In this study, we present systematic analyses of the impact of Ni doping on the structural, optical, and magnetic properties of CuS nanoparticles produced using the straightforward wet chemical ...co-precipitation approach with EDTA molecules as templates. To examine the chemical makeup of materials, energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy were also employed. It is known that Ni-doped CuS nanoparticles have a hexagonal shape, which implies that Ni
2+
can substitute for Cu
2+
sites in the CuS lattice without creating a new phase. The optical characteristics were calculated based on the UV–Vis absorption measurements. The band edge and optical band gap for Ni-doped samples both get lower as the Ni concentration rises. When Ni dopant concentration is increased in the CuS host matrix, magnetic studies (at RT via M-H curve) demonstrate that 2% and 4% Ni doped CuS nanoparticles exhibit strong ferromagnetism at ambient temperature and transition to paramagnetic nature. The 2% and 4% Ni doped CuS nanoparticle results are highly suggested for the creation of spintronic devices.
A thorough investigation was undertaken to compare ZnO nanostructured films doped with transition metals (M = Fe, Cu, and Cr) and their functional characteristics, with a specific focus on their ...potential applications in passivating silicon (Si) solar cells. Through a co-precipitation and spin coating process, both pure and M-doped ZnO nanostructured films were synthesized. Structural analyses revealed the presence of hexagonal wurtzite formation in all films. Reflectivity and lifetime measurements indicated an improvement in both parameters for doped ZnO on Si surfaces. Photocurrent density measurements exhibited a range from 2.5 µA/cm² for pristine ZnO to 25, 8, and 19 µA/cm² for Fe, Cu, and Cr-doped samples, respectively, showcasing distinct variations in performance. These compelling results position the coated layers as promising candidates for cutting-edge optoelectronic applications.
Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they do not require any statistical assumptions on time series data. FTS models can handle data sets with a small ...number of observations or with uncertainty. This is a general advantage of FTS as compared with other techniques. However, FTS models still have some criticisms, such as the optimal lengths of intervals and the proper weights, which always influence the model accuracy and still have been of many concerns in literature. The work in this paper proposes a novel FTS forecasting model based on a new tree partitioning method (TPM) and Markov chain (MC), called FTSMC-TPM, for determining the optimal partitions of intervals and the proper weights vectors respectively, and this will greatly improve the model accuracy. The efficiency of the FTSMC-TPM model is tested using two types of time series consisting of the air pollution index (API) data, which is collected from Kuala Lumpur, Malaysia and the benchmark data of the yearly enrollments for the University of Alabama. Three statistical criteria have been used for investigating the accuracy of the proposed model. The results indicate that the proposed model outperforms the existing classic and advanced time series models in terms of forecasting accuracy. In addition, the proposed model shows the ability to successfully deal with forecasting problems to obtain higher model accuracy, which is examined in comparison with the existing models to validate its superiority. Hence, this study demonstrates that the proposed model is more suitable for the accurate prediction of air pollution events as well as for forecasting any type of random time series.
The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end ...behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.
A set of experimental and computational techniques have been applied for the understanding of fundamental spectroscopic and reactive properties of 3-(3,4-dichlorophenyl)-1,1-dimethylurea (diuron) ...compound. Experimental techniques employed in this study encompassed spectroscopic characterization via IR and Raman approaches, while optical properties were studied by measurements of UV/Vis spectra. The thermogravimetric analysis was also studied in order to analyze the stability of diuron. Aside from the determination of reactive properties, DFT calculations on isolated molecules were also used to thoroughly visualize and analyze spectroscopic properties such as IR and UV/Vis. MD simulations were used in order to understand interactions with water, while periodic DFT calculations were used in order to analyze band structure and density of states of the diuron crystal structure. Since the crystal structure of diuron is known, it was used in order to extract the relevant molecular pairs and investigate interactions between them by DFT and symmetry adapted perturbation theory approaches (SAPT).
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•FT-IR, FT-Raman, TG/DTG UV spectra are investigated.•The hyperpolarizability values give the NLO properties.•Periodic DFT SAPT studies reported.•Conformational Studies predicts structural preferences.•Molecular dynamics studies predicts behavior in water atmosphere.
Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good ...near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.
Next generation wireless systems include battery operated devices which demand higher throughput and a better reliability in an energy efficient fashion. To fulfil these requirements, in this paper, ...we propose a novel scenario where we include a dynamic Wireless Power Splitting (WPS) factor for Energy Harvesting (EH) at nodes in a Drone Assisted Network Coded Cooperation (DA-NCC) system. The dynamic WPS factor used for EH in DA-NCC system is made more realistic by determining through the probability of Line-of-Sight (LoS) occurrence. Analytical framework is developed for residual Analog Network Coding (ANC) noise and variance of ANC-noise in EH scenario. We also derive the average rate and average outage probability expressions for the proposed channel model. Various algorithms are developed for deciding the Air-to-Ground (A2G) channel distributions, harvesting the energy at relay and source nodes and evaluating the performance metrics of our proposed work. Our investigations reveal that the use of EH in DA-NCC improves the lifespan of the network. Our findings play important roles in disaster management scenarios where cellular connections to base stations are disrupted due to natural calamities and battery constrained drones are deployed for assistance.
•Estimation of flow rate using solar radiation data.•Photovoltaic (PV) water pumping systems sizing.•PV pumping systems models.•Spatial simulation of flow rate Q using solar radiation data.
This ...paper presents a simple model which allows us to contribute in the studies of photovoltaic (PV) water pumping systems sizing. The nonlinear relation between water flow rate and solar power has been obtained experimentally in a first step and then used for performance prediction. The model proposed enables us to simulate the water flow rate using solar radiation data for different heads (50 m, 60 m, 70 m and 80 m) and for 8S × 3P PV array configuration. The experimental data are obtained with our pumping test facility located at Madinah site (Saudi Arabia). The performances are calculated using the measured solar radiation data of different locations in Saudi Arabia. Knowing the solar radiation data, we have estimated with a good precision the water flow rate Q in five locations (Al-Jouf, Solar Village, AL-Ahsa, Madinah and Gizan) in Saudi Arabia. The flow rate Q increases with the increase of pump power for different heads following the nonlinear model proposed.
The current number of working mothers has greatly increased. Subsequently, baby care has become a daily challenge for many families. Thus, most parents send their babies to their grandparents' house ...or to baby care houses. However, the parents cannot continuously monitor their babies' conditions either in normal or abnormal situations. Therefore, an Internet of Things-based Baby Monitoring System (IoT-BBMS) is proposed as an efficient and low-cost IoT-based system for monitoring in real time. We also proposed a new algorithm for our system that plays a key role in providing better baby care while parents are away. In the designed system, Node Micro-Controller Unit (NodeMCU) Controller Board is exploited to gather the data read by the sensors and uploaded via Wi-Fi to the AdaFruit MQTT server. The proposed system exploits sensors to monitor the baby's vital parameters, such as ambient temperature, moisture, and crying. A prototype of the proposed baby cradle has been designed using Nx Siemens software, and a red meranti wood is used as the material for the cradle. The system architecture consists of a baby cradle that will automatically swing using a motor when the baby cries. Parents can also monitor their babies' condition through an external web camera and switch on the lullaby toy located on the baby cradle remotely via the MQTT server to entertain the baby. The proposed system prototype is fabricated and tested to prove its effectiveness in terms of cost and simplicity and to ensure safe operation to enable the baby-parenting anywhere and anytime through the network. Finally, the baby monitoring system is proven to work effectively in monitoring the baby's situation and surrounding conditions according to the prototype.
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