Low Power CMOS Design of Phase Locked Loop for Fastest Frequency Acquisition at Various Nanometer Technologies

Wireless Personal Communications - The performance of any VLSI circuit depends on its design architecture. Designing a power-efficient device is the most challenging criteria. In most...     

A proportional fair scheduling strategy using multiobjective gradient-based African buffalo optimization algorithm for effective resource allocation and interference minimization

The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine-tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi-Objective Gradient-based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device-to-device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC-FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.     

Nanotechnologies in Food Science:Applications,Recent Trends,and Future Perspectives

Nanotechnology is a key advanced technology enabling contribution,development,and sustainable impact on food,medicine,and agriculture sectors.Nanomaterials have potential to lead qualitative and quantitative production of healthier,safer,and high-quality functional foods which are perishable or semi-perishable in nature.Nanotechnologies are superior than conventional food processing technologies with increased shelf life of food products,preventing contamination,and production of enhanced food quality.This comprehensive review on nanotechnologies for functional food development describes the current trends and future perspectives of advanced nanomaterials in food sector considering processing,packaging,security,and storage.Applications of nanotechnologies enhance the food bioavailability,taste,texture,and consistency,achieved through modification of particle size,possible cluster formation,and surface charge of food nanomaterials.In addition,the nanodelivery-mediated nutraceuticals,synergistic action of nanomaterials in food protection,and the application of nanosensors in smart food packaging for monitoring the quality of the stored foods and the common methods employed for assessing the impact of nanomaterials in biological systems are also discussed.     

Morphology and Mechanical Properties of Normal Blends and In-Situ Microfibrillar Composites from Low-Density Polyethylene and Poly(ethylene terephthalate)

In this work, normal blends, microfibrillar blends and composites were prepared from low density polyethylene (LDPE) and poly(ethylene terephthalate) (PET) in 85/15 and 75/25 w/w% ratio in the presence and absence of a compatibilizer polyethylene grafted with maleic anhydride (PE-g-MA). The microfibrillar composites (MFCs) were prepared using extrusion – drawing – isotropization technique. The morphology development of the microfibrillar blends and composites was studied using scanning electron microscopy (SEM). The presence of 5 wt% PE-g-MA compatibilizer affected the continuity of the fibrils differently in 75/25 and 85/15 w/w% microfibrillar blends. In the case of normal blends the addition of compatibiliser reduced the size of the dispersed PET phase. The presence of PET microfibrils improved the tensile properties of the microfibrillar composites. The normal blends exhibited a relatively ductile failure during tensile loading in comparison with the microfibrillar composites. The microfibrillar nature of the dispersed phase was found to improve the stiffness of the composite rather than their impact strength.     

Three‐dimensional visualization of an electrically conducting and viscous dissipative fluid flow over a moving permeable vertical cylinder

This study numerically scrutinizes the boundary layer flow of an electrically conducting and viscous dissipative fluid past an impulsively started permeable vertical cylinder together with thermal radiation. The solutions of the governing problem are accomplished using the Crank‐Nicholson scheme. The impressions of pertinent parameters on the flow patterns of the fluid particles as well as on the velocity, temperature, and distributed regions are captured and visualized three‐dimensionally.     

Enhancement of MRI images of hamstring avulsion injury using histogram based techniques

A novel histogram based image enhancement technique is introduced to visualize the image more effectively. The proposed method uses hamstring avulsion injury Magnetic Resonance Imaging (MRI) images from the database. First, the image is clipped using the histogram. Second, the image is subdivided into eight sub-images and enhanced individually until a better enhancement rate is maintained to obtain the final output of the proposed method. The proposed method shows effective enhancement for clear visualization of the injury. The strength of the proposed method is compared with different histogram based enhancement techniques based on the parameters such as F-measure, Contrast improvement index (CII), Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR) to determine the efficient enhancement technique. The parameters are defined to be significant for different enhancement techniques based on the statistical analysis. Further classification of the enhancement techniques are performed with the help of decision tree classifier. Based on the results of the classifier, the proposed algorithm is stated to be more significant and efficient in enhancing the region of interest in the Hamstring Avulsion Injury MRI images. Thus the proposed method shows effective enhancement for improved visualization of the hamstring injury for the diagnosis of the state of injury. With these results, the region of injury can be analysed effectively for further processing.

     

First-principles investigation on transport properties of $$\hbox {Zn}_{2}\hbox {SnO}_{4}$$ molecular device and response toward $$\hbox {NO}_{2}$$NO2 gas molecules

The transport properties of a \(\hbox {Zn}_{2}\hbox {SnO}_{4}\) device along with adsorption properties of \(\hbox {NO}_{2}\) gas molecules on \(\hbox {Zn}_{2}\hbox {SnO}_{4}\) (ZTO) molecular devices are investigated with density functional theory using the non-equilibrium Green’s function technique. The transmission spectrum and device density of states spectrum confirm the changes in HOMO–LUMO energy level due to transfer of electrons between the ZTO-based material and the \(\hbox {NO}_{2}\) molecules. I–V characteristics demonstrate the variation in the current upon adsorption of \(\hbox {NO}_{2}\) gas molecules on the ZTO device. The findings of the present study clearly suggest that ZTO molecular devices can be used to detect \(\hbox {NO}_{2}\) gas molecules in the trace level.     

Automating Transfer Credit Assessment-A Natural Language Processing-Based Approach

Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate this process. Given the unique structure, domain specificity, and complexity of learning outcomes (LOs), a need for designing a tailor-made model arises. The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs. The similarity between LOs is further aggregated to form course to course similarity. Due to the lack of quality benchmark datasets, a new benchmark dataset containing seven course-to-course similarity measures is proposed. Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency. While providing an efficient model to assess the similarity between courses with existing resources, this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.