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The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the realtime classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly.

In this work, a new protocol is proposed for sender-based responsive techniques on energy, mobility, and effective routing for Wireless Sensor Networks (WSNs). It addresses diverse challenges in packet routing especially, node mobility, energy optimization, and energy balancing in WSNs communication. The proposed protocol improves the basic Quality of Service (QoS) metrics such as Delay, Hop-Count, and Energy Level for each connection with multiple routes and predicts the best optimal path to develop efficient communication among them. It takes energy and performs mobility prediction and the time of connection failures. The main aim of this paper is to propose a secured and energy-efficient routing protocol using fuzzy rules and a node's trust values. Moreover, the proposed model provides an additional route and hence works without link failure. The observational result shows that the proposed protocol performs better than the existing secure routing protocols and achieves a packet delivery ratio of 20% higher than existing approaches. As per energy consumption, the proposed system obtains 15% lesser than recent approaches in secure energy-efficient routing protocols for WSNs.

Agri CNN-LSTM Fusion (Agricultural Convolutional Neural Network—Long Short-Term Memory Fusion) is an advanced model that uses real-time data from soil sensors to evaluate a soil's suitability for crop production. The model aims to resolve the issues with conventional farming methods by offering a data-driven substitute for spontaneity in decision-making. A significant research gap is a lack of an integrated method that incorporates temporal and spatial soil features for accurate classification. A data-intensive preprocessing workflow was used to process soil data obtained from the National Institute of Technology, Trichy field, through various sensors. Complex patterns in soil data are identified by this model, which is essential for a precise evaluation of soil health. Hyperparameter optimization further improves the model, which yields precise and consistent predictions that classify soil as “Fit” or “Not Fit” for crop cultivation. The experimental observations showed that AgriCNN-LSTMFusion attained an accuracy of 98.5%, establishing it as a dependable method for real-time soil suitability analysis. The model allows farmers to make informed decisions and optimize resource use by reducing uncertainty in soil assessment. By combining predictive modeling with soil sensor data, agricultural efficiency can be enhanced and sustainable growth encouraged.

Bismuth ferrite with manganese doping (BiFe 0.95 Mn 0.05 O 3 or BFMO) and bismuth ferrite with cobalt and manganese doping (Bi 0.95 Co 0.05 Fe 0.95 Mn 0.05 O 3 or BCoFMO) were both synthesized as nanocrystalline powders by the sol–gel autocombustion technique. X-ray diffraction examination of the powders indicates a rhombohedral distortion in the perovskite phase in both samples. The calcined powders were examined for their microstructure and elemental composition with high resolution transmission electron microscopy (HRTEM) and energy dispersive X-ray spectroscopy (EDAX), respectively. Scanning electron microscopy (SEM) was used to examine the microstructures of sintered BMFO and BCoFMO specimens at room temperature. Dielectric characteristics were studied at various frequencies and temperatures and found to follow space charge polarization. At ambient temperature, a vibrating sample magnetometer was used to analyse the materials' magnetic behaviour (M–H loops). Saturation magnetization is significantly increased with increased coercivity in the BCoFMO sample compared to the other sample. Improved structural, dielectric, and magnetic values in these doped systems, however, suggest they'd be an excellent fit for spintronic, multifunctional memories, sensors, and actuators.

Nano-crystalline powders of Bi (1-x) Tr x Fe (1-y) Mn y O 3 (where x & y = 0, 0.05 and Tr = Cr, Ni, Zn, Cu) were prepared using sol-gel autocombustion method. Structural studies on the calcined nanopowders using X-ray diffraction and Fourier transform infrared spectroscopy confirm the perovskite phase with rhombohedrally distorted structures. Microstructural studies using scanning electron microscopy and energy dispersive spectroscopy on the sintered surfaces display uniformly knitted fine grained microstructures with thin grain boundaries and the presence of all element’s constituent for the synthesis of the samples, respectively. Dielectric properties were evaluated at various frequencies and temperatures and found to follow space charge polarization with significantly reduced dielectric losses at all frequency ranges investigated. Impedance study on the samples aids in understanding the contributions of electrical conductivity and interfacial polarization, and the results verify the claims explained during the dielectric property investigation. Magnetic studies on the samples reveal that among all the samples, Cr/Mn co-doped (BCrFMO) sample shows significant enhancement in the value of saturation magnetization (3.718 emu/g) while Ni/Mn co-doped (BNiFMO) sample demonstrates higher coercivity (259.734 Oe) at room temperature. With all enhanced structural, microstructural, dielectric and magnetic order through the influence of co-doing, these materials are highly recommended for spintronic, multifunctional memories, sensors, and actuators. Graphical Abstract Highlights Wet chemical synthesis of sol-gel autocombustion produced Bi (1-x) Tr x Fe (1-y) Mn y O 3 (where x & y = 0, 0.05 and Tr = Cr, Ni, Zn, Cu) multiferroic samples with co-doping effect were studied for the first time. XRD and FTIR data shows that all samples have rhombohedral-symmetric single-phase perovskite structures. Co-doping boosts magnetic order in all Cr, Ni, Zn, Cu and Mn co-doped samples. Highest magnetization value of 3.718 emu/g was evident in Cr-Mn co-doped bismuth ferrite (BCrFMO) sample.

— The oceans are changing on a global scale. Zooplankton of the marine ecosystem are microscopic myriads of diverse, floating and drifting animal-like organisms found either on or near the surface of water bodies. Zooplankton samples were collected from the surface water and substratum of the ship hull during the study period from June 2015 to May 2016. Population density ranged from 68 000 to 194 000 cells/L was recorded in the water and substratum of the ship hull. Shannon–Wiener’s diversity index ( H ') values ranged from 4.97 to 5.57 (bits/ind) at station I and station II. Simpson’s richness ranged from 0.967 and 0.977 at station I and station II. Pielou’s evenness index ( J ') ranged from 0.976 to 0.994 at station I and station II, respectively. In total, 56 and 48 zooplankton species were recorded in water and substratum of the ship hull in the Chinnamuttom harbor water, respectively. Among the recorded zooplankton, calanoid copepod was found to be dominant followed by ciliata, cyclopoida, harpacticoida, polychaeta, amphipoda and barnacle nauplii in water samples, whereas in substratum samples the Harpacticoid copepod was predominant followed by ciliata, cyclopoida, calanoida, polychaeta, amphipoda and barnacle nauplii. Among the estimated zooplankton, the copepods were found to be dominant throughout the year in the selected site and seasons with appreciable numbers.

Water quality parameters were analysed in complex aquatic ecosystem of two major harbour waters (Ennore Fish Landing Centre and Royapuram Fishing Harbour), along the Chennai district. Monthly variations in water physicochemical parameters such as atmospheric temperature (26–34°C), surface water temperature (23.2–33.5°C), pH (7.3–8.12), salinity (28.0–33.5 psu) and dissolved oxygen (3.2–5.6 mg/L) were analysed in the selected harbours. The inorganic nutrients such as nitrate (0.042–0.18 mg/L), nitrite (0.070–0.096 mg/L) phosphate (0.078–0.197 mg/L), silicate (0.480–0.790 mg/L) and ammonia (0.040–0.158 mg/L) were estimated in selected study site. The water quality parameters have obtained important seasonal and spatial variations. The increased harbour activities in both the selected harbour have been made the noticeable water quality changes. The obtained results evidently insisting the necessity of continuous monitoring of harbour waters. The present work was contributed in the harbour water quality monitoring and impact assessment for further policy mitigation measures.

Indium (In) and chromium (Cr) doped nickel zinc ferrites with chemical formula, Ni 0.3 Zn 0.7 Fe 2- x R x O 4 (0.00 <  x  < 0.25 & R = In/Cr) were synthesized by conventional solid state reaction method. X-ray diffraction data confirm the single phase cubic spinel structures in both the series of samples. The microstructures of In substituted samples are characterized by relatively larger grains with less rounded interfaces comparing to Cr doped samples. Dielectric studies reveal the existing of typical space charge polarization in both the series of the samples at higher frequencies. There observed significant variation in the magnitudes of the dielectric constant and loss tangent, the same were attributed to the possible size differences in the substituted ions correspond to different hopping mechanisms and thus display diverse dielectric behavior. All possible reasons for the variations in structural, microstructural and dielectric behavior observed in both the series were well explained in terms of their corresponding difference in their ionic radii, cationic preferences by the doped ions and possible conduction mechanisms.

Chromium (Cr)-doped cobalt ferrite nanoparticles were synthesized using a sol–gel autocombustion method, with the chemical formula CoCrxFe2xO4. The value of x ranged from 0.00 to 0.5 in 0.1 increments. X-ray diffraction analysis confirmed the development of highly crystalline cubic spinel structures for all samples, with an average crystallite size of approximately 40 to 45 nm determined using the Scherrer equation. Pellets were prepared using a traditional ceramic method. The magnetic and magnetostrictive properties of the samples were tested using strain gauge and VSM (vibrating sample magnetometer) techniques. The results of the magnetic and magnetostrictive tests showed that the chromium-substituted cobalt ferrites exhibited higher strain derivative magnitudes than pure cobalt ferrite. These findings indicated that the introduction of chromium into the cobalt ferrite structure led to changes in the material’s magnetic properties. These changes were attributed to anisotropic contributions, resulting from an increased presence of Co2+ ions at B-sites due to the chromium substitutions. In summary, this study concluded that introducing chromium into the cobalt ferrite structure caused alterations in the material’s magnetic properties, which were explained by changes in the cationic arrangement within the crystal lattice. This study successfully explained these alterations using magnetization and coercivity data and the probable cationic dispersion.

This report discusses growth and characterization of an organic nonlinear optical (NLO) single crystal L-Tryptophan tris (4-nitrophenol) (LTT4NP). Good optical quality crystals of LTT4NP with dimension 4 × 3 × 1 mm 3 were harvested by slow solvent evaporation method. Single crystal XRD measurements proved that the crystal belongs to monoclinic system with non-centrosymmetric space group P2 1 . The optical absorption and transmission studies showed a desirable transparency window from 364 to 1100 nm with a wide optical band gap of 3.58 eV. The study explored a low refractive index of 2.111–2.112 from visible to IR region and low optical conductance below 3.5 eV indicating weak interaction of optical field with the material. The dielectric dispersion of the crystal and low dielectric loss at optical frequencies suggested positive attributes towards electro-optic applications. A highly dispersive a.c. conductivity was observed above 100 kHz which indicated strong polarization mechanism in higher frequencies. The Second Harmonic Generation (SHG) efficiency was estimated to be 2.2 times of KDP by Kurtz Perry technique. The crystal was found to be thermally stable upto 146 °C and mechanically stable upto a load of 30 g favouring the easy fabrication and processing.