University College of Rouzbahan

Spermatogenesis constitutes a complex and intricate cascade of differentiation, indispensable for the male reproductive competence. The intercellular communication conduits of Sertoli cells (SCs) are pivotal in orchestrating this cascade ensuring sustenance and development of germ cells. Single cells and bioinformatics recently demonstrated articles are used for the regulatory modalities through which SCs modulate spermatogenesis, specifically via androgen receptors (ARs), the transforming growth factor-beta/Smad axis, mitogen-activated protein kinases, cAMP/protein kinase A (PKA), phosphatidylinositol 4,5-bisphosphate 3-kinase (PI3k)/AKT serine threonine kinase (Akt), AMP-activated protein kinase, and AR pathways. Within this framework, homeostasis of gap junction dynamics, cryptic sites and the activities at tight junctions and adherens junctions, with the integrity of the testicular barrier, glucose assimilation, lactate distribution, being governed also along with SC maturation. Disruptions in activities or abnormal concentration in derangements in AR, cAMP/PKA, and PI3k/Akt pathways, and as well as the molecules that comprise them, would present male infertility.

Every day, a large amount of medical data is being produced in hospitals and medical centers. Using this data to analyze the results can save treatment costs for both patients and the government. Therefore, it is very important to collect medical data about various diseases as well as their appropriate and correct analysis. In recent years, the use of machine learning algorithms to extract and identify patterns from various diseases, including in cancer research, which is a significant challenge for humans with high morbidity and mortality, has attracted much attention. In this article, several machine learning algorithms are employed for cancer detection on UCI standard data. The obtained results show the appropriate accuracy of the proposed method.

Shaking table tests were performed on reduced-scale models of integrated and two-tiered mechanically stabilized earth walls (TMSEWs) to evaluate the effect of a tiered configuration on the dynamic behavior of geogrid-reinforced soil walls. The results of particle image velocimetry and instrumentation indicate that preventing the development of a slip surface in the lower half of the wall, improving the seismic stability by increasing the failure threshold acceleration, mitigating acceleration amplification and decreasing the reinforcement load were the main advantages of a tiered configuration. It was found that the use of an insufficient offset distance in TMSEWs not only eliminated the advantage of the tiered configuration for reducing wall deformations, but also increased the lateral displacement at the wall crest. In this regard, 0.22H was identified as the minimum offset distance required when constructing MSE walls in a tiered configuration. Moreover, comparison of integrated and tiered MSE walls showed that the effect of a tiered configuration on reducing the force of the reinforcements in the lower tier was approximately 2.2 times that for the upper tier reinforcements. It was also found that Mononobe-Okabe method can be used to find the upper bound for estimating the load of reinforcements in TMSEWs.

This study investigates the application of Artificial Neural Networks (ANN) supplemented with optimization algorithms for modeling and mapping groundwater quality in an extensive unconfined aquifer in Northern Iran, a task traditionally performed through labor-intensive and costly water sampling and lab analysis. A comprehensive collection of groundwater samples from monitoring wells scattered across the region facilitated the calculation of the Groundwater Quality Index (GWQI) for each well. These GWQI readings were subsequently categorized into four distinct quality classes very poor, poor, good, and excellent. Key variables impacting groundwater quality were identified, including proximity to industrial and residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation. These factors were compiled and processed within a GIS environment. To establish a relationship between the GWQI and these determinants, an ANN model was employed. This was enhanced by the application of two optimization algorithms, Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), to determine the optimal weight and structure of the ANN model. The study’s results indicated that the ANN-PSO model (overall accuracy = 0.88) surpassed both the standard ANN (overall accuracy = 0.71) and ANN-GWO (overall accuracy = 0.83) models in accuracy. The region with the best and poorest groundwater quality was identified in the west and the northern section of the study area respectively. The feature analysis identified precipitation and population as the critical factors influencing groundwater quality in the region.

Designing routing systems for earthquakes requires frontend us-ability studies and backend algorithm modifications. Evaluations from subject-matter experts can enhance the design of both the front-end interface and the back-end algorithm of urban artificial intelligence (AI). Urban AI applications need to be trustworthy, responsible, and reliable against earthquakes, by assisting civilians to identify safe and fast routes to safe areas or health support stations. However, routes may become dangerous or obstructed as regular routing applications may fail to adapt responsively to city destruction caused by earthquakes. In this study, we modified the A-star algorithm and designed an interactive mobile app with the evaluation and insights of subject-matter experts including 15 UX designers, 7 urbanists, 8 quake survivors, and 4 first responders. Our findings reveal reducing application features and quickening application use time is necessary for stressful earthquake situations, as emerging features such as augmented reality and voice assistant may negatively backlash user experience in earthquake scenarios due to over-immersion, distracting users from real world condition. Additionally, we utilized expert insights to modify the A-star algorithm for earthquake scenarios using the following steps: 1) create a dataset based on the roads; 2) establish an empty dataset for weight; 3) enable the updating of weight based on infrastructure; and 4) allow the alteration of weight based on safety, related to human behavior. Our study provides empirical evidence on why urban AI applications for earthquakes need to adapt to the rapid speed to Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). use and elucidate how and why the A-star algorithm is optimized for earthquake scenarios.

Parkinson's disease (PD) is one of the really frequent disorders, with hand and head tremors and rigidity being the most common sequelae. Deep brain stimulation (DBS) is a common treatment used to alleviate the symptoms of this disease. This work investigates an ultra-local model (ULM) based on a sliding mode observer (SMO) to simultaneously reduce hand tremor and rigidity. specifically, a deep deterministic policy gradient (DDPG) controller is adaptively designed in the current study to reduce observer estimation error and improve the nonlinear dynamic features of a central neural network (CNN). The DDPG is designed with an actor that produces policy demands and a critic that measures the effectiveness of the actor’s policy orders. The offered methodology employs a DDPG-based mechanism to compensate for the shortcomings of the ULM-based SMO. In the present mechanism, training of the weight values of both networks (actor and critic) is by the gradient descent way that relies on the tremor fault's reward return. Finally, the following methodology is analyses by computer simulation in a variety of contexts (robustness and controller performance) and compared to current practices to prove the benefits and adaptability of the procedure with varied models and patients. Additionally, the controllers are implemented in the hardware-in-the-loop (HiL) simulations testbed to validate the performance of the developed scheme's profitability from a realistic perspective.

Today Radio Frequency Identification systems (RFID) are one of the most usable automated wireless identification technologies in the internet of things. Identification systems can exchange data remotely by communicating between a tag and a reader with sending radio waves. The main challenge of identification systems with radiofrequency in a dense RFID network is the collision, which occurs when readers are located in each other's interference range and start reading tags simultaneously. With these collisions happening, readers cannot read all the tags around them in the efficient time durations. In this research, using a distributed method and the channel listening technique, readers select a time interval to take the control channel by the Geometric Probability Distribution Function. Also, by measuring the signal strength from neighboring readers and sharing tag information, there will be an increase in the throughput of identification systems through radio waves while avoiding all kinds of collisions in the control channel. Extensive results show that the proposed method has better throughput and has less average waiting time.

In this paper an optimized Fuzzy based controller is proposed for automatic generation control of two area hydro-thermal power system connected to the wind farm. The parameters and membership functions of the proposed controller are optimized by a modified version of Cuckoo search algorithm (CSA). Also, a weighted objective function is proposed to minimize the frequency deviation and transmission power oscillation. The suggested heuristic objective function is a weight function from maximum frequency drift and oscillations fading time. To assessing the performance of suggested controller, studies is accomplished by two different scenarios. In the first scenario, the simulations are performed without wind farm and in the second scenario, the simulation is done in the presence of a wind farm. The simulation results indicate that the wind farm presence has major effect on sustained improvement of power system and the reason is considering the load variations in the area, the demand electrical energy in the same area is provided by the wind farm and hence, the frequency oscillations are decreased in both areas.

Due to the random nature of renewable energy in the micro-grid (MG) configuration, the energy generators in such systems are not able to offer an uninterrupted power and a mismatch between the demand and generation will be occurred. To alleviate this unbalance, technically, conventional storage devices are one of the practical solutions to this issue. However, the implementation of storage system is not effective from the economic preceptive and, as an alternative, the battery of electric vehicle can be also embedded in the structure of the MGs. In this work, an ultra-local model (ULM) controller based on a single-interval type-II fuzzy logic (SIT2-FL) controller is applied for frequency regulation of multi-MG(s) with electric vehicles. In this scheme, the ULM controller plays a critical role in the reducing the dependency of the controller to the MG model. An extended observer error is embedded in the structure ULM controller to remove the uncertainties included in the system. The SIT2-FL is established as supplementary controller to remove the error of observer and further ameliorate the multi-MG performance. Comparative simulation analysis and robustness examination are made to ascertain the usefulness of the suggested ULM controller-based SIT-FL.

According to outcomes from clinical studies, an intricate relationship occurs between the beneficial microbiota, gut homeostasis, and the host’s health status. Numerous studies have confirmed the health-promoting effects of probiotics, particularly in gastrointestinal diseases. On the other hand, the safety issues regarding the consumption of some probiotics are still a matter of debate, thus to overcome the problems related to the application of live probiotic cells in terms of clinical, technological, and economic aspects, microbial-derived biomolecules (postbiotics) were introducing as a potential alternative agent. Presently scientific literature confirms that the postbiotic components can be used as promising tools for both prevention and treatment strategies in gastrointestinal disorders with less undesirable side-effects, particularly in infants and children. Future head-to-head trials are required to distinguish appropriate strains of parent cells, optimal dosages of postbiotics, and assessment of the cost-effectiveness of postbiotics compared to alternative drugs. This review provides an overview of the concept and safety issues regarding postbiotics, with emphasis on their biological role in the treatment of some important gastrointestinal disorders.

Deep brain stimulation (DBS) is a powerful tool to treat the movement disorders created by Parkinson's tremor. The stimulation of one of the two main parts of basal ganglia (BG) in DBS is often provided without the feedback signal of the tremor which leads to undesirable side effects like cognitive impairment, anxiety, and muscle twitches. In this article, two distinct intelligent controllers are designed to stimulate the parts of a non-linear BG including subthalamic nuclei (STN) and globus pallidus internal (GPi) to overcome the current challenges of the DBS. For this purpose, an interval type-2 fractional-order fuzzy proportional derivative plus integrator (IT2FO-FPD+I) is suggested to control the two parts of BG. The control signals of the IT2FO-FPD+I controller are designed based on the tremor values which are measured by a sensor mounted on the patient's finger. Since the quality of output command of IT2FO-FPD+I controller highly depends on its coefficients, these parameters are adjusted by a sine-cosine algorithm-based wavelet mutation, called SCAWM, in a heuristic scheme. The suggested controller delivers both suitable stimulatory control signals to the brain and decreases the amplitude of tremor impressively. Comparative simulation explorations are performed subsequently to ascertain the superior performance of the suggested structured intelligent controller over that of the state-of-the-art methodologies.

Parkinson’s disease (PD) is one of the most common diseases that its main complications are hand and head tremors and inflexibility of muscles. One of the prevalent treatments that employ for reducing the symptoms of that is deep brain stimulation (DBS). In practice, a sensor is located in the patient’s finger for detecting and evaluating the tremor values in PD. Using an open-loop control structure for stimulating one area of basal ganglia (BG) is the common approach, but in this work, two areas of BG, named subthalamic nucleus (STN) and globus pallidus internal (GPi) are stimulated in a closed-loop manner separately for i) reducing the intensity of electric field and consequently disappearing the side effects of DBS ii) decreasing hand tremor. In particular, an adaptive Active Disturbance Rejection Control (ADRC) based on a deep deterministic policy gradient (DDPG) and a conventional feedback controller are presented for simultaneous stimulating STN and GPi, respectively. In this way, the control coefficients of the ADRC are considered as the control objective parameters that are designed by the actor and critic neural networks (NNs) of DDPG. The suggested scheme is applied to a BG system model which is frequently studied in the literature. The comprehensive simulation studies are accomplished to confirm the supremacy of the ADRC based DDPG scheme over the state-of-the-art strategies. Moreover, hardware-in-the-loop (HiL) simulations are performed to verify the efficiency of the proposed scheme from real-time perspective.

Bioactive micro- and macro-molecules (postbiotics) derived from gut beneficial microbes are among natural chemical compounds with medical significance. Currently, a unique therapeutic strategy has been developed with an emphasis on the small molecular weight biomolecules that are made by the microbiome, which endow the host with several physiological health benefits. A large number of postbiotics have been characterized, which due to their unique pharmacokinetic properties in terms of controllable aspects of the dosage and various delivery routes, could be employed as promising medical tools since they exert both prevention and treatment strategies in the host. Nevertheless, there are still main challenges for the in vivo delivery of postbiotics. Currently, scientific literature confirms that targeted delivery systems based on nanoparticles, due to their appealing properties in terms of high biocompatibility, biodegradability, low toxicity, and significant capability to carry both hydrophobic and hydrophilic postbiotics, can be used as a novel and safe strategy for targeted delivery or/and release of postbiotics in various (oral, intradermal, and intravenous) in vivo models. The in vivo delivery of postbiotics are in their emerging phase and require massive investigation and randomized double-blind clinical trials if they are to be applied extensively as treatment strategies. This manuscript provides an overview of the various postbiotic metabolites derived from the gut beneficial microbes, their potential therapeutic activities, and recent progressions in the drug delivery field, as well as concisely giving an insight on the main in vivo delivery routes of postbiotics.

Cancer illness still is one of the most common illnesses in the world, which is constantly rising. Chemotherapy plays a crucial role in treating cancer patients. In this paper, we have presented a novel intelligent sensor for controlling and adjusting chemotherapy parameters which consist of an ultra-local (ULM) controller based on a deep deterministic policy gradient (DDPG). First, the feedback signal is provided using a sensor to calculate the population of cells. Then, a controller sends the proper control commands to the actuator (chemotherapy). In the suggested scheme, the ULM is applied to the dynamic model of cancer. In order to shrink tumor cells and rising immune and normal cells at the same time. Moreover, for improving the performance of the established ULM scheme, a DDPG algorithm with the actor-critic structure is used for tuning the parameters of ULM in an adaptive manner. To demonstrate the supremacy of the DDPG based ULM controller, the conventional ULM and proportional integrator (PI) are also designed for the cancer treatment. Simulation outcomes prove the improved cancer treatment compared to the ULM and PI schemes.

Liquefaction risk assessment is critical for the safety and economics of structures. As the soil strata of Ramsar area in north Iran is mostly composed of poorly graded clean sand and the ground water table is found at shallow depths, it is highly susceptible to liquefaction. In this study, a series of isotropic and anisotropic consolidated undrained triaxial tests were performed on reconstituted specimens of Ramsar sand to identify the liquefaction potential of the area. The specimens are consolidated isotropically to simulate the level ground condition, and anisotropically to simulate the soil condition on a slope and/or under a structure. The various states of soil behavior are studied by preparing specimens at different initial relative densities and applying different levels of effective stress. The critical state soil mechanics approach for identifying the liquefaction susceptibility is adopted and the observed phenomena are further explained in relation to the micro-mechanical behavior. As only four among the 27 conducted tests did not exhibit liquefactive behavior, Ramsar sand can be qualified as strongly susceptible to liquefaction. Furthermore, it is observed that the pore pressure ratio is a good indication of the liquefaction susceptibility.

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.

In this paper, new structures for digital code converter circuits in quantum dot cellular automata (QCA) technology are presented. The basic structure of most of these circuits is the XOR gate, which is widely used in digital design. Therefore, in the proposed, the XOR gate will be presented which will be better than previous circuits in terms of cell number and delay. Then, using the proposed circuits for the XOR gate, new circuits for generating parity bit, Binary to Gray, Gray to binary and BCD to gray code converter are introduced. Proposal designs have an efficient implementation in terms of complexity. The proposed structures are simulated using the QCAdesigner tool to evaluate the correct performance. The proposed final circuit as a digital code converter has improved by 37% in terms of cell consumption and 25% in speed.

In this paper, the optimal design of a grid-connected the hybrid energy system for a sample area in the north Iran is studied. A new innovative cost-based objective function is proposed which is combination of life cycle cost and reliability cost. Also, loss of power supply probability (LPSP) criteria, is considered as constraint for ensuring at the same time certain level of system reliability. Designing process is implemented in such a way that the total cost of the system reaches its minimum. For this purpose, a modified version of Bee algorithm has been proposed to achieve this goal. In order to carry out studies, the actual sample system, whose data has been available, has been studied. The results indicate the good performance of proposed hybrid system to reduce system cost.

In recent years, deep brain stimulation (DBS) has been one of the most effective methods for treating movement disorders, including hand tremor in advanced Parkinson’s disease (PD). In order to decrees the Parkinson’s tremor, a model of basal ganglia (BG) is often used to design a closed-loop control scheme for DBS. First, a feedback signal is provided using a sensor mounted to the patient’s finger to measure the tremor values. Then, a controller sends the appropriate control commands to the actuator, and finally, the BG areas of the brain actuator are simulated by the actuator. In the present paper, to control Parkinson’s tremor and reduce the value of stimulation intensity efficiently, two areas of BG such as subthalamic nucleus (STN) and globus pallidus internal (GPi) were controlled simultaneously. This strategy provides a reduction in the applied field and side-effects (e.g. muscle contraction and speech disorder) resulting from stimulation intensity. In particular, a new model-free scheme, called intelligent single input interval type-2 fuzzy logic (iSIT2-FL) combined with non-integer sliding mode control (SMC), is proposed to control two areas of BG. In the suggested strategy, an extended state observer (ESO) is established to approximate the unknown BG dynamics, whereas the non-integer SMC is applied to remove the ESO estimation error. Comparative simulation explorations are performed subsequently to ascertain the superior performance of the suggested intelligent control scheme over that of the state-of-the-art approaches.