Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

There is a case about a system comprising two queues of M/M(a)/1/K with a due date with FCFS service policy in each queue. Using the methods of simulation and mathematical analysis, the solution diagrams were illustrated for the fixed due date Ɵ=1 second at the phase of due date up to the commencement of the service, limited capacity K₁=5 and K₂=4, for both scenarios of different service management rate resulted. For the analytical solution, first, a SAN model from the system was created in the Mobius tool. For simulation, a java discrete systems simulation code was used. Adjustment of the initial values, selection of the scenarios for service rate, the number of the consumers in the simulation, etc. were all carried out in the simulation code.

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

Malaysia is a developing country and agro-industries contribute country’s economy. However, it is largely unknown the scenarios of agricultural practice and food supply over the years. To understand the fact, this study was designed to explore the agricultural land use changes and domestic food supply for last 50 years in Malaysia. The result showed that oil palm plantation area and yield, both are increased by folds, conforming “oil palm” mono-culture practice. Rice cultivation area remained almost unchanged but the yield increased. However, both area of plantation and yield in case of rubber, coca and coconut were decreased. When, it comes to domestic food supply, in 1973 food grain (Rice, Wheat, Maize) import was lower than domestic production. However, the import increased more than 3 times than the domestic production in 2013. Domestic production of vegetables and vegetables import was almost 50:50. Fruits import increased almost 3 times of domestic production, though domestic production was increased. In conclusion, it is understood that Malaysian agriculture economy is standing on “oil palm” wearers domestic food supply is import dependent. It is high time to reshuffle the policy for sustainable development and achieving food sovereignty.

By applying a running average (with a window-size= d), we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. this optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

Simple measures often couldn’t count a deep complexity. In the case of semantic complexity, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by 4 Experiments. The results show DASTEX is capable of measuring the semantic complexity of text in 6 application-tasks.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

In this paper, Hybrid Automata, which is a formal model for hybrid systems, has been introduced. A summary of its theory is presented and some of its special and important classes are listed and some properties that can be studied and checked for it are mentioned. Finally, the purposes of use, the most widely used areas, and the tools that provide H.A. Support are addressed.

The disease of COVID-19 comprises the most serious against human health worldwide with a high rate of virulence and mortality. The disease is caused by the 2019-nCoV virus from the beta coronavirus family. The virus makes use of its surface glycoprotein named S protein or spike to enter the human cells. The virus attached to its receptor named angiotensin-converting enzyme 2 on host cells surface via its receptor-binding domain and its fusion is mediated by cleavage at S2' site that is carried out by surface protease. Vaccines or drugs interfering with S protein binding or cleavage sites could be considered as drugs to get rid of the infection. In the current work and though docking and molecular dynamic experiments we have checked more than 100 drugs with high enough molecular weights for their shielding potency toward S protein binding sites and processing S2' sites. Our results indicate the shielding potency of: fidaxomicin > ivermectin > heparin > azithromycin > clarithromycin > eryhthromycin > niclosamide > ritonavir. Considering affluent reports regarding the complex disturbance in the immune system and multi-organ involvement in the disease there is no single or binary drug regime for cure expectedly and instead, we claim the multi-drug regime should be the choice in this context. Accordingly, we suggest our extracted drugs as an adjuvant for clinical trials.

According to many researchers, Abstraction is the basis of mathematics, computing, counting devices, and computer science and engineering. What is more, all of the above deal with complexity management in some way, and abstraction is the most basic mechanism of complexity management.Generative software development - whether in the sense of empowering humans by machine to create software or in the sense of reusing products - has been and is one of the serious concerns and goals of software engineering. The interesting thing is that in both views of generativity, the main issue is still, in a way, complexity management: whether this complexity management is to achieve diversity and reuse management (Czarnecki’s approach) or to Structuring from existing structures (the approach of Alexander and his followers in an object-oriented society).In this article, we will first look at complexity and its various definitions. The definitions that show, despite the different perspectives on complexity in different disciplines and domains, all point in one direction. We will conclude that complexity is rooted in multiplicity. In the following, we will formally define complexity. In the following discussion, we will look at the generative patterns of software development, and then we will look at the complexity management patterns at seven levels.In this article, the author has tried to maintain a comprehensive approach to complexity and to consider the approaches of different domains to complexity.

Nanofluids are a novel class of heat transfer fluid that plays a vital role in industries. In mathematical investigations, these fluids are modeled in terms of traditional integer-order partial differential equations (PDEs). It is recognized that traditional PDEs cannot decode the complex behavior of physical flow parameters and memory effects. Therefore, this article intends to study the mixed convection heat transfer in nanofluid over an inclined vertical plate via fractional derivatives approach. The problem in hand is modeled in connection with Atangana-Baleanu fractional derivatives without singular and local kernel having strong memory. The human blood is considered as base fluid dispersing carbon nanotube (CNTs) (single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes(MWCNTs )) into it to form blood-CNTs nanofluid. The nanofluids are considered to flow in a saturated porous medium under the influence of an applied magnetic field. The exact analytical expressions for velocity and temperature profiles are acquired using the Laplace transform technique and plotted in various graphs. The empirical results indicate that the memory effect decreases with increasing fractional parameters in the case of both temperature and velocity profiles. Moreover, the temperature profile is higher for blood-SWCNTs by reason of higher thermal conductivity whereas, this trend is opposite in case of velocity profile due densities difference.

Precipitation gauges are critical for measuring precipitation rates at regional and global scales and are often used to calibrate precipitation rates estimated from other instruments such as satellites. However, precipitation measured at the gauges is affected by gauge-undercatch that is often larger for solid precipitation. In the present work, two popular gauge-undercatch correction factors are assessed: one utilizes a dynamic correction model and is used in the Global Precipitation Climatology Centre (GPCC) Monitoring product and the other one employs a fixed climatology and is used in the Global Precipitation Climatology Project (GPCP) product. How much the choice of correction factors can impact the total estimate of precipitation was quantified over land at seasonal, annual, regional, and global scales. The correction factors are also compared as a function of the environmental variables used in their development, among those are near-surface air temperature, relative humidity, wind speed, elevation, and precipitation intensity. Results show that correction factors can increase the annual precipitation rate based on the gauges by ~9.5 % over the global land (excluding Antarctica), although this amount can vary from ~6.3% (in boreal summer) to more than 10% (in boreal winter), depending on the season and the method used for gauge-undercatch correction. Annual variations of correction factors can also be large, so the use of the fixed climatology correction factors requires caution. Given their magnitudes and differences, selection of appropriate correction factors can have important implications in refining the water and energy budget calculations.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the MODM-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

Cooperative learning (CL) is a teaching and learning pedagogy that has been used widely in school but there is limited information on instructors’ perceptions of CL nor observations of how CL is implemented in higher education settings. In this study, we investigated the ad-vantages and challenges of embedding CL into instructors’ teaching in Iran and Australia. Data were collected through interview and observation. Results showed that despite challenges such as the lack of familiarity of CL and how it can be implemented in university curricula, issues associated with assessment, and time constraints, CL created an interactive, pleasant and safe environment for deep learning in both countries. The findings showed that there were challenges in Iran such as a tendency to use traditional approaches to teaching, insufficient understanding of how to establish teamwork, and a lack of up-to-date teaching resources. In Australia, changing courses, working with external students, catering for individual differences, and building positive relationships were some of the challenges of implementing CL. We believe that these challenges can be overcome if university instructors are prepared to address them.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

Cluster-based routing in Mobile Ad-Hoc Networks is considered one of the convenient methods of routing. The existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. In this paper, a clustering algorithm using hierarchical routing for mobile ad hoc networks is proposed. In the proposed method, first, clusters are formed by the FLOC clustering method with low overlapping. Then, according to the most important network criteria, including hop count, noise ratio, distance, remaining energy, and link expiration time, head clusters are selected based on the weighting algorithm. To transfer data in the proposed method, two types of communication are used, e.g., inter-cluster communication and intra-cluster communication in which the shortest path to the target cluster is selected. The proposed P-METHOD technique is based on hierarchy and clustering as well as using a weighted algorithm that reduces delay in data transmission on the network. Moreover, P-METHOD is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the MANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), delay, residual energy, and lifetime.

Silencing of the fragile X mental retardation 1 (FMR1) gene and consequently lack of synthesis of FMR protein (FMRP) are associated with fragile X syndrome, which is one of the most prevalent inherited intellectual disabilities. FMRP is a multifunctional protein involved in many cellular functions in nearly all subcellular compartments under normal conditions and under conditions of cellular stress in both neuronal and non-neuronal cell types. This is achieved through its trafficking signals, nuclear localization signal (NLS), nuclear export signal (NES), and nucleolar localization signal (NoLS), as well as its RNA and protein binding domains, and is modulated by various post-translational modifications such as phosphorylation, ubiquitination, sumoylation, and methylation. This review summarizes recent advances in understanding the interaction networks of FMRP with a special focus on FMRP stress-related functions, including stress granule formation, mitochondrion and endoplasmic reticulum plasticity, ribosome biogenesis, cell cycle control, and DNA damage response.

Background: Laparoscopic cholecystectomy has gained popularity. The goal of this study was to evaluate outcome differences between open vs laparoscopic cholecystectomy. Method: Using a large NIS database and ICD-9 coding for open vs laparoscopic cholecystectomy, we compared inpatient outcomes for these two procedures regarding the occurrence of myocardial infarctions and mortality using uni- and multivariate analysis by studying 5 years of data from 2010 to 2015. Results: Open cholecystectomy was inferior to laparoscopic procedure with a higher occurrence of non-ST-elevation myocardial infarction (Non-STEMI), ST-elevation myocardial infarction (STEMI), and mortality in comparison to laparoscopic cholecystectomy. For example, using 2010 database, the occurrence of STEMI was 0.2 vs 0% (OR 3.1, CI 1.7-5.5, p<0.001), for Non-STEMI 1 vs 0.4% (OR 2.5 CI 2.0-3.0, p<0001) and for mortality 0.4 vs 0.4% (OR 9.2, CI 7.9-10.6, p<0001). After adjustment for age, obesity, diabetes, and hypertension, the occurrence of STEMI, Non-STEMI, and mortality remained higher in the open cholecystectomy cohort (mortality multivariate OR 6.4, CI 5.5-7.4, p<0001). Conclusion: Open cholecystectomy is associated with a higher rate of STEMI, Non-STEMI, and a markedly higher rate of mortality in comparison to the laparoscopic approach. Our study suggests that laparoscopic cholecystectomy should remain the first choice for patients needing gallbladder surgery.

Efficient thermal management of modern electronics requires the use of thin films with highly anisotropic thermal conductivity. Such films enable the effective dissipation of excess heat along one direction while simultaneously providing thermal insulation along the perpendicular direction. In this study, we investigate the thermal conductivity of bilayer graphene (BLG) sheets using non-equilibrium molecular dynamics, examining both in-plane and cross-plane thermal conductivities. The in-plane thermal conductivity of 10 nm × 10 nm BLG with zigzag and armchair edges at room temperature is found to be around 204 W/m.K and 124 W/m.K, respectively. The in-plane thermal conductivity of BLG increases with sheet length. For all sizes, BLG with a zigzag edge exhibits higher thermal conductivity than that of the armchair edge, with this difference becoming more pronounced as the lengths of the sheets increase. In addition, increasing temperature from 300 K to 600 K decreases the in-plane thermal conductivity of a 10 nm× 10 nm zigzag BLG by about 34%. Similarly, the application of a 12.5% tensile strain induces a 51% reduction in its thermal conductivity compared to the strain-free values. Armchair configurations exhibit similar responses to variations in temperature and strain, but with less sensitivity. Furthermore, the cross-plane thermal conductivity of BLG at 300 K is estimated to be 0.05 W/m·K, significantly lower than the in-plane results by approximately four orders of magnitude. The cross-plane thermal conductance of BLG decreases with increasing temperatures, specifically, at 600 K, its value is almost 16% of that observed at 300 K.

The issue of equipment layout in multi-floor satellites consists of two primary parts: allocating the equipment to the satellite's layers and placing the equipment in each layer individually. In reviewing the past literature in this field, firstly, the issue of assigning equipment to layers is observed in a few articles and next in the layout part, the non-overlapping constraint has always been a challenge, particularly for components that do not have a circular cross-section. In addition to presenting a heuristic method for allocating equipment to different layers of the satellite, this article presents a robust flexible programming model (RFPM) for the placement of equipment at different layers, taking into account the inherent flexibility of the equipment in placement and the subject of uncertainty. This model is based on the existing uncertainty between the distances of cuboid equipment which has not been addressed in any of the previous research, and by comparing its outputs with cases from past studies, it demonstrates a significantly higher efficiency in placing the equipment and meeting the limit of non-overlapping constraints between the equipment. Finally, it would be possible to reduce the design time in the conceptual and preparatory stages, as well as the satellite's overall size, while still satisfying other constraints like stability and thermal limitations, moments of inertia and centre of gravity.

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution with high fidelity, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained on this information directly. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems most effectively.

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

Let G be a finite group. The prime graph Γ(G) of G is defined as follows: The set of vertices of Γ(G) is the set of prime divisors of |G| and two distinct vertices p and p' are connected in Γ(G), whenever G has an element of order pp'. A non-abelian simple group P is called recognizable by prime graph if for any finite group G with Γ(G)=Γ(P), G has a composition factor isomorphic to P. In [4] proved finite simple groups ²D_n(q), where n ≠ 4k are quasirecognizable by prime graph. Now in this paper we discuss the quasirecognizability by prime graph of the simple groups ²D_2k(q), where k ≥ 9 and q is a prime power less than 10⁵.

Background: The goal of this study was to evaluate the effect of Extracorporeal Membrane Oxygenator (ECMO) on mortality in patients with cardiogenic shock excluding Impella and IABP use. Method: The large Nationwide Inpatient Sample (NIS) database was utilized to study any association between the use of ECMO in adults over age of 18 on mortality and complications with a diagnosis of cardiogenic shocks. Results: ICD-10 codes for ECMO, and cardiogenic shock for available years 2016-2020 were utilized. A total of 796,585 (age 66.5±14.4) patients had a diagnosis of cardiogenic shock excluding Impella. 13,160 (age 53.7±15.4) were treated with ECMO without IABP. Total inpatient mortality without any device was 32.7%. It was 47.9% with ECMO. Using multivariate analysis adjusting for 47 variables such as age, gender, race, lactic acidosis, three-vessel intervention, left main myocardial infarction, cardiomyopathy, systolic heart failure, acute ST-elevation myocardial infarction, peripheral vascular disease, chronic renal disease, etc., ECMO utilization remained highly associated with mortality (OR: 1.78, CI: 1.6-1.9, p<0.001). Evaluating teaching hospitals only revealed similar findings. Major complications were also high in ECMO cohort. Conclusion: In patients with cardiogenic shock, the use of ECMO was associated with the high in hospital mortality regardless of comorbid condition, high-risk futures, or type of hospital.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 Snow Telemetry (SNOTEL) sites in Alaska are used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018-2019) of the high resolution radar/rain gauge data (Stage IV) product was also utilized to add insights into scaling differences between various products. The outcomes were also used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range in which other products can be assessed. Time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tend to overestimate snow accumulation in the study area, while current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that corrections factors applied to rain gauges are effective in improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill in capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill in capturing the geographical distribution of snowfall and precipitation accumulation, so bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

In this study, a summary of the processes performed on the SPL for recycling, reduction in toxicity and treatment were examined on an industrial and laboratory scale. In writing this research, an attempt has been made to address the useful processes that have taken place in this field. Spent pot lining or SPL is a type of solid waste that is produced in the aluminum production process. After 3 to 8 years, the cathode blocks become problematic and can no longer be used, and need to be replaced due to adverse effects on cell function. SPL is known to be a hazardous waste to nature due to its fluoride and cyanide content. Research has shown that SPL ingredients have destructive and very dangerous effects on human DNA, which is why they are so important to maintain and recycle.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

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.

In the presence of aquatic plants in streams, a variety of flow structures have been reported. Re-sults of earlier investigations indicated that the hydrodynamic features of flows in rivers, and streams are influenced by a wide range of submerged and non-submerged vegetation. In the pre-sent study, eight experiment runs were conducted in two artificial pools built in a laboratory flume to assess the flow structures across vegetation cover. The slopes for entrance and exit sec-tions of these two pools are of 5 and 10 degrees, respectively. A patch of artificial grass covered the entire pool area over the gravel bed. The effect of stream flow velocity was investigated by considering two aspect ratios of 2 and 2.7. Also, distributions of flow velocity, Reynolds normal and shear stresses, turbulence intensities, turbulent kinetic energy (TKE), and higher-order mo-ments of velocity fluctuations have been investigated. By means of quadrant analysis, it is found that the skewness coefficients of velocity profiles are significant parameters to evaluate. Based on skewness values for the bursting processes, along the pool entrance section with a smaller slope, the contribution of sweep motion occurs in a more restricted zone above the canopy, and this im-plies that the ejection motion occurs in a pool entrance section with a higher slope. Immediately above the vegetation canopy, the dominant process is the sweep motion of bursting events. Whereas, at the upstream boundary of the vegetation patch, an outward motion with slightly pos-itive value occurs; and around the downstream boundary of the vegetation patch, the isotropic turbulence appears at the boundary of the vegetation and gravel bed. Over the vegetation canopy in the pool, less mixing eddies result in lower Reynolds shear stress values, and thus the turbulent flow becomes weak with lower turbulent kinetic energy (TKE). Because of the shallow flow in an artificial channel with an aspect ratio less than five and the availability of vertical non-zero ve-locity components, secondary currents become stronger in the channel. Consequently, there is a divergence from a linear dispersion of Reynolds shear stress.

Annually, millions of people worldwide are affected by skin diseases, and current therapeutic approaches have limited efficacy. Advances in the treatment of exosomes have led to the development of new therapeutic methods that hold promising potential for improving the treatment options for skin diseases. The aim of this study is to produce pharmaceutical and cosmetic compounds based on exosomes for the treatment of skin conditions such as eczema, pigmentation disorders, skin sagging and aging, cellular skin regeneration, increased skin moisture, and prevention of skin shedding, among others. This study examines the effects of exosomes derived from plants, humans, medicinal algae, and fungi to treat skin diseases. Spectrophotometric analysis reveals the presence of nucleic acids and proteins in exosome samples, indicating their cargo composition. TEM micrographs demonstrate that the exosome isolation process preserves the integrity of the membrane and the crucial spherical structure for their functionality. DLS analysis confirms that isolated exosomes have a size similar to typical exosome dimensions. MTT assays indicate concentration-dependent cytotoxic effects of exosomes on skin cells, with a calculated IC50 representing a significant decrease in cell viability. Real-time PCR shows higher gene expression levels of collagen I and collagen III, indicating the potential of exosomes to enhance collagen synthesis. Tyrosinase enzyme expression analysis demonstrates the influence of exosome treatments on pathways related to melanin production. The use of exosomes derived from various sources provides a promising platform for developing new drugs in the treatment of skin diseases. Additionally, exosomes can be utilized in the treatment of autoimmune diseases and cancer, transforming them into a hopeful field of research. Plant-derived exosomes are environmentally friendly and can serve as the next generation of drug delivery systems for treating various diseases.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a known phenomenon, called “catastrophic forgetting.” In a nutshell, the performance of the model drops on the previous data by learning from the new instances. This paper explores this issue in the table detection problem. While there are multiple datasets and sophisticated methods for table detection, the utilization of continual learning techniques in this domain was not studied. We employed an effective technique called experience replay and performed extensive experiments on several datasets to investigate the effects of catastrophic forgetting. Results show that our proposed approach mitigates the performance drop by 15 percent. To the best of our knowledge, this is the first time that continual learning techniques are adopted for table detection, and we hope this stands as a baseline for future research.

Car parking is a challenging part of urban transportation and the traffic violations around it cause many problems for citizens. In recent years, due to the fast growth and development of urbanization, temporary and unauthorized stopping of cars along the streets, especially in large cities, has led to an increased traffic, urban disorders, dangers for citizens, and violation of rules. Studies have shown that there is a direct relationship between vehicle parking violations and urban places. GIScience capabilities and tools play an important role in analysing the spatial distribution of these violations. In this study, we investigated the spatial distribution of vehicle violations in a region of Tehran, Iran that is suffering from a heavy traffic load and heavily polluted air. Although two dissimilar urban segregations exist in the north and south of the study area, our analysis indicates a similar pattern of car parking violations. In both of the areas, about 70% of all curb parks are legal, while the remaining are illegal. Also, spatial analysis reveals a direct relationship between some POIs and the occurrence of car park violations so that the density of legal curb parks is high near some POIs, and less near some others and vice versa. For example, the number of vehicle park violation around the hospitals is more than the average of the study area. However, the number of park violations around the universities is less than the average. Our findings reveal that co-location of certain POIs, for instance a hotel and a supermarket will lead to an increase in the number of park violations. In other words, there is a strong correlation between the type of POIs and curb-parks violations. Our results also show that POIs have an impact radius that leads to violations occurring in that area. For example, the area of the impact of a hospital on the creation of car park violations was estimated at 125 meters. Our presented approach along with the discussed findings along with conclusions can be useful to a large range of stakeholders including urban planner, traffic police departments, local municipalities, law enforcement agencies, and environmentalists to have a better perspective of infrastructure planning.

The global rising demand for exploiting limited shared groundwater resources, coupled with significant water deficit, especially in arid and semi-arid regions, has led to escalating conflicts among stakeholders. As such, using Bankruptcy Theory methods can be an appropriate response to the reallocation of resources. This study introduces a novel approach to weighted Bankruptcy, where the relative importance of stakeholders is determined by their contributions to sustainable development, and the impact of their claims on the shared groundwater resources is considered. The framework is implemented in a two-level bankruptcy process including the plains and their beneficiaries (agriculture, drinking and industry). The proposed two-level weighted bankruptcy process applies to the Neyshabour-Ataiyeh-Sabzevar plains in Iran. The process is implemented for two scenarios including the Baseline scenario related to the present status of demands and the Future scenario associated with the estimated demands for the year 2041. To compare the weighted proposed method with the other weighted methods such as Proportional (WPRO), Constrained Equal Awards (WCEA), Pinile (WPIN), Talmud (WTAL), Constrained Equal Losses (WCEL), Modified Constrained Equal Losses (MWCEL), two categories including the deficit-based and resource-based approaches are considered. According to the stability analysis using the Bankruptcy Allocation Stability Index (BASI) method, among the deficit-based methods, the novel approach is chosen as the preferred method for the beneficiaries’ reallocation like the Sabzevar’s beneficiaries. Accordingly, it allocates 86.95%, 10.86% and 2.17% of the shared resource to the agricultural, drinking, and industrial demands, respectively. Among the resource-based methods, the WPIN method is selected for reallocation between the stakeholders and their beneficiaries in each level and scenario of reallocation. The novel approach offers a promising solution to the water resource reallocation problem, ensuring a more equitable and sustainable management of shared groundwater resources.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

This investigation was conducted to determine the chemical composition and nutritional value of five plant species commonly used as ruminant feeds namely: Artemisia herba-alba, Acer monspessulanum, Amygdalus lycoides, Amygdalus scoparia, and Atriplex leucoclada. After the collecting samples, the chemical compositions of plants included dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE), ash (CA), cell wall (NDF), and non-lignin cell wall (ADF) were determined according to standard methods. For degradation testing, the nylon bag technique was applied using three native Sistani fistula calves. Organic matter digestibility (OMD) and metabolisable energy (ME) were determined through the gas production technique. The CP value ranged from 5.30 (Amygdalus scoparia) to 11.72% (Atriplex leucoclada) while NDF value ranged from 52.62 (Amygdalus lycoides) to 69.05% (Amygdalus scoparia). The range of OMD, DOMD, and ME was from 36.67 to 53.27%, 34.67 to 49.11%, and 5.57 to 8.08 (MJ/kg), respectively. The results showed a positive correlation between cell wall composition and dry matter digestibility in plant species. The nutritional value of Amygdalus lycoides, Atriplex leucoclada, Acer monspessulanum, and Artemisia herba-alba was acceptable composition and digestibility.

The aim of current study is adjusted and synthesized liposomal compound of N. sativa and evaluation its antifungal properties against C. parapsilosis isolates. Fifteen clinical isolates of C. parapsilosis complex isolates were obtained from hospitalized patients affected by candidemia in Mashhad city, Iran, along with a reference strain of C. parapsilosis (ATCC 22019) were assessed by flight mass spectrometry (MALDI-TOF) method, as described previously. N. sativa is encapsulated in liposomal Nanocariers by using thin film hydration technique. At the beginning liposomal nanoparticles was characterized and confirmed with the dynamic light scattering technique (DLS) and Transmission electron microscopy. Then minimum inhibitory concentration of liposomal N. sativa oil was conducted with the CLSI M27 A3 protocol and finally Cytotoxicity function of N. sativa oil liposomal nanocarriers on PBMCs was investigated and confirmed with MTT assay by the results of this research N. sativa oil-Lip-NP didn’t show any toxic effect on PBMCs and The minimum inhibitory concentration (MIC) range of free N. sativa oil and liposomal formulation with inhibitory effects on candida isolates was between 128 - 8, 250 - 31.25 µg ml also MIC50 and MIC90 were 125,187 and 32,96, µg ml respectively. Due to the hydrophobicity and hydrophilicity, biocompatibility, particle size, non-toxic effect, and higher cell viability of N. sativa oil -Lip-NP, it could be considered a more effective approach to treating fungal infections.

There was a question on “how lanthanides doping in iron oxide affects cure kinetics of epoxy-based nanocomposites?” To answer, we synthesized samarium (Sm)-doped Fe3O4 nanoparticles via electrochemical method and characterized it using FTIR, XRD, FE-SEM, EDX, TEM, and XPS analyses. The magnetic particles were uniformly dispersed in epoxy resin to increase the curability of the epoxy/amine system. The effect of the lanthanide dopant on the curing reaction of epoxy with amine was explored by modeling DSC experimental data based on model-free methodology. It was found that Sm3+ in the structure of Fe3O4 crystal participates in cross-linking of epoxy by catalyzing the reaction between epoxide rings and amine groups of curing agents. In addition, the etherification reaction of active OH groups on the surface of nanoparticles reacts with epoxy rings which prolongs the reaction time at the late stage of reaction where diffusion is the dominant mechanism.

A polymer inclusion membrane (PIM) composed of 50 wt% poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-HFP) as its base polymer, 40 wt% Aliquat® 336 as its extractant and 10 wt% dibutyl phthalate (DBP) as plasticizer provided efficient extraction of vanadium(V) from its sulfate solutions adjusted to pH 2.5. It was suggested that V(V) was extracted as VO2SO4− via an anion exchange mechanism. Quantitative back-extraction was achieved in a sulfuric acid solution (6 mol L-1) containing 1 v/v% of hydrogen peroxide. It was assumed that the back-extraction process involved the oxidation of VO2+ to VO(O2)+ by hydrogen peroxide. The newly developed PIM with the optimized composition mentioned above exhibited excellent selectivity for V(V) in the presence of metallic species present in digests of spent alumina hydrodesulfurization catalysts (i.e., Al(III), Co(II), Cu(II), Fe(III), Mn(II), and Ni(II)). The co-extraction of Mo(VI) with V(V) was eliminated by its selective extraction at pH 1.1. The optimized PIM was characterized by contact angle measurements, atomic-force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA)/derivatives thermogravimetric analysis (DTGA), and the stress-strain measurements.

Influence lines are indispensable tools for visualizing and analyzing the dynamic variations in force factors induced by external loads within structural systems. Among these methods, the energetic approach stands as a widely employed technique, rooted in the fundamental principle of work done by changing forces. It enables engineers to transform intricate structural analysis problems into manageable ones by exploring the first derivatives of the radius vector, which represent infinitesimal velocity or displacement. This methodology seamlessly interweaves concepts such as carrier motion, relative motion, and the construction of mechanisms, bringing fresh perspectives to the analysis of influence lines. In this article, we explore the nuances of these novel methods within the domain of mechanism theory. Through comprehensive elaboration and analysis, we elucidate the underlying principles and practical applications of Jacobian contours. Crucially, we introduce a straightforward, rapid, and programmable approach, promising to revolutionize influence line determination in structural engineering. This method bridges the gap between theory and practice, offering the potential to elevate the accuracy, efficiency, and adaptability of influence line analysis. As such, it represents a significant advancement in the field of structural and applied mechanics, with broad-reaching implications for engineering practice.

The aim of this study is to identify the effective institutional factors on the exploitation of entrepreneurial opportunities of the sports tourism industry in developing countries. This research is a qualitative study, the systematic method of Strauss and Corbin (1990) has been used to analyze the data. Interviewing is the main method of collecting data in this research, which semi-structured interviews were done with 45 members of the research community. According to Shane and Venkataraman individual-opportunity nexus framework (2000), interview questions were drafted and simultaneously with data collection from interviews, data analysis was performed. Based on the research results, 75 indicators affecting the exploitation of entrepreneurship opportunities in sports tourism were identified. Our research findings show that the necessary institutional arrangements in regulatory/legal/administrative dimensions (rule of law, government policies), normative/cultural (social norms, values and beliefs), cognitive/educational (promotion of elite knowledge, promotion of social knowledge) and guidance measures/supporter (public sector support, private sector support, complementary attraction and information technology) can improve the rate of entrepreneurial behavior in this area by increasing the ability and willingness of entrepreneurs to take advantage of entrepreneurial opportunities in the field of sports tourism. Finally, according to the research results, it can be suggested that for the growth of the sports tourism industry in developing countries, The existence of a legal, normative, supportive and educational environment will affect the ability and desire of market participants to identify and exploit the entrepreneurial opportunities of this emerging industry.

Nanoparticles (NPs) are widely used in diverse disciplines, including biology, medicine science. The central question that need to be answered is whether NPs have toxic effects on biological cells and molecules or are they safe. The safety of NPs including targeted drug delivery is critical and so is their toxicity in the environment. In recent years, in vitro and in vivo research on animals has generated abundant information about the toxicity of NPs. However, due to varying laboratory conditions, the comparison of the results from ensuing studies is somewhat unreliable. It should be noted that, depending on the type of production, NPs can enter the body through inhalation, skin and via digestive routes. Due to the diversity of NPs and their properties, there is paucity of accurate information on their toxicological effects; particle size, shape, surface area and the chemical levels are considered as key factors in creating health and toxicological effects. Consequently, there is a need for reliable information about their effects on various organs so as to deal with NPs effectively and their impact on health and the environment. This review covers the existing knowledge base on the subject that hopefully prepares us better to address these challenges.

The Myc gene, widely studied in numerous cancer types, serves as a pivotal regulator of crucial cellular processes implicated in tumorigenesis, such as cell growth, proliferation, invasion, metastasis, and therapy resistance. LncRNAs have been identified as relevant partners of Myc. LncRNAs modulate chromatin structure and function, transcription, splicing, stability, and translation of mRNAs, thereby emerging as pivotal regulators in cell differentiation, metabolism, signaling. An intricate crosstalk exists between Myc and lncRNAs in a wide range of cancers, wherein they can physically and functionally interact through direct and indirect mechanisms. Some lncRNAs can regulate Myc activity at post-transcriptional level, affecting its stability, translation, or interactions with other proteins, thus acting as oncogenes, while others act as tumor suppressors. On the other hand, Myc can target numerous lncRNA genes and modulate their expression, affecting the expression of downstream targets involved in tumorigenesis. Given the significant role of the Myc-lncRNA network in various cancer types, exploring therapeutic strategies targeting these crucial cellular regulators is of utmost importance. This review article focuses on breast cancer, analyzing those lncRNAs influencing Myc as well as the Myc-regulated lncRNAs.

Up to now, many studies have been performed on particle radiations before or during earthquakes (EQs). In our previous study, with the help of piezoelectricity relationships and the elastic energy formula, the MCNPX simulation code was applied to find the amount of created atomic/ nuclear particles, the dominant interactions; and the energy of the particles for various sizes of quartz and granite blocks. In this study, using the MCNPX simulation code, we have estimated the flux of the particles (created from under-stressed granitic rocks) at different distances from the EQ hypocenter inside the fractures, filled with air, water, and CO2. It was found that inside a water-filled fracture, the particles do not show the flux far from the EQ hypocenter, but inside the gases like air and CO2 with the normal condition density, different types of particles can have a flux far from the source (more than a kilometer) and they might reach themselves to the surface in the case that the EQ hypocenter is very shallow (0- 5 km). However, for deep EQs, it seems that the most detected nuclear particles on the surface should pass via the vacuum-filled fractures and reach themselves to the surface. Moreover, it was concluded that the more density of the fracture’s filling fluid, the less distance that the particles can have a flux.

This comprehensive review has systematically examined the use of heterogeneous solid acid catalysts in the production of biodiesel from wastewater-derived sludge by using the PRISMA methodology. The manuscript highlighted the composition and characteristics of wastewater-derived sludge, presenting both the opportunities and challenges associated with its use as a feedstock for biodiesel production. Various types of catalysts were dis-cussed, with a detailed exploration of heterogeneous solid acid catalysts, including zeolites, hetero-polyacid (HPA), mixed metal oxides, and sulphonic acid group catalysts. The advantages and limitations of these catalysts were critically analyzed, providing a balanced view of their potential in industrial applications. The application section delved into the catalytic transesterification reaction, mechanisms of biodiesel production, and the effects of catalyst loading on yield. Performance metrics such as catalytic activity, stability, recyclability, cost-effectiveness, and environ-mental impact were thoroughly evaluated, offering a clear understanding of the efficacy of these catalysts. Synthesis and characterization techniques were also reviewed, shedding light on the latest preparation methods and characterization techniques. Recent advances in catalyst development were presented, showcasing the innovative strides made in enhancing catalyst performance. The environmental and economic implications of using solid acid catalysts for bio-diesel production were assessed, emphasizing the importance of sustainability and economic viability.

In this paper, ground tire rubber (GTR) was mechano-chemically modified with road bitumen 160/220 and subsequently treated using a microwave radiation. The combined impact of bitumen 160/220 content and microwave treatment on short-term devulcanization of GTR were studied by thermal camera, wavelength dispersive X-ray fluorescence spectrometry (WD-XRF), static headspace and gas chromatography-mass spectrometry (SHS-GC-MS), thermogravimetric analysis combined with Fourier transform infrared spectroscopy (TGA-FTIR), oscillating disc rheometer and static mechanical properties measurements. The obtained results showed that bitumen plasticizer prevent oxidation of GTR during microwave treatment and simultaneously improves processing and thermal stability of obtained reclaimed rubber.

Background: Amyotrophic lateral sclerosis (ALS) is an incurable disease. There are no defined valid biomarkers for evaluating therapeutic outcomes of this disease. Numerous approaches have been made to meet this impact. Stem cells, one of these approaches, that might give various hopeful results that can be interpreted as a treatment for ALS This study aims to evaluate Mesenchymal Stem Cell (MSC) therapy as a promising novel treatment modality by estimating some parameters, such as immunological and biochemical factors. Methods: This study course involved 15 confirmed patients with ALS and was designed as an open-label, one-arm retrospective study to evaluate potential diagnostic biomarkers of repeated infusions of autologous bone marrow-derived mesenchymal stem cells (BM-MSC) administered at a dose of 1×106cells/kg BW with 1-month interval, in equal amounts in both intravenous (IV) and intrathecal (IT) simultaneously, by various biochemical and immunological parameters were evaluated during the three-month follow-up period. Results: Our study indicated that in the case of Immunological biomarkers ,Tumor Necrosis Factor-alpha (TNF-α) levels in the Cerebrospinal fluid (CSF) showed a significant decrease at month three after transplantation compared to levels at month zero, and the P-value was (P&lt;0.01). No statistically significant changes were observed for other immunological and biochemical parameters and P-value (P&gt;0.05). Conclusions: These results can indicate the benefit of stem cell transfusion in patients with ALS and suggest some diagnostic biomarkers. Several studies are required to approve these results.

Recently, it was reported that the physical cross-linking exhibited by some biopolymers could provide multiple benefits to biomedical applications. In particular, grafting thermoresponsive polymers onto biopolymers may enhance the degradability or offer other features, as ther-mothickening behavior. Thus, different interactions will affect the different hydrogen bonds and interactions from the physical cross-linking of CMC, the lower critical solution temperatures (LCSTs), and the presence of the ions. This work focuses on the study of blends composed of poly(N-isopropylacrylamide), poly(N-ethylacrylamide), and carboxymethyl cellulose in water and water/methanol. The molecular features, the thermoresponsive behavior, and gelation phenomena are deeply studied. The ratio defined by both homopolymers will alter the final properties and the gelation of the final structures, showing that the presence of the hydrophilic groups modifies the number and contributions of the diverse hydrogen bonds.

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

The current need to develop sustainable power sources has led to the development of ocean-based conversion systems. Wells turbine is a widely used converter in such systems which suffers from a lack of operational range and power production capacity under operational conditions. The profile named IFS which is concave in the post-mid-chord region, can produce significantly larger lift forces and show better separation behavior than the NACA profiles. In the present study, we tested this profile for the first time in a Wells turbine. The performance of six different blade designs with IFS and NACA profiles were evaluated and compared using a validated computational fluid dynamic model. Although the substitution of the NACA profile with the IFS profile in all cases increased the torque generated, the most efficient power generation and the largest efficient range were achieved in the design with varying thickness from the hub with a 0.15 thickness ratio reaching to the ratio of 0.2 at the tip. The operational span of this design with the IFS profile was 24.1% greater and the maximum torque generation was 71% higher than the case with the NACA profile. Therefore, the use of the IFS profile is suggested for further study and practical trials.

The issue of water and energy crisis have been turned into global matters which need to be tackled jointly. Accordingly, floating solar power plants, in which photovoltaic modules are used on the surface of water infrastructures, has recently been attracting much interest. This system provides some additional advantages over the ground-based system such as conserving the land and the water and increasing the efficiency of the module. This study first reviews the relevant literature comprehensively and then evaluates the potential of using floating solar photovoltaic (FSPV) on some of Iran’s water infrastructures which have experienced a large amount of evaporation every year due to high solar radiation. To this end, the five important dam reservoirs are selected as the representatives of the five important watersheds in Iran, and the advantages of the FSPV plant is analyzed in terms of energy generation, evaporation reduction, economic and environmental aspects considering different coverage percentages of reservoir’s surfaces. Considering Iran's vast potential for solar radiation, and, on the other hand, huge energy demand and critical water situation, results indicated that Iran can effectively harness solar energy through FSPV systems which help conserve the water in addition to support sustainable energy production.

Breaking up prolonged sitting by short bouts of light physical activities including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N=10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles.

In this study, the NiCrAlY coating was prepared using the atmospheric plasma spraying (APS). The sample was then exposed to a continuous fiber laser beam. Laser surface melting (LSM) can effectively homogenize the composition of the coating. It also reduced the defects and roughness. The coating was examined using optical microscopy (OM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) analysis. The process parameters of the LSM consist of laser power and scan rate. The high-temperature oxidation test was conducted for 200 hours at a temperature of 1100 °C. After a period of 200 hours, the primary coating demonstrated the emergence of an aluminum oxide layer, along with the presence of harmful spinel oxides. The laser remelting technique was used to modify the surface, which led to the uniform growth and development of an aluminum oxide layer (α-Al₂O₃) during the oxidation examination. Therefore, it appears that the reduction in grain size caused by laser remelting prevents the wrinkling of Al2O3 oxide. This process represents a novel and alternative approach to modifying MCrAlY coatings.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Introduction: Obstructive sleep apnea (OSA) is the most common sleep-breathing disorder. OSA is associated with cardiovascular diseases such as coronary artery disease (CAD) and arrhythmias with an increased risk of sudden cardiac death. Some changes in ECG markers are seen as an indicator of arrhythmogenicity, especially ventricular arrhythmias. The goal of this study was to investigate if OSA patients with CAD have QT dispersion and T peak to T end (TpTe) changes as a surrogate of arrhythmogenicity. Methods: We studied 75 patients with CAD who underwent polysomnographic studies. Patients were categorized into mild, moderate, and severe OSA according to the Apnea hypopnea index (AHI). Baseline ECG and echocardiography for assessing LV function and valvular heart disease were done for all of them. Results: Results showed that in patients with moderate and severe sleep apnea QT dispersion was higher in a group with more involvement of coronary artery disease, reduced ejection fraction, and a history of old MI. However, we didn’t find any significant statistical relationship between OSA and TpTe marker. Conclusion: Our results showed a positive relation between ECG arrhythmic indices such as QT dispersion and OSA severity in patients with CAD.

While DNA serves as the fundamental genetic blueprint for an organism, it is not a static entity. Gene expression, the process by which genetic information is utilized to create functional products like proteins, can be modulated by a diverse range of environmental factors. Epigenetic mechanisms, encompassing DNA methylation, histone modification, and microRNAs, play a pivotal role in mediating the intricate interplay between the environment and gene expression. Intriguingly, alterations in the epigenome have the potential to be inherited across generations. Ethanol abuse poses significant health issues worldwide. Ethanol has the capability to induce changes in the epigenome, which can be inherited by offspring, thus impacting them even in the absence of direct ethanol exposure. This review article reviewed the alterations caused by ethanol on the epigenome. We thoroughly gathered all relevant papers investigating the behavioral and molecular consequences of parental ethanol exposure on their offspring. In conclusion, the comprehensive analysis of the literature supports the notion that ethanol exposure induces lasting epigenetic alterations, which can influence the behavior and health of future generations. This knowledge emphasizes the significance of addressing the potential transgenerational effects of ethanol and highlights the importance of preventive measures to minimize the adverse impact on offspring.

Subarachnoid hemorrhage (SAH) is most commonly seen in patients over 55 years of age and often results in a loss of many productive years. SAH has a high mortality rate, and sur-vivors often suffer from early and secondary brain injuries. Understanding the pathophysiology of the SAH is crucial to identifying potential therapeutic agents. One promising target for diagno-sis and prognosis of SAH is circulating microRNAs, which regulate gene expression and are in-volved in various physiological and pathological processes. In this review, we discuss the poten-tial of microRNAs as a target for diagnosis, treatment, and prognosis in SAH.

Chronic kidney diseases (CKD) and end stage renal disease (ESRD) are growing threats worldwide. Tissue engineering is a new hope to surpass the current limitations such as the shortage of donor. To do so, the first step would be fabrication of an intact decellularized kidney scaffold. In the current study, an automatic decellularization device was developed to perfuse and decellularize male rats' kidneys using both sodium lauryl ether sulfate (SLES) and sodium dodecyl sulfate (SDS) and to compare their efficacy in kidney decellularization and post-transplantation angiogenesis. After anesthesia, kidneys were perfused with either 1% SDS solution for 4 h or 1% SLES solution for 6 h. The decellularized scaffolds were stained with hematoxylin and eosin (H&E), periodic acid Schiff (PAS), Masson’s trichrome, and alcian blue to determine cell removal and glycogen, collagen and glycosaminoglycans (GAGs) contents, respectively. Moreover, scanning electron microscopy (SEM) was performed to evaluate the cell removal and preservation of microarchitecture of both SDS and SLES scaffolds. Additionally, DNA quantification assay was applied for all groups in order to measure residual DNA in the scaffolds and normal kidney. In order to demonstrate biocompatibility and bioactivity of the decellularized scaffolds, allotransplantation was performed in back muscle and angiogenesis was evaluated. Complete cell removal in both SLES and SDS groups was observed in SEM and DNA quantification assays. Moreover, the extracellular matrix (ECM) architecture of rat kidney in the SLES group was significantly preservation better than the SDS group was shown. The formation of blood capillaries and vessels were observed in the kidney allotransplantations in both SLES and SDS decellularized kidneys. In conclusion, we demonstrated that both SLES and SDS could be promising tools in kidney tissue engineering. The better preservation of ECM than SDS, introduces SLES as the solvent of choice for kidney decellularization. ¬¬

Logistics Engineering has transcended over the decades into an approach for competitive benefits in organisational performance and logistics efficiency. Most industries are recognising that significant savings are available to companies that are able to ordinate and improve within their logistics operations. Companies today face great challenges because the successful supply of many products and services needs to the effective integration of logistics activities across a prolongation supply chain and an increasing geographical separation. Moreover, logistics integration approach involves both internal integration for an ordinated, unified process as well as relationships to react flexibly, changeability, and responsibility to customer's demands. In this study, by using of Fuzzy Vikor (F-Vikor), the best combination of RIR was selected and RIR Locating was done. The main aim of this paper is survey and design of the integrated operational logistics network (IOLN) and also, a proposed IOLN to integration of the Iran Khodro Co Supply Network.

Bioactive glass (BG) represents a promising biomaterial for bone healing; here injectable BG pastes biological properties were improved by 25 wt% gelatin or chitosan, as well as mechanical resistance was enhanced by adding 10 or 20 wt% 3-Glycidyloxypropyl trimethoxysilane (GPTMS) cross-linker. Composites exhibited bioactivity as apatite formation was observed by SEM and XRD after 14 days immersion in SBF; moreover, polymers did not enhance degradability as weight loss was &gt;10% after 30 days in physiological conditions. BG-gelatin-20 wt% GPTMS composites demonstrated the highest compressive strength (4.8±0.5 MPa) in comparison with 100% BG control (1.9±0.1 MPa). Cytocompatibility was demonstrated towards human mesenchymal stem cells (hMSC), osteoblasts progenitors and endothelial cells. The presence of 20 wt% GPTMS conferred antibacterial properties thus inhibiting the joint pathogens Staphylococcus aureus and Staphylococcus epidermidis infection. Finally, hMSC osteogenesis was successfully supported in a 3D model as demonstrated by alkaline phosphatase release and osteogenic genes expression.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

According to WHO, reducing salt intake to less than 5 g/day means less death, disability, and suffering from heart disease and stroke, with around 1.89 million lives saved every year. In this scoping review, we have reviewed the salt consumption pattern of South Asia. For this purpose, we searched online databases and websites and consulted key informants to address relevant unpublished reports. The inclusion criteria are English-language publications between January 2011 and October 2021, as well as research that evaluated salt intake in the South Asian region and reported salt/sodium 24-hr urine or spot urine samples involving at least 100 participants. A total of 2043 studies were screened, out of which 12 studies fulfilled all the criteria. Our review revealed that reported salt intake in the South Asian region ranges from 6.7-13.3 g/day, which was higher than the WHO-recommended maximum level of 5 g/day in all countries. Men consumed more salt than women. This review demonstrates the critical importance of implementing a salt reduction program in South Asian countries.

Software analysis, debugging, and reverse engineering have a crucial impact in today’s software industry. Efficient and stealthy debuggers are especially relevant for malware analysis. However, existing debugging platforms fail to address a transparent, effective, and high-performance low-level debugger due to their detectable fingerprints, complexity, and implementation restrictions. In this paper, we present HyperDbg, a new hypervisor-assisted debugger for high-performance and stealthy debugging of user and kernel applications. To accomplish this, HyperDbg relies on state-of-the-art hardware features available in today’s CPUs, such as VT-x and extended page tables. In contrast to other widely used existing debuggers, we design HyperDbg using a custom hypervisor, making it independent of OS functionality or API. We propose hardware-based instruction-level emulation and OS-level API hooking via extended page tables to increase the stealthiness. Our results of the dynamic analysis of 10,853 malware samples show that HyperDbg’s stealthiness allows debugging on average 22% and 26% more samples than WinDbg and x64dbg, respectively. Moreover, in contrast to existing debuggers, HyperDbg is not detected by any of the 13 tested packers and protectors. We improve the performance over other debuggers by deploying a VMX-compatible script engine, eliminating unnecessary context switches. Our experiment on three concrete debugging scenarios shows that compared to WinDbg as the only kernel debugger, HyperDbg performs step-in, conditional breaks, and syscall recording, 2.98x, 1319x, and 2018x faster, respectively. We finally show real-world applications, such as a 0-day analysis, structure reconstruction for reverse engineering, software performance analysis, and code-coverage analysis.

This study evaluates literature on the wheel of audit partners in Chinese companies and institutions by examining the impact of relationships before and after the rotation between the input and output partners. We consider the partners in the rotation before the rotation, before the work relationship. We find two different results from the previous working relationships: (a) increasing the likelihood of the outgoing partners after the cooling period, and (b) reducing the quality of the audit and lower accounting after the rotation. These findings ask whether the rotational partners are truly independent of the working relationships.

Clinical trials are research studies performed on people that are aimed at evaluating a medical, surgical, or behavioural intervention. It is a primary way that researchers find out if a new treatment, like a new drug or diet, or medical device is safe and effective in people. In this paper blockchain technology is embedded in a digital twin aiming to improve the quality of clinical trials through the boost of data integrity among the quality-related activities, and to promote participants' safety. First, a blockchain-embedded quality-enabled digital twin is proposed and the interactions among the enablers and peers are highlighted. Then, a prototype of the proposed system is developed, and the data of a pilot trial is used to justify the applicability of the system. The results showed that the proposed system is efficient, and hence it is feasible to adopt into a clinical trial. With the successful development of the proposed system, it is promising to provide effective data integration and knowledge management for improving participant safety.

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

Purpose: The present study which addressed adults who stutter (AWS), has been an attempt to investigate power spectral dynamics in stuttering state through answering the written questions using quantitative electroencephalography (qEEG).Materials and Methods: A 64-channel EEG setup was used for data acquisition in 9 AWS. Since speech, and especially stuttering, causes significant noise in the EEG, the three conditions of speech preparation (SP), imagined speech (IS), and simulated speech (SS) in a 7-band format were chosen to source localize the signals using the standard low-resolution electromagnetic tomography (sLORETA) tool in fluent and disfluent states. Results: Having extracted enough fluent and disfluent utterances, significant differences were noted. Consistent with previous studies, the lack of beta suppression in SP, especially in beta2 and beta3 and somewhat in gamma band, was localized in supplementary motor area (SMA) and pre motor area in disfluent state. Delta band frequency was the best marker of stuttering shared in all 3 experimental conditions. Decreased delta power in SMA of both hemispheres and right premotor area through SP, in fronto-central and right angular gyrus through IS, and in SMA of both hemispheres through SS were a notable qEEG features of disfluent speech. Conclusion: The dynamics of beta and delta frequency bands may potentially explain the neural networks involved in stuttering. Based on the above, neurorehabilitation may better be formulated in the treatment of speech disfluency, namely stuttering.

Objective: Regeneration of bone defects remains a challenge for maxillofacial and reparative surgeons. The purpose of this histological study was to assess the osteogenic potential of octacalcium phosphate (OCP) and bone matrix gelatin (BMG) alone and in combination in artificially created mandibular bone defects in rats. The quality of the newly formed bone was also evaluated. Methods: Thirty-six male Sprague Dawley rats (6-8 weeks old with 120-150 g weight) were randomly divided into four groups. Defects (3 mm in diameter and 2 mm in depth) were created in the mandible of rats and filled with 6 mg of OCP, BMG or a combination of both (1/4 ratio), respectively. Defects were left unfilled in the control group. To assess osteoinduction and bone regeneration and determine the quality of the newly formed bone, tissue specimens were harvested at seven, 14, and 21 days post-implantation. The specimens were processed, stained with hematoxylin and eosin (H&E) and histologically analyzed under light microscopy. Results: In the experimental groups, new bone formation was initiated at the margins of defects from seventh day after implantation. At the end of the study period, the amount of the newly formed bone increased and the bone was relatively mature. Osteoinduction and new bone formation were greater in OCP/BMG group. In the control group, slight amount of new bone had been formed at the defect margins (next to host bone) on day 21. Conclusion: Combination of OCP/BMG may serve as an optimal biomaterial for treatment of mandibular bone defects.

Background and Aim: Immunoglobulins (IgA, IgG, and IgM) are significant anti-inflammatory factors. The meta-analysis aimed to assess the serum and salivary levels of Igs as more important immunoglobulins in patients affected by oral lichen planus (OLP) compared to the healthy controls. Materials and Methods: Four databases, including PubMed/Medline, Scopus, Web of Science, and Cochrane Library as well as Iranian databases were checked up to January 2018 without language restriction. The quality of each involved study was done using the NOS questionnaire. A random-effects model analysis was done by RevMan 5.3 software applying the mean difference (MD) plus 95% confidence intervals (CIs). The CMA 2.0 software was applied to calculate the publication bias among the studies. Results: Out of 70 studies found in the databases, eight studies were involved and analyzed in the meta-analysis. The meta-analysis included 282 OLP patients and 221 healthy controls. The pooled MDs of serum levels of IgA, IgG, and IgM were -0.13 g/L [95%CI: -0.24, -0.02; P = 0.02], 1.01 g/L [95%CI: -0.91, 2.93; P = 0.30], and -0.06 g/L [95%CI: -0.25, 0.14; P = 0.56], respectively; whereas, the salivary IgA and IgG levels were 71.54 mg/L [95%CI: 12.01, 131.07; P = 0.02] and 0.59 mg/L [95%CI: -0.20, 1.38; P = 0.14], respectively. Conclusions: Considering the few studies performed on saliva, the results suggested that the salivary levels, especially IgA level had a higher diagnostic value than the serum levels. Therefore, the salivary immunoglobulins can play a significant function in the OLP pathogenesis.

Gold- and MnO₂- nanozymes are well-known for their enzyme-like activity. In this regard, initially, gold- and MnO₂- nanozymes were synthesized by simple and green methods. Afterward, the kinetic studies were performed using the Michaelis–Menten model for both gold- and MnO₂- nanozymes. The kinetic parameters including K_m and V_max were calculated via the construction of the linear plot of Lineweaver–Burk for both nanozymes. The results showed a V_max and K_m of 185 nM sec^-1 and 47 nM sec^-1 for the gold- and MnO₂- nanozymes, in order. The ratio of V_max(gold)/V_max(MnO₂) was found to be about 4.0 which pointed that the catalytic efficiency of gold-nanozymes is 4.0-fold higher than the catalytic efficiency of MnO₂- nanozymes. The K_m value was found to be 0.72 mM and 1.6 mM for the as-prepared gold- and MnO₂- nanozymes, respectively, and the K_m of MnO₂-nanozymes is 2.2-fold higher than that of gold nanozymes. Since the Km shows the affinity of substrate for binding to nanozyme active nodes (lower K_m=higher affinity), it is consultable that the substrate affinity toward MnO₂-nanozymes is 2.2-fold lower than that of the gold-nanozymes. Considering the above results, the as-prepared gold nanozymes are very stronger peroxidase-like mimics than the metal oxide MnO₂-nanozymes.

MnO₂ nanoparticles are considered nanozymes with intrinsic peroxidase-like activity and there are several reports on their application in the field of nanozyme-based sensing and detection, however, up to now, there is no report on the investigation of their biochemical and kinetics properties toward enzyme-mediated oxidations. Hence, in this work, the MnO₂ nanozymes were synthesized and characterized with different characterization methods. Thereafter, their biochemical properties including pH stability, thermal stability, and salt stability as well as their kinetics perfromances were evaluated toward n-electron irreversible oxidation of 3,3’-diaminobezedine. The results showed that the as-prepared nanozymes reveal their maximal enzyme-like activity over a wide pH range of 3.0-6.0 at a temperature of 23-25 ℃ toward oxidation of 3,3’-diaminobezedine. Besides, the salt stability studies exhibited that the as-mentioned nanozymes can save their maximal activity over a wide range of high salt concentrations over 3-7 M of NaCl. Moreover, the kinetics studies revealed a K_m as low as 1.6 mM and a V_max as high as 47 nM sec^-1 for the MnO₂ nanozymes toward irreversible oxidation of DAB to produce the brown-colored polyDAB.

In this study, considering the peroxidase-like activity of the SiO₂@AuNPs nanocomposite, their biochemical properties including, pH stability, cycling stability, shelf stability, and kinetic parameters were investigated. The results revealed that the as-mentioned SiO₂@AuNPs nanocomposite reveal its maximal activity at pH=4.0 along with saving 83.3% of its maximal activity at pH=5.0. Besides, the reusability and shelf-storage studies exhibited that the SiO₂@AuNPs nanocomposite retained 90% and 100% of its initial activity after 5 operational cycles and 30 days of storage, in order. The kinetic parameters of the as-prepared nanozymes were calculated using Menten kinetic model, revealing a V_max of 1.35 µM min^-1 and a K_m as low as 0.06 mM for the SiO₂@AuNPs nanocomposite. Based on the results of this work, the as-prepared SiO₂@AuNPs nanocomposite with intrinsic peroxidase-like activity shows high substrate affinity and catalytic efficiency along with excellent cycling- and shelf-stability, making them suitable for application in peroxidase-mediated reactions instead of the native enzyme.

The spread of fake news poses a serious threat to public trust and informed decision-making. The emergence of AI-generated fake news exacerbates this issue, as it can be produced more rapidly and convincingly than human-generated content. Addressing this issue, our paper introduces a robust approach to detect AI-generated fake news that is applicable across major disinformation domains: Politics and Elections, Health and Medicine, Science and Technology, and Entertainment and Celebrities. Our models differentiate between AI-generated and human-generated news articles while assessing their truthfulness. Utilizing large language models such as ChatGPT, we curated datasets spanning both AI and human generated fake and genuine news. Following extensive data preprocessing and the application of diverse feature extraction techniques, our models were trained using multiple machine learning algorithms. Our results show the feasibility of accurately identifying AI-generated fake news, thereby adding a significant layer of defense in combating misinformation across various sectors. In addition, we have explored the potential utility of transferable machine learning models trained on data from one domain (e.g., politics), when tested on another domain (e.g., health).

In this contribution, unmodified silver nanoparticles were synthesized and characterized for their size and morphological properties. Thereafter, their peroxidase-like activity as the common catalytic property of silver nanoparticles was investigated by catalyzing the oxidation of 3,3’,5,5’-tetramethyl-benzidine (TMB) as peroxidase substrate, exhibiting, a specific activity as high as 5.4 µM min^-1 for the as-prepared unmodified silver nanoparticles. The stability of the catalytic activity of the as-prepared nanozymes was also checked upon their storage at ambient temperature within 7 days at different storage conditions. The results revealed that the peroxidase-like activity of unmodified silver nanoparticles was approximately retained at about 75%, and 63% after 7 days exposing daylight and air oxygen, in order. The shelf-life (storage stability) of the as-prepared nanozymes was also investigated at usual storage conditions (i.e., 4 ℃ under dark), revealed that the nanozymes saved their activity about 96% of their initial activity after 10 days of storage at 4 ℃ under dark conditions.

Rosavin, a phenylpropanoid in Rhodiola rosea's rhizome, an adaptogen, is known for enhancing the body's response to environmental stress. It significantly affects cellular metabolism in health and many diseases, particularly influencing bone tissue metabolism. In vitro, rosavin inhibits osteoclastogenesis, disrupts F-actin rings formation, and lowers osteoclastogenesis-related gene expression (cathepsin K, CTR, TRAF6, TRAP, MMP-9). It also impedes NFATc1, c-Fos, NF-κB, and MAPK signaling pathways, and blocks phosphorylation processes crucial for bone resorption. Moreover, rosavin promotes osteogenesis, osteoblast differentiation and increases Runx2 and OCN expression. In vivo studies show its effectiveness in enhancing BMD in PMOP mice, re-straining osteoclast maturation, and increasing active osteoblast percentage in bone tissue. It modulates mRNA expressions (increasing EEF2 and decreasing HDAC1, thereby activating os-teoprotective epigenetic mechanisms) and alters various serum markers, including decrease of CTX-1, TRAcp5b, RANKL, M-CSF, TRAP, and increase of ALP and OCN. Additionally, when combined with zinc and probiotics, it reduces pro-osteoporotic MMP-3, IL-6, TNF-α, and en-hances anti-osteoporotic IL-10 and TIMP3 expressions. This paper aims to systematically review rosavin's impact on bone tissue metabolism, exploring its potential in osteoporosis prevention and treatment, and suggesting future research directions.

It is well-known that the peroxidase enzyme catalyzes the oxidation of different substrates such as 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine, however, the affinity of different substrates to the enzyme and consequently and catalytic performances of the enzyme toward different substrates is not same, resulting in different kinetics indexes. As the BSA-gold nanozymes are considered artificial peroxidase enzymes, hence, the kinetics indexes of these nanozymes should also be varied by varying their substrate. Hence, in this work, a comparative study was performed on the kinetics performances of BSA-gold nanozymes for enzyme-mediated oxidations of 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine. The results showed that the Km value of BSA-gold nanozymes was 0.03 mM and 0.72 mM toward TMB and DAB, in order, to reveal the higher affinity (lower Km) of TMB for binding to nanozyme active nodes compared to its alternative substrate, DAB. In contrast, the Vmax was found to be 263 nM sec^-1 and 185 nM sec^-1 for nanozmye-mediated oxidation of TMB and DAB, respectively. The higher Vmax of the nanozyme-mediated oxidation of TMB revealed that the catalytic efficiency of BSA-Au nanozymes toward TMB oxidation is higher (about 1.5-fold) than that of the DAB oxidation. The difference between the kinetic indexes of TMB and DAB may be related to their different oxidation pathways and their different reactivity. In fact, the DAB oxidizes via an n-electron irreversible oxidation pathway to produce an indamine polymer. While TMB nanozyme-mediated oxidation has occurred upon a 2-electron reversible mechanism for the production of a cation radical. These different pathways resulted in different kinetic performances.

Background: Nowadays vital signs in clinics are electronically monitored excepted for urine output which still requires, manual recording, which is subject to human errors. We, in-vitro, assessed in this study the ability a novel electronic device to measure by a combination of two different methods, the real time flow-output, like urine output. Methods: Flow or urine output was measured by a combination technique of gravimetry using a dynamometer and capacitive sensing to improve precision. Data were obtained in 3 different situations, low, medium, and high flow. Results: Three different situations were tested: full or empty dead space and Pasteur’s chamber. The differences between the injected and measured volume varies from 0 to 5 ml representing a maximal error of 3 % regardless of the volume injected and the flow in ml/hour. The capacitive sensors behave as expected with a parallel curve of the expected and measured flow. Conclusion: We have shown in this in-vitro study that the combination of gravimetry and capacitive sensors analysis allow accurate measurement of volume and flow-output. Clinical trials should be conducted to confirm its usefulness in urine output measurement in the real life.

Fire accidents are causing a huge human death as well as economic losses throughout the world every year. But a novel fire detection and extinguishing system could reduce these losses to a great extent. We are proposing a cost effective and yet very efficient system which has been developed in our laboratory and evaluated while in operation. Our system consists of two units: detection unit and extinguishing unit. For detection purposes we used smoke, IR and temperature sensors. While for extinguishing purposes we used both foam and water. We used a PC to process and control our system through LabVIEW software of National Instrument, USA. The detection unit will send signal through a wireless module to PC if fire is detected. Then PC (LabVIEW) will process the signal and using relay module first of all the electric power and gas lines will be turned off. The extinguishing unit will then release foam or water to extinguish the fire. The system will send SMS to the predefined numbers using GSM module. Our system is location independent as it uses GSM and can be operated from remote location using wireless module. After performance evaluation it was found to be fast and reliable.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

Leptospirosis is an important zoonotic disease that is transmitted worldwide through infected small mammals such as rodents. In Malaysia, there is paucity of information on the animal reservoirs that are responsible for leptospirosis transmission, with only few studies focusing on leptospirosis risk in recreational areas. Therefore, in this study, we characterized the species composition and the prevalence of pathogenic Leptospira spp. in non-volant small mammals of Hutan Lipur Sekayu, Terengganu. We performed ten trapping sessions totaling 3,000 trapping efforts between September 2019 and October 2020. Kidney samples from captured individuals were extracted for the PCR detection of pathogenic Leptospira spp. Overall, we captured 45 individuals from 8 species (1.56% successful trapping effort), with 9 individuals testing positive for pathogenic Leptospira, that is 20% (n = 9/45) prevalence rate. Rattus tiomanicus (n = 22) was the most dominant captured species and was found to harbour the highest positive individual with pathogenic Leptospira (44.4%, n = 4/9). Despite the low successful trapping effort in this study, the result shows that the non-volant small mammals of Hutan Lipur Sekayu are capable of maintaining and transmitting pathogenic Leptospira, thus making this recreational area a potential infestation ground for leptospirosis.

This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

There is a marked momentum towards the use of clean hydrogen energy as an alternative for fossil fuels. Renewable energies such as solar and wind are being used to generate hydrogen from the water hydrolysis process. Indeed, this approach stores renewable energies in the form of combustible hydrogen for other energy uses. The other alternative that could be economically more cost-effective at the current technology stage is to explore the natural “Hydrogen System” where the natural hydrogen is generated and accumulated within the earth system, the same that stands for a “Petroleum System”. The Discovery of a large accumulation of relatively pure natural hydrogen (H2) in Mali has triggered the opportunity of searching for natural hydrogen accumulations in other countries. The generation of hydrogen from a circular depression in Mali and some other countries is linked to the presence of geologically very old iron-rich basement rocks. Solid-liquid redox reactions between iron-rich minerals and groundwater that split water are a possible source of H2 in deep basement rocks. It is believed that the hydrogen degassing may be detected by surface topographic circular to sub-circular shallow depressions. Chemical processes such as dissolution by hydrogen are considered to play the main role in the formation of the circular depressions through preferential vertical hydrogen migration channel. Archean iron-rich Yilgarn Craton that covers a vast area of Western Australia (WA) contains abundant iron-rich mafic-ultramafic rocks. The craton reveals many surficial circular depressions visible through satellite images. The area has abundant fault systems and is blanketed with Eocene sedimentary rocks containing high-quality reservoir rocks. All these characteristics seem to provide most of the required elements, such as hydrogen source, migration pathway, and reservoir rock for a “Hydrogen System” in this area.

Decision-makers are concerned with the inherent complexity of the modern world's markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21^st century's more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This paper proposed that artificial intelligence can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective purified input features subset refer to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improves prediction accuracy and reliability.

Based on a large sample of publicly listed and non-listed US commercial banks from 1996 to 2011, we find robust evidence consistent with banks using realized available for sale (AFS) securities gains and losses to smooth earnings and increase low regulatory capital. We also find that (i) banks with positive earnings smooth earnings, and banks with negative earnings generally take big baths; (ii) regulatory capital constrains big baths; (iii) banks with more negative earnings and more unrealized beginning-of-quarter losses (gains) take big baths (smooth earnings); and (iv) banks with low regulatory capital and more unrealized gains realize more gains. Also, banks with negative earnings tak big baths (avoid or reduce the earnings loss) if their unrealized gains are insufficient (sufficient) to offset the negative earnings. Our inferences apply to listed and non-listed banks, which indicates that the earnings management incentives do not derive solely from public capital markets. Our findings reveal that the accounting for AFS securities gains and losses enables banks to manage regulatory capital and earnings in a variety of ways.

Extracellular vesicles (EVs) are enclosed by a lipid-bilayer membrane and secreted by all types of the cells under various physiological conditions. EVs play important roles in intercellular communication and crosstalk between tissues in the body. They are classified into three groups, such as apoptotic bodies, microvesicles, and exosomes. Exosomes were isolated from biofluids including blood, urine, milk, and cell culture media. Exosomes have significant potential for drug delivery and diagnosis. However, the method of isolation affects the physical and biological properties of exosomes. Several methods based on different principles have been developed for exosome isolation. These include ultrafiltration, precipitation, ultracentrifugation, size-exclusion chromatography, and microfluidics. In this study, we applied three common methods, such as ultrafiltration, precipitation, and ultracentrifugation, to isolate exosomes from the cell culture medium and investigated the effects of these different isolation methods on the size distribution and quality of the isolated exosomes. Field emission scanning electron microscopy (FESEM) images, size distribution, total protein content, and the effect of exosomes on the viability of hypoxic cells were analyzed in this study. The analysis revealed that compared to other methods, the ultracentrifugation method can isolate exosomes with smaller diameter (ranging from 20 to 80 nm), lower total protein content (50 µg/ml), and causing the increased viability of the hypoxic cells. The precipitation method does not require special equipment and is inexpensive, if the quality and purity of this method are solved, and it can be used as the best method for exosome isolation. This study can serve as a guide for choosing the best exosome isolation method for applications in medicine according to the needs, time, cost, and equipment.

Yogyakarta City is one of the big city which is located in Java Island, Indonesia. Yogyakarta City, including study area (Pleret Sub District), are very prone to earthquake hazards, because close to several active earthquake sources. For example, Sunda Megathrust which often generates a big earthquake which can affect the study area. The Sunda Megathrust extends from north to south and west to east along the Sumatra and Java Islands. Furthermore, an active normal fault called as Opak Fault pass through right in the middle of Study area and divides the study area into east and west zone. Recently, after the devastating earthquake in 2006, the population of the study area increases significantly. As a result, the housing demand is also increasing. However, due to the absence of earthquake building code in the study area, locals tend to build improper new houses. Furthermore, in some part of the mountainous area in the study area, there are some building found in unstable slopes area. Due to this condition, the multi-hazard and risk study needs to be done in Pleret. The increasing of population and improper houses in Pleret Sub-District can lead to amplify the impact. Thus, the main objective of this study is to assess the multi-hazards and risk of earthquake and other related secondary hazards such as ground amplification, liquefaction, and coseismic landslide. The method mainly utilised the geographic information system, remote sensing and was fit up by the outcrop study. The results show that the middle part of the study area has a complex geological structure. It was indicated by a lot of unchartered faults was found in the outcrops. Furthermore, the relatively prone areas to earthquake can be determined. In term of the coseismic landslide, the prone area to the coseismic landslide is located in the east part of the study area in the middle slope of Baturagung Escarpment. The highly potential area of liquefaction is dominated in the central part of the study area. In term of building collapsed probability, the result shows that the safest house based on statistical analysis is the residential house with the building attribute of wood structure, roof cast material, distance more than 15 km from the earthquake source, and located above the Nglanggran Formation. Finally, the multi-hazard and risk analysis show that the middle part of the study area is more vulnerable than the other part of Pleret Sub-District.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

This study introduces a neural network-based approach to predict dust emission, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), the research predicts PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. The model is trained on extensive field data from the almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a Mean Squared Error (MSE) of 0.02 and a Mean Absolute Error (MAE) of 0.01, indicating a high degree of precision in forecasting PM2.5 levels. The study also finds a strong correlation between certain operational parameters and PM2.5 emissions, highlighting specific areas for optimization in harvesting techniques. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Molecular and morphological properties of poly(vinyl chloride) grains produced by suspension polymerization of VCM in the presence of a mixture of three kind of initiators (i.e. fast, mild and slow) (named as Cok process) was experimentally investigated in a pilot-scale reactor. The results obtained here were initially compared with a isothermal regular process (named as control process) and then with those obtained already for other productivity-enhancing polymerization techniques (i.e. nonisothermal and fast initiator dosage process). The results showed that, in contrast to nonisothermal and fast initiator dosage process, the addition of initators mixture at the beginning of the reaction has the smallest influence on molecular weight and polydispersity index compared to control process. It is obvioused that Cok-PVC grains have the lowest cold plasticizer absorption and porosity among these mentioned processes. Scanning electron microscopy (SEM) showed that the particles produced by Cok process are more regularly shaped, with a smoother surface compared with the control product. According to the literatures, all three productivity-enhancing techniques lead to an apparent quality enhancement, higher flowability and greater bulk density of final grains. While both processes of continous initiator dosage and nonisothermal polymerization broaden the particle size distribution of final PVC grains, applying initiator mixture produces particles with the same particle size distribution as control process. The SEM images processing showed that Cok process deccelerates the formation of a three-dimensional skeleton of primary particles relative to the control polymerization. In comparison with nonisothermal trajectory and continous fast initiator dosage system, the Cok polymerization process leads to the most delay of motionless conversions and fusion time as well.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study is to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying quantum potential beyond the microscopic world, we compute quantum potentials for the S&amp;P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

Today, mobile phones are at the center of many consumers' lives and a part of people's lives. Although this tool was considered a luxury initially, it has gradually become a necessary tool with the reduction of the price and the increase of its functions. Consumers choose specific models of mobile phones according to their needs. Mobile phones have quality features that are expressed in the form of quality dimensions. These dimensions are mostly expressed by word of mouth (WOM). In this research, first; we collected the opinions of 350 students about 4 brands of mobile phones based on Garvin's quality of dimensions, finally; the students’ opinions about mobile phones were evaluated with integrated TOPSIS with interval-valued intuitionistic fuzzy numbers technique, and the priority of 4 phone brands was determined based on students' opinions.

In this paper, we compile the fractional power series method and the Laplace transform to design a new algorithm for solving the fractional Volterra integro-differential equation. For that, we assume the Laplace power series (LPS) solution in terms of power q=1m,m∈Z+, where the fractional derivative of order α=qγ for which γ∈Z+. This assumption will help us to write the integral, the kernel, and the nonhomogeneous terms as a LPS with the same power. The recurrence relations for finding the series coefficients can be constructed using this form. To demonstrate the algorithm's accuracy, the residual error is defined and calculated for several values of the fractional derivative. Two strongly nonlinear examples are discussed to provide the efficiency of the algorithm. The algorithm gains powerful results for this kind of problem. Under Caputo meaning the obtained results are illustrated numerically, and graphically. Geometrically, the behavior of the solution declares that the changing of the fractional derivative parameter values' in their domain alters the style of the attained solution in a symmetrical meaning and fully coinciding to the ordinary derivative value'. From these simulations, the results report that the recommended novel algorithm is a straightforward, accurate, and superb tool to generate analytic-approximate solutions for Integral, and integro-differental equations of fractional order.