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The Cis-2,6-Dimethylpiperazin has undergone wave function analysis to determine the topological characteristics. The vibrational and ultraviolet-visible spectroscopy investigations were carried out. By advocating Density Functional Theory as a technique, the optimized structure is computed. Polarizability, nucleophilicity, and electrophilicity have also been estimated using Non-Linear Optics, Fukui, and Molecular Electro Static Potential. Additionally, the Highest occupied molecular orbital to Lowest unoccupied molecular orbital studies were carried out to know biological activity. It has been hypothesized that drug-like properties can explain a biological reaction. We predicted and examined the various thermodynamic properties. In addition to previous studies, the docking process was employed to calculate the energies of binding and size of bonding hydrogen with various proteins. This work describes molecular, electrical, chemical, and biological actions.

Abstract Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

The Intelligent Moving Robots (IMR) are designed to understand instructions and act accordingly in an independent manner. They use sensors that operate with the help of the Internet of Things(IoT) and Deep Learning (DL) for interpreting and navigating the directions following environmental conditions. Recent advancements use the Artificial Neural Network(ANN) and an Adaptive Neuro-Fuzzy Inference System(ANFIS) to model an efficient engineering system. In this work, a hybrid fuzzy inference system, MSFIS(Mamdani-Sugeno Fuzzy Inference System), is proposed along with Adaptive Deep Belief Networks(ADBN) for identifying and tracing the Direction Finding(DF) capability of the IMR. The MSFIS uses parameters in the range 4:4 (4 Input and 4 Output Parameters). The inputs used are Front View (FV), Left View(LV), Right View(RV), and Back View(BV), and the output (4 directions) might depend on the speed of the wheels used in the Robot. Four directions are used at the output for navigation purposes. The results obtained from simulating the experiments confirm that the suggested navigation controller demonstrates superior viability, efficiency, and resilience. Compared with the existing system, the proposed system outperforms well in accuracy and sensitivity, proving it is well efficient in navigating any new environment.

Measurement(s) Full length transcriptome of Cobia (Rachycentron canadum) Technology Type(s) PacBio Sequel 2 Sample Characteristic - Organism Rachycentron canadum Sample Characteristic - Environment Marine environment Sample Characteristic - Location India

Gingival enlargement is caused by various factors such as inflammation, medications and malignant diseases. Acute leukemic enlargement is commonly seen in children. If prompt diagnosis is not made properly and treated early, it may result in significant morbidity and mortality. Gingival enlargement is seldom seen in adult patients with acute lymphoblastic leukemia. This case report presents an interesting rare case of gingival enlargement in an adult patient with acute lymphoblastic leukemia and management of the disease.

Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications. This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks. Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network. This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas. We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography. The challenges and research directions in these domains are explored as well. Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.

Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively.

In recent years, immense development in Structural Health Monitoring (SHM) of bridges helps address the life span and reliability of bridge structure at contrasting phases of their service life. This article provides a detailed understanding of bridge monitoring, and it focuses on sensors utilized and all kinds of damage detection (strain, displacement, acceleration, and temperature) according to bridge nature (scour, suspender failure, disconnection of bolt and cables, etc.) and environmental degradation under static and dynamic loading. This paper presents information about various methods, approaches, case studies, advanced technologies, real-time experiments, stimulated models, data acquisition, and predictive analysis. Future scope and research also discussed the implementation of SHM in bridges. The main aim of this research is to assist researchers in better understanding the monitoring mechanism in bridges.

Background: Management of native uncomplicated coarctation in neonates remains controversial with current evidence favoring surgery. The logistics of organizing surgical repair at short notice in sick infants with critical coarctation can be challenging. Methods and Results: We reviewed data of 10 infants mean age of 2.9 ±1.6 weeks) who underwent catheter intervention for severe coarctation and left ventricular LV) dysfunction between July 2003 and August 2007. Additional cardiac lesions were present in 7. Mean systolic gradient declined from 51±12 mm Hg to 8.7±6.7 mm Hg after dilation. The coarctation segment was stented in fi ve patients. Procedural success was achieved in all patients with no mortality. Complications included brief cardiopulmonary arrest n =1), sepsis n = 1) and temporary pulse loss n = 2). LV dysfunction improved in all patients. Average ICU stay was 5±3.4 days and hospital stay was 6.5±3.4 days. On follow-up 14.1±10.5 months), all developed restenosis after median period of 12 weeks range four to 28 weeks). Three (two with stents) underwent elective coarctation repair, two underwent ventricular septal defect VSD) closure and coarctation repair and one underwent pulmonary artery PA) banding. Two patients who developed restenosis on follow-up were advised surgery, but did not report. Two one with stent) underwent redilatation and are being followed with no signifi cant residual gradients. Conclusion: Balloon dilation ± stenting is an effective interim palliation for infants and newborns with critical coarctation and LV dysfunction. Restenosis is inevitable and requires to be addressed.

The exfoliation of graphene from pristine graphite in a liquid phase was achieved successfully via sonication followed by centrifugation method. Ultraviolet–visible (UV–vis) spectra of the obtained graphene dispersions at different exfoliation time indicated that the concentration of graphene dispersion increased markedly with increasing exfoliation time. The sheet-like morphology of the exfoliated graphene was revealed by Scanning Electron Microscopy (SEM) image. Further, the morphological change in different exfoliation time was investigated by Atomic Force Microscopy (AFM). A complete structural and defect characterization was probed using micro-Raman spectroscopic technique. The shape and position of the 2D band of Raman spectra revealed the formation of bilayer to few layer graphene. Also, Raman mapping confirmed the presence of uniformly distributed bilayer graphene sheets on the substrate.

Fabrication of wear resistant and low friction carbon films on the engineered substrates is considered as a challenging task for expanding the applications of diamond-like carbon (DLC) films. In this paper, pulsed laser deposition (PLD) technique is used to deposit DLC films on two different types of technologically important class of substrates such as silicon and AISI 304 stainless steel. Laser power density is one of the important parameter used to tailor the fraction of sp2 bonded amorphous carbon (a-C) and tetrahedral amorphous carbon (ta-C) made by sp3 domain in the DLC film. The I(D)/I(G) ratio decreases with the increasing laser power density which is associated with decrease in fraction of a-C/ta-C ratio. The fraction of these chemical components is quantitatively analyzed by EELS which is well supported to the data obtained from the Raman spectroscopy. Tribological properties of the DLC are associated with chemical structure of the film. However, the super low value of friction coefficient 0.003 is obtained when the film is predominantly constituted by a-C and sp2 fraction which is embedded within the clusters of ta-C. Such a particular film with super low friction coefficient is measured while it was deposited on steel at low laser power density of 2 GW/cm2. The super low friction mechanism is explained by low sliding resistance of a-C/sp2 and ta-C clusters. Combination of excellent physical and mechanical properties of wear resistance and super low friction coefficient of DLC films is desirable for engineering applications. Moreover, the high friction coefficient of DLC films deposited at 9GW/cm2 is related to widening of the intergrain distance caused by transformation from sp2 to sp3 hybridized structure.

Skin cancer is most top three critical kinds of cancer due to damaged DNA, which is cause death. This damaged DNA begins cells for growing uncontrollably and currently it can be obtaining improved quickly. It is several researches on the computerized examination of malignancy from the skin cancer image. But, study of these images are very difficult taking several troublesome issues such as light reflections on the skin surface, differences from the color illumination, sizes of lesions, and distinct shapes. Thus, the outcome, evidential automatic detection of skin cancer are appreciated for developing the accuracy and efficiency of pathologists at the beginning phases. This manuscript develops a Stacked Ensemble Machine Learning based Skin Cancer Detection and Classification (SEML-SKCDC) approach. The presented SEML-SKCDC technique majorly aims to offer ensemble of three ML models for skin cancer classification. In the presented SEML-SKCDC technique, median filtering and contrast enhancement is performed at the pre-processing stage. To generate feature vectors, the honey badger algorithm (HBA) with EfficientNet method has been exploited in this work. At last, an ensemble of k-nearest neighbor (KNN), random forest (RF), and feed forward neural network (FFNN) approaches are applied for skin cancer classification. The simulation evaluation of the SEML-SKCDC system on skin cancer database depicts the developments of the SEML-SKCDC algorithm with recent methods. [ABSTRACT FROM AUTHOR]

Robocasting is a highly effective method to fabricate 3D bone scaffolds for bone tissue regeneration as it offers precise material deposition and great control over pore size and interconnectivity at low cost. In this study, bentonite (BEN)–hydroxyapatite (HAP) scaffolds reinforced with graphene (G) and graphene oxide (GO) in varying concentrations separately were fabricated through robocasting. All the fabricated scaffolds were sintered at 1100 °C. However, the BEN–HAP scaffolds (BH) were sintered in normal air and a controlled Argon atmosphere was used for the GBH and GOBH scaffolds. The printed samples were subjected to physical and mechanical characterizations. The results revealed that the higher concentrations of G and GO (2GBH and 2GOBH) resulted in decreased density and modified water absorption patterns. The compression test among the scaffold groups shows higher strength in graphene reinforced scaffolds than graphene oxide, making them appropriate for load-bearing bone tissue engineering applications. FTIR and XRD data revealed the chemical and structural changes in the scaffolds. The rough surface morphology of the printed scaffolds was analysed via FESEM. The EDAX data confirmed the presence of calcium, iron, alumina, magnesium, silica, phosphate, and sodium ions in both the GBH and GOBH scaffolds. The assessment of biological characterization through MTT and live–dead assays shows no cytotoxic effect among the scaffolds. However, the higher concentrations of graphene oxide were shown to reduce the cell viability. The findings of this study emphasize the interplay between scaffold composition, structural properties, and biological outcomes. [ABSTRACT FROM AUTHOR]

Dredged soils are often considered as waste and pose significant environmental challenge. The objective of this study is to find the feasibility of utilizing dredged soil from Vizhinjam (MH soil) and Vallarpadam (CH soil) sites for land-filling applications through Chemical-Physical Combined Method (CPCM). Lime and fly ash were used as chemical stabilizers. The optimum percentage of the lime is determined as 6 (wt.%) and fly ash is 10 (wt.%) and 15 (wt.%) for MH and CH soil, respectively. The California Bearing ratio (CBR) increased by 55 and 60%, and Unconfined Compressive Strength (UCS) was enhanced by 61 and 53% for MH and CH soil, respectively. The chemically treated soil is then subjected to various physical treatments. The CPCM effectiveness was evaluated by determining shear strength by fall cone test. Curing by geobags enhanced 77 and 69% increase in shear strength for MH and CH soil, respectively, while vacuum preloading resulted in two times increase over the curing method. Among the physical methods, surcharge using geo-bags was found to be the most effective, as it improved the shear strength by 20–30% when compared to other approaches, making them suitable for reuse as an increase in shear strength contributes to the enhancement of bearing capacity. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the functional outcomes of nerve reconstruction for nerve gaps exceeding 7 cm, using sural nerve autografts. The cases primarily involved brachial plexus injuries and peripheral nerve injuries. The most frequent causes of these injuries were motorcycle accidents and workplace incidents, predominantly affecting young individuals. The best outcomes for nerve injuries are achieved through primary coaptation. All the patients in our study underwent delayed nerve reconstruction rather than primary repair for various reasons, resulting in outcomes that were not as favourable as those of primary repair. Despite using avascular nerve grafts to bridge long nerve gaps, many of our patients still experienced meaningful recovery. Motor function recovery outperformed sensory function recovery across all types of reconstructions. Hence, in situations where facilities and expertise for vascularized nerve grafts are unavailable, attempting reconstruction with an extended nerve graft for long nerve gap is justified. [ABSTRACT FROM AUTHOR]