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Evaluation of the capability of NARX neural network in predicting ground water level changes

Efficient monitoring and tracking of groundwater level changes is critical for sustainable management of water resources, especially in light of population growth, climate change, and increasing water demand. This study evaluates the ability of the Non-linear AutoRegressive with eXogenous input (NARX) model to simulate groundwater level trends in Ajabshir, Iran, using groundwater level data from 2006 to 2019 as a baseline period. The model was trained using time, groundwater levels, and delay times between 1 to 2 as the input training samples. The results indicate that the NARX model performed exceptionally well in simulating historical trends of groundwater levels, achieving a Coefficient of Determination (R2) value of 0.87 and a Root Mean Squared Error (RMSE) of 0.03 (m). The excellent performance can be attributed to the optimal hyperparameters and long-term simulation capabilities of the NARX model. The findings have significant implications for managing groundwater resources in Ajabshir and other regions facing similar challenges. The NARX model can be used to predict future trends in groundwater levels, taking into account current and projected climatic conditions, population growth, and other key factors. Such predictions can inform decision-making and help develop effective water management strategies that promote sustainable use of this vital resource.

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Optimizing the Energy Efficiency in 5G security systems for Intrusion Detection with an emphasis on DDOS assaults
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In response to the rising demand for new and existing use cases of Energy Efficiency, the telecoms sector is going through a dramatic shift towards 5G technology. High data speeds, extensive coverage provided by dense base station deployment, higher capacity, improved Quality of Service (QoS), and extremely low latency are required for 5G wireless networks. New deployment methods, networking architectures, processing technologies, and storage solutions must be created to satisfy the anticipated service requirements of 5G technologies. These developments further increase the need to secure the security of 5G systems and their functionality as well as Energy Efficiency problems. 5G system security is the target of intense efforts by developers and academics in this industry. Significant security concerns for 5G networks have been identified through extensive research. Attackers can make use of vulnerabilities by introducing malicious code and performing other nefarious deeds to take advantage of the system. On 5G networks, attack techniques as Model node map (MNmap), power depletion assaults and Man-in-the-Middle (MiTM) assaults can be effectively used. However, this study analyses 5G technology's current Energy Efficiency problems. We recommend an unusual Intrusion Detection system (IDS) which makes use of Traffic Volume methods considering this investigation, we propose the enhancing training process by including statistical analysis on Distributed Denial-of-Service (DDoS) threats, which is how prior research recommended using OMNET and NS-3 on IDS for optimization. Additionally, the methodology for incorporating the suggested Intrusion Detection systems within a typical 5G architecture is presented by our research using NETSIM. The paper also offers a planned system's correction method, providing a useful implementation after making analysis.
Keywords—5G, security, Intrusion Detection systems, Energy Efficienc

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A Survey of Machine Learning and Optimization Algorithms in Plant Tissue Culture
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Although significant efforts have been made to increase the world's food supply, hunger and undernutrition continue to be significant challenges worldwide. As the global population is expected to exceed 10 billion by 2050 and agriculture facing numerous challenges due to climate change, finding sustainable solutions to food insecurity is crucial. Plant tissue culture has emerged as a promising technology for crop improvement and rapid multiplication of various crops. However, this approach generates vast amounts of data resulting from the complex interactions between plant genetic and environmental components, making it difficult to analyze using traditional statistical methods. To tackle this issue, researchers have turned to artificial intelligence (AI) technologies, particularly machine learning (ML) algorithms, which are well-suited for handling large and complex datasets. Artificial neural networks, support vector machines, genetic algorithms, and other ML techniques have been extensively employed in the analysis, prediction, and optimization of plant in vitro breeding processes. Thus, this mini-review provides an up-to-date assessment of machine learning applications in plant in vitro culture research, emphasizing their various strengths and limitations and proposing potential future research directions.

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In vivo biocompatibility and biodegradability of bilayer films based on hyaluronic acid and chitosan for ENT surgery
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Nasal septal perforations are now a frequent pathology, occurring in 2% of the population. They are caused by a number of factors: uncontrolled use of topical drugs for nasal breathing recovery, synthetic steroids, long rehabilitation period after septal surgery, nasal trauma with massive cartilage destruction.

Conversely, persistent tympanic membrane defects could be caused by the long-term inflammatory processes (e.g. chronic otopyosis), and trauma.

These defects cannot be restored even with modern reconstructive microsurgery due to the lack of suitable material for making prostheses able to provide both volumetric reparation processes of functional character - restoration of cartilage, mucosa and delicate connective tissue matrix for these organs while replacing the significant areas of the lost organ elements.

The aim of this study is to evaluate the in vivo biocompatibility and biocompatibility of bilayer cast films based on hyaluronic acid with molecular weight equal to 1300 kDa and chitosan with molecular weights equal to 500 and 900 kDa with potential use as devices for surgical reconstruction of acute posttraumatic defects of the tympanic membrane and septal cartilage. For the in vivo assay 20 Wistar rats (weight 220-240 g) were used. Total toxicity, pro-inflammatory activity, biodegradation rate and proliferative potential of the connective tissue of the dermis in the implantation area were evaluated on days 7, 14, 30, 50 after implantation. All studied materials were demonstrated to have low overall acute and chronic toxicity. The analysis of influence of the manufacturing technique, e.g. temperature treatment 100°Cx5 min, as well as chitosan molecular weight on the bioresorption intensity is demonstrated. It could be concluded, that the temperature treatment has higher influence of biodegradation period in comparison with the chitosan molecular weight and allows obtaining films with tunable biodegradability. After the additional analysis these bilayer films could be recommended for the ENT surgery, in particular, for the reconstruction of nasal septum and tympanic membrane.

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Recent Advances in Extractive Distillation

Distillation is widely recognized as the preferred method for separation due to its operational and control benefits. Traditional distillation processes, however, cannot successfully separate azeotropic mixtures with near boiling points. Numerous special distillation processes have been developed to address this limitation. Extractive distillation, in particular, has gained significant popularity in the chemical, petrochemical, pharmaceutical, and refining industries. This review examines the state-of-the-art advances in extractive distillation. The importance of the proper selection of a solvent was discussed. Several configurations of extractive distillation processes were presented. Additionally, alternative extractive distillation systems have been elaborated. However, significant research gaps remain, such as the need for an exhaustive investigation of various control variables, the impact of certain entrainers on distillation processes, and cost comparisons across specialized distillation systems. Furthermore, process intensification strategies require additional research to solve complexity and operability issues. The integration of energy-efficient technologies, developments in renewable energy consumption, and the development of cost-effective reactive or split distillation columns will shape the future of distillation operations. These advances will help the chemical process sector achieve improved energy efficiency, lower environmental impact, and increased sustainability.

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Structural, morphological and mechanical properties of concrete slab in traditional buildings, case study: Casablanca-Morocco
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Morphological, structural and mechanical analyses were performed on reinforced concrete slab samples used in traditional buildings in Casablanca, Morocco. Spectral (X-ray diffraction) and morphological analysis revealed that all samples had a low Ca/Si intensity, which could be the primary factor responsible for the reduction in compressive strength of the studied concrete slabs. In the mechanical part, the compressive strength ranges between 30.5 and 29.1 MPa, and the flexural strength ranges between 13 and 15 MPa, both of which are too low compared to existing buildings. Several factors, including porosity, hardening, age, aggregate type, water-cement ratio, and Ca/Si ratio, could account for these results. These results aim to obtain the basic knowledge necessary to propose a correct diagnosis, useful for planning conservation projects compatible with the specificity of the local culture of the building.

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Vanadium complexes as potential anticancer agents
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Published: 26 October 2023 by MDPI in 4th International Electronic Conference on Applied Sciences session Poster Session

Metals are required for the structure and functioning of biomolecules. Now a days, the main goal of bioinorganic chemistry is the design and synthesis of novel metal-based complexes and metal ion binding substances for the treatment of human diseases. This paper summarizes the anticancer activity of vanadium with different ligands. The potential anticancer activity of vanadium based drugs is one of the area of research in this field. Vanadium is a well-known transition metal and its complexes have received extensive studies for their pharmacological properties. The medicinal properties of vanadium complexes particularly their anticancer activity is a rapidly expanding research field. Vanadium complexes such as VO-salen (N,N’bis(salicylidene)ethylenediamine, Metvan [VIVO(SO4)(4,7-Mephen)2], vanadocene dichloride, vanadium(III)-L-cysteine complexes, vanadium complexes with flavonoids and other polyphenols, semicarbazone derivatives and vanadium Schiff base complexes exhibited anticancer activity. Majority of the reported complexes contain vanadium in +4 and +5 oxidation states. In past it has been established that vanadium in different oxidation states exhibited well-defined geometry and coordination power making them thermodynamically stable. The literature was reviewed and collected from leading indexing database of last 10 years to find the potentiality of vanadium as anticancer agents. The biological potential of vanadium complexes with different ligands open new horizons for future interdisciplinary studies and investigation focussed on understanding the biochemistry of these complexes.

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Enhancing Gamma Stirling Engine Performance through Genetic Algorithm Technique

The Stirling engine, invented in 1816, was initially lacking comprehensive scientific understanding, which only surfaced after a considerable 50-year period. In the present era, impressive strides have been made in enhancing the performance of Stirling engines through the implementation of thermodynamics cycles. Despite these advancements, there remains untapped potential for further improvements through the application of soft computing methods. To address this, the focal point of this research paper centers around optimizing the Stirling engine, specifically focusing on a gamma type double piston Stirling engine and leveraging genetic algorithms to achieve the desired enhancements. The obtained results from this meticulous analysis are meticulously compared with experimental data, validating the efficacy of the approach. Additionally, the paper explores the potential impact of utilizing cryogenic fluids as coolants on the Stirling engine's performance

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Computational study of the effects of dual air swirlers on swirling combustion of kerosene-air at a high pressure
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The air compression ratio in a modern aero engine has been significantly increased to enhance the engine’s thermal efficiency, thereby leading to high-pressure combustion with the combustor pressure exceeding the fuel’s critical pressure (~23 atm for the aviation kerosene). In this work, large eddy simulations are conducted to investigate the effects of two air swirling injection on the turbulent flow and combustion of kerosene-air in a dual-swirl model combustor at a supercritical pressure of 4 MPa. The flamelet progress-variable (FPV) model is applied to handle turbulent combustion, and the extended corresponding states (ECS) method is adopted to evaluate the thermophysical property variations at the high pressure. Results indicate that the inner air swirler controls the mixing process and chemical reactions inside the injector. The precessing vortex core (PVC) is generated by the inner swirling flow, and the PVC frequency increases significantly as the inner air swirler angle varies from 25° to 40°. On the other hand, the outer air swirler exerts strong impacts on the flow field and flame characteristics in the combustor. As the outer air swirler angle increases, the PVC frequency decreases at a moderate inner air swirler angle. A modified Strouhal number is proposed, and the detailed analyses reveal that the PVC frequency is influenced by the swirl number and the maximum axial velocity in the inner injector. Results obtained herein would help gain fundamental understanding on swirling flow and flame dynamics at high pressures.

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Optimizing Traffic Flow: Utilizing IR and Load Cell Sensors for Cost-Effective Traffic Congestion Alleviation at Smart City Intersections
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Cities face a significant challenge of increasing traffic congestion due to the rising number of vehicles. Developed countries have introduced smart traffic management systems as a solution to mitigate congestion and improve traffic flow. These systems employ various techniques such as image processing, radar sensing, ultrasonic and microwave detectors, and other sensors. However, each of these methods has its drawbacks, including susceptibility to weather conditions, high costs, and lower accuracy. Intelligent traffic control methods like inductive loop detection, wireless sensor networks, and video data analysis have proven to be efficient. However, they suffer from lengthy installation processes and high installation and maintenance expenses. In response to this issue, this article proposes a system that can detect lane density and adjust traffic signal timers accordingly to optimize traffic flow. The proposed system utilizes IR sensors and load sensors to calculate the density of each lane at an intersection, and an RFID system is implemented to accommodate emergency response vehicles. The system is centered around an ATmega 2560 chip. To demonstrate the effectiveness of the proposed approach, real-time experiments are conducted on a scaled-down model of the system. The results showed promising outcomes. The authors argue that this system could serve as a cost-effective and efficient solution for managing traffic in cities, particularly in Pakistan.

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