Research Work Research Articles

Recent developments in synthetic strategies and pharmacological outcomes of synthetic xanthine oxidase inhibitors: A comprehensive review

AbstractXanthine oxidase is an important enzyme in purine metabolism that converts hypoxanthine to xanthine and subsequently xanthine to uric acid. Its elevated levels result in an abnormal accumulation of uric acid that results in gout and other pathological conditions, which makes it a prime target for the management of gout. Considering this, numerous reports have been published by the various research groups across the globe focusing on the development of effective xanthine oxidase inhibitors. On that ground, numerous review articles are also available describing the pharmacological outputs of these reports, but a composition highlighting the synthetic strategies used in these research works is still missing. This review will focus on various synthetic strategies adopted by different research groups in developing xanthine oxidase inhibitors with their pharmacological outcomes, along with structure activity relationships. This review will help researchers and pharmaceutical chemists in the design and development of new xanthine oxidase inhibitor leads that are highly efficient and have fewer side effects than existing ones.

Wrist-Based Fall Detection: Towards Generalization across Datasets.

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets.

Interactive Teaching in Virtual Environments: Integrating Hardware in the Loop in a Brewing Process

The teaching–learning process developed was based on the effective integration of the Hardware in the Loop (HIL) technique to control a brewing process. This required programming the autonomous control of the system and uploading it to a physical controller consisting of a PLC S7-1200, which communicates in real time with a virtual brewing environment, in addition to a SCADA system, providing engineering students with a unique practical experience. The system allows the emission of input signals from sensors and the reception of output signals in actuators, which is reflected accurately and in real time in the virtual environment. Students cannot only observe but also control and manipulate the system using specifically developed programs. This methodology enriched the understanding of industrial processes and fostered the acquisition of control skills. This research work reveals that the combination of the physical and the virtual through the HIL technique offers an effective approach for the training of engineers, improving their understanding of industrial control processes and their ability to intervene practically in real industrial situations.

Comparative Technical and Economic Analyses of Hydrogen-Based Steel and Power Sectors

Decarbonizing the current steel and power sectors through the development of the hydrogen direct-reduction iron ore–electric arc furnace route and the 100% hydrogen-fired gas turbine cycle is crucial. The current study focuses on three clusters of research works. The first cluster covers the investigation of the mass and energy balance of the route and the subsequent application of these values in experiments to optimize the reduction yield of iron ore. In the second cluster, the existing gas turbine unit was selected for the complete replacement of natural gas with hydrogen and for finding the most optimal mass and energy balance in the cycle through an Aspen HYSYS model. In addition, the chemical kinetics in the hydrogen combustion process were simulated using Ansys Chemkin Pro to research the emissions. In the last cluster, a comparative economic analysis was conducted to identify the levelized cost of production of the route and the levelized cost of electricity of the cycle. The findings in the economic analysis provided good insight into the details of the capital and operational expenditures of each industrial sector in understanding the impact of each kg of hydrogen consumed in the plants. These findings provide a good basis for future research on reducing the cost of hydrogen-based steel and power sectors. Moreover, the outcomes of this study can also assist ongoing, large-scale hydrogen and ammonia projects in Uzbekistan in terms of designing novel hydrogen-based industries with cost-effective solutions.

Heat Transfer and Flow Characteristics of Channel Impingement Cooling Structure at Leading Edge Inside Turbine Blades Using Large Eddy Simulation

Abstract As a main part of multichannel wall jet cooling structure, channel impingement cooling is a cooling strategy of great concern at the leading edge inside of the turbine blade. In this paper, heat transfer and flow behavior in the channel impingement cooling structure are investigated by large eddy simulation (LES). The results imply that impingement created by curvature-induced centrifugal instabilities in the turning region of the cooling channel is dominated by a streamwise vortex system containing a counter-rotating Dean vortex, which presents high heat transfer streaks along the streamwise direction on the target wall. The intensely unsteady nature of the cooling jet induced by a lack of equilibrium between the pressure gradient and the centrifugal force is precisely captured herein by LES. An attaching-wall jet formed on the outer wall downstream of the cooling channel has highly three-dimensional characteristics not observed by Reynolds-averaged Navier–Stokes equations (RANS). Heat transfer augmentation on the target wall of the cooling channel is mainly due to the intensifying streamwise vortex system developing in the turning region as driven by the centrifugal force. This research work will provide a reference for the optimization and application of multichannel wall jet cooling for gas turbine blades.

Seidel Laplacian Energy of Fuzzy graphs

Seidel Laplacian Energy of Fuzzy graphs

Preparing a New Type of Concrete Based on Sulfur-melamine Modifier

في هذا البحث تم تحضير نوع جديد من الخرسانة يعتمد على تحوير الكبريت والميلامين ودراسة خصائصها المختلفة. حضر هذا النوع الجديد من الخرسانة بناءً على تعديل الكبريت والميلامين والمكونات المختلفة. تم اثبات هذه التعديلات من خلال التحليل الطيفي للأشعة تحت الحمراء وتحليل .TG تمت دراسة التشكل السطحي الناتج عن هذا التعديل من خلال تحليل SEM وEDS وتم دراسة نسب المكونات في الخواص الخرسانية والكيميائية والفيزيائية الناتجة عن تعديل الكبريت والميلامين، كما تم دراسة المقاومة الكيميائية والتآكل للخرسانة، وثبات الخرسانة ضد امتصاص الماء، وثبات الخرسانة ضد التجمد، والخصائص الفيزيائية والميكانيكية والمتانة، ومعامل المرونة، والحرارية ومعامل التمدد للخرسانة الكبريتية المدروسة. أكدت نتائج الأشعة تحت الحمراء المجموعات الوظيفية الأمينية (حلقة الميلامين المرفقة) وتشكيل سلاسل متعدد الكبريت. كانت خسارة الكتلة الحرارية لتعديل الكبريت والميلامين خطوة واحدة. كانت عمليات فقد الكتلة للمنتج المعدل عمليات ماصة للحرارة. كشفت نتائج SEM التي تم الحصول عليها أن تعديل الكبريت والميلامين يؤدي إلى بنية مسامية أكثر ، بدون أي أشكال بلورية. أظهر تحليل EDS أن ذرات النيتروجين شكلت 51.33٪ من الكتلة الكلية بينما كان الكربون 30.94٪ من الكتلة الكلية. كان ثبات الخرسانة المعدلة الكبريت والميلامين عاليًا جدًا في المحاليل القوية المختلفة. كان الحجم المنخفض للخرسانة القائمة على الركام أكثر كثافة \، 2417 كجم / م 3. انخفضت كثافة الخرسانة ببطء مع زيادة حجم الركام. كان معدل التشوه للخرسانة المعدلة 0.0030-0.0033 مما يؤكد على ان فعالية الخرسانة المعدلة أفضل من الخرسانة التقليدية. كما أكدت النتائج المتحصل عليها أن قيمة معامل التمدد الحراري للخرسانة المعدلة من الكبريت والميلامين كانت 17.2×10-6\oC درجة مئوية.

LEGISLATIVE REGULATION OF BOTH CRIMES OF THREAT AND INFORMATIONAL BLACKMAIL- ANALYTICAL FUNDAMENTAL STUDY OF SAUDI INFORMATIONAL CRIMES LAW

Objective: This research discusses one of the most important issues resulting from the rapidly developed technological revolution, as coercion and electronic extortion have become one of the negative results of that revolution.
 
 Theoretical Framework: the coercion and electronic extortion is the most common negative results of that revolution, particularity that they do not require more tools or effort, as all the blackmailer needs is a picture, or an audio recording, or something that combines both of the picture and audio recording, or even a piece of information, on any social networking site, so that he, then, weaves the threads of his planned crime, behind which he intends to achieve specific goals, which are most cases achieving material or moral gain, engaging in a forbidden sexual relationship, or similar benefits. In addition, this crime is rapid spread due to its electronic nature. Accordingly, it is necessary to learn about these serious crimes.
 
 Methods: The researcher used the analytical fundamental approach to study the crime and its elements and analyze the legal provisions with the objective down to reach forms of the implementation of the crime and its elements and the penalty to be imposed on the criminal regarding this crime.
 
 Results and Discussion: This research work was divided into two sections. The first section discusses the definition of crime of coercion and its conditions, and the definition of crime of the electronic blackmail, and types and goals of the blackmail. As for the second section, it discusses elements of realization the crime and the punishment determined thereto by Law.
 
 Concluion: The crime of coercion and extortion goes through several stages, and the offender’s goal is to achieve his desires, whether material, moral, or sexual.

To access the prevalence of undiagnosed type 2 diabetes mellitus in patients with acute coronary syndrome.

Objective: To access the prevalence of undiagnosed type 2 Diabetes Mellitus in patients with Acute Coronary Syndrome. Study Design: Cross-sectional study. Setting: Department of Medicine, of Tertiary Care Hospital of Islamabad. Period: 17th July 2023 to 17th October 2023. Material & Methods: WHO calculator was used to calculate the sample size and 100 subjects were selected fulfilling the inclusion criteria (Both male and female of age 18 to 65 years retained in ER or admitted with the diagnosis of ACS). Blood samples were obtained for baseline investigations including blood sugar and HBA1C. Results were entered on EXCEL Sheets and analyzed using SPSS V.20. Results: The data showed 47% males, 70% of the participants belonging to the age group 41-65 years ACS presenting with STEMI in 37%, 40% patients with NSTEMI and unstable angina was found in 23%. The Mean BMI was 27±3.2SD mean HBA1C level was 4.3±1.7SD and the mean FBG was 129±17.9SD. Among all the male patients, there were (27.65%) patients with undiagnosed T2DM while among all the female patients, (18.8%) were reported with undiagnosed T2DM. Conclusion: A high prevalence of undiagnosed DM was observed in ACS patients admitted to tertiary care hospital. Research work done in developing countries showed data which was consistent with our study, however, research data from developed world was in contrast to our study.

Advances in industry 4.0: from intelligentization to the industrial metaverse

AbstractOne of the characteristic features of the next-generation of Industry 4.0 is human-centricity, which in turn includes two technological advancements: Artificial Intelligence and the Industrial Metaverse. In this work, we assess the impact that AI played on the advancement of three technologies that emerged to be cornerstones in the fourth generation of industry: intelligent industrial robotics, unmanned aerial vehicles, and additive manufacturing. Despite the significant improvement that AI and the industrial metaverse can offer, the incorporation of many AI-enabled and Metaverse-based technologies remains under the expectations. Safety continues to be a strong factor that limits the expansion of intelligent industrial robotics and drones, whilst Cybersecurity is effectively a major limiting factor for the advance of the industrial metaverse and the integration of blockchains. However, most research works agree that the lack of the skilled workforce will no-arguably be the decisive factor that limits the incorporation of these technologies in industry. Therefore, long-term planning and training programs are needed to counter the upcoming shortage in the skilled workforce.

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

Research on environmental aspects of retention/release of pollutants in soils and sorbents. What should be next?

Although studies dealing with adsorption/desorption (and/or retention/release) of pollutants present in environmental compartments is a classical field of research, recent papers are focusing on some weak points of investigations and publications within the area. In addition, an increasing number of works are being published related to new possibilities and alternatives in this kind of research works, many of them in relation to the use of artificial intelligence (AI). Considering the existence of eventual controversies, eventual mistakes, and the convenience of suggesting alternatives to go ahead in the future, in this work, after taking into account some relevant publications in the previous literature, a simple workflow is proposed as a kind of protocol to revise successive steps that could guide the direction to follow when programing research dealing with the retention/release of pollutants in soils and sorbent materials.

Investigation of micro-electro-discharge machining process parameters during machining of M2 hardened die-steel

Micro-electro-discharge machining (micro-EDM) is a well-known micromachining method to create microstructures on hard-to-cut materials. To further exploit the capability of micro-EDM, the present research work investigates the effect of various important micro-EDM process parameters on the material removal rate (MRR), tool wear rate (TWR), and overcut (OC) during micro-cavity machining of M2-hardened die-steel. A Taguchi experimental design was used to study the effects of three process parameters namely pulse-on-time (Ton), peak current (Ip), and gap voltage (Vg). The experimental results showed that the most influencing process parameter in the present experimental investigation is peak current, Ip followed by pulse-on-time, Ton and gap voltage, Vg. Furthermore, a recent and effective method i.e. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) has been successfully employed to determine optimal process parametric combination for micro-EDM process to achieve maximum MRR and minimum TWR and OC during machining of micro-cavity on M2-hardened die-steel. From the present research investigation, the optimal micro-EDM process parametric combinations obtained for higher MRR, lower TWR and OC are found as 1 A of peak current, 30 μs pulse-on time and gap voltage of 60 V. Furthermore, through various scanning electron microscope micrographs of micro-cavities and micro-machined surfaces, analysis has been done to find out the micro-cavity with best geometrical accuracy and surface feature to validate the best and most ideal combinations of micro-EDM input parameters for micromachining operations. The results of this study show that micro-EDM is a promising process for machining M2-hardened die-steel. The optimization of process parameters using the MARCOS methodology can further improve the machining efficiency and accuracy of micro-EDM.

Design and Development of a Secure Website for Asaba Real Estate Industry

This research work borders on development of a secure functional website for Asaba real estate industry where potential buyers can visit and make their choice. Survey carried out using web research, study of documents and oral interview showed that all is not well with the way and manner real estate transaction is carried out in Asaba. The main objectives of this research work are to develop interactive website that can bring estate owners and customers together in real time without the stress of travelling to locate the property, thus saving time and cost and provide real time information on availability of property through proper advertisement. The methodology adopted for this research is Structural Systems Analysis and Design Methodology (SSADM). The system was also implemented using Object Oriented PHP and MYSQL. The new system developed brought effectiveness and efficiency to the process of selling, buying and renting houses in Asaba. This further increased transparency, accountability, customer satisfaction and effective data collection from Asaba real estate industry. Keywords: Website, Real Estate, Transparency, Methodology and Encryption.

Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading

There is still insufficient data on the behavior of tubed-reinforced concrete columns (TRCCs) under the eccentric compression. Thus, this research work comprehensively examines the eccentric compression behavior of TRCCs using nonlinear finite element modeling and machine learning (ML). To do this, numerical simulation and parametric analysis based on existing investigations were conducted. In addition to the existing 22 specimens with limited test variables, additional 188 specimens were developed to cover a wide range of parameters, including the load eccentricity, transverse reinforcement spacing, columns’ slenderness ratio, yield strength of steel, and outer steel tube diameter. Additionally, six ML models were created to estimate the ultimate load results. The results indicated that increasing the outer steel tube yield strength and diameter, and reducing the load eccentricity, slenderness ratio, and spacing of the transverse reinforcement enhanced the load-carrying capacity of the columns. The Gaussian process regression model demonstrated superior performance metrics in comparison to other ML models, with the highest R2 values (0.998613 in training and 0.99823 in testing stages) and lowest root mean square error values (0.007213 in training and 0.008471 in testing stages). To save money, time, and resources compared to laboratory testing, an online-based prediction program is finally presented to predict the columns’ ultimate load.

School gardening through the perspective of the Whole School Approach

ABSTRACT Inadequate dietary patterns are a major contributor to the increasing prevalence of malnutrition in all its forms, while the processes of food production, consumption and wastage further contribute to environmental degradation. School gardens serve as important educational tools for promoting healthy, sustainable food systems. However, there are obstacles in setting up and operating a school-integrated, long-sustained school garden. This research work employs the Whole School Approach framework to address these obstacles utilizing a school case study involving in-depth interviews with school stakeholders. Findings suggest that a long-sustained school garden requires collective vision and strategic planning. Having a holistic school garden-curriculum connection across school subjects and year groups, continuous school staff capacity building and involvement of the school community are also important. Finally, integrating a school garden into the school daily life and environment and aligning its operation with the health and sustainability school policy can enhance its longevity over time.

Opinion Mining on Social Media Text Using Optimized Deep Belief Networks

In the digital world, most people spend their leisure and precious time on social media networks such as Facebook, Twitter. Instagram, and so on. Moreover, users post their views of products, services, political parties on their social sites. This information is viewed by many other users and brands. With the aid of these posts and tweets, the emotions, polarities of users are extracted to obtain the opinion about products or services. To analyze these posts sentiment analysis or opinion mining techniques are applied. Subsequently, this field rapidly attracts many researchers to conduct their research work due to the availability of an enormous number of data on social media networks. Further, this method can also be used to analyze the text to extract the sentiments which are classified as moderate, neutral, low extreme, and high extreme. However, the extraction of sentiment is an arduous one from the social media datasets, since it includes formal and informal texts, emojis, symbols. Hence to extract the feature vector from the accessed social media datasets and to perform accurate classification to group the texts based on the appropriate sentiments we proposed a novel method known as, Deep Belief Network-based Dynamic Grouping-based Cooperative optimization method DBN based DGCO. Exploiting this method the data are preprocessed to attain the required format of text and henceforth the feature vectors are extracted by the ICS algorithm. Furthermore, the extracted datasets are classified and grouped into moderate, neutral, low extreme, and high extreme with DBN based DGCO method. For experimental analysis, we have taken two social media datasets and analyzed the performance of the proposed method in terms of performance metrics such as accuracy/precision, recall, F1 Score, and ROC with HEMOS, WOA-SITO, PDCNN, and NB-LSVC state-of-art works. The acquired accuracy/precision, recall, and F1 Score, of our proposed ICS-DBN-DGCO method, are 89%, 80%, 98.2%, respectively.

Psychometric properties and factor structure of a motivation scale for higher education students to graduate and stimulate their entrepreneurship

AbstractThe purpose of this research work is to provide a measurement instrument, through the validation of a proposed scale, to determine the relevant factors that affect the motivation of university students, and that can be used as anticipatory indicators of personal entrepreneurship to achieve educational and work success. To carry it out, exploratory and confirmatory factor analyses have been carried out, based on the principal components’ method, which have been validated through the usual model fit measures in the literature, considering an analysis of reliability and reliability of the measurement instrument. To obtain this purpose, a sample of the university population was selected, through a simple random sampling, considering heterogeneity of courses, subjects, areas, and teaching modalities, of 596 individuals, with a higher percentage of women compared to men, as can be seen from the total number of enrolled in university degrees in the Spanish education system. The scale proved to have good psychometric properties, obtaining good internal consistency and validity. Among the main findings, we can highlight several dimensions for motivation, for instance, emotional self-management and adversity management; and learning strategies, such as: active self-management of study material, study management and self-management of effort, among others. In conclusion, a scale has been validated to determine which dimensions should be considered to promote student motivation as a method of personal entrepreneurship, and which can be used by educational authorities to propose extracurricular training that affects the improvement of students' competence, both in academic and emotional management. The dataset was analyzed using exploratory and confirmatory factor analysis.

Solvent efficiency and role of dispersion and electrostatic forces for chiral discrimination of sulfur-containing amino acids by tetra-protonated CBPQT macrocycle

Two enantiomeric forms of amino acids in a chiral medium behave quite differently because of the different orientation of their functional groups in space. Thus, the phenomenon of chiral recognition is crucial with the focus on applications in molecular sensing and enantioselective separations. The present research work is focused on the illustration of the potential chiral recognition of a porous CBPQT4+ macrocycle for sulfur-containing amino acids, which is elucidated by the conformational energies landscape with quantitative non-covalent analysis and their electronic behaviour. Herein, we report the chiral recognition of sulfur-containing amino acids e.g., cysteine (CY), homocysteine (HCY), and methionine (MT), by tetra-protonated (4+) CBPQT macrocycle via density functional theory (DFT) calculations. Geometry optimization, thermodynamic stability, noncovalent interaction analysis, symmetry-adapted perturbation (SAPT), and electronic properties analyses are employed to characterize the chiral response of the complexes formed by CBPQT4+ with D- and L-isomers of selected amino acids. The interactive conformations of complexes indicate physisorption of amino acids through the central cavity of the macrocycle. The maximum chiral discrimination is observed in the case of D- and L-cysteine isomers, which is 3.56 kcal/mol. It is revealed that the complex; D-CY@CBPQT4+ is energetically more stable than the L-CY analogue, whereas, L-HCY and L-MT show higher stability as compared to D-type counterparts, probably due to the interaction of the thiol groups with π-electrons of macrocycle in respective stable complexes. Non-covalent interaction (NCI) analyses including, reduced density gradient-based NCI index and SAPT reveal that the methionine-based complexes show the highest attractive components e.g., electrostatic, dispersion, and induction with the lowest repulsive exchange contribution, which is followed by homocysteine and cysteine. Overall, results show that the CBPQT4+ macrocycle has an excellent ability to differentiate between L- and D-amino acids, the difference is more pronounced when the structure of amino acid is small and rigid.

Development and Evaluation of a Maturity Model for AI Deployment Capability of Manufacturing Companies

ABSTRACT This research work addresses the development and evaluation of a maturity model for artificial intelligence (AI) deployment capability of manufacturing companies. Based on the literature research and 11 maturity models, an industry-specific maturity model is developed. The maturity model contains sequential maturity levels with defined maturity dimensions and 29 indicators. Among the main findings is that the developed model is capable of calculating an AI deployment capability level and fully counterbalance the maturity indicators.