Search

Ahmad M Mansour,1,2 Hana A Mansour,3 J Fernando Arevalo4,5 1Department of Ophthalmology, Rafic Hariri University Hospital, Beirut, Lebanon; 2Department of Ophthalmology, American University of Beirut, Beirut, Lebanon; 3Department of Biology, American University of Beirut, Beirut, Lebanon; 4Retina Department, The King Khaled Eye Specialist Hospital, Riyadh, Kingdom of Saudi Arabia; 5Retina Department, Wilmer Eye Institute, The Johns Hopkins University, Baltimore, MD, USA Abstract: This patient presented for surgery at the age of 32 years, 14 months after his initial complaint of metamorphopsia and visual loss in the right eye. Past tests demonstrated a whitish epiretinal membrane (ERM) with translucent stress lines over a thickened macula. Visual acuity was found on last presentation to be normal with minimal alteration on Amsler grid testing. A torn ERM was found in the center with left-over ERM temporally and rolled-over ERM nasally at the site of the epicenter with no posterior vitreous detachment. Visual recovery occurred gradually over several days 2 months prior to presentation apparently following heavy weight-lifting with a sensation of severe eye pressure. Sequential funduscopy and optical coherence tomography scans demonstrated the peeling of an ERM accompanied by normalization of foveal thickness. Valsalva maneuver had put excessive tension on ERM which tore in its center at the weakest line with gradual contraction of the ERM away from the fovea towards the peripapillary area. This is a new mechanism of self-separation of ERM induced by Valsalva. ERM in young subjects is subject to rupture and subsequent separation by tangential traction. There are three mechanisms for spontaneous separation of ERM: 1) posterior vitreous detachment with pulling of ERM by detaching vitreous (most common in adults); 2) the contracting forces of the immature ERM become stronger than its adhesions to the retina resulting in slow tangential traction on the edges of the ERM and gradual separation from the edges towards the center (remodeling common in youngsters); and 3) acute tearing of ERM at its weakest central point and retraction of part of the membrane towards the epicenter (current case report). Keywords: valsalva maneuver, posterior vitreous detachment

Understanding the factors affecting internal combustion engine performance is crucial for improving emissions and fuel efficiency in real traffic. This paper investigates the influence of date-specific factors, such as working days and holidays on fuel consumption and emissions of a representative internal combustion engine in the context of Shiraz city. The data was collected by measuring the speed of vehicle on a specific route during peak traffic times on both working days and holidays. The determined route was calculated by a qualitative and quantitative method. K-means clustering and principal component analysis are employed to design representative driving cycles, then combining micro-trips and smoothing them to develop driving cycles. Characteristics of both driving cycles, emissions such as HC, NOx, CO, and fuel consumption were specified under a simulated vehicle model of Peugeot 206 and placed through both real driving cycles by using advanced vehicle simulation software. It has been observed that variations in fuel consumption and emissions between holidays and working days can be attributed to distinct driving patterns and characteristic parameters, such as acceleration, speed, and driving time. Contrary to common assumptions, the study found that fuel consumption was approximately 8 % higher on holidays compared to working days, primarily due to increased driving time and higher average speeds. Moreover, higher acceleration and speed on holidays led to a significant increase in CO emissions (about 36 %) and NOx emissions (about 4 %) compared to working days. However, HC emissions was found to be 27.03 % higher on working days according to lower speed. This study helps in reducing fuel consumption and emissions by uncovering internal combustion engine factors, optimizing driving patterns, and promoting sustainable transport on working days and holidays.

Key Clinical Message Vincristine therapy can be effective in refractory Immune thrombocytopenia (ITP) following COVID‐19 vaccination. Our case report highlights the need for further research to establish standard management guidelines for COVID‐19‐vaccine‐associated ITP. Abstract Adult immune thrombocytopenia (ITP) can occur as a rare complication following several viral infections or a rare adverse event or complication of vaccination. In this paper, we report a case of a 39‐year‐old male patient with severe refractory ITP that began 4‐weeks after receiving his third (booster) dose of the COVID‐19 vaccine (BNT162b2, Pfizer‐BioNTech). He was given oral dexamethasone 40 mg daily for 4 days followed by prednisone at 1 mg/kg (85 mg daily) for 10 days. In the following weeks, we attempted several other lines of therapy to treat his ITP, including anti‐RhD immunoglobulin, which, unfortunately, caused moderate hemolysis requiring packed red blood cell transfusion, intravenous immunoglobulin (given at a subtherapeutic dose of 0.4 g/kg for only 1 day since it was not available), rituximab, and eltrombopag. The patient, unfortunately, showed no response to any of these treatments. This was an indicator to initiate salvage therapy with vincristine 2 mg weekly for 3 weeks. The patient's platelet count started to increase remarkably during the third week of vincristine and normalized after 4 weeks. We review the findings, clinical characteristics, and management approaches that were reported in the literature regarding COVID‐19‐vaccine‐induced ITP. More in‐depth research is needed to delineate standard guidelines for the management of such cases. This report underscores the importance of resorting to vincristine and eltrombopag as great options for severe and refractory ITP related to the COVID‐19 vaccine.

Late-onset Alzheimer’s disease (AD) has become the paradigm of a non-mendelian complex neurodegenerative disease, for which a major genetic determinant is known, the APOE locus. A rare APOE variant named Christchurch (APOEch) yielding a missense mutation from Arginine to Serine at amino acid 136, has been suggested to exert a protective effect in an individual carrying the most penetrant form of Familial AD (Paisa mutation in PSEN1 gene, E280A). We describe here a new set of induced pluripotent stem cell (iPSC) lines, where the Christchurch mutation (Ch) has been introduced by gene editing into the APOE locus of three isogenic iPSC lines carrying the more common APOE variants (APOE 2/2, APOE 3/3, and an APOE 4/4) in homozygosity. Brain cells derived from these iPSC lines will enable a better understanding of APOE biology in general and facilitate the study of how the Christchurch variant affects the function of each APOE genotype. This set of iPSC lines are globally available via the European Bank of iPSCs, EBiSC.org.

Abstract The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

Energy generated from renewable sources is exposed to extremely dynamic variations in climatic conditions as well as uncertainties (current/voltage variability, noise, measurement errors.). These conditions are relevant issues to be considered when monitoring renewable energy conversion (REC) systems. In fact, these uncertain systems are subjected to many failures leading to performance degradation and long downtime maintenance periods. Therefore, fault detection and diagnosis (FDD) are essential to ensure its high dependability. This paper proposes an FDD under climatic conditions variability of uncertain REC systems using deep recurrent neural networks (DRNNs) techniques. Firstly, a novel modeling strategy for REC systems is built. Secondly, different DRNN-based interval-valued data methods are intended to differentiate between the various REC systems operating states. Finally, the hyperparameters of the proposed techniques are tuned using the Bayesian optimization algorithm. The efficiency and robustness of the novel strategy are demonstrated through REC application, using grid-connected photovoltaic (GCPV) systems. The obtained results show the efficiency of the developed strategy by reaching an accuracy rate of 92.40%.

This paper proposes effective Random Forest (RF)-based fault detection and diagnosis for wind energy conversion (WEC) systems. The proposed technique involved two major steps: feature selection and fault classification. Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. Therefore, a hybrid feature selection based diagnosis technique, that can preserve the advantages of wrapper and filter algorithms as well as RF model, is proposed. In the first phase, the neighborhood component analysis (NCA) filter algorithm is used to reduce and select only the pertinent features from the original raw data. This phase helps in improving data by removing redundant and unimportant features. In the second step, we applied a wrapper technique called equilibrium optimizer to get optimized features and better classification accuracy. The main idea behind using a hybrid feature selection step is to select a small subset from original data that can achieve maximum classification accuracy and reduce the computational complexity of the RF technique. Then, the sensitive and significant characteristics are transmitted to the RF model for classification purposes. The presented results prove that the proposed methods offer enhanced diagnosis accuracy when applied to WEC systems.

Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic conditions. Accordingly, it becomes extremely difficult to distinguish if it refers to faulty status in the system or healthy status under the irradiance variation (IV). Therefore, PV monitoring considering IV condition is fundamental in ensuring high reliability as well as improving power production of PV systems.In fault detection and diagnosis (FDD) field, researchers have considered the variation of irradiance (especially under low irradiance level) as faulty operating mode while others have considered it as fixed parameter during detecting faults.In this paper, therefore, firstly, the IV is introduced in the dynamic model of the grid connected PV (GCPV) system in different operating conditions. Then, an efficient and robust FDD approach based on machine learning and deep learning techniques is proposed in order to identify the healthy and faulty operating conditions.The obtained results through simulated data of a 12 kW PV module are extremely encouraging with a high accuracy under different studied cases.

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems.

Manual harvesting of sugarcane requires skilled labourers, as improper harvesting leads to a significant loss of yield. Therefore, it is necessary to move towards designing a system capable of harvesting sugarcane on smallholdings, with high harvesting efficiency. This study aims to determine some physico-mechanical properties of sugarcane stalks related to harvester by making a comprehensive analysis of the compressing, bending, and shearing properties of sugarcane stalks. These properties were measured at three moisture content levels at three positions (base, middle, and top) of the stalk with three sugarcane varieties (G.T.54-9, IK 76-66, and HAO3-55-24). The different moisture contents were fresh stalk, after 7 days, and after 15 days. The values of different content with different varieties were (70.3, 42.1, and 23.2%), (68.7, 61.3, and 42.7%) and (66.4, 48.6, and 30.5%), respectively. The physico-mechanical properties of stalks were measured in terms of diameter, length, fresh mass, moisture content, sugarcane stalk bulk density, compression stress, shear stress and bending stress at three different positions (top, middle, and base).The most important results showed that the decreasing stalk moisture content of three sugarcane varieties led to an increase in compression, shear, and bending stresses.

In today's world, the amazing and unimaginable growth of the media has made them a powerful tool that not only fulfills the informational task inherent in their nature; Rather, they have gone further and entered and influenced all aspects of human life. The diversity of the social roles of women and men reflects the fact that the concepts of femininity and masculinity (gender) are strongly influenced by social factors. Men and women are each reflected in the media. The image of women in mass media has an ideological aspect for the continuation of patriarchal relations. Against the subjectivity of women in patriarchal discourse; The strategy of struggle to deconstruct and break stereotypes has flowed in late modernity. The aim of the current research is to analyze the discourse of Iranian women's subjectivity in the Instagram social network. The research method in this study is qualitative content analysis. The technique used is critical discourse analysis and specifically Fairclough's discourse analysis approach. The community studied in this research is the pages related to the theoretical class of discourses of subjectivity in the Instagram social network of Iranian users, which were selected with theoretical considerations and qualitative content analysis. The final categories obtained in the research include the objectification of women, glorification of narcissism, gender class system, de-importance of role, naturalization of superior status, deprivation of rights, double otherization, which is consistent with the discourse of women's subjectivity.

Abstract This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%).

Abstract Macrophages play major roles in the pathophysiology of various neurological disorders, being involved in seemingly opposing processes such as lesion progression and resolution. Yet, the molecular mechanisms that drive their harmful and benign effector functions remain poorly understood. Here, we demonstrate that extracellular vesicles (EVs) secreted by repair‐associated macrophages (RAMs) enhance remyelination ex vivo and in vivo by promoting the differentiation of oligodendrocyte precursor cells (OPCs). Guided by lipidomic analysis and applying cholesterol depletion and enrichment strategies, we find that EVs released by RAMs show markedly elevated cholesterol levels and that cholesterol abundance controls their reparative impact on OPC maturation and remyelination. Mechanistically, EV‐associated cholesterol was found to promote OPC differentiation predominantly through direct membrane fusion. Collectively, our findings highlight that EVs are essential for cholesterol trafficking in the brain and that changes in cholesterol abundance support the reparative impact of EVs released by macrophages in the brain, potentially having broad implications for therapeutic strategies aimed at promoting repair in neurodegenerative disorders.

In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the PV1 array, simple faults in the PV2 array, multiple faults in PV1, multiple faults in PV2, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset.

Background: Smoking is a major cause globally of morbidity and mortality hence life years lost, this issue manifested in 399 Million Jordanian dinars (JD)($562.3 million USD) lost yearly due to productivity lost as a consequence of smoking in Jordan1. It is no surprise that quitting smoking will reduce the loss in life years and hence productivity. In this study, using cohort simulation, we want to quantify the gains in productivity from smoking cessation aids usage for one course of smoking cessation aid varenicline or nicotine replacement therapy in comparison to physician counseling only without pharmacological therapy, in the population that intends to quit at a point of time, through campaigns nationwide, among the working-age population followed up until retirement. Methodology: We present a transparent, generic model based on accepted analytic methods that allow users to assess the present value of lifetime earnings gained (PVLE) in smokers who intend to quit. It is shown in previous studies that smoking cessation aids are cost-effective in Jordan (Madae’en et al. 2020), yet the benefit of using smoking cessation aids goes further to reduce productivity loss by reducing life years lost. Our model incorporates life-years gained from the Markov Model in Madae’en et al. (2020), simulation of Jordanian male smokers’ cohort in three scenarios of either using varenicline or nicotine replacement therapy or only physician consultation, to estimate life-years gained and hence reduction in lost productivity costs. Results: We found productivity loss was reduced in males who attempted to quit in their productive years. Using Varenicline, the researchers calculated the expected future payments (wages) count for years gained due to varenicline use for a wage average of 507 JDs ($714.5 USD) per month discounted by 8% for the rest of their productive life. As well as for the other two scenarios, the gained productivity from one course of varenicline to the male adults over 30 who intend to quit will reduce loss by more than 72 billion JDs ($101.42 billion USD) among the working-age population followed up until retirement. Conclusion: policy change must be approached to reimbursement of smoking cessation aid in the Jordanian formulary.

The purpose of this study is to investigate the impact of monetary policy through the channel of bank facilities on macro variables in Iran's economy. In order to analyze the results, dynamic stochastic general equilibrium (DSGE) was used in terms of the structure of the banking system in the period of 1370-1399 based on the frequency of seasonal data. In the statistical analysis section, the impact of monetary policy on banking and macroeconomic variables was compared and evaluated. Based on the obtained results, it can be stated that most of the macroeconomic variables have shown the most positive reaction to the shock caused by the banking facilities as a channel for influencing the monetary policy. Based on this, the independence of the Central Bank and not imposing the results of the government's budget policies on the monetary policies of the Central Bank is a solution to make the monetary policies efficient in the medium and long term.

This research aims to construct a three-dimensional numerical model for modeling friction stir extrusion using the completely Lagrangian method, smoothed particle hydrodynamics (SPH). For extrusion simulations, the Finite Element Method (FEM) is extensively utilized; however, it has limitations due to excessive element deformation. Because the particle-based method eliminates the usage of volumetric elements, SPH can be a viable alternative. The performance of the SPH model was evaluated using different particle sizes. The results showed that the smaller particle size improves the temperature results as well as the shape of the wire produced. Then the mechanical and microstructural properties of the produced wires were investigated. The results show that the grain size in the center of the wire is larger than its perimeter due to the lower strain rate in this area. Increased strain reduces grain size in the produced microstructure by increasing nucleation sites during recrystallization, as is well known. The wire microhardness in the centre is 121 HV, whereas it is 129 HV in the periphery. Grain size is the main reason of increased hardness near the sample's periphery.

Over the past decade, there have been significant developments in treatment for ovarian cancer, yet the lack of targeted therapy with few side effects still represents a major issue. The cytochrome P450 (CYP) enzyme family plays a vital role in the tumorigenesis process and metabolism of drugs and has a negative impact on therapy outcomes. Gaining more insight into CYP expression is crucial to understanding the pathophysiology of ovarian cancer since many isoforms are essential to the metabolism of xenobiotics and steroid hormones, which drive the disease’s development. To the best of our knowledge, no review articles have documented the intratumoral expression of CYPs and their implications in ovarian cancer. Therefore, the purpose of this review is to provide a clear understanding of differential CYP expression in ovarian cancer and its implications for the prognosis of ovarian cancer patients, together with the effects of CYP polymorphisms on chemotherapy metabolism. Finally, we discuss opportunities to exploit metabolic CYP expression for the development of novel therapeutic methods to treat ovarian cancer.

This paper designs two DC-DC converter configurations integrated with solar PV renewable energy resource. Its focuses on comparing two converter topologies: the conventional boost converter and the switched capacitor boost converter. The Perturb and Observe (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) algorithms are employed to dynamically enhance the Maximum Power Point Tracking (MPPT) performance for both converters. The simulation results demonstrate that both converter topologies, when integrated with appropriate MPPT algorithms, can effectively harvest maximum power from the solar PV. However, the switched capacitor topology converter exhibits advantages in terms of current capabilities and voltage performance. In addition, combing the switched capacitor boost converter with the GA-MPPT algorithm improved the output voltage profile. The switched capacitor topology demonstrates distinct advantages by exhibiting enhanced current control, enabling improved handling of dynamic load changes and varying irradiance conditions. It shows voltage regulation, resulting in reduced output voltage fluctuations and enhanced stability, thereby optimizing energy extraction. The GA-MPPT simulation demonstrates a substantial increase in maximized output current for the switched capacitor boost configuration (70 A) when compared to the conventional type (10 A). The validation and implementation of the system models are carried out using MATLAB/Simulink.

Cannabinoids are abundant signaling compounds; their influence predominantly arises via engagement with the principal two G-protein-coupled cannabinoid receptors, CB1 and CB2. One suggested theory is that cannabinoids regulate a variety of physiological processes within the cells of skeletal muscle. Earlier publications have indicated that expression of CB1 receptor mRNA and protein has been recognized within myotubes and tissues of skeletal muscle from both murines and humans, thus representing a potentially significant pathway which plays a role in the control of skeletal muscular activities. The part played by CB1 receptor activation or inhibition with respect to these functions and relevant to targets in the periphery, especially skeletal muscle, is not fully delineated. Thus, the aim of the current research was to explore the influence of CB1 receptor stimulation and inhibition on downstream signaling of the nuclear receptor, NR4A, which regulates the immediate impacts of arachidonyl-2′-chloroethylamide (ACEA) and/or rimonabant in the cells of skeletal muscle. Murine L6 skeletal muscle cells were used in order to clarify additional possible molecular signaling pathways which contribute to alterations in the CB1 receptor. Skeletal muscle cells have often been used; it is well-documented that they express cannabinoid receptors. Quantitative real-time probe-based polymerase chain reaction (qRT-PCR) assays are deployed in order to assess the gene expression characteristics of CB1 receptor signaling. In the current work, it is demonstrated that skeletal muscle cells exhibit functional expression of CB1 receptors. This can be deduced from the qRT-PCR assays; triggering CB1 receptors amplifies both NR4A1 and NR4A3 mRNA gene expression. The impact of ACEA is inhibited by the selective CB1 receptor antagonist, rimonabant. The present research demonstrated that 10 nM of ACEA notably amplified mRNA gene expression of NR4A1 and NR4A3; this effect was suppressed by the addition of 100 nM rimonabant. Furthermore, the CB1 receptor antagonist led to the downregulation of mRNA gene expression of NR4A1, NR4A2 and NR4A3. In conclusion, in skeletal muscle, CB1 receptors were recognized to be important moderators of NR4A1 and NR4A3 mRNA gene expression; these actions may have possible clinical benefits. Thus, in skeletal muscle cells, a possible physiological expression of CB1 receptors was identified. It is as yet unknown whether these CB1 receptors contribute to pathways underlying skeletal muscle biological function and disease processes. Further research is required to fully delineate their role(s).

Pain represents an international burden and a major socio-economic public health problem. New findings, detailed in this review, suggest that adenosine plays a significant role in neuropathic and inflammatory pain, by acting on its metabotropic adenosine receptors (A1AR, A2AAR, A2BAR, A3AR). Adenosine receptor ligands have a practical translational potential based on the favorable efficacy and safety profiles that emerged from clinical research on various agonists and antagonists for different pathologies. The present review collects the latest studies on selected adenosine receptor ligands in different pain models. Here, we also covered the many hypothesized pathways and the role of newly synthesized allosteric adenosine receptor modulators. This review aims to present a summary of recent research on adenosine receptors as prospective therapeutic targets for a range of pain-related disorders.

The dynamic and complex interactions between the urban freight-transportation system and population, economy, traffic flow, fuel consumption, and environmental pollution, make policymaking in this system one of the fundamental challenges of urban management. In this regard, a systemic approach in urban freight-transportation system modelling should be considered to solve the problems of the system. One of the main problems of this system is the mismatch between the freight-transportation capacity and the total freight-transportation demand. Considering the lack of sufficient studies in the field of macro and quantitative modeling of this system, the main goal of this article is to model the urban freight-transportation system in order to identify the factors affecting the urban freight-transportation demand and capacity. The main focus of the research is to develop quantitative scenarios which balance the freight-transportation capacity and freight-transportation demand. The urban freight-transportation system is modelled by the System Dynamics (SD) approach and their basic behaviors; as well as this the results of some policy-making scenarios are simulated. The model is validated by the real data of Shiraz. Five quantitative scenarios are designed with two approaches of managing the freight-transportation demand and freight-transportation-capacity sectors. The scenarios are based on four control variables, including the distribution coefficient, trip numbers, vehicle capacity, and vehicle numbers. The simulation results show that the total gap between freight-transportation capacity and freight-transportation demand will decrease by optimizing each of the control variables. However, the combined scenario is the most applicable policy in order to maintain the balance between freight-transportation capacity and demand. Generally, the proposed model can be used to design different quantitative scenarios in order to optimize the freight-transportation system’s performance. This study can also help policymakers to manage the urban freight-transportation system more efficiently.

This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical, economic, and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university’s electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, and environmental factors and addressing unmet load, this research contributes to advancing renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design, and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University’s search for energy resilience and environmental surveillance, which has a total peak load of 9.958 MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of USD 30 M, with a levelized energy cost (LCOE) of 0.00274 USD/kWh. In addition, the minimum desired load will be unmetered on some days in September.

The majority of energy sources being used today are traditional types. These sources are limited in nature and quantity. Additionally, they are continuously diminishing as global energy consumption increases as a result of population growth and industrial expansion. Their compensation is made from clean energy and renewable energy. Renewable energy is strongly dependent on climatic conditions; therefore, an aspect of energy management is needed, which is essential in distribution systems, because it enables us to calculate the precise energy used by the load as well as by its many components. It also helps us understand how much energy is required and its origin. The energy management aspect contains two main phases: forecasting and optimization. In this study, we are focused on the forecasting level using intelligent machine learning (ML) techniques. To ensure better energy management, it is very important to predict the production of renewable energy over a wide time period. In our work, several cases are proposed in order to predict the temperature, the irradiance, and the power produced by a PV system. The proposed approach is validated by an experimental procedure and a real database for a PV system. The big data from the sensors are noisy, which pose a major problem for forecasting. To reduce the impact of noise, we applied the multiscale strategy. To evaluate this strategy, we used different performance criteria, such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), nRMSE and the coefficient of determination (R2). The obtained experimental results show good performance with lower error. Indeed, they achieved an error for nRMSE criteria between 0.01 and 0.37.

The present study, which employs a survey method, is applied in terms of the purpose. Its statistical population is the "members" of "active" agricultural cooperatives in Lorestan. The research instrument was a questionnaire whose face validity was determined according to the opinions of 15 members of the statistical community, its content validity was calculated using content validity ratio (CVR) and its reliability was calculated by Cronbach's alpha test which was 0.76 and the data was analyzed by SPSS and Amos software. According to the results of the one-sentence t-test, members' participation in the management of the cooperatives studied is above average, as well as the results of correlation tests of variables and the results of structural equation analysis of the research model. (Members’ participation in general assemblies, members' participation in cooperative oversight, members’ participation in management and members' economic participation,) and "success" of agricultural cooperatives was confirmed and the relationship between them with 99% confidence level showed a direct, positive and significant relationship. The results obtained in relation to the fit indices of the model showed that the research model has a good fit. In this regard, there was no significant difference between married and single men and women in terms of participation in cooperative activities, while there was a direct and significant relationship between the variables of age, membership history, education and members' shares in the success of cooperatives.

Abstract Background Macrophages play a dual role in neuroinflammatory disorders such as multiple sclerosis (MS). They are involved in lesion onset and progression but can also promote the resolution of inflammation and repair of damaged tissue. In this study, we investigate if and how phloretin, a flavonoid abundantly present in apples and strawberries, lowers the inflammatory phenotype of macrophages and suppresses neuroinflammation. Methods Transcriptional changes in mouse bone marrow-derived macrophages upon phloretin exposure were assessed by bulk RNA sequencing. Underlying pathways related to inflammation, oxidative stress response and autophagy were validated by quantitative PCR, fluorescent and absorbance assays, nuclear factor erythroid 2–related factor 2 (Nrf2) knockout mice, western blot, and immunofluorescence. The experimental autoimmune encephalomyelitis (EAE) model was used to study the impact of phloretin on neuroinflammation in vivo and confirm underlying mechanisms. Results We show that phloretin reduces the inflammatory phenotype of macrophages and markedly suppresses neuroinflammation in EAE. Phloretin mediates its effect by activating the Nrf2 signaling pathway. Nrf2 activation was attributed to 5′ AMP-activated protein kinase (AMPK)-dependent activation of autophagy and subsequent kelch-like ECH-associated protein 1 (Keap1) degradation. Conclusions This study opens future perspectives for phloretin as a therapeutic strategy for neuroinflammatory disorders such as MS. Trial registration Not applicable.

Inefficient traffic signal control at intersections causes vehicle delays, increased fuel consumption, and vehicle emissions. With respect to the large number of fixed signalized intersections along the 7th line of Tehran BRT, passengers experience long delays and travel time while BRT is defined as a high-quality bus-based transit system that delivers fast and efficient service according to literature. Due to constraints on increasing supply, actuated traffic signal (ATS) and public transportation priority (PTP) are some of the possible signal control methods that can reduce the above-mentioned problems. In this paper, the 7th line of Tehran BRT is simulated using Aimsun. ATS and PTP have been simulated and traffic, environmental and economic indicators of transit and non-transit vehicles have been derived. Finally, a cost-benefit analysis is conducted to calculate the net present value (NPV), internal rate of return (IRR) and payback period (PP) of investment. Results show a decrease in travel time, delay, stop time, fuel consumption and pollutant emissions and an increase in average speed and flow at intersections. The payback period is estimated to be approximately two months, indicating a short-term policy and other economic indices confirm the profitability of project.

Introduction In the new form of social relations, Habermasian’s ‘public sphere’ has been embedded in ‘cyberspace’. Online social platforms have created an alternative to the public sphere that has led to the recognition of individuals, the definition of social interactions, and even the creation of love among members of the society. The impact of the internet on the social and personal life of users is important in several ways. Media consumption at home has significantly changed in recent years. New forms of media and media-related devices such as iPads, tablets, laptops, and smart phones have become common among families. Internet, digital cables, Wifi, Wimax, application software, and portable devices have created a world in which users’ contents are accessible wherever and whenever they want. With respect to the above discussion, it is clear that, since the introduction of cyber communication in 2008 as well as the installation and use of recently-released applications such as Telegram, WhatsApp and Instagram, which have provided the bases for the formation of a revolution in modern communications, it can be argued that the present study can be one of the newest researches in this area. In addition, Iran ranks the first among telegram users among 57 Islamic countries. Despite the increasing use of Iranian users, few studies have been done on this issue. Material & Methods The current study was conducted with the aim of analyzing the semantic implications of married men who were active in the cyberspace to find a spouse through the grounded theory approach. To this end, 11 users were selected through theoretical sampling. The data collection was conducted through semi-structured interviews and MAX Q DA 12. Two main sampling strategies in this study were purposive and theoretical samplings. The former was used to select participants with diverse characteristics. Theoretical sampling as one of the most common methods of sampling in the Grounded Theory was used for directing the general process of sampling via the sampling of the concepts during collecting and analyzing the data. The situation, people, time, and place were considered during theoretical sampling. Making comparisons in this process helped to diversify the categories and improve the theory. According to the logic of theoretical changes and the maximum sampling, the criteria for the entry of participants were as follows: •The participant must be a married man. • Their activities in the cyberspace are devoted to romantic conversations with men. • The participant must be older than 20 years of age. • The participant must be online in virtual marriage forums during the last 6 months. The participants remained in the study until they had been inclined to cooperate with the researcher, in case of the absence of any of these conditions, the participants were excluded from the study. Discussion of Results & Conclusions Based on the data analysis, three causal conditions including the crisis of meaning, diversity, and pleasure along with three contextual conditions including differentiation, separation of values, and preservation of independence have created a particular type of interaction. The interactive conditions of users had two main aspects including resistance to the ethical system of the society and the agreed economy in the family relationship system. The results of this study showed that the goal of a significant number of users to sign up and join these sites was to find an alternative structure for the predominant form of marriage in Kerman. In this study, the existence of the empty lives, a monotonous atmosphere at home, and routine relationships have led unemployed men to become more familiar with online dating than other groups. The majority of the participants referred to the disagreement of their wives with online gaming and entertainment because of their potentially addictive nature and online infidelities. Thus, anonymity was used as a very important strategy to facilitate online sexual activities among committed partners in committed relationships. According to the reports of the participants, ‘secret cyber entertainment’ has much more excitement and variety. They often want to hide their real identity to be safe from family and cyberspaces have become an alternative public arena for interaction, courtship, and love.

Music therapy is the use of musical skills to improve physical, mental function and emotional-social reactions, to help develop motor, academic, communication, social and emotional abilities, and ultimately to improve the quality of life. The aim of this study was to investigate the difference in quality of life of the elderly aged 60 years and older living in 60 nursing homes in the two modes of receiving and not receiving inactive music therapy. The statistical population was 1800 people; The calculated sample size was calculated using Cochran's formula of 318 people and the questionnaires were distributed using simple cluster and random sampling method. Data collection tool is a standard 26-item quality of life questionnaire based on the Likert scale. The research method is methodologically quantitative and comparative. Findings indicate that the hypotheses about the difference in quality of life (Sig = 0.000), environmental health (Sig = 0.001), social relations (Sig = 0.011), physical health (Sig = 0.048), Mental health (Sig = 0.000) of the elderly has been confirmed in terms of receiving and not receiving inactive music therapy and the quality of life of elderly men is better than elderly women. Numerous music therapies such as playing music, singing, etc. can be taken to improve the physical and mental performance of the elderly to help increase their life expectancy and reduce mental illness.

This research work was carried out to evaluate drought stress for the differentiation of bread wheat (Triticum aestivum L.) genotypes in their ability to tolerate drought. An experiment was carried out on six genotypes (‘Sides 1’, ‘Shindwell 1’, ‘Gemmiza 9’, ‘Sakha 93’, ‘Saheel 1’ and ‘Masr 2’). A randomized complete block design with three replications along two separate tests under the drip and the sprinkler irrigation systems was used in this experiment. One of the irrigation treatments applied the normal amount of irrigation water and the other applied end-season drought stress conditions through two successful agricultural seasons 2016/2017 and 2017/2018. The impact on biomass, grain yield and water productivity of the six genotypes was measured. Random amplified polymorphic DNA (RAPD) markers were used to evaluate genetic variation among the six genotypes. PCR–RAPD analysis showed that there were several differences in both the size and number of bands between the varieties. Based on these markers, genetic similarity coefficients were calculated and a dendrogram was constructed. The dendrogram analysis delineated three major clusters. The current study showed that RAPD markers are useful in the assessment of the genetic diversity among the wheat genotypes. The drip irrigation system gave the highest values of both the biomass and the grain yield for the six genotypes, while the sprinkler irrigation gave the lowest values. Comparing the six genotypes in terms of the biomass, grain yield and water productivity, it was concluded that, the highest water productivity (WP) genotype was Sides 1, except during water stress with the sprinkler irrigation system. It was followed by genotype Sakha 93, then genotype Shindwell 1, while genotype Gemmiza 9 gave the lowest in all cases, except without water stress under drip irrigation.

One of the most important simulation models in the field of water produc"tivity (WP) management in the world is the Aquacrop model, which depends on many factors and conditions related to climate, soil, irrigation water, etc. Aquacrop model program simulates vegetative growth and the yield of both grains, biomass and irrigation WP. The purpose of this study is to evaluate the Aquacrop model of two Egyptian wheat varieties, Gemeza-9 (G-9) and Misr-1 (M-1), under the two modern irrigation systems, sprinkler and drip, and the application of algae solution. Experiment location of fieldwork for this research was in El-Nubaria area, El-Beheira Governorate, and Aquacrop in Egypt. Results obtained show that under both irrigation systems, the deviation percentages between simulated by Aquacrop and observed WP values were 40.6% and 68.34%, in the case of using untreated algae and treated algae, respectively. From LSD 0.05% values, there are significant differences between all study factors (irrigation system, wheat varieties, and applied algae). Moreover, the interactions between all factors were significant under wheat grain yield of two varieties but no significant differences were found under observed or simulated WP by the Aquacrop model. It can be concluded that it is possible to recommend to use the Aquacrop simulation model for different wheat varieties in the future, to predict the WP in these semi-arid areas, especially under different irrigation systems (Bradford and Hsiao 1982; Entz et al. 1992; Johnston et al. 2002; Heng et al. 2009; Araya et al. 2010; Bennett and Harms 2011).

Abstract Background The presence of foamy macrophages and microglia containing intracellular myelin remnants is a pathological hallmark of neurodegenerative disorders such as multiple sclerosis (MS). Despite the importance of myelin internalization in affecting both central nervous system repair and neuroinflammation, the receptors involved in myelin clearance and their impact on the phagocyte phenotype and lesion progression remain to be clarified. Methods Flow cytometry, quantitative PCR, and immunohistochemistry were used to define the mRNA and protein abundance of CD36 in myelin-containing phagocytes. The impact of CD36 and nuclear factor erythroid 2–related factor 2 (NRF2) on the phagocytic and inflammatory features of macrophages and microglia was assessed using a pharmacological CD36 inhibitor (sulfo-N-succinimidyl oleate) and Nrf2 −/− bone marrow-derived macrophages. Finally, the experimental autoimmune encephalomyelitis (EAE) model was used to establish the impact of CD36 inhibition on neuroinflammation and myelin phagocytosis in vivo. Results Here, we show that the fatty acid translocase CD36 is required for the uptake of myelin debris by macrophages and microglia, and that myelin internalization increased CD36 expression through NRF2. Pharmacological inhibition of CD36 promoted the inflammatory properties of myelin-containing macrophages and microglia in vitro, which was paralleled by a reduced activity of the anti-inflammatory lipid-sensing liver X receptors and peroxisome proliferator-activated receptors. By using the EAE model, we provide evidence that CD36 is essential for myelin debris clearance in vivo. Importantly, CD36 inhibition markedly increased the neuroinflammatory burden and disease severity in the EAE model. Conclusion Altogether, we show for the first time that CD36 is crucial for clearing myelin debris and suppressing neuroinflammation in demyelinating disorders such as MS.

Background: Chronic Hepatitis C is common problem worldwide treated by combination of interferon alpha and ribavirin. Retinopathy is one of the side effects of interferon therapy. Aim: determine the frequency and risk factors of retinopathy in patients with chronic hepatitis C treated by interferon and ribavirin combination therapy in Egypt. Methods and Results: Total 100 patients were enrolled among which, 16 (16%) developed retinopathy in the form of cotton wool spots and 14(14%) developed atypical complications. Conclusion: Retinopathy may occur in patients with chronic hepatitis C receiving interferon alpha and ribavirin.

Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.

We consider the t-extension of the p-Fibonacci Pascal matrix. First, we study the k-th power of the t-extension of the p-Fibonacci lower and upper triangular Pascal matrix. Then, we obtain a new code which is named the t-extension of the p-Fibonacci Pascal matrix coding/decoding by using them.

Compton cameras use a pair of detectors to record the energy deposited during Compton scattering and photoabsorption of gamma ray photons. By inverting the forward model for these detection events, the 3D radioactive source distribution can be recovered computationally. However, existing methods of describing the forward model ignore the ambiguity in the Compton cone axis introduced by the detector pixel size. In this paper, we introduce approximations to the axis ambiguity that make the description of the source ambiguity computationally tractable and improve the accuracy of the detection forward model. Specifically, we model the pixel active areas as discs, so the intersection between all possible Compton cone axes and a plane parallel to the detectors is also a disc. Because of ambiguity in the Compton cone axis, the gamma source position is constrained to lie within a more complicated volume that we approximately bound by ellipses or hyperbolas at discrete depth slices. We perform simulations of single point gamma sources using the Geant4 software. Our forward model leads to improved source distribution recovery using both backprojection and iterative reconstruction methods, demonstrating that accurate localization can be performed from a smaller number of detected photons.

Background and Objectives: breast cancer remains the most common health burden affecting females worldwide. Despite developments in breast cancer diagnostic approaches and treatment strategies, the clinical management of metastatic breast cancer remains challenging. Thus, there is a need to identify new biomarkers and novel drug targets for breast cancer diagnosis and therapy. Recently, aberrant glypican-3 (GPC3) expression in cancers has gained considerable interest in cancer research. The studies, however, have yielded contradictory results about GPC3 expression in breast cancer. Therefore, the current study aims to analyse GPC3 expression across a large panel of different breast cancer subtypes. Materials and Methods: GPC3 expression was immunohistochemically evaluated in 230 breast cancer patients along with eight normal tissues and its associations to clinical and demographic characteristics, as well as immunohistochemical biomarkers for breast cancer. Moreover, a public database consisting of breast cancer patients’ survival data and GPC3 gene expression information was used to assess the prognostic value of GPC3 in the survival of breast cancer patients. Results: GPC3 expression was only characterised in 7.5% of different histological breast cancer subtypes. None of the normal breast tissues displayed GPC3 expression. Interestingly, all cases of Paget’s disease, as well as 42.9% of intraductal and 16.7% of mucinous carcinomas were found to have GPC3 expression, where it was able to significantly discriminate Paget’s disease and intraductal carcinoma from other breast cancer subtypes. Importantly, GPC3 expression was found more often in tumours that tested positive for the expression of hormone receptors and human epidermal growth factor receptor 2 (HER2), indicating more favourable histological subtypes of breast cancer. Consequently, longer relapse-free survival (RFS) was significantly correlated with higher GPC3 mRNA expression. Conclusions: Our study proposes that GPC3 is a promising breast cancer subtype-specific biomarker. Moreover, GPC3 may have the potential to be a molecular target for the development of new therapeutics for specific subtypes of breast cancer.

We studied the antimicrobial effect of gold quantum dots (AuQDs), femtosecond laser irradiation, and the combined effect of laser irradiation and AuQD treatment against common infectious eye pathogens. The INSPIRE HF100 laser system (Spectra Physics) provided a femtosecond laser, which was pumped by a mode-locked femtosecond Ti: sapphire laser MAI TAI HP (Spectra Physics), while a Quanta-Ray nanosecond Nd: YAG laser (Spectra-Physics) was used to precisely synthesize 7.8, 8.7, and 11.6 nm spherical AuQDs. Then, the in vitro growth kinetics and growth rate analysis of E. coli, methicillin-resistant Staphylococcus aureus, Enterococcus faecalis, Listeria monocytogenes, and Candida albicans (treated with the AuQDs, femtosecond laser irradiation, or combined laser and AuQDs treatment) was measured. The biocompatibility of the AuQDs with the retinal epithelial cell lines (ARPE-19) and their toxicity to the cells was assayed. The results showed that (1) in vitro irradiation using a 159 J/cm2 energy density obtained from the 400 nm femtosecond laser suppressed the growth of each of the five pathogens. (2) Similarly, treatment with the AuQDs was antimicrobial against the four bacteria. The AuQDs with an average size of 7.8 nm were more highly antimicrobial and biocompatible and were less cytotoxic than the larger AuQD sizes. (3) The combined femtosecond laser irradiation and AuQD treatment was more highly antimicrobial than each treatment alone. (4) The AuQD treatment did not impair the rate of wound closure in vitro. These findings suggest that combined femtosecond laser irradiation and AuQD treatment is significantly antimicrobial against Candida albicans, Gram-positive L. monocytogenes, S. aureus, and E. faecalis, as well as Gram-negative E. coli. The nontoxicity and biocompatibility of the AuQD particles tested suggest that this form of treatment may be clinically viable.

In the light of crop shortage production, water scarcity and environmental pollution. The utilization of biofertilizers as an algae extract and modern irrigation systems has become paramount important to reduce the chemical fertilizers and ensure good distribution for plants. Therefore, two field experiments were conducted in the Nubaria district at the Agricultural Station of the National Research Centre, El-Behira Governorate within the two winter seasons 2015/2016 & 2016/2017 to demonstrate the influence of two irrigation systems, i.e., drip and sprinkler and two rates of algae extract foliar application i.e., 0 g/L and 1.5 g/L on growth parameters and spike characteristics of two wheat varieties i.e., Gemiza-9 and Misr-1 grown in sandy soil. Results indicated that irrigation systems were significant effective on all characters except leaf area, plant height and spike length. On the other hand, all growth parameters and weight of spike of the two wheat varieties showed significantly differences under the studied treatments. The results clarified that some tested characters of Gemiza-9 variety were considerably improved. However, the Misr-1 variety surpassed Gemiza-9 in all characters in both seasons. While the algae foliar extract was significantly effective on all characters of study. The rate of 1.5 g/L of algae extract gave the highest values of all the studied parameters. Respecting to the first and second order interacted of factors under study effect on such parameters; the results showed that there were many parameters that improved significantly. According to the results of this study, it could be concluded that the combined treatments of foliar spray with 1.5 g/L of algae extract and two wheat varieties under two irrigation systems were found to be highly effective in improving wheat growth in sandy soil condition.

Introduction Destabilization and weakness of the foundation of family and the reduction of love and affection among family members are examples of the consequences of today’s civilized and industrialized society. But, since the family is regarded as the footstone of the society, emotional divorce has changed from being a private problem into a public problem in the structure of a society which requires a lot of attention. The maladjustment of married couple who did not get divorced formally has more negative, physical, emotional, moral and social effects for the couple and their children than for the family members of the divorced couple. Emotional divorce is one of the important cultural problems in a society. The basis of family is formed through the marriage of a man and a woman. Without having a healthy life, no society can have a healthy community and all of the social harms are influenced by the family. Emotional divorce is not recorded anywhere and is related to families in which, because of the reasons such as the dominant customs in family, negative attitudes of the society towards the women, and the incapability in meeting the needs of the life, women forcibly decide to continue their common life with their husbands. The purpose of this study was to investigate the social consequences of emotional divorce among married women in Tehran city. Emotional divorce and the problems following it are reflected directly in social problems of the society and increase the social harms which influence the children who live in this society. Emotional divorce has been examined in Iran in descriptive and qualitative forms and there has been less effort to analyze it sociologically, and because of social and cultural conditions there has been less interest towards it. The purpose of the present research is to sociologically examine the social consequences of emotional divorce of women settled in Tehran. Material & Methods From the viewpoint of purpose and data collection, the present research was practical and qualitative, respectively. It also had a cause-and-effect nature. The analysis was based on structural equation. Data collection was descriptive and survey. To collect data, a questionnaire was used. Statistical population of the research included 622 married women who have experienced their common life for at least one year and have referred to consulting centers and health houses of Tehran. Then, to examine the relationship among the variables the structural model of Amos Software was used. Discussion of Results & Conclusions According to the collected data, emotional divorce had a positive and meaningful effect on improper marital relationships, social harms, and social isolation, but had a negative effect on marital satisfaction. It had no meaningful effect on social growth and shouldering responsibility. Emotional divorce had the most effect on social harms and improper relationships, marital satisfaction, social isolation, respectively. Model values indicated that the effect of emotional divorce was 63% in inappropriate marital relationships (betrayal), 58% in social harms, -49% in marital satisfaction, and 34% in social isolation, respectively. Therefore, by reducing emotional divorce, the level of marital inappropriate relationships, social harms, academic failure and social isolation can be reduced, and the body image, child education, marital satisfaction, and religious beliefs can increase. Regarding the findings of the research, emotional divorce was effective on the amount of social isolation, improper marital relationships, life satisfaction and social harms. Some efforts should be done in the society so as to reduce the harms resulting from divorce. Reducing emotional divorce and at last the lawful divorce, and improving social conditions, doing cultural measures to modify ancient and false views of the society such as man-domination, sexual discrimination, informing women of their personal and social rights, preventing imposed marriages through training parents and passing proper laws in this regard, and increasing the skills of the women exposed to divorce to face emotional problems of common life via providing consulting services are suggested.

The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is developed in order to select and extract the most efficient features and the final features are fed to the BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performance of the developed technique when compared to the conventional FDD methods. To evaluate the effectiveness of the proposed KPCA-based BiLSTM approach, we utilize data obtained from a healthy WTC, which are then injected with several fault scenarios: simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mixed fault on both sides. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the developed KPCA-based BiLSTM technique compared to the classical FDD techniques (an accuracy of 97.30%).

The present study addresses the role of globalization of culture and lifestyle on the sexual satisfaction of married women. In recent decades, globalization has made profound changes in cultural elements, lifestyles, ideas, ideals and thoughts that need to be explored. To this end, the present study was conducted by a survey method using a questionnaire based on Erik Forem, Adler and Giddens theories. A sample of 384 married women living in Zanjan City was selected through cluster sampling. The dependent variable of this research is sexual satisfaction of the married women in dimensions of satisfaction from relationship, relation with spouse, adaptability, relationship anxiety and personal anxiety. The independent variable is globalization of culture in three dimensions: Attitude towards globalization, tendency to modernity and new cultural values. Second independent variable is lifestyle in terms of spiritual health, social health, physical health, psychological health, exercise and well-being, nutrition and weight. The results of the research show that there is a reverse and significant relationship between cultural globalization and sexual satisfaction, and there is a direct and significant relationship between sexual satisfaction and lifestyle. Therefore, the results confirm the main hypotheses of this research.

The genetic diversity in local Tunisian watermelon (Citrullus lanatus) was studied using RAPD markers. Eight watermelon cultivars originating from south of Tunisia and belong to four populations were analysed and compared to two commercial varieties (Giza and Sugar-baby) widely produced in this area. Five of nine RAPD primers generated a total of 86 reproducible bands, 85 of which were polymorphic (98.4%). Cluster analysis of the accessions considered in this study employing RAPD data indicated that commercial varieties are significantly different of all the local cultivars. The relationships among the local cultivars (four populations) showed that Medenine population and the most genotypes of Benguerdane population were grouped together and significantly different from Kebili population. The AMOVA showed significant differentiation between populations (27%). In addition, the data showed clusters according to some fruit characteristics such as fruit shape and fruit weight. This proved that RAPD markers are useful for germplasm discrimination as well as for investigation of patterns of variation in watermelon.