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Abstract: To kill cancer cells, photodynamic therapy (PDT) utilizes a light-sensitive medication and light. Light-sensitive drugs are absorbed by cancerous cells. The cells are then illuminated with a specific light or laser by a specialist and oxygen that destroys cancer cells is released. The present study aims to discuss the roles, advancements, and limitations of photodynamic therapy in cancer treatment. Photosensitizers and photosensitizing agents are used in photodynamic treatment to destroy cancer cells. Lasers or other light sources, such as LEDs, may provide illumination. There are reports of a novel nanoplatform for the treatment of HER2-overexpressed breast cancer, as well as other primary and metastatic cancers, using fluorescent electron microscopy PDT/photothermal therapy (PTT) dualtherapy. PDT has a significant benefit over conventional cancer therapies because it may cause fewer adverse effects. PDT-induced immunostimulatory cell death and the production of a robust local inflammatory response might lead to effective local anticancer therapy. PDT has a lower long-term morbidity and recovery time than surgery, chemotherapy, or radiation.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1005-1010, Advanced analysis and monitoring of photovoltaic solar modules is required to maintain the reliable operations of photovoltaic plants. Hence it requires diagnostics through current-voltage (IV) curves, electroluminescence (EL) imaging and other measurement techniques. The analysis through IV characterization provides the discerning insight about the quantitative measure of solar module performance, while the image characterization methods on EL images can capture spatial defects with microscopic resolution such as micro cracks, broken cells interconnections, shunts, among many other defect types. The fusion of these two methods with supervised and unsupervised machine learning can generate unique insight with classification, regression, and dimension reductions on IV-EL data. In this study, we have performed the IV-EL correlation by classifying the IV data based on EL image annotation (where the class information is coming from EL image). The feature vectors consist of IV curve parameters as well as statistical features. We have done the binary classification for identifying defect vs. no defect. Furthermore, we had applied feature selection algorithms before applying the classification algorithms. We have performed the classification of various defect types by applying the Random Forests (RF) and XGBoost algorithm while identifying the top features. The accuracy was achieved greater than 91% and 94% respectively for supervised methods on the top 5 features. This correlation of IV- EL measurement could benefit in quick identification of various defect types in PV modules with only IV curve parameters, given the classification models are modeled using large scale data sets and tuned optimally.

As the world is engulfed with COVID-19 pandemic and the glimpse of vaccine is still a distant dream, taking precautions and maintaining the norms suggested by WHO will keep us safe. With this, we present in this paper a solution that would help travelers induce confidence in traveling while keeping in mind the guidelines that must be followed. The solution focuses on an end to end service that will not only help the travelers to make informed and safe decisions but also allow the hospitality industry to monetize from this application. This paper is focused on a detailed analysis of the solution that is being presented to tackle the problems faced by various industries and their fear of resuming the work. A software-based approach is taken for providing a simple and engaging user experience to the user along with an AI approach to detect and predict the COVID trend in various cities. Along with this, a system that detects if people are wearing a mask or not will also be verified by thealgorithm.

An idealized shock/boundary-layer interaction problem that is two dimensional in the mean but with a threecomponent mean velocity field is studied using large-eddy simulations. The problem isolates some aspects of three-dimensionality while avoiding others and can thus offer insights into the differences between two-and threedimensional problems. The addition of a crossflow increases the size of the separation bubble quite substantially when normalized by the boundary-layer thickness or the momentum thickness but less substantially when normalized by the displacement thickness. All cases studied are found to have a very thin precursor separation bubble before the point of true separation that extends for at least one boundary-layer thickness but remains fully within the viscous sublayer. Simulations at different Reynolds numbers show that the flow reattaches after the precursor bubble at sufficiently high Reynolds number.

Vehicular traffic is endlessly increasing everywhere within the world and may cause terrible traffic jams at intersections. Traffic congestion and tidal flow are major facts that cause delays to emergency vehicles. Fire brigade officials, ambulances, and police officers often get delayed due to such congestion and traffic. With the use of the right technology, like Artificial Intelligence and real-time monitoring of the traffic, such a predicament can be moderated thereby saving the lives of the needy. The solution presented allows emergency Vehicle (EV) drivers to select the route of commute on the mobile application. Modules on that route selected are activated for the adaptive traffic control system. The driver’s GPS is dynamically updated on the cloud and fetched by the Raspberry Pi module. As the vehicle enters a given radius, the module checks for other EVs in the vicinity for priority assignment. The module starts traffic density detection and changes the traffic light states to clear the traffic if the density is above a certain threshold. After the EV comes closer to the signal, the light turns green irrespective of the traffic density. The camera cross validates if the EV has passed. Once it does, traffic signals switch to regular operation. This will allow the emergency vehicles to reach the destination on time and save the lives of those in need without delay. The entire solution is robust and reliable and can manage traffic efficiently keeping in mind the imperativeness of the emergency vehicles.

Wind energy is anticipated to play a vital role to fulfill the worldwide energy requirements, whereas existing bare wind turbines can convert only a fraction of flow energy into electricity. To bridge the gap between the escalating demand and the generation capability, we propose a shrouded-twin-rotor turbine design, whose power coefficient exceeds the Betz-Joukowsky limit. We analyze the flow through the wind turbine assembly, using the actuator disk theory, and estimate the power output for a pair of rotor-loading coefficients. We find the existence of two regimes in which the proposed design performs better than the single-rotor configuration: (a) a turbine-turbine mode where both rotors work as turbines and (b) a turbine-fan mode where one rotor is a turbine while the other is a fan- an idea proposed by Betz (A. Betz, Wind-Energie und ihre Ausnutzung durch Windmuhlen, Vandenhoeck, 1926). Both modes enable achieving maximum power for multiple combinations of the loading coefficient pair. Moreover, power output depends solely on a single parameter, defined using the area weighted sum of the loading coefficients. We derive the optimum performance criterion and present the effects of the shroud geometry, back pressure, and flow-efficiency parameters on the power output and the performance envelope.

This paper presents an application of optical character recognition(OCR) which can extract information from images of bills and receipts; store it as machine-processable text; in an organized manner for ease of access. It can do this efficiently even in the presence of watermarks on the bills or any shadows in the images of the bills. In developing this application, OpenCV has been used for the processing of the images and the Tesseract OCR engine has been used for optical character recognition and text extraction. The image is first processed using OpenCV for the removal of any shadows or watermarks present in it. For longer invoices, by employing the image bifurcation process, the data can be easily extracted which was not possible earlier. Furthermore, the processed image is passed on to the Tesseract OCR engine for the retrieving of text present in it. The text is then searched for important information, such as the total amount spent and the date on the receipt, using string processing.

The impact dynamics of a water droplet on a flexible substrate is useful for designing pesticide sprays and understanding insects flying in rainfall. We experimentally analyze the impact dynamics of a microliter water droplet on a superhydrophobic cantilever beam for Weber number in the range of 30–76. A thin copper sheet was coated with a commercial coating to render it superhydrophobic and high-speed imaging was used for visualization. During the impact, the spreading droplet converts its inertial energy into surface energy and elastic energy of the substrate. While retraction of the contact line, the latter energies convert to the kinetic energy of the droplet, and the droplet could bounce off the deforming cantilever beam. The characteristics timescales of droplet and cantilever beams are varied by changing the droplet diameter and impact velocity, and beam length, respectively. We show that the overall system dynamics, i.e., bouncing of the droplet and oscillations of the cantilever, is dependent on the interplay of these two timescales. A spring-mass system has been used to model this coupling and to explain the experimental observations. These findings can help to design systems to achieve desirable contact time, droplet rebound kinetic energy, energy transfer to the cantilever beam, and the droplet spreading diameter.

Flow-induced vibration (FIV) of a D-section cylinder is computationally studied and utilized to augment convective heat transfer in a heated laminar channel flow. An in-house fluid–structure interaction (FSI) solver, based on a sharp-interface immersed boundary method, is employed to solve the flow and thermal fields. In conjunction, a spring–mass system is utilized to solve for the rigid structural dynamics. Using numerical simulations, we highlight that the oscillations of a D-section cylinder are driven by vortex-induced vibration and galloping. It is observed that as the cylinder vibrates, vortices are shed from the apex of the cylinder due to the separating shear layers. These vortices, categorized using shedding patterns, interact with the heated channel walls. This interaction results in disruption of the thermal boundary layer (TBL), thus leading to heat transfer augmentation. The enhancement in thermal performance has been quantified using time and space-averaged Nusselt numbers at the channel walls. It is observed that the oscillation amplitude of the D-section cylinder and the extent of vortex–TBL interaction are crucial for determining heat transfer augmentation. Both symmetric and asymmetric thermal augmentation at the top and bottom channel walls have been reported. Finally, to assess the effectiveness of heat transfer augmentation, the D-section cylinder FIV is compared to other FSI systems operating under similar conditions.

New techniques for more accurate segmentation of a 3D cerebrovascular system from phase contrast (PC) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe PC-MRA images and desired maps of regions by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modified the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.

New techniques for more accurate segmentation of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe TOF-MRA images and desired maps of regions (blood vessels and the other brain tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a GMRF model with analytically estimated potentials. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.