP), and 24 U of platelets (PLT). Mortality was 46% at 24 hours and 65% at discharge. Transfusion of RBC/FFP ≥1.5:1 or RBC/PLT ≥1.5:1 was significantly associated with mortality, most pronounced for the 18% of patients who received both RBC/PLT and RBC/FFP ≥1.5:1 (odds ratios, 3.11 and 2.81 for mortality at 24 hours and discharge; both p < 0.01). Classification and Regression Tree identified that age older than 50 years, low initial Glasgow Coma Scale, thrombocytopenia, and resuscitative thoracotomy were associated with low likelihood of survival (14-26%), while absence of these factors was associated with the highest survival (71%).ConclusionDespite modern massive transfusion protocols, one half of trauma patients receiving UMT are transfused with either RBC/FFP or RBC/PLT in unbalanced ratios ≥1.5:1, with increased associated mortality. Maintaining focus on balanced ratios during UMT is critical, and consideration of advanced age, poor initial mental status, thrombocytopenia, and resuscitative thoracotomy can aid in prognostication.Level of evidencePrognostic, level III.

Purpose of the Study: The overall purpose of this study was to investigate whether or not the incorporation of combined flipped classroom, and collaborative learning pedagogical schemes in two restructured sections of principles of marketing course, had any positive impact on students' learning of 100 key marketing concepts, processes, and strategies. Results: By comparing the students' total assessment scores in Experimental and Control Group groups, it was concluded that the re-structured course had significantly improved students' learning of key marketing concepts, processes, and strategies.

Abstract Today, ‘security challenge’ is considered a commonly‐used catchword when it comes to emerging technologies such as the internet of things (IoT). Turning a blind eye to this challenge can sometimes lead to irreparable human and financial damage in everyday life. Threat modelling, despite being time consuming, complex, and error‐prone, is still recognized as a preventative solution in the design phase. The method proposed in this paper, called automatic generation of threat paths, is suggested as an automated solution to the problem of identifying and determining potential threats. The proposed method is the improved and robust development of automated modelling and the introduction of the integration of new features (such as conditional probability and security) with the techniques of previous generations (such as Petri Nets (PNs)). The proposed method is evaluated using different security scenarios, and the results show that the proposed method outperforms other manual methods in this domain in terms of both time and cost (99.9% and 98.05%, respectively).

Abstract The evolution of technology has led to the appearance of smart cities. An essential element in such cities is smart mobility that covers the subjects related to Intelligent Transportation Systems (ITS). The problem is that the ITS vulnerabilities may considerably harm the life quality and safety status of human beings living in smart cities. In fact, software and hardware systems are more exposed to security risks and threats. To reduce threats and secure software design, threat modelling has been proposed as a preventive solution in the software design phase. On the other hand, threat modelling is always criticised for being time consuming, complex, difficult, and error prone. The approach proposed in this study, that is, Automated Security Assistant of Threat Models (ASATM), is an automated solution that is capable of achieving a high level of security assurance. By defining concepts and conceptual modelling as well as implementing automated security assistant algorithms, ASATM introduces a new approach to identifying threats, extracting security requirements, and designing secure software. The proposed approach demonstrates a quantitative classification of security at three levels (insecure, secure, and threat), twelve sub‐levels (nominal scale and colour scale), and a five‐layer depth (human understandability and conditional probability). In this study, to evaluate the effectiveness of our approach, an example with various security parameters and scenarios was tested and the results confirmed the superiority of the proposed approach over the latest threat modelling approaches in terms of method, learning, and model understanding.

Task scheduling problem in a cloud computing environment is one of the most important issues in this field. Because task schedulers should be aware of underlying platform heterogeneity, task interdependencies, and virtual machine (VM) variable configurations. An efficient task scheduling algorithm can potentially increase the efficiency of cloud computing, by choosing the appropriate virtual machine to do a specific task-scheduling problem, for dependent tasks on heterogeneous resources which is a well-known NP-Hard problem. To reduce minimum total execution time, makespan, in cloud computing many heuristics were presented in literature but lots of them suffer from low efficiency. In this article, a new task priority strategy and applying task duplication methods are proposed, for solving the task scheduling problem of the dependent tasks in heterogeneous cloud computing systems. The novelty of the current paper is to present a new list scheduling algorithm with a new task priority strategy and applying pertinent task duplication techniques. This paper uses optimistic cost table downward (OCTd) and optimistic cost table upward (OCTu) procedures to prioritize tasks in an efficient ordered list; then, it utilizes Heterogeneous Earliest Finish Time (HEFT)-duplication method for performing task duplication technique which significantly reduces makespan. To validate the proposal, we experimentally analyzed the proposed scheduling algorithm with different scientific workflows such as Molecular, LU-Like, FFT, and Montage datasets. The performance comparison of the novel heuristic scheduling algorithm against other existing approaches proved the superiority of proposed algorithm in terms of makespan, speedup, SLR, and efficiency which are prominent scheduling evaluation metrics.

The purpose of this study was to design a model of the factors affecting the revitalization of small and medium- scale ailing food companies and industries in Ardabil province. To this end, through reviewing the related literature and interviewing experts, first, 97 factors affecting the revitalization of these companies were identified, using the Fuzzy Delphi Method (FDM). Next, through exploratoratory factor analysis, the relationship between the factors was investigated. For this purpose, the factors in 10 demensions were determined and using fuzzy hierarchical analysis, these dimensions were ranked based on the views of 48 managers and senior experts of small and midum-scale food industreis in Ardabil. The results revealed the periorty of economic factors, factors related to infrastructure, factors related to research and development processes, factors related to financial problems, legal and judicial factors, respectively, and factors due to the sanctions, market-related factors, legislation related to work-place, organizing managers and staff, and social and cultural factors were the next priorities. According to the objectives of the research, the clustering of active food industries in Ardabil was done based on the available documents and the relevant industries were divided into five clusters. The effect size of each set of factors in each cluster was determined using multivariate regression method and finally the appropriate revitalization model for each cluster was identified and presented.

Summary: In "Internet of Things" (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan. [ABSTRACT FROM AUTHOR]

This study considers four elements for a consumer-based chain coffee shop brand equity and investigates the responses of consumers, as their purchase intentions and brand attitudes, to each of the elements. It also probes brand attitude as a mediator of the elements' impacts on purchase intention. Moreover, the chief important aim of the current study is to detect the potential differences among three different consumer segments for responding to the conceptual model, in terms of their usages of the brand. Data were collected from 448 coffee shop chain brands' consumers in Tehran. Findings demonstrate the mediation role of brand attitude, but not among the heavy users. Only perceived quality does not directly affect purchase intention but does among the light users. Brand awareness's greatest impact is on the light users' brand attitude, and the self-congruity impact on purchase intention rises by growing usage among consumers. Also, self-congruity generally has the most direct impact on purchase intention. Managerial implications for better brand equity management in the coffee shop chain industry are provided. [ABSTRACT FROM AUTHOR]

During the process of cutting andesite stones, the waste mud is kept in powder form once fully dried. It is difficult to store the waste that is produced as a consequence of the extensive utilization area and consumption of andesite. Thus, eliminating waste storage challenges and incorporating these wastes into the economy are crucial. For this reason, this study examined the effects of waste andesite dust (WAD) on the flexural behavior of reinforced-concrete beams (RCBs) using experimental testing and 3D finite-element modeling (FEM) via ANSYS. Thus, different rates of WAD up to 40% were used to investigate the influence of the WAD rate on the fracture and bending behavior of RCBs. While the RCB with 10% WAD had a slightly lower load-bearing and ductility capacities, ductility capacities significantly drop after 10% WAD. At 40% WAD, both the load-bearing capacity and ductility significantly reduced. Based on the experimental findings, using 10% WAD as a replacement for cement is a reasonable choice to obtain eco-friendly concrete. Moreover, the outcomes of 3D FEM were also compared with those of experiments conducted using ANSYS v19 software. The displacement values between the test and FEM findings are quite similar. [ABSTRACT FROM AUTHOR]

Abstract Multi‐object tracking (MOT) can be considered as an interesting field in computer vision research. Its application can be found in video motion analysis, smart interfaces, and visual surveillance. It is a challenging issue due to difficulties made by a variable number of objects and interaction between them. In this work, a new method for online MOT based on time and frequency domain features is presented. The features are obtained from the wavelet transform and fractal dimension. The modified cuckoo optimization algorithm is utilized for feature selection, which has the ability such as fast convergence and global optima finding. The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. To evaluate the performance of the presented technique, simulations are performed using the ETH Mobile Platform and VS‐PETS 2009 datasets. The simulation results show the superiority of the presented technique for MOT compared to earlier studies in terms of accuracy. The mostly tracked values for the datasets are 74.3% and 97.2%, which leads to at least 4.2% and 2.5% better performance according to the other methods, respectively.

Energy management is considered the major concern in cloud computing, which supports the rapid growth of data centers and computing centers; therefore, energy and load balancing have become crucial issues in cloud data centers. To address this issue, the present paper proposed a two-phase energy-aware load balancing (EALB) scheduling algorithm using the virtual machine migration through the Particle Swarm Optimization (PSO) algorithm to be applicable to dynamic voltage frequency scaling-enabled cloud data centers, which is called EALBPSO. In the first phase, an objective function was employed to deactivate a large number of physical machines in order to reduce energy consumption. The main idea of the algorithm was to maximize load balancing in the second phase, in which the remaining virtual and physical machines were used as the PSO inputs, and an objective function was also defined to distribute the load appropriately among the physical machines. In addition, a dataset was developed to test different parameters and scenarios with the aim of assessing the effectiveness of the proposed EALBPSO algorithm in comparison with other algorithms already proposed in the literature for similar purposes. The experimental results demonstrated that the proposed algorithm was capable of saving up to 0.896%, 9.716%, and 10.8% energy compared with the MDPSO algorithm, Kumar et al.’s algorithm, and Dahsti and Rahmani algorithm, respectively, and also it showed 5.91%, 16%, and 16.267% improvements for the number of virtual machines migrations, and 3.867%, 8.623%, and 6.953% improvements for the deviation of processors, all compared with their competitors stated above, respectively.

Increasing demand for computational resource as services over the internet has led to the expansion of datacenter infrastructures. Thus, datacenter authorities are striving to adopt optimal power usage schemes to minimize costs, emissions and Service Level Agreement (SLA) violations in their task scheduling for heterogeneous computation centers. One of the most effective strategies to reduce datacenter energy consumption is to maximize the utilization of physical machines and shut down the idle ones. This can be realized through two main algorithms, namely virtual machine placement and virtual machine consolidation. The VM placement method is a dynamic process to put these virtual devices on physical machines. The consolidation technique, however, tries to improve physical machine efficiency through grouping and live migration of dispersed virtual machines on lower number of active physical machine. In this paper, a novel approach is proposed for improving the physical machine efficiency. The approach employs heuristics and meta-heuristic algorithms with eight performance criteria and is implemented on small to medium scale data centers using simulated cloud module. The results indicates that the proposed method showed up to 10.3%, 5.3%, and 12.5% the more significant efficiency rather best previous algorithms, respectively, in terms of the energy consumption, number of SLA violation and number of VMs migration.