Performance Of Proposed System Research Articles

A novel high-temperature water cooling system utilizing cascaded cold energy from underground water plants in northern China

In alignment to China's ambitious ‘Dual Carbon' strategy and the urgent need to address energy shortages, optimizing energy consumption in air conditioning systems has become a critical imperative in thermal engineering.This study introduces an innovative high-temperature water cooling system that leverages cascaded cold energy from underground water plants in northern China. Initially, the DeST software was utilized to simulate and quantify the hourly cooling load and annual cooling consumption of a representative building. Subsequently, based on these simulation results, a comprehensive case study was conducted on an air conditioning project in Jinan city, Shandong Province, to rigorously analyze the proposed system's performance in terms of energy efficiency, economic feasibility, and environmental impact.The results demonstrate significant improvements over conventional air conditioning systems: a 24.6 % increase in energy efficiency, corresponding to a reduction of 4.7 kWh/m2 in energy consumption and cost savings of 2.5 CNY/m2. The economic viability of the system extends to a radius of 3.5 km from the underground water plant. Projections for a typical cooling season indicate potential electricity savings of approximately 691,000.0 kWh, translating to emission reductions of 691.0 tons of CO2, 20.7 tons of SO2, and 10.4 tons of NOx.

A Novel Hybrid FSO-mm Wave System for Enhanced Mobile Network Capacity and Reliability

The ever-growing demand for high-bandwidth communication in mobile networks necessitates innovative solutions that provide exceptional capacity and reliability. This paper introduces a novel dual hop hybrid system model that leverages the complementary strengths of free space optics (FSO) and millimeter-wave (mm W) communication, while incorporating a direct mm W link for added robustness. This paper analyzes the comprehensive performance of the proposed hybrid system using a single threshold selection combining. We consider a scenario where the channel state for the FSO link adheres to a Log-Normal distribution under conditions of weak turbulence. For the millimeter wave link, we assume it follows a Nakagami-m distribution, which encompasses a broad range of commonly encountered radio frequency (RF) fading distributions. The proposed system's performance is analyzed by deriving analytical expressions for average bit error rate (ABER) and outage probability (OP). The theoretical results of the proposed system, which are verified using Monte-Carlo simulations, unveil that under turbulence and adverse weather conditions, the performance of the proposed system over a standalone FSO system is improved significantly due to mm W backup link.

Blockchain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output nonorthogonal multiple access

One of the greatest ways to guarantee that networks designed for fifth generation (5G) and beyond reach the required levels of spectrum efficiency (SE) is through nonorthogonal multiple access (NOMA). This work presents two new blockchain-based techniques that take advantage of massive multiple input multiple output in a single-cell network to improve performance. NOMA power domain is used in the 5G cooperative cognitive radio network (CCRN) to improve the SE of the downlink. This study investigates a novel cooperative NOMA-based CCRN for underlay spectrum sharing. A cooperative NOMA technique is proposed by considering the access modes of relays and secondary users (SUs) on the secondary network. The proposed system's performance is assessed with respect to random channel characteristics, frequency-selective Rayleigh fading and perfect successive interference cancellation (SIC). The first signal decoded by SU identifies the relay states with the best channel quality between users and the destination users to offset the bit error rate. The throughput dropped during the period because of the perfect SIC. The precise closed-form expressions for the system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the efficacy of the suggested cooperative strategy. The suggested approaches are assessed under various conditions using the MATLAB application by considering varying transmit power levels, power location coefficients and lengths. In every case, four users are assumed to be using a 90 MHz bandwidth and M-ary Quadrature Amplitude Modulation technology.

Efficient and Reliable Wireless Power Transfer Stent Rectenna System for Abdominal Aortic Aneurysm Surveillance

AbstractThis study presents an innovative wirelessly powered stent rectenna system for endovascular aneurysm repair (EVAR), targeting abdominal aortic aneurysm (AAA) treatment. The system includes an on‐body flexible transmitter (Tx) antenna and an in‐body rectifier‐integrated stent receiver (Rx) antenna, operating in the industrial, scientific, and medical (ISM) band of 868 MHz. The Rx stent antenna, made from a biocompatible nylon‐coated metallic wire, ensures mechanical stretchability, biocompatibility, and precise current distribution. The flexible Tx antenna is designed for wearable use. A miniaturized rectifier on flexible polyimide, coated with biocompatible polydimethylsiloxane, converts received power into DC energy. The EVAR stent system's performance is validated through measurements using a saline‐filled American Society for Testing and Materials (ASTM) phantom. Magnetic resonance imaging (MRI) compatibility studies using Sim4Life simulations assess potential heating and image artifacts, confirming the proposed stent's MRI suitability. Wireless power transfer experiments demonstrate reliable power delivery to the Rx in various real‐world scenarios, achieving a power transfer efficiency of 3.6% at 18 cm inside the phantom. The flexible rectifier shows a peak efficiency of 87% at 15 dBm input power. The proposed system's exceptional performance and efficiency highlight its potential for effectively wirelessly powering advanced AAA stents.

Modeling and control of a new multi-port converter for hybrid energy system integration

This research introduces a new topology called the quasi-Z-source integrated isolated multiport bidirectional resonant DC-DC converter. The aim is to achieve cost-effective and efficient multi-directional power flow between photovoltaic (PV), battery/supercapacitor, and DC loads. The proposed topology ensures uninterrupted power supply to the loads and supports reverse power transfer through its bidirectional power flow capability. By combining quasi z-source and H-bridge resonant converters with an existing switch, a four-port converter is achieved without the need for separate converters or additional switches. The high-gain quasi z-source converter reduces the required voltage ratings of the battery and supercapacitor packs while enabling the utilization of a high-frequency transformer (HFT) with a low turns ratio. Isolation between the ports is achieved using a secondary center-tapped HFT equipped with a controlled full-wave rectifier on the secondary side, allowing bidirectional power flow. A power flow management system and corresponding control scheme are proposed to optimize the system's performance. The proposed system's performance is evaluated under various operating conditions, demonstrating its effectiveness in power flow management and overall efficiency. The results confirm the feasibility and effectiveness of the proposed system.

Comparative multi-objective optimization using neural networks for ejector refrigeration systems with LiBr and LiCl working agents

Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar-powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi-objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two-objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchanger have also been studied in the ejector asset system. To find the best values of decision variables, multi-objective optimization for both arrangements is conducted, and results are presented. The results have indicated that the refrigeration system using lithium chloride working fluid without an ejector achieves a coefficient of performance of 0.766 and a cost of 0.922 $/h at the optimal point, while the system with an ejector yields a higher coefficient of performance (1.047) and a slightly lower cost rate (0.991 $/h). The outcomes of this work can play a critical role for higher education institutions in advancing innovative solutions to pressing energy challenges. Lifelong learning, at the heart of educational innovation, can benefit from the integration of sustainable energy systems as a core component of informal education through the optimization of ejector refrigeration systems.

Optimization of a hybrid system (PV-fuel cell) of energy production for an isolated site

In recent years, there has been a significant shift in the design and implementation of autonomous micro-grid systems, moving away from the traditional reliance on fossil fuels. Instead, these systems are now integrating existing renewable energy sources available in specific geographic locations to promote sustainability and environmental responsibility in the energy sector. One particularly promising approach for energy storage and transmission is through the utilization of hydrogen and solar energy, especially in remote regions where hybrid techniques can effectively meet the required energy demands. Our research focuses on electrifying an agricultural site located in the SBAA region, specifically in the city of ADRAR, southwest Algeria. We propose the implementation of an independent small electric network that combines various renewable energy sources, including photovoltaic (PV) systems and fuel cells. To ensure cost-effectiveness and optimize the system's performance, we aim to carefully control and size the system's components through a meticulous selection process. The primary objective of this study is to evaluate the technical and economic feasibility of implementing renewable energy systems in the targeted region. Our goals are to reduce the total net cost, minimize energy expenses and unfulfilled load, while simultaneously mitigating CO2 emissions. To achieve these objectives, we employ the HOMER Pro simulation program, which enables us to conduct a comprehensive analysis of the proposed system's performance and economic viability. The proposed system incorporates a hybrid configuration of PV panels and fuel cells, leveraging the complementary nature of these two renewable energy sources. PV panels harness the abundant solar energy during daytime hours, while fuel cells provide a reliable and consistent energy source during periods of low solar irradiance or at night. By integrating these two technologies, we aim to establish a self-sufficient and sustainable energy system capable of meeting the energy demands of the remote agricultural site. Overall, the proposed renewable energy system demonstrates positive results in electrifying remote areas while maintaining a conservative cost of electrical energy. By implementing this system, we not only contribute to the establishment of sustainable regions but also promote environmental stewardship by reducing carbon emissions and dependence on fossil fuels.

Performance investigation and operation optimization of an innovative hybrid renewable energy integration system for commercial building complex and hydrogen vehicles

With the increasing in energy demands and carbon emissions of building and transport sectors, improving the penetration of renewable resources in these areas are inevitable trends. To further develop the theoretical method of energy integrated system in building/transportation area, load forecasting of commercial building complex and hydrogen vehicles are determined, a novel hybrid renewable energy integration system is presented and studied. Thermal, economic and environmental performances for poly-generation system are performed and optimized, optimal performance and configuration are obtained by NSGA-II algorithm and TOPSIS-Shannon entropy weight method. In addition, operating performance characteristics of integration system during typical weekday and weekend in different seasons are analyzed, the influences of critical parameters on comprehensive performances of integrated system are also conducted. The results showed that optimal exergetic efficiency, levelized cost of product (LCOP) and levelized CO2 emissions of product (LCEP) are 12.61 %, 0.0711 $/kWh and 0.0185 kg/kWh. Furthermore, thermodynamic performances of proposed system decreases while LCOP and LCEP rise with the increasing in HVs stock. Biomass and purchased electricity prices have different impacts on system comprehensive performances. In general, this study provides a new alternative for multiple renewable energy integrated system optimization in building and transportation sectors.

Brake-actuated steering control strategy for turning of articulated vehicles

The use of high-capacity vehicles has been shown to significantly reduce carbon dioxide emissions from heavy goods transport and improve the efficiency of the transport sector. Despite their benefits, current legislations regarding the dimensions and weight of the vehicles have limited the widespread use of heavier and longer vehicles. Long vehicles are less manoeuvrable and often necessitate technological interventions to pass manoeuvrability tests. Various rear-steering systems proposed to solve these issues lead to high costs and mass penalties compared to standard fixed axles. This paper presents a new steering concept which utilises the axles’ existing brake actuators to steer, eliminating the need for dedicated actuators on the axle and associated steering hardware. Brakes are individually controlled on each side of a freely steerable axle to generate the necessary torque about the kingpins causing it to steer. A validated vehicle model is used to design a feedback steering strategy based on path-following steering control for low-speed manoeuvring. The proposed system's performance is compared to conventional vehicles and current state-of-the-art active-steering systems through simulations. It demonstrates comparable steering performance to that of electro-hydraulic active-steer systems, with an estimated 50% reduction in both mass and costs.

Control and fault diagnosis for two-interleaved boost converter associated with to PEMFC

This paper presents an enhanced control and fast fault detection method, localisation and isolisation for a two-phase interleaved boost DC/DC converter specifically designed for fuel cell applications. Fuel cells are known for their rapidly changing output voltage and current due to fluctuating load demands. Conventional converters might not be able to respond fast enough to maintain stable operation under such conditions.The two-phase interleaved design offers significant advantages. such as reduced ripple current delivered by dividing the Input current, the converter significantly lowers input and current ripple compared to single-phase designs. Each phase handles a smaller portion (1/N) of the total current, leading to reduced stress on individual components and enhanced reliability and operating margins. Additionally, Interleaving minimizes conduction losses by dividing the current among multiple converter stages. This work presents a model of a two-phase interleaved boost DC/DC converter and implements a PI controller for its output voltage regulation. This control method guarantees good performance even under varying reference voltages and load conditions. The proposed algorithm addresses the challenge of open circuit switch failures by utilizing current slopes for swift fault detection and rapid corrective action. This ensures accurate reference tracking, desired dynamic response, and timely fault detection and localization. The entire system was rigorously tested using MATLAB/Simulink software under various conditions, including reference voltage and load variations, as well as open circuit switch faults. The results showcase the proposed system's superior performance in terms of: Dynamic performance: The system exhibits rapid response to changes in operating conditions. Fast fault detection, localization and isolation.

Chemical looping combustion-driven cooling and power cogeneration system with LNG cold energy utilization: Exergoeconomic analysis and three-objective optimization

This study conducts thermodynamic and exergoeconomic analyses for a novel chemical looping combustion-driven cooling and power cogeneration system consisting of a supercritical carbon dioxide cycle, an air cycle, an organic Rankine cycle, and a liquid natural gas regasification process. Comprehensive parametric analyses are performed to investigate the impact of design variables on the proposed system's performance metrics, including electrical efficiency (ηel), exergy efficiency (ηex), and total product unit cost (cp,tot). A three-objective optimization is implemented to obtain the optimal system performance. The results underscore that the two reactors exhibit the highest exergy destruction of 17.29 MW, followed by the condenser's exergy destruction of 6.50 MW. The two reactors, expander, gas turbine, and condenser are the most important components within the system, and evaporator 1 and condenser need to be given more attention from exergoeconomic aspects. An optimum split ratio exists to maximize system performance by optimizing the conditions of the recycled stream into the fuel reactor. Optimization results reveal that ηel, ηex, and cp,tot cannot be simultaneously optimal and are 52.68%, 40.72%, and 27.43 $/GJ, respectively, after weighing. Moreover, the weighed performance of the proposed system outperforms those of similar systems despite employing components with lower design efficiencies.

Performance evaluation and optimization of a multigeneration hybrid system for power, hydrogen, cooling, and freshwater production from renewable sources

Designing a hybrid system emerges as a highly efficient strategy for augmenting the performance of renewable-based power plants, owing to their adaptability in integrating diverse subsystems. In this study, a new multigeneration system is introduced, showcasing its capability to concurrently generate power, hydrogen, cooling, and freshwater through a synergy of methanol-steam reforming, modified absorption refrigeration cycles, humidification-dehumidification, and multi-effect desalination. The operational foundation of the system is rooted in the harmonious integration of solar and geothermal energy sources. To comprehensively evaluate the proposed system's performance, a combination of exergy and economic analyses is employed. This research studies the impacts of varying key parameters on the system's operation. Notably, the water-to-methanol ratio is identified as the most influential variable affecting reformer performance, underscoring the pivotal role of reforming pressure. The estimated total fixed cost of the system stands at $4.039 million, with a calculated payback period of 6 years and a net present value of $3.454 million. Furthermore, in its optimized state, the system achieves an impressive exergy efficiency of 49.80 % and a unit product cost of $32.38/GJ. These findings unequivocally validate the feasibility and economic viability of the proposed hybrid renewable-based power generation system. In conclusion, the amalgamation of solar and geothermal energy, coupled with meticulous parameter optimization, substantiates the potential of this innovative system to serve as a sustainable and economically viable solution for power generation.

ADSBAN: Anomaly detection system for body area networks utilizing IoT and machine learning

SummaryBody area networks (BANs) play a pivotal role in modern healthcare, enabling real‐time data collection and monitoring of vital patient parameters, thereby empowering healthcare providers to respond swiftly to any serious health conditions. These networks depend on strategically placed sensors on the patient's body to collect important health data. The integrity of this data is really important, as problems with a component can cause doctors to make mistakes that could be life‐threatening. Consequently, developing and evaluating robust anomaly detection methods for BANs is paramount. To address this concern, an anomaly detection system for body area networks (ADSBAN) has been proposed in this article. Different machine learning methods have been tested and implemented to improve the proposed system's performance. These include decision tree, K‐nearest neighbor, logistic regression (LR), random forest, AdaBoost (Adaptive Boosting), and XGBoost (Extreme Gradient Boosting). Further, this framework has been tested with emulated and standard datasets and compared with existing methodologies. The research employs an IoT Flock emulator to replicate BAN conditions and simulate two distinct attack scenarios. Wireshark aids in thoroughly analyzing network traffic, while Python, in conjunction with tools like Keras and Pandas, is instrumental in implementing the ML models. Further, a CIC flowmeter has been utilized to convert the .pcap file format into .CSV file format. Comprehensive evaluation metrics such as precision, recall, accuracy, F1 score, and the Mathew correlation coefficient (MCC) demonstrate the consistent superiority of LR on emulated (i.e., traffic generated through a simulator IoT Flock) and standard datasets (BoT‐IoT). LR shows the highest accuracy of 99.92% on emulated and standard datasets, while XGBoost has the highest average accuracy of 99.92% on standard dataset. This research significantly bolsters the reliability of healthcare data, instilling confidence among healthcare professionals in their decision‐making processes. By safeguarding the integrity of vital health data in BANs, it advances the quality of patient care. It underscores the indispensable role of ML techniques in fortifying the resilience of healthcare systems.

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

Stress and anxiety detection: deep learning and higher order statistic approach

<p>Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress.</p>

Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system's performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist's report.

Fruit Disease Detection using Image Processing

These days within the agrarian assiduity we've a good reference in fruit field. As we know that India’s frugality is truly dependent upon the agrarian assiduity, so an advanced field is must necessary and important. That is why growers want the homemade process of covering the fruits from crop till its growing phase. Purely homemade observing will not give reasonable results and also consumes a lot of your time, hence proposing an resourceful smart husbandry approach. That helps growers to descry conditions in crops. The proposed work uses image processing ways like preprocessing to enhance the quality of an image and segmentation to partition the images also feature birth to prize some useful information and type to classify the diseases. Fruit conditions are a major problem in profitable losses and product in the agrarian assiduity worldwide. In this paper, an image processing approach is proposed for relating fruit conditions. conditions in fruit cause ruinous problem in profitable losses and producting agricultural assiduity worldwide. According to the Sri Lankan terrain, treatment details are taken by the growers from the field officers. But it can take a multitudinous days. So, this proposed system can be used to identify fruit conditions snappily and automatically. This proposed approach is composed of the following main way; Preprocessing, point birth, Bracket, Training &Testing. Our experimental results express that the proposed result can significantly support accurate discovery and automatic type of fruit diseases. Paper demonstrates the system for discovery of fruit complaint. Present days as there's restrictive interest for husbandry assiduity, successful development and enhanced product of fruit is abecedarian and imperative. For this reason agronomists bear homemade observing of fruits. But all the time primer observing will not give satisfactory results and they generally bear guidance from master. So there's demand for proposing an effective cultivating system which helps for better product and advancement with truly lower mortal trouble, Image processing strategies are employed for performance of proposed system. For image segmentation Four point vectors are used in the proposed system those are colour, morphology, texture and structure of hole on the fruit. The system utilizes two image databases, one for training of formerly stored complaint images and one further for performance of query images. For matching of patterns and identification of fruit conditions GLCM or CNN generality is used.

Self-Centering Precast Unit as Energy Dissipation Members in Precast Segmental Bridge Columns

This research aims to present a new generation of seismic-resisting systems designed for precast reinforced concrete (RC) bridge piers in modern sustainable cities to withstand moderate to high seismic activity. The proposed system consists of two self-centering (SC) systems operating in parallel to bring together all features of the required resiliency during a seismic action. The first/main system is a hollow core precast segmental bridge column, and the second is composed of an SC precast unit and energy dissipation (ED) steel reinforcements positioned in the main pier segment’s hollow core. To study the performance of the proposed system, a finite element model was first developed to capture the behavior of experimentally tested precast bridge columns. After validation, the created model was systematically studied to investigate the performance of the entire proposed system under cyclic loading. The effects of three parameters related to the ED system were investigated, including the reinforcement ratio, the unbonded length of ED bars, and the SC post-tensioned force ratio. Furthermore, the impact of FRP wrapping on the lower part of the core column of the ED system was also investigated. An analytical model predicting the characteristic points of the lateral response of the proposed system based on the superposition concept is also proposed. The FE results showed that the entire proposed system is a new design-based resilient system with the ability to dissipate energy without compromising the SC capacity of the main resisting system. Compared to the typical precast hollow core segmental column, a 6% reinforcement ratio of the ED unit can cause a 60% increase in lateral resistance and a 220% increase in the ED capacity. The analytical model can successfully be applied in the design of the proposed system to provide customized ED capabilities and controlled lateral resistance.

Cloud Intrusion Detection Using Hybrid Convolutional Neural Networks

Instead of storing data on a hard drive, cloud computing is seen as the best option. The Internet is used to deliver three different kinds of computing services to users all over the world. One advantage that cloud computing provides to its customers is greater access to resources and higher performance while at the same time increasing the risk of an attack. Intrusion detection systems that can handle a large volume of data packets, analyse them, and generate reports based on knowledge and behaviour analysis were developed as part of this research. As an added layer of protection, the Convolution Neural Network Algorithm is used to encrypt data during end-to-end transmission and to store it in the cloud. Intrusion detection increases the safety of data in the cloud. In this paper demonstrates the data is encrypted and decrypted using a model of an algorithm and explains how it is protected from attackers. It's important to take into account the amount of time and memory required to encrypt and decrypt large text files when evaluating the proposed system's performance. The security of the cloud has also been examined and compared to other existing encoding methods

Carbon capture from biomass flue gases for CO2 enrichment in greenhouses

Heating and CO2 enrichment systems can improve yields in intensive greenhouse agriculture Combining both techniques, which are currently applied commercially, can potentially enhance their effect. The CO2 must be separated from the other noxious gases present (such as CO, NOX, and SO2) to avoid them becoming part of the supply. The CO2 is then provided to the greenhouse on demand in the same way as the heating. In this work, we show that an improved food productivity of a pilot-scale greenhouse system combined with CO2 capture by adsorption using activated carbon and heating with alternative fuel. The proposed system's overall performance was evaluated and optimized. The best values were 46.7 g/kg of CO2 storage capacity on the adsorbent bed, 99.99 % removal rate harmful gases from the gas supplied to the greenhouse, CO2 levels of 1851.0 ± 262.8 mg/Nm3 of the CO2 levels in the greenhouse, and an enrichment time of 2.18 ± 0.92 h/day. The system's effective performance over extended periods (November–February) was confirmed and the productivity of a crop species (tomato) was compared to a control, showing an increment of 18 %. The results indicate that this is a valuable option for increasing the crop yield. By integrating this combined system with advanced climate control strategies, it is possible to maximize the CO2 provided per day, leading to higher yields. The system proved to be stable under real pilot-scale conditions over winter periods (four months).