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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

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.

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&P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

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.

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.

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.

This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics such as Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). Regarding the F1-Score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.

This article presents and discusses analytical data on the scientific publication record from 1910 to 2020 on two topics: "climate" and "climate change/global warming/climate emergency". The goal is to visualize how the publication record on these two topics has evolved over time, from different classification perspectives (year, country, source and organization). Three hypotheses are tested using data collected from Web of Science and various graphical representations of the data. It is found that research output related to the Earth’s contemporary changing climate overtook that of general climate research in 2011, and the publication ratio has been expanding in the last decade. There are significant differences in the publication countries and sources between the two topics, and conversely less significant differences in terms of organizations publishing these works. Differentiation factors that affect the level of research output and engagement on the climate challenge include: island versus landlocked nations, specialized versus general scientific journals, academic versus institutional organizations. The future of the publication records is discussed, such as the emergence of new terms to refer to the climate challenge, such as “climate emergency”.

As formally is known, nanozymes are the nanomaterials or nanoscale materials which reveal enzyme-like properties. These nanomaterilas can be utilzied instead of the natural enzymes for simulation of enzymatic reactions in harsh environmental conditions. Among different nanoscale enzyme-like materials, the majority of them reveal peroxidase-like activity, due to the high catalytic efficiency and stability of peroxidase-like nanozymes, they had been widely utilized for sensing and detection via constructing the colorimetric and fluorescence-based analytical and bioanalytical sensors. There are several high impact nanozymatic sensors which reported in the literature. Although the review articles published in this field, however, the current reviews focus on the introducing nanozyme field not description on the best articles published in this filed. Hence, this article aimed to present a brief introduction on the nanozyme-based chemistry with emphasizing on the historical overview of recent nanozymatic sensors. In this regard the best nanozymatic sensors which published over 2016-2023 were briefly described and overviewed.

Considering higher stability and lower cost of nanozymes than native enzymes, the nanozymatic systems had been utilized for several practical application, especially for sensing and detection. Most of common nanozymatic sensors are single-nanozyme based systems, however, recently a new generation of nanozyme-based systems called “multinanozyme system’ was introduced by Hormozi Jangi et al. (2020). Since the first report of multinanozyme systems, several multinaozyme systems have been developed and utilized for highly sensitive and selective sensing aims. The main advantages of multinaozyme systems compared of common nanozymatic sensors are their impact on simultaneous enhancing selectivity and sensitivity of sensor in a well-designed detection process. Since, the principles of design and detection mechanism of this new generation is not well-described in the literature, the aim of this article is the fast review of the principles of design of this new generation of nanozyme-based sensing and detection.

Influence of reaction time on silver nanoparticles-catalyzed oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) was evaluated via probing the oxidation process by recording the UV-Visible absorbance of the resulting colored products of TMB oxidation by hydrogen peroxide in the presence of silver nanoparticles as peroxides mimics. The time curve of the oxidation process was constructed, revealed that the concentration of the oxidation product was increased by increasing the reaction time and then levled off because the nanozyme active nodes were saturated after a certain reaction time and consequently the steady-state condition can be observed. The maximal activity of the silver nanozymes was achieved after a very short reaction time as short as 3.0 min and after this time, the color intensity of the oxidation products remained constant, revealing saturation of active nodes of nanoparticles with nanozyme-substrate.

In this contribution, time course studies were performed toward oxidation of different peroxidase substrates in the presence of silver nanozymes. In this regard, two common peroxidase substrates, TMB and DAB, were selected as model chromogens. The process of the oxidation reaction was probed by recording the absorbance of the colored products using UV-Vis spectrophotometer. Thereafter, the nanozymatic activity was calculated and used as an index for comparing the performance of the silver nanozymes against different substrates in a certain reaction time. The time course curves, revealed that the maximal activity of the silver nanozymes were achieved after a very short time of 3.0 min toward TMB oxidation while regarding DAB, the maximal activity was observed after 25.0 min which is 6.5-fold slower than that of the TMB oxidation. The difference between the DAB and TMB can be assigned to their different reaction pathways and different reactivity. In fact, the oxidation of DAB has occurred via n-electron irreversible pathway while the TMB oxidation has occurred through a simple 2-electon reversible pathway. Besides, DAB is a high stable and less relative compound compared of TMB, resulting different oxidation times, different affinity for binding to nanozyme active nodes which lead to different nanozymatic activities.

In this work, a high throughput nanozymatic method was developed for green nanozyme-mediated biodegradation of organic dyes in real water media. The nanozymes were synthesized and characterized for their size and morphology by TEM and DLS analysis. The nanozymatic properties of the as-prepared nanomaterials were evaluated by standard enzyme activity assay, revealing high peroxidase-like performances for the as-prepared nanozymes. Therefore, for exploring more precise, the kinetics behavior of the as-prepared nanozymes was evaluated by the standard Lineweaver-Burk method, revealing a V_max as high as 0.263 µM sec^-1 and a K_m as low as 0.03 mM, revealing high catalytic efficiency and affinity of the as-prepared nanozymes. Hence, the as-prepared nanozymes were utilized for organic dye degradation in real water media using methylene blue as a model organic dye. The effective factors on the dye degradation including pH, ionic strength, degradation time, nanozyme amount, etc. were optimized by the one-factor-at-a-time optimization method. At optimal experimental conditions, the as-prepared nanozymes can degrade about 99.0% of the organic dye within a degradation time as low as 7 min. The developed method was finally employed for the nanozyme-mediated degradation of methylene blue from real water media such as pool, mineral, river, and tap water samples. The results of this study revealed an excellent biodegradation yield of over 94.3%-99.0% for the different real samples, proving the suitability of the developed method for dye degradation in real water media.

Water treatment efficiency of several filter media such as perlite, modified (silicated) perlite, zeolite and sand were studied on wastewater. This study contains 2 distinct experiments which the first one emphasize on the modified perlite’s performance in removing turbidity which has been gone through 3 phases including low turbidity model water, mid turbidity and high turbidity model water; the second experiment underlines the differentiation between 3 evolutionary material based on perlite which undergoes the filtration process with certain turbid model water. It was dedicated that modified perlite removed more than 90% of turbidity and it functions better than other materials with high turbidity (more than100 NTU). The experiments have been conducted with sodium silicate perlite, normal perlite and synthetic zeolite perlite in order to determine the optimized material which is able to be substituted with perlite in the filtration process.

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.

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.

Commonly macrophages are categorized into M1 and M2 subsets. M1 macrophages are defined as inflammatory cells and may contribute to tissue injury, while, M2 macrophages play a central role in tissue remodeling and control of immune responses. It seems that the existence of these cells in graft location is effective for control of inflammation and improvement of transplantation consequences. Although the relative contribution of M2 cells in organ transplantation is not clear, the accumulation of these cells in acute and chronic injury models of transplantation was shown. In some cases, the depletion of M2 cells leads to the amelioration of disease; however in other causes, skewing the response in M2 cells leads to an augmentation of graft fibrosis and worsening of graft condition. In spite of these findings, the benefits of such strategies and their implications in human disease are not clearly understood. The purpose of this mini-review is to highlight the role of alternatively activated M2-type macrophages in the improvement or reduction of the kidney transplantation outcome.

Electronic-health applications rely on large computer networks to facilitate patients' information access and to communicate various types of medical data. To examine the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially when a real time operation is needed. Sampling allows a subset of packets that accurately represents the original traffic to be formed. In this study an adaptive sampling method based on regression and fuzzy inference system was developed. It dynamically updates the number of packets sampled by responding to the traffic variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

Background: Three drug-combinations, ipilimumab-nivolumab (Ipi-Nivo), pembrolizumab-axitinib (Pembro-Axi) and avelumab-axitinib (Ave-Axi), have received regulatory approvals in USA and Europe for the treatment of metastatic renal cell carcinoma with clear cell component (mRCC). However, no head-to-head comparison data are available to identify the best option. Therefore, we aimed to compare these new treatments in the first-line setting. Methods: We conducted a systematic search in Pubmed, the Cochrane library and clinicaltri-al.gov website from January 2015 to October 2019, for any randomized controlled trials of treatment-naïve patients with mRCC. The process was performed according to PRISMA guide-lines. We performed a Bayesian network meta-analysis with two different approaches. The out-comes for analysis were overall survival, progression-free survival, and objective response rate. Results: Our search identified 3 published phase 3 randomized clinical trials (2835 patients). In the contrast-based model, Ave-Axi (SUCRA: 83%) and Pembro-Axi (SUCRA: 80%) exhibited the best ranking probabilities for PFS. For OS, Pembro-Axi (SUCRA: 96%) was the most pref-erable option against Ave-Axi and Ipi-Nivo. Objective response rate analysis showed Ave-Axi as the best (SUCRA= 94%) and Pembro-Axi as second best option. In the parametric models, risk of progression was comparable for Ave-Axi and Ipi-Nivo, whereas Pembro-Axi exhibited a lower risk during the first 6 months of treatment and a higher risk afterward. Furthermore, Pembro-Axi exhibited a net advantage in terms of OS over the two other regimens, while Ave-Axi was the least preferable option. Conclusions: Overall evidences suggested pembrolizumab plus axitinib may be the best option.

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH₄, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

Since the spread of the deadly virus SARS-CoV2 in late 2019, researchers have restlessly been seeking for unraveling how the virus factually enters the host cells. Some proteins on each side of the interaction between the virus and the host cells are involved as the major contributors to this process: 1- the nano-machine Spike protein on behalf of the virus, 2- angiotensin converting enzyme II, the mono-carboxypeptidase and the key component of renin angiotensin system on behalf of the host cell, 3- some host proteases and proteins exploited by SARS-CoV2, In this review, the complex process of SARS-CoV2 entrance into the host cells with the contribution of the involved host proteins as well as the sequential conformational changes in the Spike protein tending to increase the probability of complexification of the latter with angiotensin converting enzyme II, the receptor of the virus on the host cells, are discussed. Besides, the release of the catalytic ectodomain of angiotensin converting enzyme II as its soluble form in the extracellular space and its positive or negative impact on the infectivity of the virus are considered.

The purpose of this study was to explore and develop high-resolution infrared thermal (HRIT) imaging for screening toddler’s fracture. A Toddler’s fracture is a common tibial fracture of children younger than 6 years old. Initial x-ray radiographs may not show the fracture and another x-ray during a follow-on visit is usually required. The study included 39 participants admitted to an emergency department with a suspected toddler’s fracture. X-ray confirmed 8 cases with a toddler’s fracture (20.5%). Infrared images of participants were recorded on their index visit, focusing on region-of-interests on the injured and the contralateral (uninjured) legs. The uninjured leg acted as a thermal reference. Nine statistical measures were extracted from the images: maximum, mini-mum, mean, standard deviation, median, mode, interquartile range, skewness, and kurtosis. Wilcoxon rank sum test indicated that maximum, mean, standard deviation, median, and inter quartile range temperature measures were significantly different (p<0.05) when comparing fractured and non-fractured legs. Principal component analysis of these measures highlighted distinct separation of toddler’s fracture and non-fracture cases. Similarly, plots of the statistical measures further confirmed clustering of toddler’s fracture cases. The study demonstrated that HRIT imaging can be a valuable tool for screening for toddler’s fracture.

A systematic and comprehensive review of critical applications of Blockchain Technol-ogy with Differential Privacy integration lies within the privacy and security enhancement. This paper aims to highlight the research issues in the e-health domain (e.g., Electronic Medical Rec-ords) and to review the current research directions in Differential Privacy integration with Blockchain Technology.(1) Background: The current state of the art in the e-health domain is identified as follows: (a) healthcare information poses a high level of security and privacy concerns due to its sensitivity; (b) due to vulnerabilities surrounding the healthcare system, a data breach is common and presents a risk for attacks by an adversary; and (c) the current privacy and security apparatus needs further fortification. (2) Methods: The methodology uses a systematic literature review (SLR) to identify and select relevant research papers and academic journals in DP and BT. (3) Results: The results are categorized into: e-Health Record Privacy, Real-Time Health Data, and Health Survey Data Protection to identify inherent issues with Differential Privacy integra-tion with Blockchain and technical challenges.(4) Conclusion: This review thoroughly surveyed and summarized Differential Privacy mechanisms in EMR privacy, real-time health data, and health survey data protection while highlighting challenges.

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on the strategy for renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with life-cycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and environmental impact. It was further observed that optimizing the building design with the goal to reduce embodied energy and carbon in compliance with modern energy standards, was crucial to improve overall energy performance. This work demonstrated that the developed BIM-based framework to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

In this article, rst we are going to review the concept of ordinary frames , in more general case in measure spaces, namely, gc-frames. We try to develop the use of measure space in describing frames. Then by means of the gc-frames, we shall introduce gn-operators, which we shall show that each trace class operator has a vector-valued integral representation and vice-versa.

Unmanned aerial systems (UASs) create an extensive fighting capability of the developed military forces. Particularly, these systems carrying confidential data are exposed to security attacks. By the wireless’s nature within these networks, they become susceptible to different kinds of attacks, hence, it seems essential to design the appropriate safety mechanism in such networks. The sinkhole attack is one of the most dangerous and threatening attacks amongst types of attack in UAS. A malicious UAV exists in such a threat attacking as a black hole for absorbing all traffic in the network. Mainly, in a Flow-based protocol, the attacker considers the requests on the route, then, it replies to the target UAV such as high quality or the best route towards Gard station. The malicious UAV is able to only insert itself on one occasion between the nodes relating to each other (such as sink node and sensor node), and act for passing packets among them. In this study, the malicious attacks are detected and purged using two stages were. In the first stage, some principles and rules are used to detect black hole, gray hole, and sinkhole attacks. In the second stage, using a smart agent-based strategy negotiation procedure for three steps, a defense mechanism is designed to prevent these attacks. The smart agent is used by reliable neighbors via the negotiation procedure for three steps, hence, the traffic formed by the malicious UAV is not considered. The suggested protocol is called SAUAS. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the UAS network performance metrics are enhanced based on the packet delivery rate, detection rate, false-negative rate and false-positive rate.

UAVNs (unmanned aerial vehicle networks) may become vulnerable to threats and attacks due to their characteristic features such as high mobility, highly dynamic network topology, and open-air wireless environments. Since previous work has focused on classical and metaheuristic-based approaches, none of these approaches have a self-adaptive approach. In this article, we examine the challenges of cyber detection methods to secure UAVNs and review exiting security schemes proposed in the current literature. Furthermore, we propose an agent-based self-protective method (ASP-UAVN) for UAVNs that is based on the Human Immune System (HIS). In ASP-UAS, the safest route from the source UAV to the destination UAV is chosen according to a self-protective system. In this method, a multi-agent system using an Artificial Immune System (AIS) is employed to detect the attacking UAV and choose the safest route. In the proposed ASP-UAVN, the route request packet (RREQ) is initially transmitted from the source UAV to the destination UAV to detect the existing routes. Then, once the route reply packet (RREP) is received, a self-protective method using agents and the knowledge base is employed to choose the safest route and detect the attacking UAVs. The method is evaluated here via extensive simulations carried out in the NS-3 environment. The experimental results of four scenarios demonstrated that the ASP-UAS increases the Packet Delivery Rate (PDR) by more than 17.4, 20.8, and 25.91%, and detection rate by more than 17.2, 23.1, and 29.3%, and decreases the Packet Loss Rate (PLR) by more than 14.4, 16.8, and 20.21%, the false-positive and false-negative rate by more than 16.5, 25.3, and 31.21% those of SUAS-HIS, SFA and BRUIDS methods, respectively.

The aim of this study was to investigate the effect of discoursal approach on Iranian intermediate EFL learners’ reading comprehension ability. Quick Placement Test (QPT) was used to select 60 intermediate EFL learners as the participants of this study. Then, they were randomly divided into experimental and control groups. Each consisted of 30 learners. Prior to the treatment, the participants of both groups were given a pretest to ensure their reading comprehension. The experimental group was exposed to the Hymes’ model. In the control group the researcher used placebo for teaching reading skill. Then a posttest was administered to both groups. An Independent samples t-test between posttests of the study and a paired-samples t-test between the pretest and posttest of the groups of the study were run. The results of the study revealed and emphasized that the Hymes’ model improved the learners’ reading comprehension.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

G-protein-coupled receptors (GPCR) belong to a large family of molecules eliciting different responses to a variety of signaling molecules. These receptors participate in various physiologic pathways such as metabolism, growth, immune responses, inflammation, vision, taste, olfaction, neurotransmission and even and pathologic responses including chronic inflammatory and vascular diseases. Receptors contributing to the biological responses of renin-angiotensin system (RAS) are members of GPCR family. COVID-19-induced inflammatory cascade has been attributed to acute ACE2 downregulation and imbalance of proinflammatory ACE/AngII/AT1R and anti-inflammatory ACE2/angiotensin (1-7)/Mas axes in favor of the former. Some of the receptors contributing to activities of proteins in RAS including AT1R, AT2R and Mas receptors are members of GPCR family. It is notable that these receptors induce their effects both through G protein and β-arrestin pathway; the former exerts temporary and the latter more sustained effects. In addition to the imbalance of GPCR responses contributing to RAS activities, it has been suggested that SARS-CoV2 pathogenesis might be attributed to the activation of GPCRs or modulating G-proteins involved in adenosine-CFTR regulation system and epithelial Na channel function.This article includes a minireview about the physiological functions of GPCRs and their contribution to COVID-19.

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.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

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

Molecular dynamics(MD) simulations are carried out to characterize the mechanicalproperties and failure behavior of van der Waals heterostructures composed ofgraphene and hexagonal boron nitride(hBN) single layer sheets. The graphene–hBNand hBN–graphene–hBN heterostructures simulations are carried out under tensileand shear deformation. Accordingly, stress versus strain curves of each systemare plotted and various properties of heterostructures, namely elastic modulusand shear modulus as well as failure stress and failure strain, are evaluatedand compared with one another as well as with the pristine graphene and hBNsheets. Subsequently, the failure mechanism/characterization of each sheet in theheterostructures is described. Alternatively, the elastic and shear modulus of eachheterostructure are computed by means of rule of mixture(ROM) which are in goodagreement with results that are obtained from MD simulations.

As a new approach, for a nonzero normed algebra $A$, we will define some different classes of algebra fuzzy norms on $A$ generated by homomorphisms and continuous homomorphisms. Also as a source of examples and counterexamples in the field of fuzzy normed algebras, separate continuity of the elements of each class are investigated.

In the present study, we implemented species distribution models (SDMs) to determine the potential invasive distribution range of a cichlid fish, Nile tilapia, Oreochromis niloticus, in a very sensitive inland water system in the southeastern Arabian Peninsula where it is now established and spreading. By using the “Biomod2” package, and the latest CMIP6 climate data, four single species distribution models and EM were built, and the results were evaluated by operating characteristic (ROC) and the true skill statistic (TSS). Based on the relative importance of climate factor analysis the mean diurnal range and precipitation seasonality were useful predictors of habitat suitability for O. niloticus. The response curve analysis indicated that the presence probability of O. niloticus decreased with increasing mean diurnal range and decreasing precipitation seasonality. The suitable distribution ranges for O. niloticus in the studied area were mainly distributed in the northeast of this region. The ensemble model results specified a significant impact of climate change on O. niloticus distribution, so highly suitable areas for this species will be reduced, while areas with low to moderate suitability increase slightly or remain unchanged. Based on the available data, Nile tilapia fulfills all four criteria to be considered as an invasive species: colonization in a new area, direct or indirect impacts of anthropological activity on its distribution range, geographical discontinuity between its native area (Africa) and the Arabian Peninsula (remote dispersal), and finally, its successful breeding in situ without human assistance resulting establishment of this biopollutant. Species distribution modeling provides insights into the factors that can reduce the risks and negative impacts of this biological pollutant on aquatic ecosystems of the southeastern Arabian Peninsula which harbors about 22 recognized fish species in 16 genera, 10 families, seven orders, and a class including 20 natives (seven endemic) species.

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.

Controlling and managing risk is always a critical point for the managers, investors and even consultants. Having clear and better percepts about future can reduce the risk of fail. In addition it is important for a risk analyzer to consider the stakeholders views and senses about the risk. This research aimed at framing a comprehensive risk management model which builds up on stakeholder requirements and forecasting techniques. This conceptual model attempts to use the stakeholder’s points of view to write prefer and probable scenarios to neglect critical risks and increase the chance of success in long-term plannings. Using futures studies methods prepare decision makers for the possible upcoming events and donate them a powerful tool to handle the uncertainties cover the future.

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

Infertility affects approximately 15% of couples worldwide, an estimated 30% of which is related to male factor infertility. Application of low level laser therapy (LLLT) to improve fertility status is a rapidly growing discipline in medicine. Laser therapy triggers a variety of biological processes through interaction with primary cellular photoacceptors. The present review aims at evaluating the literature available in the MEDLINE/ PubMed on cellular and molecular mechanisms of photobiomodulation in the domains of reproductive and veterinary medicine. We primarily focused on the clinical application of laser treatment on seminal quality, in particular its role in promoting spermatozoa motility, as well as the role of phototherapy in modulating assisted reproduction (ART). Additionally, we investigated the strength of evidence in support of the positive impact of photobiomodulation on facilitating ART.

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.

Early stroke diagnosis remains a big challenge in healthcare partly due to the lack of reliable diagnostic blood biomarkers, which in turn leads to increased rates of mortality and disability. Current screening methods are optimised to identify patients with a high risk of cardio-vascular disease, especially among the elderly. However, in young adults and children, these methods suffer low sensitivity and specificity and contribute to further delays in their triage and diagnosis. Accordingly, there is an urgent need to develop reliable blood biomarkers for triaging patients suspected of stroke in all age groups, especially children and young adults. This review explores some of the existing blood biomarkers, as single biomarkers, or biomarker panels, and examine their sensitivity and specificity for predicting stroke. A review was performed on PubMed and Web of Science for journal articles published in English during the period 2001 to 2021 which contained information regarding biomarkers of stroke. In this review article, we provide comparative information on the availability, clinical usefulness, and time-window periods of eight single blood biomarkers and six biomarker panels that have been used for predicting stroke in emergency situations. The outcomes of this review can be used in future research for developing more effective stroke biomarkers.

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH4, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

There is a relatively high risk of virus transmission in dental procedures and oropharyngeal examinations. We investigated the effects of mouthwashes on covid-19 viral load reduction during dental practices and oro-pharyngeal assessments. We performed a systematic search in PubMed, EMBASE, Scopus, Web of Science, Cochrane library for relevant studies up to February 2021. Papers evaluating patients with covid-19 infection (patients) who rinse mouthwashes (intervention) compared to patients who don’t rinse them (comparison) for reducing covid-19 viral load or reducing cross-infection of covid-19 (outcome) in the randomized and non-randomized clinical trial and quasi-experimental studies (study) were included due to PICOS question. Three independent authors conducted literature screening and data extraction. We extracted the most relevant data and we evaluated the risk of bias from the included studies. Out of 344 potentially eligible articles, six studies were included in this systematic review. Regarding viral load and negative cycle threshold (ct) values, 1% PVP_I and Listerine mouthwash were effective. 0.12% CHX mouthwash was effective 0-2 hours post rinsing, but it was not effective after 2 hours. A mixture solution of 0.2% Chlorhexidine gluconate and 6% Hydrogen peroxide was effective on day 5 of intervention. Gargling 1% hydrogen peroxide, 0.075% Cetylpiridinum Chloride (CPC), 0.5%PVP-I and 0.2% CHX mouthwashes was not effective on SARS-COV-2. It cannot be guaranteed that rinsing a specific kind of mouthwash prevents cross-infection of covid-19; however, the viral load of SARS-COV-2 in saliva will be decreased after rinsing mouthwashes containing 1%PVP-I and Listerine.

Antibiotic resistance represents a significant threat to the modern healthcare provision. The ESKAPEE pathogens, in particular, have proven to be especially challenging to treat, due to their intrinsic and acquired ability to rapidly develop resistance mechanisms in response to environmental threats. The development of biofilm has been characterised as an essential contributing factor towards antimicrobial-resistance and tolerance. Several studies have implicated the involvement of efflux pumps in antibiotic resistance, both directly, via drug extrusion and indirectly, through the formation of biofilm. As a result, the underlying mechanism of these pumps has attracted considerable interest due to the potential of targeting these protein structures and developing novel adjunct therapies. Subsequent investigations have revealed the ability of efflux pump-inhibitors (EPIs) to block drug-extrusion and disrupt biofilm formation, thereby, potentiating antibiotics and reversing resistance of pathogen towards them. This review will discuss the potential of EPIs as a possible solution to antimicrobial resistance, examining different challenges to the design of these compounds, with an emphasis on Gram-negative ESKAPEE pathogens.

Strain induced melt activation process (SIMA) creates a globular microstructure which improves the hardness and ultimate tensile stress (UTS) of 7075 aluminum alloys. But, at the grain boundaries of SIMA processed 7075 aluminum alloy, presence of continuous and brittle intermetallic compounds and the eutectic structure result in decreasing mechanical properties, such as elongation, formability and etc. Hence, in order to improve microstructure and formability of 7075 aluminum alloy respect to SIMA process, a new process was done that is called two-step strain induced melt activation (TSSIMA). This process, which has been mentioned in the experimental procedure section, both organized globular structure and significantly modified the microstructure of the alloy compared to that of the SIMA method. It promoted discontinuity and more homogeneity in distribution of the precipitates and, approximately removed the eutectic mixture. Performing the rolling process on the alloy also revealed that it is more effective in formability enhancement in comparison with SIMA process. Also, it increased the hardness of 7075 aluminum alloy respect to that of SIMA process.

Chemical and mechanical properties of plasma-assisted nitrided (PAN), TiN cathodic arc coated, and PAN with TiN coated samples were thoroughly evaluated on tool steel samples of 1.2379. The evaluation involved SEM, nano-indentation, profilometer, micro scratch, and corrosion tests. The results revealed that PAN treatment alters the roughness of the surface, while it has minimal impact on the coefficient of friction. However, it increases the substrate's hardness, making it twice as hard and three times more resilient against scratch tests, along with improved corrosion resistance. TiN-coated samples are four times harder than untreated ones, but they exhibit a higher elastic modulus (E), leading to a lower H/E index. The most promising results were obtained with PAN+TiN treatment, combining high hardness (H) with lower E, resulting in a high H/E index, a crucial criterion for wear resistance. These samples also demonstrated superior scratch hardness and corrosion resistance, despite being thinner and rougher.

In the present day and age, cancer is still a life-limiting factor whose one of the therapeutic strategies is immunotherapy with vaccines. This research aims to prepare and characterize nanoliposomal vaccine formulation attached to HER2/neu-derived peptide (AE36) with or without CpG-ODN, and to evaluate its immunological responses to the therapy using BALB/c mice with HER2 overexpressing breast cancer. Methods: AE36 was conjugated to the liposomes containing DOTAP, DOPE and Cholesterol. Such formulations are able to produce CD8+ and CD4+ responses and induce synthesis of cytokines detectable via Enzyme-linked immunosorbent assay kits, cytotoxicity testing and intracellular cytokine staining combined with flow cytometry. Therapeutic and prophylactic effectiveness were evaluated through the formulation in samples. The highest effectiveness was found in DDC-peptide + CpG-ODN in both prophylactic and therapeutic studies, which decreased the size of tumors significantly and increased time of survival. These nanoliposomes linked to AE36 could be regarded as an appropriate candidate for the treatment and also for prophylaxis of HER2+ breast cancer; however further studies are needed.

Bandung and Lembang are the cities that are chosen by many tourists (local and foreign) as their destinations. Geographically, these cities are placed side-by-side and each city has its own characteristics. As in Bandung, there are many hotels and culinary spots, meanwhile, in Lembang, there are many scenery tourism spots. Tourists usually have limited time to visit all the destinations on holiday, which makes them choose several tourist destinations to visit. In this paper, the tourism itinerary recommendation system is proposed by calculating the most optimal route between destinations and approaching the Vehicle Routing Problem with Time Windows. The optimal route is defined by using Dijkstra the shortest path algorithm by using the collaboration between the road information (road length, traffic condition, travel time, and weather condition) and criteria weight that is determined using the Analytics Hierarchy Process as the graph weights. According to the simulation results, the criteria weights are 6.9%, 62.7%, 18.6%, and 11.9% for Route Length, Traffic Condition, Travel Time, and Weather Condition respectively. Moreover, the optimal number of tourism itinerary plans for the tourist in this research is 4 destinations. As the usage of computational resources, it takes 31.8% of CPU and 61.9% of Memory usage. For the time processing, it increases exponentially as the increment of number of requested stops. The output of this research is expected to be a solution to the tourist itinerary plan, especially in Bandung and Lembang.

To systematically investigate the epidemiology of breast cancer risk factors in Iran, we performed a systematic search via PubMed, Scopus, Web of Science and Persian databases for identifying studies published on breast cancer risk factors up to March 2019. Meta-analyses were done for risk factors reported in more than one study. We calculated odds ratios (ORs) with corresponding 95% confidence intervals (CIs) using a fixed/random-effects models.Thirty-nine studies entered into the meta-analysis. Pooling of ORs showed a significant harmful effect for risk factors including family history (OR: 1.80, 95%CI 1.47-2.12), HRT (OR: 5.48, 95%CI 0.84-1.74), ER positive (OR: 1.87, 95%CI 1.41-2.33), PR positive (OR: 1.84, 95%CI 1.38-2.29), stress condition (OR: 2.67, 95%CI 1.84-3.50), passive smokers (OR: 1.68, 95%CI 1.34-2.03), full-term pregnancy at age 30 (OR: 3.41, 95%CI 1.19-5.63), abortion (OR: 1.84, 95%CI 1.35-2.33), sweets consumption (OR: 1.71, 95%CI 1.32-2.11) and genotype Arg/Arg (crude OR: 1.59, 95%CI 1.07-2.10), whereas a significant protective effect for late menarche (OR: 0.58, 95%CI 0.32-0.83), nulliparity (OR: 0.68, 95%CI 0.39-0.96), 13 to 24 months of breastfeeding (OR: 0.68, 95%CI 0.46-0.90), daily exercise (OR: 0.59, 95%CI 0.44-0.73) and vegetable consumption (crude OR: 0.28, 95%CI 0.10-0.46).This study suggest that factors such as family history, HRT, ER and PR positive status, stress condition, passive smokers, late full-term pregnancy, abortion, sweets consumption and genotype Arg/Arg might increase risk of breast cancer development, whereas late menarche, nulliparity, 13-24 months breastfeeding, daily exercise and vegetable consumption had an inverse association with breast cancer development.

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.

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al.). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), mainly affects the respiratory system with some patients rapidly progressing to acute respiratory distress syndrome (ARDS). The most common symptoms of the patients are fever, cough, dyspnea, myalgia, and fatigue. Nasopharyngeal and oropharyngeal swab specimens tested by real-time reverse transcription-polymerase chain reaction (RT-PCR) are the most commonly used methods to diagnose COVID-19. Herein, we investigate and discuss a young case of COVID-19, without any pre-existing medical conditions, whose both nasopharyngeal and oropharyngeal swab tests of SARS-CoV-2 were negative in the prodromal phase. However, after three days, with severe dyspnea and rapidly progressed acute respiratory distress syndrome (ARDS), the case was identified as infected by COVID-19 by testing bronchoalveolar lavage fluid (BALF). The patient was intubated in the intensive care unit (ICU) but expired on the fourth day. This case shows the importance of active and accurate monitoring of the patients showing COVID-19 symptoms. Although the BALF test has a higher exposure risk, it is considered more accurate and recommended if performed by an expert operator.

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.

Investigation of oomycete diversity in rice paddies of Fars Province in Iran led to the identification of two new Pythium sensu lato (s.l.) species as Globisporangium izadpanahii sp. nov. and Pythium banihashemianum sp. nov. The identification was based on morphological and physiological features as well as the phylogenetic analysis of nuclear (ITS and βtub) and mitochondrial (cox1 and cox2) loci using Bayesian inference and Maximum Likelihood. A major characteristic of G. izadpanahii was the production of globose hyphal swellings; this species did not produce vesicles and zoospores. The present paper describes formally these two new species and defines their phylogenetic relationships with other congeneric species. According to multiple gene genealogy analysis, G. izadpanahii grouped with other species of Globisporangium in the former clade G of Pythium s.l. and was closely related to both G. nagaii and the recently described G. coniferarum. The second species, designated P. banihashemianum, grouped with other species of Pythium sensu stricto in the former clade B of Pythium s.l. and according to the phylogenetic analysis shared an ancestor with P. plurisporium. In pathogenicity tests on rice seedlings, P. banihashemianum isolates were highly pathogenic causing severe root and crown rot, while G. izadpanahii isolates were not pathogenic.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

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).

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.

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.

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.

Background: Malnutrition has negative consequences in hospitals, like poor disease recovery, increased mortality, length of hospitalization, readmission, and health care costs.Aims: This review was designed to determine malnutrition prevalence in hospitals located in eastern Mediterranean region (EMR).Study design: To conduct this systematic review, papers, including original articles, related references and conference proceedings on the prevalence of malnutrition in adult hospitalized patients published in international journals and scientific databases from 1 Jan 2017 to 31 Jan 2022 will be included. The following keywords and their alternatives will be used in the literature search: malnutrition, patient, and EMR. The protocol has been registered at PROSPERO (code: CRD42022339426).Methods: After initial screening based on inclusion and exclusion criteria, the full text articles will be read carefully and eligible documents will be selected according to the defined objectives. Afterward, the selected papers will be reviewed for methodological quality and the required information will be extracted. Finally, in case of sufficient data, a meta-analysis will be performed. Screening, selection, quality assessment, and data extraction will be done by at least two independent reviewers.Results: This review is ongoing and will be completed until the end of year 2023.Conclusion: This review aims to provide comprehensive evidence about the prevalence of malnutrition among adult inpatients in EMR hospitals. This can provide regional authorities, health managers and policy makers good information regarding current status to make appropriate decisions for promoting the health status in hospitals.

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

Grain legumes are fair sources of protein, amino acids and energy, and can be used as a replacement for soybean meal in poultry feed formulations as the soybean meal becomes short in supply and costly. However, a concern associated with the use of grain legumes in poultry feeding is the presence of antinutritional factors. Effective processing and utilisation of these grain legumes in poultry feeding are well documented. The current review focuses on four selected grain legumes (lupins [Lupinus albus and Lupinus angustifolius], field peas [Phaseolus vulgaris], faba beans [Vicia faba] and chickpeas [Cicer arietinum]) and their nutrient content, the presence of antinutritional factors, processing methods and feeding value, including updated data based on recent research findings.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

Background: Systemic lupus erythematous (SLE) is an autoimmune, episodic, and chronic disease that can affect multiple organs in the body and consequently it can present with variety of sign and symptoms. Hypercalcemia is a considered to be a rare presentation in patients with systemic lupus erythematous. In this case report we present a patient with SLE flare up who presented with hypercalcemia, which her overlapping manifestations endured a challenging diagnosis of lymphoma.Case Presentation: A 41-year-old man with a ten-year history of SLE presented to us with symptoms of SLE flare up. During laboratory evaluation, high calcium level was detected which was unresponsive to the treatment. Bone marrow revealed marginal B cell lymphoma as the ultimate diagnosis. He was started on R-CHOP chemotherapy regimen, and reported relative improvement in clinical and laboratory conditions during follow-ups.Conclusion: Our case is the first report of concurrent SLE flare up, with a diagnosis of lymphoma and a primary clinical presentation of hypercalcemia. Physicians should be aware of the fact that patients with SLE flare up can present with hypercalcemia and lymphoma as concomitant conditions that needs further exploration.

This paper examines some of the largely neglected areas of opportunity to utilize skeletal space structure systems in support of modular, industrialized, economical, sustainable and digital future of the construction industry. In this context, the feasibility of future use of skeletal space structures is studied for a few classes of engineering structures, namely, residential apartment buildings, and offshore platforms, along with their suitability for reconstruction, renovation, modernization and retrofit of damaged buildings and urban areas of cultural heritage significance. Finally, the particulars of lean project management of space structures are discussed with emphasis on engineering and economic factors, production management, environmental aspects, quality management, and reliability, maintainability and sustainability considerations. This article concludes that skeletal space structures can fulfil many of the essential construction requirements of modern societies, especially those facing environmental challenges, all while allowing for design flexibility and mass customization.

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.