Abstract:This This article proposes a revolutionary idea that seamlessly blends algebraic structures and machine learning techniques. The primary goal is to explore the pseudo-metric free semigroup\'s structure because it has been potential to be used in more intriguing ways in the future. One of these applications is the algebraic structure of genome. DNA and RNA are the hereditary material that play a vital role in the metabolism process of living things, especially the protein synthesis. In this context, we demonstrate that the complexity of genetic codes is examined by defining pseudo-metric free semigroup over DNA basis. Additionally, we proposed two numerical mappings for DNA bases in relation to the pseudo-metric free semigroup representation. These mappings show that there is an isomorphism between the suggested algebraic structure and another algebraic structure defined in a different numerical set. The suggested study is implemented using customized machine learning models, including multilayer perceptron, recurrent neural networks, and convolutional neural networks, to identify changes between the DNA sequences. Experimental results show that Multilayer Perceptron with proposed numerical values for DNA bases offers 99.06% accuracy on testing data. This novel technique effectively figures out gene mutations that will aid in the prognosis of complex diseases.
Abstract:This experimental study explores the impact of Magnetic Activation of Water (MAW) on the fresh and hardened properties of Reused Crumb-Rubber Mortar (RCRM). Eight different mortar formulations were produced, comprising four variants using potable water (DW) and four utilizing MAW, with varying percentages of Reused Crumb Rubber Sand (RCRS) substituted for natural sand at rates of 0%, 4%, 8%, and 12%. The analyses of fresh properties indicated that MAW effectively reduced entrapped air and improved the consistency of the RCRM mixtures. In terms of hardened properties, MAW demonstrated a significant enhancement in compressive strength and hydration kinetics of RCRM compared to those made with DW. This innovative approach of employing magnetic activation not only addresses compatibility and heterogeneity challenges typically associated with rubber-cement mixtures but also offers a viable solution for incorporating waste rubber into cement composites. Ultimately, this research presents a promising avenue for improving the mechanical and physical characteristics of recycled materials in sustainable construction practices.
Abstract:Forage crops are used as livestock feed, so any anomalies or drops in quantity or quality in the production of these plants will influence the milk and meat markets. The years 2021/2022/2023 saw a real crisis in dairy products, largely attributed to the lack of forage. This shortage is often attributed to the country\'s meteorological factor. There is a high correlation between forage production and available moisture.\nThe objective of this study is to provide an overview of forage production in the wilaya of Medea. In other words, the aim is to characterize forage production by studying a number of variables, namely the areas devoted to this crop, the species grown, crop management, rainfall, harvested yield, etc. \nThe results show a very strong correlation between precipitation and natural grassland forage production (0.8660). This shows a strong upward trend in natural grassland production with increasing precipitation. Moreover, the interaction between production and area (production x area) was highly significant, indicating that the relationship between production and cultivated area varies depending on the type of forage.\nThe maximum achievable yield was obtained in the 2021 and 2017 seasons, with almost 85.66 and 41.16 t.ha-1 respectively, for the artificial oat-vegetable, while the minimum yield was obtained in the 2022 season, with almost 0.56 t.h-1 in the natural grassland, despite its large cultivated area occupied during this season. Other species showed variations in area and production. There were seasons when no crops were grown in the wilaya, except in 2017 for natural alfalfa and in 2018 for natural oat vetch. This situation is having an impact on forage stocks in the region.
Abstract:The rising need for lithium batteries and growing environmental issues have prompted the develop3 ment of new sustainable recycling techniques. In this study, the new optimization framework, multi4 objective goal programming, enhances the efficiency and sustainability of lithium battery recycling pro5 cesses. The model represents a significant advancement in recycling techniques as it can simultane6 ously address multiple objectives such as cost reduction, environmental impact mitigation, workforce 7 optimization, and compliance with recycling regulations. An effectively simulated case study utilizing 8 random data highlights the effectiveness of the model by optimizing decision variables and attaining op9 timal objective function values. The study presents the efficiency of the proposed model by conducting 10 a thorough sensitivity analysis, which includes changing parameters and observing their impact on the 11 performance of the objective functions.
Abstract:Background\n Sion\'s local paralysis (SLP) is the local application of neuromuscular blocking agents (NMBAs) to achieve local paralytic effects while avoiding the side effects of systemic paralysis.\n\nObjective:\nWe examined the changes in foreleg raising power after Sion\'s local paralysis (SLP) in an advanced porcine model.\n\nMethods:\nThis was a randomized, double blind, placebo-controlled, porcine study. Ten male Korean native Jeju black pigs were randomized into an intervention group (n = 5) and a control group (n = 5). The injection points were in the middle of the left trapezius muscle and middle of the left deltoid muscle, as described in a previous study. The control group received 2 ml normal saline (NS), with 1 ml injected at each point. The intervention group received 0.5 mg/kg succinylcholine diluted to 2 ml in NS, and 1 ml was injected at each point. The height of the left foreleg from the baseline (experimental table) was measured. We measured foreleg height and oxygen saturation at −4, −2, 0, +2, +4, +6, +8, +10, +20, +30, and +60 min. Unlike previous experiments, the study animals received pain stimuli before each measurement using the Kelly forcep that were clamped on their dew claws.\n\nResults:\nAfter SLP, foreleg height values immediately declined in the intervention group by approximately 20%. This effect was sustained for a few minutes. After satisfying the sphericity assumption (P-value = 0.015), repeated-measures analysis of variance (ANOVA) identified a significant main effect of group (P-value = 0.039) and time (P-value = 0.001) at 0, 2, 4, and 6 min. Hypoxic events were not observed.\n\nConclusions\nCompared with the control group, the foreleg raising power in the intervention group decreased significantly after SLP, without hypoxia, in an advanced pig model.
Abstract:Digital twin (DT) has provided numerous opportunities for product development businesses to leverage data acquired from the physical model. Ideally, many companies are improving their use cases by using the collected physical data in a virtual model. Thanks to emerging communication technologies, 5th generation mobile network (5G), the internet of things (IoT), industrial internet of things (IIoT) sensors, etc., which help to collect data from the physical product and send data to cloud computing, called big data. With the gap in the non-availability of standard architecture, many companies are using big data and building their digital models. This paper proposes a novel DT-enabled new product design framework that has built an interaction between the DT and product design environments. This framework enables the data to be captured from various physical entities, stored in a centralized location, and pushed into the digital entity, which processes the data and converts the raw data into information used for decision-making. In the digital entity, multiple technologies are used to process data into information, such as artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. The processed data that emerges from these models are called synthetic or DT data; this data is later used for control / monitoring and visualization / interaction purposes. In this paper, the data classified as visualization and interaction is integrated with the new product design framework in all four design stages, viz., concept design, detailed design, design verification, and redesign, with various data contributions to the design. A case study was discussed by applying the DT data processed using the AI / ML model in the concept design phase. Using synthetic data, the medium-duty truck frame assembly�s design concept was validated using ten load cases iteratively, and the design was finalized. The results are encouraging that the overall weight of the frame assembly was optimized by 9% without impacting the strength and factor of safety of the frame assembly.
Abstract:In the urban and suburban areas of Lanzhou, a petrochemical industry city located in northwest China, 99 Volatile Organic Compounds (VOCs) excluding aldehydes and ketones were measured using both PAMS and TO-15 methods between 2021 and 2022. The analysis of the collected data aimed to explore the seasonal trends in air pollution and its correlation with respiratory diseases in Lanzhou. The average annual concentrations of VOCs were found to be 121 ppbv for urban living areas, 165 ppbv for industrial areas, and 114 ppbv for background areas, with industrial areas exhibiting the highest concentration. These VOCs consist of various organic compounds, including alkanes, olefins, halogenated hydrocarbons, and aromatic hydrocarbons, which act as organic tracers of specific marker molecules or their sources. By employing principal component analysis and grouping the species using the maximum variance method, the VOC concentration levels were assessed across different regions. Subsequently, the associated changes were calculated based on the number of reported respiratory morbidity cases from public health hospitals in each region. The results suggest a correlation between VOC concentration levels and the onset of respiratory illnesses during the fall and winter seasons. Therefore, reducing VOC emissions during these months can effectively mitigate the public health risks associated with respiratory illnesses.
Abstract:This study analyses combined impacts of radiation and ion slip on unsteady MHD\nchemically reacting fluid flow past on plate with variable wall temperature and mass transfer in the presence of Hall current. The geometry of the flow model has been consider exponentially accelerated inclined surface and medium of the flow taken porous. A set of non-dimensional governing MHD fluid model is obtained and the governing equations of this fluid model are linear, an exact solution of our model can be obtained by using Laplace transform method. The impact of various parameters on flow\nof the fluid are discussed with the help of graph and table.
Abstract:Abstract\n\nFinancial services have been fundamentally transformed by FinTech, an innovation in technology that has caused significant shifts in the financial sector. This study delves into the impact of two prominent FinTech platforms, Stripe and Square, on the banking industry. Stripe specializes in online payments, customization, and agility, with a focus on in-person transactions, catering to technologically adept startups. Conversely, Square offers tools tailored for small retail and restaurant enterprises. The study aims to bridge the gap between user perceptions and actual performance, focusing on security and reliability concerns. Despite the convenience offered by FinTech, concerns over security persist, highlighting the need for improved user confidence through robust security standards and transparency. The research explores the historical evolution of FinTech, emphasizing the integration of advanced technologies like AI and blockchain. The research analyzes FinTech performance evaluation methodologies, including key performance indicators (KPIs), to showcase the significance of assessing financial success, customer satisfaction, and user experience. The study outlines the criteria for selecting Stripe and Square, considering industry prominence, market share, a diverse user base, service offerings, innovation, accessibility, and financial inclusion initiatives. The methodology employs a qualitative approach to systematically analyze the performance and user experience of both platforms. Features, pricing, target audience, security, and user experience influence user preferences, according to key findings. The comparative analysis between Stripe and Square indicates a market trend where businesses choose platforms based on operational nature, settlement speed, and specific financial needs. The study underscores the transformative role of FinTech within the financial sector, driven by technological innovation, financial inclusion initiatives, diverse user bases, user-centric design, and robust security measures. The implications suggest a future FinTech landscape characterized by continued emphasis on innovation, inclusivity, user-centric approaches, and security. The research concludes by highlighting the significant impact of Stripe and Square on the financial sector and providing insights for stakeholders to adapt strategies in the dynamic FinTech landscape. Further research opportunities lie in exploring evolving user preferences, emerging technologies, and regulatory developments shaping the future of FinTech.
Abstract:Higher education institutions worldwide have been greatly affected by the COVID-19 pandemic about two years ago. As a consequence, learning and teaching mode were then changed into online platform. Today, even though most of the class activities have resumed back to physical mode, online learning is still remained as an important platform for learning. Apart from desktop computers and laptops, students use smartphones considerably for online learning. However, the physical characteristics of a smartphone could hinder its ability to work as an effective tool for academic task. In the context of online learning, this study aims to explore the adoption of an advanced feature of a modern smartphone: the desktop mode, which could possibly overcome the physical limitations of a standard smartphone. The factors that influence the undergraduate students� intention to use the smartphone�s desktop mode for online learning were examined. By using constructs from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Learning Value (LV) and Empowerment in Learning (EiL), the PLS-SEM method was used to analyze the data collected with a self-administered survey. The results revealed that Performance Expectancy, Social Influence, Hedonic Motivation, Habit and EiL positively influence students� behavioral intention (BI) to use smartphone�s desktop mode in online learning. Additionally, gender was found to have moderating effects on the relationship between some constructs and BI.