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The reemergence of Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper, we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for a huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data are easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide a theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other competing learning approaches.

Increasing and heterogeneous service demands have led to traffic increase, and load imbalance challenges among network entities in the Internet of Things (IoT) environments. It can affect Quality of Service (QoS) parameters. By separating the network control layer from the data layer, Software-Defined Networking (SDN) has drawn the interest of many researchers. Efficient data flow management and better network performance can be reachable through load-balancing techniques in SDN and improve the quality of services in the IoT network. So, the combination of IoT and SDN, with conscious real-time traffic management and load control, plays an influential role in improving the QoS. To give a complete assessment of load-balancing strategies to enhance QoS parameters in SDN-based IoT networks (SD-IoT), a systematic review of recent research is presented here. In addition, the paper provides a comparative analysis of the relevant publications, trends, and future areas of study that are particularly useful for the community of researchers in the field.

Colloidal quantum dots (CQD) have attracted considerable attention for biomedical diagnosis and imaging as well as biochemical analysis and stem cell tracking. In this study, quasi core/shell lead sulfide/reduced graphene oxide CQD with near infrared emission (1100 nm) were prepared for potential bioimaging applications. The nanocrystals had an average diameter of ~4 nm, a hydrodynamic size of ~8 nm, and a high quantum efficiency of 28%. Toxicity assay of the hybrid CQD in the cultured human mononuclear blood cells does not show cytotoxicity up to 200 µg/ml. At high concentrations, damage to mitochondrial activity and mitochondrial membrane potential (MMP) due to the formation of uncontrollable amounts of intracellular oxygen radicals (ROS) was observed. Cell membrane and Lysosome damage or a transition in mitochondrial permeability were also noticed. Understanding of cell-nanoparticle interaction at the molecular level is useful for the development of new fluorophores for biomedical imaging.

This systematic review and meta-analysis study was conducted to estimate the pooled prevalence of CD in low and high risk groups in this region. Following keywords were searched in the Medline, PubMed, Scopus, Web of Science and Cochrane database according to the MeSH terms; celiac disease, prevalence, high risk population and Asian-Pacific region. Prevalence studies published from January 1991 to March 2018 were selected. Prevalence of CD with 95% confidence interval (CI) was calculated using STATA software, version 14. The pooled sero-prevalence of CD among low risk group in Asia–Pacific region was 1.2% (95% CI 0.8–1.7%) in 96,099 individuals based on positive anti-tissue transglutaminase (anti-t-TG Ab) and/or anti-endomysial antibodies (EMA). The pooled prevalence of biopsy proven CD in Asia–Pacific among high and low risk groups was 4.3% (95% CI 3.3–5.5%) and 0.61% (95% CI 0.4–0.8%) in 10,719 and 70,344 subjects, respectively. In addition, the pooled sero-prevalence and prevalence of CD in general population was significantly higher in children compared with adults and it was significantly greater in female vs. male ( P  < 0.05). Our results suggest high risk individuals of CD are key group that should be specifically targeted for prevention and control measures, and screening may prove to have an optimal cost–benefit ratio.

Recent literature on multi-project scheduling problems has been focused on the transfer of available resources between project activities, especially in decentralized projects. Determining the right location for storage facilities where resources are moved between activities is major topics in decentralized multi-projects scheduling problems. This paper introduces a new decentralized resource-constrained multi-project scheduling problem. Its purpose is to simultaneously minimize the cost of the project completion time and the cost of facilities location. A mixed integer linear programming model is first presented to solve small-size problems by using conventional solvers. Then, three heuristic/meta-heuristic methods are proposed to solve larger-size problems. To this end, a fast constructive heuristic algorithm based on priority rules is introduced. Then, using the heuristic method structure, a combinatorial genetic algorithm is developed to solve large-size problems in reasonable CPU running time. Finally, the lagrangian relaxation technique and branch-and-bound algorithm are applied to generate an effective lower bound. According to the computational results obtained and managerial insights, total costs can be significantly reduced by selecting an optimal location of resources. By the use of a scenario-based TOPSIS approach, the heuristic methods are ranked based on changes in the importance of metrics. Friedman’s test result of TOPSIS shows that there is no significant difference among the heuristic methods.

Endoscopic skull base surgery has transformed craniofacial treatment by reducing morbidity and enhancing precision. Traditional outcome measures often overlook the multidimensional aspects of quality of life (QoL) in recovery. Patient-reported tools, such as the anterior skull base questionnaire (ASBQ), offer a comprehensive view of QoL. However, the ASBQ’s effectiveness is limited in non-English-speaking populations without cultural adaptation. This study aims to culturally adapt and psychometrically validate the Persian version of the anterior skull base questionnaire (ASBQ-P) for patients undergoing transnasal transsphenoidal surgery. Translation and cross-cultural adaptation adhered to ISPOR guidelines, encompassing forward and backward translation, expert review, and cognitive debriefing. Internal consistency was evaluated through Cronbach’s α, and test-retest reliability was assessed using intraclass correlation coefficients (ICCs). Convergent validity was assessed using Pearson’s correlation with SNOT-22 scores, while responsiveness was evaluated through effect sizes and standardized response means. Statistical analyses were performed using SPSS version 27, with a significance threshold set at P  < 0.05. A total of 36 patients completed the Persian version of the ASBQ and SNOT-22 questionnaires. The ASBQ demonstrated excellent internal consistency (Cronbach’s α = 0.936). Test-retest reliability was high, with an ICC of 0.927 for average measures. Convergent validity was supported by a significant moderate negative correlation between ASBQ and SNOT-22 scores ( r =–0.544, P  = 0.001). A known-groups validity analysis revealed significant differences in ASBQ scores across SNOT-22 severity categories ( P  = 0.001), supporting the instrument’s discriminative capacity. Our findings confirm the Persian ASBQ as a reliable and valid tool for assessing outcomes in anterior skull base surgery.

In Internet of Things (IoT), load-balancing helps enhance resources utilization through efficient and fair task allocations between computing resources. On the other hand, a lack of load balancing means that some resources are under-loaded or idle, while others are overloaded. This problem will affect resources performance and the satisfaction of users and service providers is negatively affected. Since tasks need to be served by a set of resources, load-balancing towards optimizing and managing resource allocation can improve Quality of Service (QoS) parameters and maximize the task acceptance rate. Computational tasks are allocated to resources by the controller’s decision, taking into account the load-balancing aspect to achieve QoS. Consequently, one of the critical factors for managing resources in IoT efficiently and avoiding overloaded is load-balancing. In this paper, A load-tolerance technique based on load-balancing is also proposed to increase the task acceptance rate when the resource capacity is full. It is a graphical and mathematical model in resource allocation for load-balancing to evaluate and analyze the network performance. The effectiveness of the load-tolerance technique is modeled with Petri-nets to investigate the task acceptance rate. Petri-net design, by providing a graphical model based on mathematical logic, is a valid criterion for analyzing and evaluating QoS-aware workload allocation management. This paper deals with modeling and analyzing the optimal allocation of processing resources to arrived tasks using Petri-nets. This achievement is the use of Petri-nets to model the steps of computing resource allocation in order to improve resource allocation and QoS parameters. To the best of knowledge, none of previous work has applied Petri-net to resource allocation problem in IoT. Simulation results of the task acceptance rate of the proposed approach showed that it reached 86.2%, which increased the acceptance of more tasks by about 8.4% compared to other approaches.

Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

Non-celiac gluten sensitivity (NCGS) and irritable bowel syndrome (IBS) frequently overlap. Although, gluten-free diet (GFD) and low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP) improve the IBS clinical picture, many aspects remain unclear. Therefore, we designed a study to evaluate gluten tolerance, anxiety and quality of life in a specific study population. Fifty IBS patients were asked to follow a low FODMAP strict GFD for 6 weeks and were then randomly allocated to the following groups for a further 6 weeks: (A) receiving 8 g/day of gluten for 2 weeks; gluten-tolerating subjects received 16 g/day for 2 weeks and then 32 g/day for a further 2 weeks; (B) continuing to follow a low FODMAP strict GFD; and (C) receiving a gluten-containing diet. After the first 6 weeks, symptom scores significantly improved. Pain severity, bloating and total score were significantly decreased in the GFD and in the high-gluten groups, while the satiety score significantly increased in group C. Between-group analysis revealed significant differences for pain severity ( p  = 0.02), pain frequency ( p  = 0.04) and impact on community function ( p  = 0.02) at the end of the study. Our findings suggest that low FODMAP strict GFD could be prescribed in IBS patients and would reduce anxiety and improve the quality of life.