Modeling of single mode optical fiber having a complicated refractive index profile by using modified scalar finite element method

A numerical method based on modified scalar finite element method (SC-FEM) is presented and programmed on MATLAB platform for optical fiber modeling purpose. We have estimated the dispersion graph, mode cut off condition, and group delay and waveguide dispersion for highly complicated chirped type refractive index profile fiber. The convergence study of our FEM formulation is carried out with respect to the number of division in core. It has been found that the numerical error becomes less than 2 % when the number of divisions in the core is more then 30. To predict the accurate waveguide dispersion characteristics, we need to compute expression (d^2 (Vb))/(dV^2 ) numerically by the FEM method. For that the normalized propagation constant b (in terms of β) should be an accurate enough up to around 6 decimal points. To achieve this target, we have used 1 million sampling points in our FEM simulations. Further to validate our results we have derived the higher order polynomial expression for each case. Comparison with other methods in calculation of normalized propagation constant is found to be satisfactory. In traditional FEM analysis a spurious solution is generated because the functional does not satisfy the boundary conditions in the original waveguide problem, However in our analysis a new term that compensate the missing boundary condition has been added in the functional to eliminate the spurious solutions. Our study will be useful for the analysis of optical fiber having varying refractive index profile

The Four Domains Model of Artificial Intelligence in Surgical Education: Adapting Applications to Trainee Level

Since the inception of modern medicine, surgical trainees have benefitted from coaching and feedback from senior surgeons. In the high-demand operating room environment, granular feedback is often limited by time constraints. Artificial intelligence (AI) represents a novel frontier in surgical education in that it may be able to provide reliable, objective, and comprehensive feedback in an automated manner. While prior studies have given broad overviews of AI in surgical training, there has been no study to our knowledge that has codified the myriad applications and offered guidance for which applications best suit trainees at various levels of surgical education. We aim to describe four key domains under which surgical training AI applications can be classified. We define which applications would best suit junior, intermediate, and experienced surgeons by matching their educational needs. The four domains include: enhanced simulation (realistic and safe training environment), real time visual cues (guidance for safe planes of dissection and steps of the operation), automated performance metrics (analyzing kinematic data with immediate feedback and potential improvements in operative efficiency and surgical technique), and surgical decision-making (utilizing metacognition to develop surgical gestalt). Each of these tenants represents a key step in surgical trainees’ development, from practicing technical skills on high fidelity models as a junior trainee to more advanced surgical decision-making as an advanced practitioner. We aim to provide a comprehensive framework for surgical mentors, training programs, and trainees in order to maximize benefits with the use of AI technologies

Engineered Peptides for Applications in Cancer-Targeted Drug Delivery and Tumor Detection

Cancer-targeting peptides as ligands for targeted delivery of anticancer drugs or drug carriers have the potential to significantly enhance the selectivity and the therapeutic benefit of current chemotherapeutic agents. Identification of tumor-specific biomarkers like integrins, aminopeptidase N, and epidermal growth factor receptor as well as the popularity of phage display techniques along with synthetic combinatorial methods used for peptide design and structure optimization have fueled the advancement and application of peptide ligands for targeted drug delivery and tumor detection in cancer treatment, detection and guided therapy. Although considerable preclinical data have shown remarkable success in the use of tumor targeting peptides, peptides generally suffer from poor pharmacokinetics, enzymatic instability, and weak receptor affinity, and they need further structural modification before successful translation to clinics is possible. The current review gives an overview of the different engineering strategies that have been developed for peptide structure optimization to confer selectivity and stability. We also provide an update on the methods used for peptide ligand identification, and peptide-receptor interactions. Additionally, some applications for the use of peptides in targeted delivery of chemotherapeutics and diagnostics over the past 5 years are summarized

TinyML: Tools, Applications, Challenges, and Future Research Directions

In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.Comment: 12 pags, 3 tables, 4 figure

CHARACTERIZATION OF XANTHOMONAS AXONOPODIS PV. PUNICAE ISOLATES FROM WESTERN MAHARASHTRA AND THEIR SENSITIVITY TO CHEMICAL TREATMENTS

Bacterial blight of pomegranate caused by Xanthomonas axonopodis pv. punicae is a major biotic constraint in peninsular India. Field survey was undertaken in the major pomegranate growing regions of Western Maharashtra, which revealed the high prevalence of bacterial blight incidence in Solapur, Sangli and Nashik districts. Four different isolates of this pathogen were obtained from the highly infected plant materials collected during the field survey. X. axonopodis pv. punicae was detected from infected plant material and its identity was confirmed by morphological, physiological, hypersensitive and pathogenicity tests. Nashik isolate was most virulent. On Inter Simple Sequence Repeat (ISSR) analysis they formed separate clusters with Akkalkot-Solapur isolate being most divergent, while Deola-Nashik and Sangamner-Ahmednagar isolates were most similar. Six chemical treatments showed complete control under in vitro conditions while rest varied in their response to isolates. Complete control in all four isolates was observed with Bordeaux mixture (1%); captan (0.25%) + Copper oxychloride (0.3%), captan (0.25%) + copper hydroxide (0.3%), bromopol (500 ppm) + copper oxychloride (0.3%), streptocycline (250 ppm) + copper hydroxide (0.3%), streptocycline (500 ppm) + copper hydroxide (0.3%) during in vitro study

Reverse hydrotropy by complex formation

Alkylated azacrown ethers lower significantly interfacial tension and are capable of solubilising water-soluble dyes, despite not being able to aggregate in non-polar solvents.</p

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

Fingerprints Indicating Superior Properties of Internal Interfaces in Cu(In,Ga)Se2 Thin-Film Solar Cells

Growth of Cu(In,Ga)Se2 (CIGS) absorbers under Cu-poor conditions gives rise to incorporation of numerous defects into the bulk, whereas the same absorber grown under Cu-rich conditions leads to a stoichiometric bulk with minimum defects. This suggests that CIGS absorbers grown under Cu-rich conditions are more suitable for solar cell applications. However, the CIGS solar cell devices with record efficiencies have all been fabricated under Cu-poor conditions, despite the expectations. Therefore, in the present work, both Cu-poor and Cu-rich CIGS cells are investigated, and the superior properties of the internal interfaces of the Cu-poor CIGS cells, such as the p-n junction and grain boundaries, which always makes them the record-efficiency devices, are shown. More precisely, by employing a correlative microscopy approach, the typical fingerprints for superior properties of internal interfaces necessary for maintaining a lower recombination activity in the cell is discovered. These are a Cu-depleted and Cd-enriched CIGS absorber surface, near the p-n junction, as well as&nbsp;a negative Cu factor (∆β) and high Na content (&gt;1.5 at%) at the grain boundaries. Thus, this work provides key factors governing the device performance (efficiency), which can be considered in the design of next-generation solar cells

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Abstract Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Abstract Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations