Thanks to rapid advances in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, Smart Public Safety (SPS) system has become feasible by integrating heterogeneous computing devices and different types of networks to collaboratively provide seamless public safety services. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service oriented architecture (SOA) architecture is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging hybrid blockchain fabrics, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability and traceability for secured data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed Blend-SPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

A study on the selection of hyperelastic constitutive model for polymeric materials is performed using a hybrid experimental-computational approach. Bis-GMA polymer is used as a case study of hyperelastic material to describe the polymer characteristics by determining its Poisson’s ratio and its valid range of the hyperelastic stress-strain curves. These two parameters are then used to determine the hyperelastic constitutive model by using the hybrid approach. Several uniaxial compression tests along with their finite element simulations are implemented in a systematic way, to identify the polymer behavior under the compressive loading conditions. Nano-indentation experiments are conducted to verify the hyperelastic behavior of the polymer. The experimental and computational evidences confirm that the Poisson’s ratio of Bis-GMA is 0.40 and the appropriate hyperelastic constitutive model for this polymer is of a second order polynomial. It is shown that, the results can be used to determine the true stress-strain curve of hyperelastic materials.

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Background: Neonatal cholestasis (NC) initiates in the first trimester of a newborn, comprising extra and intrahepatic medical conditions, with a high risk of fatality warranting early diagnosis and treatment to prevent morbimortality. The differential diagnosis of NC is a challenge demanding an accurate diagnosis for disease detection. The current study evaluates NC's clinical symptoms and differential diagnosis using ultrasound, liver biopsy, histopathology, and biochemistry. Methods: Infant registry data from Hevi Pediatric Teaching Hospital (January 2016 - January 2022) were obtained and screened for subject selection. The inclusion criteria include infants with direct hyperbilirubinemia within an onset of 1 to 90 days of birth. As indicated, the recruited subjects underwent liver ultrasound, blood biochemistry, and liver biopsy. Results: Seventy-two children presented with the criteria for inclusion in the study. The study found that ultrasound helped diagnose 43.1% of subjects for BA compared to 34.7% through histopathology. The histopathology confirmed 13 children (18.1%) having neonatal hepatitis. Test sensitivity of the ultrasound method for BA and NH was 60% (40.74, 76.6) and 38.46% (17.71, 64.48), respectively. Conclusion: The study found both ultrasound and liver biopsy to be critical diagnostic methods to differentiate the etiology of NC. Ultrasound has a higher specificity and sensitivity for diagnosing BA than NH. Histopathology and blood biochemistry should be considered, too, for effective diagnosis. In the future, larger sample and multicenter studies should be conducted to develop practically implementable strategies.

Hearing loss in infancy leads to preventable speech, language, and cognitive developmental delay [1, 2]. Sensorineural hearing loss (SNHL) is caused by damages, problems, or issues related to the inner ear such as the cochlea with or without the auditory nerve; cranial nerve VIII, involvement. There are three anatomic areas which include the outer ear: composed of the auricle and external auditory canal and the middle ear: which includes the tympanic membrane, ossicles, and the middle ear space, the inner ear: composed of the cochlea, semi-circular canals, and internal auditory canals. The unique anatomical shape of the auricle catches the incoming sound waves to send them down the external auditory canal. Hearing risk assessment should be part of all health visits while regular hearing screening checks are done for all children from 4 to 21 years [1, 2]. Assessment of hearing loss includes history, physical examination and specific hearing assessment tests.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

The most modern technique utilized to create intricate and complexly manufactured parts for a variety of applications is called additive manufacturing (AM). Fused deposition modeling (FDM) has received the greatest attention in the development and manufacturing sectors. The main objective of this study was to investigate how printing factors affected the mechanical characteristics of printed samples. Samples were produced using an FDM 3D printer in compliance with ASTM D638 using a variety of input settings, including orientation, layer thickness, speed, and infill pattern. On the printed samples, tensile tests and morphological analysis using a scanning electron microscope (SEM) were done. The findings of this study demonstrate that factors including layer thickness, printing speed, and orientation significantly affect the tensile strength of ABS printed samples. Discovering the process settings that could lead to superior mechanical and physical characteristics would undoubtedly assist designers and manufacturers worldwide as the FDM 3D printer becomes more and more crucial in manufacturing engineering parts.

Recently, most countries suffering from global warming due to the waste gases coming out of the combustion engines used to generate electric power by traditional methods. Therefore, many countries are currently trying to find alternative solutions to this problem by using renewable energies such as solar energy, wind energy, and energy generated from the waves of the ocean and sea to overcome pollution problems. The waves of the seas and oceans have enormous and largely untapped energy, so this work presents an efficient way to use the energy of sea waves to generate electricity. And also, trying to be a way to produce electricity from wave energy in the future. The best suitable places along the shores of Jizan city were inspected to install the buoy system to revenue advantage of the wave supreme height to achieve the height amount of electrical energy. A mathematical model was made to analyze the wave energy and convert it into energy extracted by mechanical force. The mathematical analyzes used the data collected from satellite maps of the numerous severe waves in the Red sea of the Jazan area, and the data published in previous research along that area. It was found that the beach of the Al Shuqaiq area is the greatest for installing the buoy and obtaining the highest electrical energy due to the presence of the highest wave intensity, followed by the beach of the Baysh and Al Morgan area. Also, one of the objectives of this research is to study the design of a device powered by a buoy to use the waves of the Red Sea to generate electric power. The influence of the buoy system design parameters such as the buoy diameter, length of the cylinder, and the length of the connecting rod on the electrical energy generation from wave energy was investigated. The current device is designed with a gearbox to produce continuous power with a single electric generator. A floating mooring device uses the rise and fall of bulges to convert sea wave energy into electrical energy. The device consists of a float, arm, two wheels of different diameters, a gear set, and an electric generator. The effect of the design of several factors on the performance of the device for converting the sea waves energy into electrical energy, including the length of the buoy arm, the wave height by changing the cam diameter, and the conversion ratios between the gear set, to optimize the output power of the wave energy.

The neural system has limited resources to interact with the environment, and this is where attention comes into play. Attention is a complex process that provides numerous benefits, but there has been a longstanding debate about the mechanisms involved in attentional orientation. Two widely proposed mechanisms are top-down and bottom-up, which relate to the organism's current behavioral goal and the salience of environmental stimuli, respectively. However, distinguishing between these mechanisms can be challenging in both behavioral and neural studies. This review introduces a new conceptualization in which both top-down and bottom-up mechanisms are viewed as aspects of the same process. This unified approach aims to prepare the organism to face stimuli and events based on its prior experiences, immediate (the current task that he/she is aware of), and innate (to be alive) demands.

This study investigated the impact of seven-strain probiotic supplementation on the frequency of T helper 17 (Th17) cells, regulatory T (T-reg) cells, Treg/Th17 ratio, and their gene expression in individuals with severe traumatic brain injury (STBI). Probiotics can influence the gut microbiota, modulate the immune response, and prevent or alleviate certain immune diseases. However, it is necessary to clarify whether probiotics can effectively impact Th cells in STBI patient. Despite the great attention to improving the immune system in most diseases, there is currently no accurate mechanism for the use of probiotics in diseases with damage to the head injury, which can be a basis for the use of probiotics. The primary objective of this study is to address this research gap. The present study was a randomized, double-blind, placebo-controlled trial that enrolled 48 STBI patients. They were treated with probiotics or placebo: 4 capsule/day for 14 days. The pre- and post-study evaluation included examining the frequency of Treg, Th17 cells, as well as the expression of associated immunological parameters (Foxp3 and RORγt) and Treg/Th17 ratio in both groups. No significant differences were found between the two groups in terms of subject characteristics (P>0.05). GCS scores at 14 days were significantly higher in the probiotic compared to the placebo group. The probiotic group showed a significant decrease in Th17 cell and RORγt gene expression (P = 0.022 and P< 0.001, respectively), a notable increase in T-reg and FoxP3 gene expression (P= 0.025 and P< 0.001, respectively), and a higher Treg/Th17 ratio compared to the placebo group. Our study provides promising evidence of the beneficial effects of seven-strain probiotic supplementation on Th cells in individuals with STBI.

On the basis of the golden ratio and the compasseses, every angle was divided into three equal parts. In general, it has been proven that the problem of triangulation of an angle cannot be solved on the basis of the golden ratio and the expanding of the angle. The relationship of golden relativity with Pi number and Euler's number has not been investigated to solve impossible problems. According to the investigation of events in six-dimensional space-time, with the simultaneous movement of two arms of the compasses based on the golden ratio, every angle was divided into three equal parts. The growth rate based on the golden ratio is the key to solving the most intractable mathematical and physical problems.

Consciousness results from comprehending the living creature from its existence in time and space. Thought and consciousness are distinguishing factors between humans and machines having artificial intelligence. No algorithm has been offered for artificial self-awareness based on Thinking. Previous studies have not studied the relationship between consciousness, thinking and time. In this study, the relationship between consciousness, thinking, memories and speech over time has been studied. A deep logical connection exists between consciousness, thinking, and time. An algorithm can be designed for artificial consciousness based on this research findings.

This paper presents a method for producing a gravity field through an electromagnetic field. The electromagnetic field can produce a positive or negative gravity field. The relationship between gravity force and electromagnetic is unknown. Former studies by theoretical physics predict the relationship between these two forces. Based on the general balance theory in six dimensions of space and time, the electromagnetic field was studied from this viewpoint. Three-dimensional waves oscillate in the fourth dimension and electromagnetic waves distort space-time. Aggregating these distortions can behave like a positive or negative gravity field. Consequently, the production of energy is possible from gravitational fields.

A human brain can communicate with another one’s brain using quantum entanglement. Similar particles can entangle without interaction. Sensory communication between two individuals from far distances is still not known. Former studies primarily have been performed regarding brain neurons' quantum behavior. The test was performed on two individuals far from each other. These individuals were put under the same music on the basis of brain neuroplasticity property. By activating the brain reward system, aligned thinking was time scheduled in these individuals’ brains. On the basis of entanglement, compressed information was sent and received between these two individuals.

On the basis of the findings of this paper, the theories of quantum mechanics and general relativity can be expressed in a comprehensive theory. The events were surveyed length of the time, in the Euclidean six-dimensional space-time (R6=x1+x2+x3+t1+t2+t3). Previous studies have not studied the relationship between the origin of matter's inherent properties, fundamental constants, and quantum mechanics phenomenon with space-time geometry. This theory prediction the with certainty obtained probabilistic results of quantum mechanics. This prediction is on the basis of metric resulting from the Eccentricity of the Ellipse due to the object density, in the length of time. The cause and nature of dark matter and dark energy are the findings of this article.

Although lithium-ion batteries have extensively been used in various applications because of their high energy capacity, fracture and failure, the by-products of large strains and stresses caused by fast charging and discharging need yet to be addressed. The size effects on the mechanical behavior of the nano-sized structures are significant; however, the classical elasticity theory may not consider such effects. On the other hand, surface stress theory, as a robust and potential theory, is suitable in considering size effects in nano-scale structures. Therefore, in this paper, in order to involve the surface stress effects on the fracture behavior of Li-ion batteries, the following steps are taken. Firstly, a phase-field model is used to determine the evolution of the concentration profile. Subsequently, the stress distribution is obtained by using the surface stress theory combined with chemical equations for a planar electrode. Afterward, by using the weight function method for an edge crack in the plate, the stress intensity factor is derived for all time steps and possible crack lengths during the process. It is found that with increasing phase boundary thickness parameter or decreasing phase-separation phenomenon, the surface mechanics parameters become more influential. Furthermore, in the presence of positive surface stress, the diffusion-induced stress distribution decreases, which in turn reduces the stress intensity factor. In addition, in this paper, the two states of surface stress are compared either for elastic or total strain. Concerning stresses and concentrations, the results indicate a big difference at the beginning of the deintercalation process showing, in particular, 2% for stresses, but the differences diminish gradually.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

Despite the 12-lead electrocardiogram (ECG) being the gold standard in evaluating clinical presentations of arrythmias of various kinds, several notable limitations, namely in accuracy and localization, have recently garnered concern with respect to administering appropriate therapeutic measures. The following text puts these limitations under the spotlight, highlighting the difficulty and challenging nature of standard ECG analysis and therapeutic measures, while simultaneously showing a novel body surface electrocardiographic mapping system that utilizes 3D spatial imaging and pinpoint localization algorithms in order to compensate for the shortcomings of the standard ECG, thereby providing higher precision data collection. We provide a brief overview of the novel device in a selection of arrhythmic demographics, along with its validity and reliability, while illustrating the benefits and positive outcomes of its utility.

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, environmental factors, and addressing unmet load, this research contributes to the advancement of 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 in its search of energy resilience and environmental surveillance which has a total peak load of 9.958MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of 30M$, with a levelized energy cost (LCOE) of 0.00274$/kWh. In addition, the minimum desired load will be unmeted on some days of September.

This bibliometric review is aimed to analyze the top 100 most-cited publications in dentistry and to compare its outcomes with similar analysis done by Feijoo et al., [1]. A literature search was performed using the Elsevier's Scopus without any restriction of language, publication year or study design. Of 336381 articles, the top 100 were included based on their citation count which ranged from 638 to 4728 citations (Feijoo et al., 326 to 2050). Most productive decade was the 2000s with 40 articles on the list (Feijoo et al., 1980s: 26). Marx RE (7%) was the major contributor in this study (Feijoo et al., Socransky SS: 9%) and a whopping 48% articles generated from the USA. 26% of top 100 articles focused periodontology (Feijoo et al., periodontology: 43%), while 17% of the total were published in the Journal of Dental Research (Feijoo et al., Journal of Clinical Periodontology: 20%). Most of the publications were narrative reviews/expert opinion (36%), (Feijoo et al., case series: 22%) and were within the evidence level V (64%) (Feijoo et al., 54%). The citation count that a paper secures is not necessarily a reflection of research's quality, however, the current analysis provides latest citation trends in dentistry.

An oil and vinegar scheme is a signature scheme based on multivariate quadratic polynomials over finite fields. The system of polynomials contains $n$ variables, divided into two groups: $v$ vinegar variables and $o$ oil variables. The scheme is called balanced (OV) or unbalanced (UOV), depending on whether $v = 0$ or not, respectively. These schemes are very fast and require modest computational resources, which make them ideal for low-cost devices such as smart cards. However, the OV scheme has been already proven to be insecure and the UOV scheme has been proven to be very vulnerable for many parameter choices. In this paper, we propose a new multivariate public key signature whose central map consists of a set of polynomials obtained from the multiplication of block matrices. Our construction is motivated by the design of the Simple Matrix Scheme for Encryption and the UOV scheme. We show that it is secure against the Separation Method, which can be used to attack the UOV scheme, and against the Rank Attack, which is one of the deadliest attacks against multivariate public-key cryptosystems. Some theoretical results on matrices with polynomial entries are also given, to support the construction of the scheme.

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

The fundamental challenge for the structural design of complicated shapes and lightweight structures such as Tensile Membrane Structures (TMS) is a reliable estimation of lateral loading such as wind load. Wind pressure coefficients can’t be correctly calculated by standards for a large range of intricate forms of structures. Computational Fluid Dynamics (CFD), a strong computational technique for evaluating wind pressure distribution on complicated geometry, can be used to determine wind-related parameters. The average wind related-distributions for the tensile membrane surface are validated with a few contributions from prior studies, which are important to validate with benchmarks in experimental wind tunnel experiments. For the double-curved surface, the results show good agreement between experimental tests and CFD simulations. In this study, CFD simulation was used to calculate the mean surface pressure coefficient (Cp) for generic shapes and tensile membrane forms using the steady Reynolds-Averaged Navier-Stokes (RANS) technique. Wind force coefficients and the effect of wind directions are also explored across a wide range of classical and tensile forms. The purpose of the current research is to look at the effect of wind assault angle on the Cp distribution for tensile membrane structures and general shapes.

In the previous work, the pure fatigue behavior of AM60 magnesium alloy (PF-AM60) was compared with the corrosion fatigue behavior of these specimens (CF-AM60). In this research, in continuation of the previous job, the pure fatigue behaviors of AM60 with polylactic acid (PLA) coating (PF-AM60-PLA) and also, corrosion fatigue behaviors of magnesium alloy with PLA coating (CF-AM60-PLA) were evaluated. Polymer coating was made by fused deposition modeling (FDM) with a 3D printer and attached to standard fatigue test specimens with a glue. Then, they were immersed in the simulated body fluid (SBF) for 27 days. In the end, a high-cycle bending fatigue test was performed on samples. The fracture surface of the samples was also observed using the field emission scanning electron microscopy (FESEM). Due to corrosion, the weight of the specimens redcued by an average of 35%. The corrosion rate decreased in the first 7 days and then increased. PF samples with coating had an average of 49% increase in fatigue lifetime. Regarding the CF samples, despite the use of a 10 times stronger solution, the fatigue lifetime of these samples decreased by only 35%. FESEM results also showed cleavage plates and striations. In addition, the separation of the glue from the coating and Mg was observed. Corrosion products including microcracks and holes were seen on the fracture surface of CF specimens, which caused stress concentration and crack growth. Holes caused by the release of gases were also observed in polymer coatings.

In this paper, the problem of predicting future failure times based on a jointly type-II censored sample from k exponential populations is considered. The Bayesian prediction intervals and point predictors were then obtained. Generalized Bayes is a Bayesian study based on a learning-rate parameter. This study investigated the effects of the learning rate parameters on the prediction results. The loss functions of squared error, Linex, and general entropy were used as point predictors. Monte Carlo simulations were performed to show the effectiveness of the learning rate parameter in improving the results of prediction intervals and point predictors.

Aims: Adolescence is a critical period because the brain is involved in the process of maturation that entails cognitive functions. On the way of maturation, an individual’s inhibitory control undergoes many changes and becomes vulnerable to different thrill-seeking like smoking, drinking, and so on. Smoking is highly prevalent among teenagers that are trying to take control of their behaviors in order to join society. They experience antisocial behavior too which is a trait that can lead adolescents to addiction. This trait is an inevitable part of psychopathy. Inhibitory deficits and psychopathy have been widely reported in addiction studies. The current study tried to investigate the relationship between psychopathic traits and behavioral inhibition between male smokers and non-smoker teenage students.Materials & Methods: Statistical sample of this research is 62 teenage students aged 17 years that are divided into smoker and non-smoker groups. The participants have been chosen through random sampling from the population of 10 high schools. The data have been gathered in Kordkoy and Gorgan in Golestan province. Behavioral bias has been measured by Go/NoGo task and psychopathic traits through youth psychopathic traits inventory. Also, the short form of Wechsler Adult Intelligence Scale test has been executed and used as a control variable.Findings: A meaningful difference has been found between the performance of smoker and non-smoker groups in Go/No Go task and psychopathic traits that are smokers performed weaker in comparison with non-smokers and psychopathic traits of smokers were meaningfully higher than non-smokers. On the other hand, there was no significant difference between these two groups in their Wechsler Adult Intelligence Scale scores.Conclusion: The results have shown that smokers have higher psychopathic traits and lower behavioral inhibition when compared with their non-smoker peers. According to the results of current research, smoking can decline the cognitive functions.

One of the major problems caused by repeated subcutaneous insulin injections in diabetic patients is insulin amyloidosis. Understanding the molecular mechanism of amyloid fibril formation of insulin and finding effective compounds to its inhibit or eliminate is very important and extensive research has been done on it. In this study, the anti-amyloidogenic and destabilizing effects of the pyrogallol, as a phenolic compound, on human insulin protein were investigated by CR absorbance, ThT and ANS fluorescence, FTIR spectroscopy and atomic force microscopy. According to the obtained results, the formation of amyloid fibrils at pH 2.0 and 50 °C was confirmed by CR, ThT, ANS, FTIR assays. Microscopic images also showed the twisted and long structures of amyloid fibrils. Simultaneous incubation of the protein with pyrogallol at different concentrations reduced the intensities of CR, ThT and ANS in a dose-dependent manner and no trace of fibrillar structures was observed in the microscopic images. FTIR spectroscopy also showed that the position of amide I band in the spectrum of samples containing pyrogallol was shifted. Based on the findings in this study, it can be concluded that pyrogallol can be effective in preventing and suppressing human insulin amyloid fibrils.

An in-depth study of the failure of granular materials, which is known as a mechanism to generate defects, can reveal the facts about the origin of the imperfections such as cracks in the carbon anodes. The initiation and propagation of the cracks in the carbon anode, especially the horizontal cracks below the stub-holes, reduce the anode efficiency during the electrolysis process. In order to avoid the formation of cracks in the carbon anodes, the failure analysis of coke aggregates can be employed to determine the appropriate recipe and operating conditions. In this paper, it will be shown that a particular failure mode can be responsible for the crack generation in the carbon anodes. The second-order work criterion is employed to analyze the failure of the coke aggregate specimens and the relationships between the second-order work, the kinetic energy, and the instability of the granular material are investigated. In addition, the coke aggregates are modeled by exploiting the discrete element method (DEM) to reveal the micro-mechanical behavior of the dry coke aggregates during the compaction process. The optimal number of particles required for the failure analysis in the DEM simulations is determined. The effects of the confining pressure and the strain rate as two important compaction process parameters on the failure are studied. The results reveal that increasing the confining pressure enhances the probability of the diffusing mode of the failure in the specimen. On the other hand, the increase of strain rate augments the chance of the strain localization mode of the failure in the specimen.

Background and Aim: Cancer is a disease characterized by uncontrolled cell growth and invasion of tissues. It seems that personality differences and psychological factors are important factors that lead to different reactions to cancer. Therefore, the main purpose of the present study was to investigate the relationship between type D, stress level of life events, personality traits, and anxiety sensitivity in people with breast cancer attending to Tehran Bu-ali Hospital. Methods: The study was a correlational study in which 100 cancer patients referred to Bu-ali Hospital in Tehran, selected by the available sampling method from April to June 2018. The tools used included the Denollet DS-14 Type D Questionnaire, the Paykel Life events Questionnaire, the Eysenck Personality Questionnaire, the ASI Anxiety Scale, Floyd et al., Data analysis in this study is taken place by using SPSS software, two descriptive levels (percentage, frequency, etc.) and regression. Results: The study results showed that there is a relationship between type D personality with stress of life events, neurosis, extraversion, and psychopath dimensions of personality and anxiety sensitivity in breast cancer patients. Conclusion: Psychological factors play a role in the incidence and exacerbation of breast cancer, and ultimately patients with personality type D and personality traits such as psychosis and high anxiety sensitivity and more stress of life events show more symptoms.

CO2 injection into geological formations is considered one way of mitigating the increasing levels of carbon dioxide concentrations in the atmosphere and its effect on and global warming. In regard to sequestering carbon underground, different countries have conducted projects at commercial scale or pilot scale and some have plans to develop potential formations for carbon dioxide storage. In this study, pure CO2 injection is examined on a model with the properties of bunter sandstone and then sensitivity analyses were conducted for some of the important parameters. The results of this study show that the extent to which CO2 has been convected in the porous media in the reservoir plays a vital role in improving the CO2 dissolution in brine and safety of its long term storage. We conclude that heterogeneity plays a crucial role on the saturation distribution and can increase or decrease the amount of dissolved CO2 in water. Furthermore, the value of absolute permeability controls the effect of the Kv/Kh ratio on the CO2 dissolution in brine. In other words, as the value of vertical and horizontal permeability decreases (i.e. tight reservoirs) the impact of Kv/Kh ratio on the dissolved CO2 in brine becomes more prominent. Additionally, reservoir engineering parameters, such as well location, injection rate and scenarios, also have a high impact on the amount of dissolved CO2.

Manufacturing environments, characterized by rapid, unpredictable changes, uncertainties, risks, and uncontrollable fluctuations, pose significant challenges to minimizing disruptions in processes. This study introduces an innovative approach that prioritizes curbing risk propagation among processes to enhance robustness. It emphasizes the integration of engineering management principles and modular design within manufacturing. Adopting a system engineering perspective, all manufacturing process activities are viewed as interrelated components within a unified system. By employing Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, manufacturing process architecture is modularized, yielding heightened robustness. The proposed mathematical model equips engineering and manufacturing managers with a potent tool for designing robust processes while adeptly managing system complexity. The study's outcomes underscore a substantial enhancement in modularization, leading to elevated overall robustness in manufacturing processes. To validate the methodology, the architectural design of manufacturing processes is examined in a real-case scenario, specifically the Barez Industrial Group in Iran. This verification substantiates the 'manufacturing processes' of the case, presenting an optimally modularized architecture. The results affirm the proposed approach's efficacy, demonstrating improved modularization that contributes to bolstered robustness in manufacturing processes.

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

An investigation of compressible flow over two-dimensional obstacles is presented in this paper. When shock waves are formed on surfaces due to compressibility and these shocks interfere with the boundary layer, undesirable effects such as friction, separation, and flow instability occur. To weaken the shock and reduce its effects, various methods have been employed in the past, including surface suction, surface blowing, and vortex generators. Using numerical solutions, the present work attempts to reduce the negative effects of the shock generated by the obstacle by creating porosity on its surface. Numerical methods such as the finite volume method and the Navier-Stokes equations as well as the Spalart-Allmaras model are used. By creating suction and blowing and increasing the cross-sectional area of the flow, porous surfaces reduce the effects of shock. Achieving around 50% porosity on the obstacle weakened the shock and reduced the Mach number after it, according to this paper's results. Also, the area under the skin friction diagram has decreased by about 38% as a result of less interference between the shock and the boundary layer and blowing caused by the porous surface.

In this paper, we demonstrate a useful interaction between the theory of clique partitions, edge clique covers of a graph, and the spectra of graphs. Using a clique partition and an edge clique cover of a graph we introduce the notion of a vertex-clique incidence matrix for a graph and produce new lower bounds for the negative eigenvalues and negative inertia of a graph. Moreover, utilizing these vertex-clique incidence matrices, we generalize several notions such as the signless Laplacian matrix, and develop bounds on the incidence energy and the signless Laplacian energy of the graph. %The tight upper bounds for the energies of a graph and its line graph are given. More generally, we also consider the set $S(G)$ of all real-valued symmetric matrices whose off-diagonal entries are nonzero precisely when the corresponding vertices of the graph are adjacent. An important parameter in this setting is $q(G)$, and is defined to be the minimum number of distinct eigenvalues over all matrices in $S(G)$. For a given graph $G$ the concept of a vertex-clique incidence matrix associated with an edge clique cover is applied to establish several classes of graphs with $q(G)=2$.

Compton cameras detect scattered gamma rays and estimate the distribution of gamma-ray sources. Nonetheless, crafting a camera tailored to a specific application presents formidable challenges, often necessitating the implementation of diverse image reconstruction techniques. Delving into the factors influencing these cameras can pave the way for design optimization and performance enhancement. This study introduces an inventive detector design for Compton imaging systems, building upon the achievements of prior designs. The proposed system contains eight scatterer detectors and a semiconductor absorber detector, spaced at 1 mm and 30 mm intervals, respectively. The source-to-first-scatterer-detector distance is 5 mm, with scatterer and absorber detector plates measuring 70× 70× 2.125 mm³ and 70× 70× 10 mm³, respectively. Geant4 simulation toolkit models the Compton imaging system, and an analytical method reconstructs Compton camera images. Unlike more straightforward techniques, the analytical method directly reconstructs Compton camera images by solving the equation relating to the reflected image data. This approach is implemented in the C++ programming language. The study's findings reveal that the analytical method discerns optimal conditions and parameters that significantly influence efficiency, yielding a full width at half maximum (FWHM) of 3.7 mm with an angular uncertainty of approximately 2.7 degrees at an energy level of 0.662 MeV. Compared to another experimental design employing the analytical image reconstruction approach, the FWHM value decreased by 0.7 mm. This study presents an innovative detector design and an analytical reconstruction method for Compton imaging systems, showcasing improved efficiency and accuracy.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

On a global scale, food safety and security aspects entail to be considered throughout the farm to fork continuum considering food’s supply chain. Generally, the agri-food system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as Machine learning (ML) and Deep Learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of artificial intelligence tools in the domain of food-crops safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of Inclusion and Exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Antibiotic resistance among bacterial pathogens is recognized as a major threat to human health worldwide. The emergence of multidrug-resistant bacteria can also be found in the community settings, apart from hospital environment, which indicates that reservoirs of antibiotic resistance genes do exist outside the hospital. The growth of antibiotic resistance is a consequence of bacterial adaptations in response to selective pressures. To survive in this hostile environment, bacteria develop defence mechanisms such as chemical modification of antibiotics, enzyme-catalysed antibiotic degradation, altered permeability, antibiotic efflux, mutation of target sites and biofilm formation, resulting in resistance to nearly all currently available antibiotics used in the clinical practice. The present review summarizes insights into the molecular mechanisms underlying the antibiotic resistance which is useful for planning strategies to combat antibiotic resistance and devise innovative therapeutic tools to fight against multidrug-resistant bacterial species.

Human papillomavirus (HPV) infections, primarily transmitted through sexual contact, have been linked to various cancers, including cervical, penile, anal, oropharynx, breast, and prostate cancers. This study presents a unique case of concurrent high-risk HPV35, HPV45, and HPV59 infections in both prostate and bladder cancer tissues from a single patient, representing the first documented instance worldwide with identical HPV types detected in two adjacent organs of the same individual. Employing a multiplex-PCR approach, gel electrophoresis, and Sanger sequencing, we confirmed the presence of these high-risk HPV types. Additionally, Western blot analysis using an HPV E7 antibody demonstrated the active expression of HPV oncoproteins in both cancer types. This discovery underscores the potential for HPV intra-organ transmission and necessitates further exploration of alternative transmission routes. The implications of our results offer new insights into the complex dynamics of HPV transmission in cancer pathogenesis.

This study will lead to the development of a numerical data set that will assist in the design of Savonius wind turbines. A major objective of this study is to develop a better design for Savonius turbine blades in order to increase their torque coefficients, rotational speeds, and pressure coefficients. As part of the experimental design methodology, a full dataset was generated by simulating three-dimensional scale models utilizing computational fluid dynamics (CFD) simulations that were validated by wind tunnel data. This process is multi-objective optimization for optimizing turbine performance. The twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. The group method of data handling (GMDH) algorithm was utilized to model objective functions based on input-output data. The Pareto fronts were plotted using polynomial models obtained from the evolutionary Pareto-based optimization approach (modified NSGA-II), and the optimal commercial points were determined using TOPSIS. A comparison of three- and two-objective optimization data revealed that two-objective optimization data lie within the boundaries of a three-objective optimization problem. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively, using multi-objective optimization. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.

Introduction: Circulating tumor DNA (ctDNA) is newly diagnosed tumor DNA that can easily represent a tumor’s genetic and epigenetic change. Pheochromocytomas (PCCs) and Paragangliomas (PGLs) are rare tumors of adrenal gland tissue that have the potential to be detected by ctDNA. We aimed to study the potential of the methylation status of RDBP, SDHB, and SDHC genes in ctDNA of PCCs/PGLs patients as a diagnostic biomarker. Materials and Methods: Clinical data, fresh frozen tissue, the blood of 12 PCCs/PGL patients, and the blood of 12 age/sex-matched normal patients were collected. The methylation status of RDBP, SDHB, and SDHC was compared between cases and controls by MS-HRM analysis. Results: Amongst six promoter regions of RDBP, SDHB, and SDHC, promoter methylation quantification of SDHCa and RDBPb was significantly different between PCCs/PGLs and controls. SDHCa was methylated in 49.93% of PCCs/PGLs cases vs. 8.33 % of control samples, p-value: 0.026, area under curve AUC=0.757, and RDBPb in 74.9% of PCCs/PGLs cases vs. 25.0% of control samples, p-value: 0.032, AUC=0.750. Conclusions: This study suggests the ctDNA potential for a less invasive source of tumor epigenetic modification in PCCs/PGLs malignancies. The SDHCa and RDBPb hypermethylation warrant further exploration as diagnostic tools for PCCs/PGLs.

Background: Migraine is one of the most common health conditions in the reproductive male and female population. Premenstrual syndrome (PMS) is a common condition that affects many women during their menstrual cycle. around 50 to 60% have menstrual headaches among the migraineur women. The symptoms in the majority were mild, but 5–8% may suffer moderate to severe symptoms, accompanied by considerable distress or functional dysfunction. Premenstrual migraine (PMM) refers to migraines that occur with the menstrual cycle. It affects approximately 50% of women with migraine and can be debilitating in terms of both frequency and severity of symptoms. This information is important for medical professionals in Saudi Arabia to be aware of so, as to provide better medical care and support which can significantly improve the person's quality of life of women who suffer from PMS and menstrual migraines. Objectives: To estimate the prevalence of menstrual migraines in women, to evaluate the severity and frequency of menstrual migraines in women with PMS, and to identify potential risk factors aggravating menstrual migraines in women with PMS. Methodology: A cross-sectional community-based study was conducted on reproductive age (18-50Years) women who have regular menstrual cycles with experience migraine headaches and are diagnosed with PMS and menstrual migraine, using a self-administered questionnaire between December 2022 to May 2023 in Saudi Arabia. Results: Out of 2130 women participants 397 were suffering from migraine which is 18.6%. Of the total 397(18.6%) Migraineurs individuals 230 (57.9%) were suffering from menstrual migraine and 167 (42.1%) were non-menstrual migraineurs, in general, reproductive women menstrual migraine is 10.7% and non-menstrual migraine is 7.8%. The BMI is increasing the incidence of menstrual migraines is increasing. Migraine Disability Around one-third of the participants were suffering from III–moderate disability 134 (33.8) Most of the menstrual migraine victims at least 3 days in the last three months were absent from their duties due to their menstrual migraines. Conclusions: Migraine attacks during the menstrual cycle cause the impairment of their social and physical ability to perform their routine household, and academic activities and unable to fulfill their professional commitments. To better understand the Menstrual Migraine and Non-menstrual migraine large prospective studies are required to plan the best management methods.

Gasca D herbal formulation is an antidiabetic medicine for the management of diabetes mellitus produced exclusively from natural ingredients. The level of some essential elements (Copper, chromium, Cobalt, Iron, Manganese, Nickel and zinc) and toxic (Cadmium, Arsenic, Mercury, and Lead) heavy metals were determined using microwave plasma-atomic emission spectrometry (MP-AES). The aim of this study is to evaluate the levels of essential and toxic heavy metals and also identify bioactive compounds present in Gasca D herbal formulation. The result shows no traces of Lead, Mercury, Zinc, Arsenic and Zinc, Iron was found to have highest concentration 67.16 + 7.5 µg/g and Cadmium lowest concentration 0.4 + 0.03µg/g. GC-MS analysis of Gasca D herbal formulation revealed the presence of 14 biologically active compounds which include N-Formyl-beta-alanine, Paromomycin, 3,4-Altrosan, Benzamide, 1,3,4-Thiadiazol-2-amine, Carbamodithioic acid, Carbonic acid, alpha-D-Glucopyranoside, Ethyl isocyanide, 2-Propanesulfinic acid, Propanamide, 2-Butenenitrile, Dicyclopropyl carbinol, Isoxazolidine, 1,5-Hexadiene 10-Azido-1-decanethiol. Conclusion: The result indicates that the mean levels of toxic metals in Gasca D herbal formulation were below WHO permissible levels. Gasca D herbal formulation also contains various bioactive compounds that can have various medicinal application which can be used for the treatment of various diseases.

Yemen is a water-scarce country with inadequate fresh water and considerable groundwater depletion, as well as a lack of adequate surface water. The study region is considered an arid region, and there is insufficient water to meet the needs of the region's yearly population growth rate of 4 %. This study aims to assess the water resources in the Sana'a region and to identify the current water situation and forecast for the future. Rainfall changes spatial and temporal in very few quantities and an annual average of 267 mm. Water harvesting facilities are entirely filled by 75% of the total water facilities in the rainy seasons. The groundwater level in Sana’a Basin decreases about 6-8 meters annually due to the increase in the number of wells, the abundance of abstraction, and the lack of recharge. The amount of abstraction exceeds 400% of the recharge in the Sana'a basin. The water per capita is 70 - 85 m³ annually. It is an abstraction from the aquifers by private wells. The crop cultivated area decreased from 184217 hectares in 2007 to 122583 hectares in 2018 due to lack of water. The sewage treatment plant treats 18.25 Mm³ annually, with less than 70 % efficiency. The water deficit is about 500 to 723 Mm³ annually; it is an abstraction from the aquifers by private wells. The current water situation in the Sana'a region is catastrophic, and the planning and management must ensure a water balance in the future.

The regulatory mechanism of PCSK7 gene is still unknown, although its encoded protein PC7 is the most ancient and highly conserved of all proprotein convertases and exhibits enzymatic and non-enzymatic functions in liver triglyceride regulation. Bioinformatics algorithms were used to predict regulatory microRNAs (miRNAs) of PCSK7 expression. This led to the identification of 4 miRNAs, namely miR-125a-5p, miR-143-3p, miR-409-3p, and miR-320a-3p, with potential binding sites on the 3’-untranslated region (3′-UTR) of human PCSK7 mRNA. The expression patterns of these miRNAs and PCSK7 mRNA were assessed in three different cell lines with quantitative polymerase chain reaction (qPCR), which revealed reciprocal expression patterns between the expression levels of the four selected miRNAs and PCSK7. Next, the interactions and effects of these miRNAs on PCSK7 expression levels were investigated via cell-based expression analysis, dual-luciferase assay, and Western blot analysis. The data revealed that PCSK7 mRNA levels decreased in cells transfected with vectors overexpressing miR-125a-5p, miR-143-3p, and miR-409-3p, but not miR-320a-3p. The dual-luciferase assay demonstrated that the above 3 miRNAs could directly interact with putative target sites in PCSK7 3′-UTR and regulate its expression, whereas miR-320-3p exhibited no interaction. Western blot analysis further revealed that overexpression of miR-125a-5p in Huh7 cells inhibits the expression and ability of PC7 to cleave human transferrin receptor 1. Our results support a regulatory role of these miRNAs in PCSK7 expression and function and opens the way to assess their roles in the regulation of PC7 activity in vivo in the development of hepatic steatosis.

This work is related to the environmental toxicology risk assessment and evaluation of possible transformation of carbon-based nanomaterials (CNMs) after the contact with marine microalgae. The materials used in the study represent common and widely applied multiwalled carbon nanotubes (CNTs), fullerene (C60), graphene (Gr), and graphene oxide (GrO). The toxicity was evaluated as growth rate inhibition, esterase activity, membrane potential, and reactive oxygen species generation changes. The measurement was performed with flow cytometry after 3, 24, 96 h, and 7 days. Biotransformation of nanomaterials were evaluated after 7 days of microalgae cultivation with CNMs by FTIR and Raman spectroscopy. The calculated toxic level (EC50 in mg/L, 96 h) of ued CNMs reduced in the following order: CNTs (18.98) &gt; GrO (76.77) &gt; Gr (159.40) &gt; C60 (414.0). Oxidative stress and membrane depolarization was the main toxic action of CNTs and GrO. At the same time, Gr and C60 decreased the toxic action with time and had no negative impact on microalgae even at concentration of 125 mg/L. Moreover, C60 and Gr after seven days of the contact with microalgae cells obtained structural deformations.

The high figure of merit and earth abundance of Cu₁₂Sb₄S₁₃ thermoelectric ma- terials have recently attracted many attentions toward these type of complex compounds. Intrinsic low thermal conductivity, as well as tunable electronic transport properties, make them suitable for thermoelectric power generation. In this study, we perform a comparative theoretical study on the substituted compounds, primarily at the Cu site including known tetrahedrite Cu₁₂Sb₄S₁₃, by means of first-principles calculations. The density functional theory of electric structure is applied to investigate the result of substitution.

The monitoring of sick newborns is a challenging task that health care providers in Neonatal Intensive Care Units (NICU) must contend with each day. Conventionally, newborns are monitored via probes that are affixed to their skin and attached to processing monitors (Fig.1). However, an alternative exists in contactless imaging to record such physiological signals (Physio-Markers), surface changes and internal structures which can be used independently of, or in conjunction with conventional monitors. Advantages of contactless monitoring methods include: i) quick data generation; ii) lack of contact with skin, which reduces skin breakdown and decreases risk of infection; and iii) minimizing the number of probes and monitors affixed to the skin, which allows greater body surface-area for other care. This paper is an attempt to build a foundation for and to provide a vision of the potential neonatal clinical applications of technologies that use non-contact modalities such as Visible Light Imaging (VLI), Near InfraRed Spectrum (NIRS), and Thermal Imaging (TI) using InfraRed Spectrum (IRS).

Renewable jet fuel (RJF) production has been recognized as a promising approach for reducing the aviation sector's carbon footprint. Over the last decade, commercial production of RJF has piqued the interest of airlines and governments around the world. However, RJF production can be challenging due to its dispersed supply resources. Furthermore, the production of RJF is more costly compared to producing conventional jet fuel. In this study, using a mixed integer linear program (MILP), we design an RJF supply chain network in which we obtain an optimized configuration of the supply chain and determine operational decisions required to meet RJF demand at airports. To accelerate commercialization of RJF production, we considered four different monetary incentive programs, which could cover the supply chain’s costs. This study is validated by employing the model to design an RJF supply chain in the Midwestern US. Results from this study are promising as they show the supply chain can achieve commercialization with partial financial coverage from the incentive programs. Based on the findings of this study, policymakers can devise policies to commercialize RJF production and accelerate its adoption by industry.

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman Neural Network (ENN), Boosted Tree Algorithms (BTA), Multi-layer Perceptron (MLP), and Gaussian Processes Regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages their strengths for accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction, the fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out, the first scenario is done for PV array fault detection with DC power (DCP) as output. The second scenario is done for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified reliably among all the models with MAPE=0.0393, and MAE=0.002 for inverter fault detection and MAPE=0.091, and MAE=0.000 for PV array fault detection.

In recent years, excessive heat in the urban texture has become the main problem in the humid and calm wind city of Babolsar with high density, especially in high-rise areas. Therefore, in order to create comfort in this region, it is necessary to establish and continue the wind circulation in space with an environmentally compatible and optimal configuration. The study applies combination of literature, field measurement, experimental validation of CFD simulation output, and comparative analysis. After field measurement and validation of FLOW-3D simulation software (V11.2.2), the relationship between these parameters (height, the width of passages, enclosure between buildings, and buildings' orientation) will be studied which affects the wind’s velocity and direction. The factors of the buildings' orientation and enclosure based on the passages' width have opposite reactions in the direction of the prevailing wind especially from perpendicular side. In this study, two effective factors are on wind velocity: 1- The orientation of the buildings towards the wind flow by creating permeability 2- Reducing the enclosure by increasing the width of the passage's perpendicular to the wind flow (w '= 3w, E' = 0.33E). According to the theoretical and practical study, first, the creation of permeability in the body of the block and the separation of buildings instead of aggregation has been studied, and then reducing the confinement of streets perpendicular to the wind flow has been discussed as effective solutions to improve the wind velocity and circulation between the urban environment.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

Studying Active Transportation (AT) is widespread in America and Europe's research and stud-ies that include Latent variables (LV) are growing to identify exact results of finding what to do to increase the utility of AT. LVs help us to have more accurate research. LVs are defined as psy-chological factors like feeling safe while you ride at night and thus they are not subjective and hard to understand but very important to increase the utility of using AT modes. In this study, most of the previous studies about bicycling have been reviewed. In this research, different var-iables including subjective and LVs are conducted for maximizing the use of bicycle utility and introduced to have better sight for future researchers to deal with modeling AT mode choice. Results represent the importance of having 'will' for using a bicycle, especially in difficult situa-tions and cultural barriers that affect the cycling of women.

Population growth and increasing global demand for freshwater have raised a serious challenge for the depleting sources of freshwater in the 21st century. Desalination technologies can be a reliable technique for providing freshwater. Capacitive deionization is one of the innovative desalination methods that has received increasing interest. Flow-electrode capacitive deionization (FCDI) (a new architecture of capacitive deionization) is one of the efficient, cost-effective, and environmentally-friendly desalination methods for freshwater production. In this experimental research, the performance of an FCDI system was investigated and the influence of important parameters such as flow rate of flow-electrodes, electrolyte salt concentration of flow-electrodes, and initial feed water concentration will be assessed on the efficiency of desalination operation. In this study, the flow-electrodes operated in short-circuited closed-cycle operation (SCC) mode, and also the feed water operated similarly to the flow-electrodes in closed-cycle. Moreover, in all the experiments, the salt adsorption capacity (SAC) and salt removal efficiency (SRE) was calculated. Herein, by optimizing the above mentioned parameters, the salt removal efficiency of 83% and a SAC value of 29.12 mg/g dry carbon were achieved in 5 hours.

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

The construction industry is one of the most fatal industries, so it is important to pay more attention to safety solutions. Even though work-related accidents are known as major waste in construction projects, little attention has been paid so far to incorporating safety into the lean construction framework. In this research, lean construction theory is reviewed through the lens of safety. That being so, the identified challenges in previous research on improving safety in construction projects are categorized and those related to the concept of lean project delivery are introduced. Then, the principles of the lean construction framework are explained and the relevant changes for incorporating safety into the framework are introduced and discussed. It is expected that this hybrid model would further enrich the lean construction framework. The careful attention of project executives to this model may improve the safety situation in construction projects.

The growing production and application of carbon-based nanomaterials (CNMs) represent possible risks for aquatic systems. However, the variety of CNMs with different physical and chemical properties, and different morphology complicated the understanding of their potential toxicity. This paper aims to evaluate and compare the toxic impact of four most common CNMs, namely multiwalled carbon nanotubes (CNTs), fullerene (C60), graphene (Gr), and graphene oxide (GrO) in marine microalgae Porphyridium purpureum. The microalgae cells were exposed to the CNMs for 96 h and measured by flow cytometry. Based on the obtained results, we determined no observed effect level (NOEL), calculated EC10 and EC50 concentrations for growth rate inhibition, esterase activity, membrane potential, and reactive oxygen species (ROS) generation changes for each tested CNMs. According to the sensitivity (growth rate inhibition) of P. purpureum, the used CNMs can be listed in following order: CNTs &gt; GrO &gt; Gr &gt; C60. The toxicity of CNTs was significantly higher than the toxic effect of the other used CNMs and only this sample caused increase of ROS generation in microalgae cells. This effect caused by trace metal residuals in CNTs and high affinity between particles and microalgae associated with the presence of exopolysaccharide coverage on P. purpureum cells.

Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, we proposed a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) to simplify and accelerate the immunization administration for nurses that may result in improving the completion and timeliness of immunizations among infants. Considering the narrow research about the acceptance of these systems in the healthcare sector, we examined factors influencing nurses’ attitude and intention to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is proposing new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares-structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses’ attitude and intention to use VHC. Moreover, we applied artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives toward the system. Moreover, the nurses felt optimistic about the system when considering it to be useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in raising their intention. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitude towards using VHC, followed by perceived ease of use and perceived usefulness.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

Energy storage systems (ESSs) are the key elements to improve the operation of power systems. On the other hand, these elements challenge the power system planners. The difficulties arise as a result of the ESSs' economic and technological features. The cycle life of ESSs is a critical aspect that influences the choices made during expansion planning processes. In this manuscript, we have focused on a new model for the expansion planning of ESSs considering the impacts of technical properties, such as the cycle life and depth of discharge. For this purpose, the proposed model consists of the hourly operation planning of ESSs in the sample days of year. A new indicator is proposed to determine the daily charging/discharging cycles of ESSs. Numerical results show the ability of the proposed model to determine the optimal technology and capacity of ESSs.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

As 180-degree meanders are observed in abundance in nature, a meandering channel with two consecutive 180-degree bends was designed and constructed to investigate bed topography variations. These two 180-degree mild bends are located between two upstream and downstream straight paths. In this study, different mean velocity to critical velocity ratios have been tested at the upstream straight path to determine the meander's incipient motion. To this end, bed topography variations along the meander and the downstream straight path were addressed for different mean velocity to critical velocity ratios. In addition, the upstream bend's effect on the downstream bend has been investigated. Results indicated that the maximum scour depth at the downstream bend has increased as a result of changing the mean velocity to critical velocity ratio from 0.8 to 0.84, 0.86, 0.89, 0.92, 0.95, and 0.98 by respectively 1.5, 2.5, 5, 10, 12, and 26 times. Moreover, increasing the ratio increased the maximum sedimentary height by 3, 10, 23, 48, 49, and 56 times. The upstream bend's incipient motion was observed for the mean velocity to critical velocity ratio of 0.89, while the downstream bend was equal to 0.78.

The performance of modified electrode of nanocomposite film consisting of polypyrrole-chitosan-titanium dioxide (Ppy-CS-TiO2) has been explored as non-enzymatic glucose biosensors. The synergy effect of TiO2 nanoparticles and conducting polymer on the current response of electrode resulted in higher sensitivity for nanocomposite modified electrode. The incorporation of TiO2 nanoparticles in the nanocomposite films were confirmed by XPS spectra. The FESEM and HR-TEM provided more evidences for the presence of TiO2 in Ppy-CS structure. Glucose biosensing properties were determined by amperommetry and cyclic voltammetry (CV) methods. The interfacial properties of nanocomposite electrodes were studied by electrochemical impedance spectroscopy (EIS). The developed biosensors showed a good sensitivity over the liner range of 1-14 mM with a detection limit of 614 μM for glucose. It also exhibited good selectivity and long term stability with no interference effect. The Ppy-CS-TiO2 nanocomposites film presented high electron transfer kinetics.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

Background and Objective: Intimate relationships hold significant importance during emerging adulthood. This study aimed to investigate the critical determinants of perceived well-being in friendship relationships during emerging adulthood.Method: Using a quantitative approach, a sample of 726 undergraduate students from the Ferdowsi University of Mashhad were selected through convenience sampling. They completed the Interpersonal Behaviors Questionnaire (IBQ), the Compassionate and self-image goals scale (CSIGS), the Basic Need Satisfaction in Relationships Scale (BNS-RS), the Interpersonal Needs Questionnaire (INQ-10), Friendship Self-Regulation Questionnaire (SRQ-F), Interpersonal Behaviors Questionnaire-Self (IBQ-self), State Level Subjective Vitality Scale (SVS-SL), the Perceived Relationship Quality Component (PRQC), and the Basic Empathy Scale (BES). Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results: The results indicated that the critical determinants in predicting perceived well-being in friendship relationships were, in order, goals in friendship relationships, friendship self-regulation, perceived support of basic psychological needs by friends, satisfied basic psychological needs, frustrated psychological needs, and supporting the basic psychological needs of friends.Conclusion: The findings demonstrate that self-determination theory (SDT) provides a coherent framework for explaining perceived well-being in friendships.

Heart failure (HF) diagnosis, phenotyping, prognosis, and treatment decisions are all based on the left ventricular ejection fraction (EF). Heart failure with mid-range EF (HFmrEF) is a third EF category for heart failure with an EF between 40 and 49 percent. Comparatively, heart failure with preserved EF or reduced EF has been largely unexplored. 10–25% of patients with HF have HFmrEF. Although HFmrEF has an intermediate clinical profile, it is more similar to HF with reduced EF, particularly because these patients have a high incidence of ischaemic heart disease. Heart failure with preserved EF has a lower risk of cardiovascular events than heart failure with reduced EF, which is milder. HFmrEF and HF with preserved EF patients have a similar or higher risk of non-cardiovascular adverse events than patients with HF with reduced EF. There is still a great deal of mystery surrounding HFmrEF. The purpose of this review is to describe the major pathophysiology of HFmrEF and its similarities and differences with the other two types of EF. It is the purpose of this paper to present a comprehensive overview of the currently available evidence in support of the pathophysiology of patients with HFmrEF. Further investigation of the pathophysiology, treatment, and prognosis of this condition will be needed in the future in order to better understand it.

Hyperlipidemia is the fat particles excess in the serum. Hyperlipidemia increases the mortality rate that occurs with other metabolic disorders. Hyperlipidemia is classified into familial and acquired subgroups. Moreover, Hyperlipidemia etiology is based on environmental or genetic factors. For instance, abnormal HMG-CoA regulation down-regulates ubiquitin ligase and targets variable oxidative stress-related condition proteins. There has been proven reactive oxygen species (ROS) overshoot happens during hyperlipidemia occurrence. OS, directly and indirectly, regulates molecular cascades within the cell and leads to gene expression alteration. At this molecular juncture, AMPK is affected by ROS. AMPK is a serine-threonine kinase and a critical energy balance. Low energy conditions result in AMPK activation due to the down-regulation of protein and lipid synthesis. Furthermore, ER stress and activated unfolded protein proteasomal response and autophagy are AMPK mediated. Polyphenols are widespread dietary plant-based compounds that regulate gene expression and signal conduction. Through the hyperlipidemic state, FFAs releasing indirectly connect to AMPK/NF-κB pathway then polyphenols target them. AMPK, during this FFA exposure, down-regulates de novo lipid molecules generation. Likewise, if AMPK/mTOR pathway failure is prolonged, the hyperlipidemic state may be explicit, activated by natural herbal mediators, e.g., polyphenols. Polyphenols activate the AMPK signaling pathway and influence lipid metabolism. Polyphenol-mediated AMPK activation results in lipogenesis inhibition and lipophagy. Cholesterol efflux mediated polyphenols lipid-lowering effects, accessing LXR pathway. All these clues persist on direct or indirect AMPK-related polyphenolic anti-hyperlipidemic effects.

In this paper, the effect of preheating on the mechanical properties and micro structure of similar friction stir welded AA6061 aluminum alloys sheets was investigated. Aluminum alloy 6061 sheets with a thickness of 5 mm and threaded cylindrical pins were used. Rotation and traverse speeds were 1200 rpm and 75 mm/min, respectively. The results of tensile tests performed on the welded samples showed that compared to the non-preheated samples, preheating had increased the strength and elongation of the joints by 58% and 46%, respectively. In the present study, during the welding process preheating cause emerged heat with lower slope from stir zone. This phenomenon may result in Increase the deformation resistance of material and consequently decrease of grain size. This grain refinement can improve the mechanical properties of welds. Accordingly, hardness and strength of the material will be increased.

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

This study presents the consecutive modification of orange peel (OP) by NaOH and sodium dodecyl sulfate (SDS) for simultaneous elimination of basic dyes from the binary system and modeling the adsorption process using an intelligent tool. The natural and modified biosorbents were characterized by variety of analyses such as: field emission scanning electron microscopy with energy dispersive X-ray, N2 physisorption and Fourier transform infrared spectroscopy techniques. The influence of various variables on dye removal like pH, the quantity of biosorbents, dyes concentration, contact time, and temperature in the binary system were investigated and optimized by an artificial neural network (ANN) model as an intelligent tool. The optimum quantity of the sorbent was found to be 0.30 g for orange peel (OP) and 0.25 g for NaOH-treated OP (NOP) and SDS-decorated NOP (SNOP) at pH = 7. The kinetics and thermodynamics investigations showed that the removal of dyes obeyed the pseudo-second-order kinetic model and were spontaneous and exothermic in nature. Moreover, in order to describe the mechanism of sorption process, desorption studies of dyes were carried out. The desorption percentages of methylene blue (MB) in water and HCl were found to be in the range of 1.93%–4.76% and 18.87%–28.76%, respectively; in addition, the desorption percentages of crystal violet (CV) in water and HCl were obtained to be in the range of 4.11%–7.41% and 32.84%–43.00%, respectively; which could be a recommendation ion exchange or electrostatic attachment of dyes onto biosorbents. The ANN predictions matched with the experimental data very well (0.95308 < R2 < 0.99191 and 0.98335 < R2 < 0.99773 for MB and CV, respectively) which indicated high accuracy of the ANN model. In addition, the relative importance of each parameter was calculated by Garson’s equation.

Today, due to recent developments in technology, children devote plenty of time for screen viewing. However, its harmful effects are not yet clear. The purpose of present study was to examine the associations among screen viewing and sleep duration, and body mass index (BMI) in under-five years old children. This cross-sectional study was conducted with 322 under-five healthy children that were selected using multistage stratified cluster sampling method in 2017. The data that were gathered by time-use diary method were analyzed using Kolmogorov-Smirnov test, Spearman correlation tests, multiple linear regression analysis, one-way ANCOVA, two-way ANCOVA. There was a negative correlation between screen time and sleep duration (rs = -0.42, p = 0.00), positive correlation between screen time and BMI (rs = 0.38, p = 0.00) and sleep duration negatively correlated with BMI (rs = -0.22, p = 0.00). screen viewing was a predictive factor for both sleep duration (β = -0.26, p = 0.00) and BMI (β = -0.26, p = 0.00). screen viewing had a significant impact on sleep duration (4, 314) = 5.02, P = 0.001) and BMI (F (4, 314) = 1.16, P=0.298). Results of this study indicated that screen viewing is related to sleep duration and BMI in under-five children. furthermore, screen time has an impact on sleep duration and BMI scores of children. findings of our study suggest that sleep duration negatively is associated with BMI in under-five-year-old children.

The main objective of this study was to compare the removal efficiency of nutrients using Lactuca sativa, Medicago sativa and Phragmites australis in subsurface flow constructed wetlands with horizontal flow. In order to test water quality, fabricated reactors designed and the plants cultivated in the soil while their root were inside the wastewater. A long time study carried out from spring till end of autumn (9 months) in order to evaluate the difference in removal rate based on the seasonal changes. The highest removal rate was during summer which followed by spring and autumn. Thus, the effect of plants on the removal efficiency of organic matter (COD, BOD), TSS and nutrient (P and TN) appeared to be dependent on the seasonal growth. Phragmites australis the most sensitive species in order the removal of nutrient from wastewater.

This article discusses a focused study on visualizing the flow patterns in a two-phase pulsating heat pipe (PHP) using Fe₃O₄/water as the working fluid at 3 V/V% concentration. The research also aims to meticulously examine phase change phenomena in the heating section, particularly focusing on bubble formation and expansion processes. A high-speed video camera was utilized to capture dynamic insights into the behavior of the Fe₃O₄/water mixture. Based on the findings, a straightforward model was developed to explain bubble generation and growth in the mixture, serving as a useful reference for future PHP designs and optimizations. Visual observations also noted the stable nature of the Fe₃O₄/water nanofluid over a four-day period, confirming its consistency throughout the experiments. Moreover, the impact of heat load variation on the evaporator section was assessed using controlled heat inputs ranging from 10 to 80 Watts. Observations on the arrangement of slugs and plugs at a 50% filling ratio revealed interesting self-adjusting flow patterns in response to increasing heat inputs, providing valuable insights into PHP operational dynamics. Notably, the oscillatory flow behavior of Fe₃O₄/water, the chosen working fluid, exhibited greater activity in comparison to water. This distinctive flow behavior contributed to achieving heightened thermal performance efficiency for the Fe₃O₄/water system, attributed to its faster attainment of the annular flow condition.

Natural product especially secondary metabolites that produced by plants under the stressed condition shown to have a different pharmacological impact. Aeluropus lagopoides is one of the typical halophyte plants survivals under stressed conditions. It has been used for wound healing and as a painkiller. The bioactivity and the chemical composition of this plant have poorly investigated. Consequently, chemical components of A. lagopoides leaves were extracted using hexane (nonpolar), ethyl acetate (semi-polar), n-butanol (polar) to extract the most extensive variety of metabolites. The cytotoxicity and anticancer impact of extracted secondary metabolites evaluated against breast (MCF-7), colon (HCT-116), and liver (HepG2) cancer cell lines using SRB test. The mechanism of action verified by observing the appearance of apoptotic bodies using the fluorescent microscope while their antiproliferative impact had been evaluated using flow cytometer. Results revealed that secondary metabolites extracted using hexane and ethyl acetate were having the highest cytotoxicity and thus anticancer activity effect on HepG2 with IC₅₀ (24.29 ± 0.85, 11.22 ± 0.679 µg/mL) respectively. Where apoptotic bodies observed, flow cytometer results exhibited that secondary metabolites can inhibit cell cycle in G0/G1 phase. Accordingly, A. lagopoides hexane and ethyl acetate extracts may consider as a candidate anti-cancer drug.

Dementia, characterized by cognitive decline beyond age-related expectations, poses a formidable global health challenge. With an estimated 55 million individuals affected worldwide and 10 million new cases annually, dementia profoundly impacts memory, comprehension, language, and behavior. Then, the global diabetes population increased from 108 million in 1980 to 422 million in 2014, with a more rapid rise in prevalence observed in low- and middle-income countries compared to high-income countries. Individuals with diabetes have an increased risk of cognitive problems, including a higher likelihood of developing dementia, Alzheimer's disease, and vascular dementia, especially in older age. Managing diabetes through diet, physical activity, medication, and consistent screening and treatment for complications can prevent or delay its consequences.Globally ranking as the seventh leading cause of death, dementia's societal implications are substantial. The chapter delves into Mild Cognitive Impairment (MCI), a precursor to dementia, emphasizing its global epidemiological trends. Neuroimaging, particularly Functional Near-Infrared Spectroscopy (fNIRS), emerges as a non-invasive diagnostic tool, surpassing traditional imaging methods' portability. The chapter investigates the effects of non-invasive electrical stimulation on neurovascular tissues, shedding light on its impact including potential to address metaboreflex related reduced exercise tolerance. Furthermore, the chapter presents a clinical trial (ClinicalTrials.gov NCT04626453 and NCT04812288) examining the effects of a 2-month exercise program on cognitive function and muscular oxidative capacity in older adults with type 2 diabetes. The study's design, individualized exercise regimens to address muscle mechanoreflex and metaboreflex responses, and their physiological changes during exercise are discussed, offering insights into the interplay between exercise tolerance, cognition, and vascular health. The findings contribute to tailored and effective interventions for cognitive well-being. Here, our exploration extends to the application of transcranial electrical stimulation (tES) in optimizing exercise performance, with a focus on its various forms, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS). A physiological modeling approach based on the neurovascular unit (NVU) is employed, revealing potential avenues for also treating vascular dementia (often noted to have a strong association with type 2 diabetes) through tACS, guided by human-in-the-loop approaches. Here, modal analysis for control is applied to assess the NVU model's characteristic dynamics under tACS, providing a comprehensive understanding of neurovascular coupling modes.In summary, this chapter synthesizes diverse facets in type 2 diabetes comorbidity and cognitive decline, ranging from global impact and diagnostic criteria to innovative interventions like exercise and transcranial electrical stimulation. The multidisciplinary approach integrates epidemiology, clinical trials, and physiological modeling, contributing valuable insights into the holistic management of cognitive health and neurovascular function.

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips’ design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

This research evaluates the mineral ions and their concentrations in formation water from five well samples of the Bibi Hakimeh oil field. The study reveals the presence of calcium (Ca2+), so-dium (Na+), and magnesium (Mg2+) cations, as well as sulfate (SO42-), bicarbonate (HCO3-), and chloride (Cl-) anions, which are soluble in water within the Bibi Hakimeh oil formation. Fur-thermore, mineral deposits of CaSO4, CaSO4.2H2O, CaCO3, and MgCO3 are investigated. The findings highlight the significant formation of calcium sulfate and calcium carbonate mineral deposits under the operating conditions of the Bibi Hakimeh oil well. Additionally, the study examines the interaction between rock and water of the Bibi Hakimeh formation, revealing a notable correlation between the concentration of calcium and magnesium ions and the water-rock reaction in this field

Beta thalassemia major (β-TM) disorder characterized by the lack, or severe reduction in the production of hemoglobin β-globin chains. The standard protocol for the management of β-TM is blood transfusion and iron chelation therapy to reduce the iron overload state. The present study aimed to investigate the relationships between two iron regulatory hormones, hepcidin (HEPC) and erythroferrone (ERFE) levels and iron status parameters (ISPs) in Iraqi patients with β-TM. ISPs and hormones were measured in sixty patients and compared with thirty healthy controls. The results indicated significant changes in different iron status parameters, while ferritin (FRT) with the ~11 fold increase showed the most change. Significant reduction in HEPC and increase in ERFE levels were detected in patients as compared to the control group, while no direct correlation was identified with the other measured ISPs. Receiver operating characteristic (ROC) analysis showed that the z-score of the composite of ERFE+FRT has a full diagnostic ability for β-TM. In conclusion, our finding indicated the correlation between different ISPs, FRT as the leading predictor of iron overload and tow main iron regulatory hormones.

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost, and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. %by high-power and mobile nodes. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, i.e., to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the MAC layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput, and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack.

The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants of (height, 168 ± 8 cm; mass, 61 ± 17 kg) to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.

Polyethyleneimine (PEI) has been reported to have good potential for the adsorption of metal ions. In this work, PEI was covalently bound to NiFe2O4@SiO2 nanoparticles to form the new adsorbent NiFe2O4@SiO2–PEI. The material allowed magnetic separation and was characterized via powder X-ray diffraction (PXRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform IR spectroscopy (FT-IR), and thermogravimetry-differential thermal analysis (TGA-DTA). The adsorption of CrO42‒, Ni2+, and Pb2+ ions from aqueous solutions was studied at different pH, temperatures, metal ion concentration, and adsorbent dosage. Maximum adsorption capacities of 149.23, 156.68, and 161.25 mg/g were obtained for CrO42‒, Ni2+, and Pb2+, respectively under optimum conditions using 0.075 g of the adsorbent material 250 mg/L ion concentration, at pH = 6.5 and room temperature.

The relationship between physics and information processing is a topic of great significance in the realm of science and technology. The laws of physics, which govern the behavior of matter and energy, also have a profound influence on the way information is processed and utilized. While the mathematics governing information processing has not extensively utilized the laws of physics, it is evident that there is potential for these laws to play a more significant role, particularly in the context of quantum systems. based on the equivalence between information and energy, we can explore how other laws of physics can effectively contribute to more efficient and advanced information processing techniques. This research sheds light on the interconnectedness of physics and information processing, and its findings have the potential to significantly enhance our ability to process information, particularly in cutting-edge quantum systems. By recognizing and leveraging the influence of physics, we can pave the way for further advancements in information processing technologies.

Hand gesture recognition has many valuable applications in engineering and health care. This study proposes a novel model which can accurately distinguish hand gestures using forearm muscles' surface electromyogram (sEMG) signals. A deep learning algorithm with hyper parameters impacting the final model’s accuracy and a convolutional neural network (CNN) were employed in the recognition stage. The number of convolutional layers, kernels per layer, and neurons in the dense layer were selected for optimization, while the remaining parameters, such as the learning rate, batch size, and number of epochs, were chosen based on trial and error and prior knowledge. The optimal values for the selected hyperparameters were obtained using a genetic algorithm to achieve maximum recognition accuracy. The UC2018 Dual-Myo database was used for training and testing the model based on EMG signals characterizing the activity of eight different hand gestures. The final structure of the model consisted of two convolutional layers with 131 and 28 kernels, a dense layer with 111 neurons, and a softmax layer with eight neurons. Upon optimizing the hyperparameters using the genetic algorithm, the accuracy of the proposed model increased from 91.86% to 96.4% at best and 95.3% on average in real-time applications and 99.6% in an offline mode. Future work is warranted towards improving the architecture and the computational cost.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

SARS-CoV-2 was detected in China in December 2019. Myocardial injury is a crucial presentation of COVID-19, based on the association of ACE-2 and SARS-CoV-2. Down-regulating ACE-2 decreases the cardioprotective effects of angiotensin, leading to a higher TNF-α activation. TNF-α causes the inflammatory response in the myocardial damage as an apoptotic inducer. Moreover, as an inducer of necroptosis, TNF-α binds to a part of TNF receptor 1, which involves receptor-interacting protein 1 (RIP1) and causes cell death through RIP1 inhibition and NF-κB stimulation, which are also done through Tpl-2. Calcineurin controls the Tpl-2-driven NFAT stimulation. Bcl-2 or Bcl-X_L entirely blocks these pathways. Bcl-2 overexpression reduces FasL expression with a mechanism based on Bcl-2 inhibiting the NFAT. Moreover, the Fas/FasL system activates apoptosis in various cells. Bcl-XL stimulates Fas-related cell death. Additionally, TNF-α, as a part of inflammatory cytokine storms, indirectly interacts with NFAT/Bcl-2 through Tpl-2/NF-κB. Diversely, TNF-α and IL-1ẞ, the basis of inflammatory cytokine storms in COVID-19, can stimulate generating NO. Also, IL-2 is highly up-regulated in COVID-19 patients and stimulates NO generation in patients. TNF-α can provoke the generation of superoxides in neutrophils. A well-determined mechanism is the intracellular production of NO via calcium-calmodulin-dependent NO synthase (NOS). NO enhances NFAT’s calcium-dependent activity. Also, Intra/extracellular calcium exchange activates calcineurin and its related molecule, NFAT. Nitration provokes RIP1 necroptosis cascade, with respiratory complex I. Nitrites converse protection against ischemia-reperfusion injuries in the myocardium. Regulating this intrinsic molecular pathway can prevent the necroptosis of cardiomyocytes.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

A Savonius wind turbine is one of the simplest vertical-axis turbines. Several researchers have been attracted to it because of its simple structure, low noise, appropriate torque, and independence from wind direction. SR3345 and SR5050 blade profiles will be investigated for their effects on the performance of Savonius wind turbines. First, the fabricated wind turbine is placed in front of an open circuit wind tunnel with a velocity of 6 m/s. Furthermore, governing relationships are used to calculate power and torque coefficients as well as tip speed. In this study, the power and torque coefficients of the turbine are compared with the two profiles considered based on overlap ratios between 0 and 0.3. Despite its increased stability, the central shaft reduced the power coefficient of the turbine. For blade SR3345, the best performance occurs at tip speed ratios of less than 1, while for blade SR5050, the best performance occurs at higher tip speed ratios. Both blades SR3345 and SR5050 achieve the highest power coefficients at overlap ratios of 0 and 0.18 when a central shaft is present.

The aim of this research study was to develop hydrogels samples for using in potential biomedical applications. Hydrogels consisting of different volumes of keratin, polyvinyl alcohol (PVA), Polyvinylpyrrolidone (PVP), and starch. The keratin is derived from the chicken was the primary material on the hydrogels due to attractively for the potential wound healing application. The hydrogel samples were made by using the freeze‑thawing method, and they were examined using the Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), porosity amusement, swelling ratio and keratin release kinetic models that analyzed using (zero-order, first-order Higuchi and Korsmeyer-Peppas models). These results were indicated that feather keratin could use with formulated hydrogels suitably for controlled keratin release studies .

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R²=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

Background: the present study aimed to examine the effect of rehydration with mineral water on cardiorespiratory fitness in athletes. Methods: Twenty athletes (21.7±3 years) were ran-domly underwent a crossover design experimental trail. Three visits were arranged. The first visit was for baseline measurement. The second visit included three phases (pre-dehydration, post-dehydration, and post-rehydration), and either Zamzam (mineral water) or bottle water (control water) was used. The third visit was similar to the second visit with exchange the type of water. Cardiorespiratory fitness and blood parameters have been evaluated. Data were an-alyzed to compare results of Zamzam water with bottle water and to compare between the phases for each type of water. Results: although there was no significant difference found be-tween Zamzam and bottle water in the cardiorespiratory fitness markers, Zamzam water maintained cardiorespiratory function including VO2peak, VT1, VT2, and VEpeak even with re-hydration equal to 100% of losing body mass following exercise-induced dehydration (>-2% body mass), where rehydration with bottle water reported significant reduction in both VO2peak and VEpeak. Conclusion: rehydration with mineral water such as Zamzam water may not impair cardiorespiratory fitness even with amount equal to 100% of losing body mass.

This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ∆QSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ∆QSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ∆QSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ∆QSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ∆QSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ∆QSD has enabled challenging throughput targets to be met for a globally distributed blockchain operating on the public internet.

In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space's lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is needed that not only controls the glare near the windows but also increases the light at the end of the room and provides uniform daylight. One of the widely used systems is the “light shelf“which has three main functions: shading, increasing the depth of light penetration and reducing glare. Review articles about light shelves have been done in 2015 and 2017, while more than 80% of the studies have been carried out since 2016 and light shelves with more diverse forms and dynamic elements and many consolidations were proposed. Therefore, there is a need for a more comprehensive review. The main question of this research is how different parameters (including climate, material, ceiling and integrated systems) can help to increase the efficiency of light shelves. By using a systematic review, studies in the past three decades were classified in order to determine the effect of these parameters on improving lighting performance and controlling solar heat gain.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

It is well known that fiber sizing is one of the most important component in the manufacturing of composites, and it does affect the mechanical properties including strength and stiffness. In this work, the influence of fiber sizing levels on the mechanical properties of carbon fibers (CF) is reported at room temperature by using single fiber tensile testing (Favimat+), single fiber pullout testing (SFPO), and interfacial elemental analysis using X-ray photoelectron spectroscopy (XPS). Standard modulus of CF (7±0.2 μm in diameter) were sized using two Michelman commercially available sizing formulations. The average solid content for each sizing formulation is 26.3 ± 0.2% and 34.1 ± 0.2%, respectively. HEXION RIMR 135 with curing agent RIMH 137 was used as a model thermoset epoxy matrix during the SFPO measurements. A predictive engineering fiber sizing methodology is also developed. Sizing amounts of 0.5, 1, and 2 wt. % on the surface of the fiber is achieved for both sizing formulations. For each fiber sizing level, 50 single fiber tensile testing experiments and 20 single fiber pull-out tests were conducted. The ultimate tensile strength (σult) of the carbon fibers and the interfacial shear strength (τ_app) of the single fiber composite were analyzed. it is also investigated the effect of the sizing levels on the interfacial shear stress and the O/C (Oxygen/Carbon) surface composition ratio. As a result, generalized fiber sizing and characterization methods were established. The developed methods can be used to characterize the strength and interfacial shear strength of any man-made fibers with different sizing formulations and solid contents, irrespective of the matrix i.e. thermoset or thermoplastic.

Indoor localization is an active area of research dominated by traditional machine-learning techniques. Deep learning-based systems have shown unprecedented improvements and have accomplished exceptional results over the past decade, especially the Transformer network within natural language processing (NLP) and computer vision domains. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer with multiple classification heads (one per class) and learnable embeddings, to investigate the effectiveness of Transformer-based models for received signal strength (RSS) based WiFi fingerprinting. HyTra leverages learnable embeddings and the self-attention mechanism to determine the relative position of the wireless access points (WAPs) within the high-dimensional embedding space, improving the prediction of user location. From an NLP perspective, we consider a fixed order sequence of all observed WAPs as a sentence and the captured RSS value(s) for every given WAP at a given reference point from a given user as words. We test our proposed network on public and private datasets of different sizes, proving that the quality of the learned embeddings and overall accuracy improves with increments in samples.