Search

Image enhancement of low light images is an important research area in computer vision and image processing. In recent years, deep learning has emerged as a powerful tool for enhancing low light images, with convolutional neural networks (CNNs) being the most used architecture. In this study, we propose a deep learning approach for enhancing low light images using a modified CNN. Our approach involves training the network on a large dataset of low light images and their corresponding enhanced images and using the trained model to enhance new low light images. We also propose a new loss function that encourages the network to enhance details and reduce noise in the images. Our experiments show that our approach outperforms existing state-of-the-art methods in terms of both objective metrics and visual quality, making it a promising technique for real-world low light image enhancement applications

The design and construction of a multi-drink vending machine is the primary goal of this little project. This machine's function is to offer people a wide variety of goods, including water, drinks, coffee, tea, etc. A machine that can sell things is a vending machine.Different products are being produced in what is more akin to an automated process without the need for man handling that we typically see in busy cities. These systems are now accessible in some of the best-rated restaurants and hotels in India as well as MNCs. In most systems the operation are carried manually by the operator in which the billing and change making is carried out by the \sowner who running the shop or restaurant. There are strict requirements for interactive gadgets used in public settings, including user-friendly interfaces. This model's vending machine is made using plywood, fevicol gum, a DC motor, and other materials. batteries, etc.

This paper presents the implementation of separating the linear and non-linear data using support vector machine (SVM) algorithm. First let us understand what linear and non-linear datasets are. Linear datasets are the data that can be easily separable using a straight line. Such data are usually easy to implement in Artificial Neural Networks as they require a smaller number of hidden layers for its computation. Less layers implies a smaller number of weights assigned to the nodes present between the layers and less amount of time needed is needed to compute and update the weights for the current neural network. Hence linear datasets are easy to train and model. Whereas non-linear datasets are those datasets which cannot be separated by a straight line. For such datasets more hidden layers and weights are required and also more time and computational power is needed for a system to update the weights and train the model to give a better and sophisticated output data. As a result, training and modeling such neural network is tedious due to its complexity. To solve this problem SVM comes into picture. SVM stands for Support Vector Machine which is a machine or an algorithm that helps in classification and diversifying the data given to it. The data provided to the Support Vector Machine (SVM) should be a labeled one. Then these datasets are given to a training model where the training process of the neural network is being undergone. Once the training is completed, the next step is to predict the output. For this process we have to provide a new data that may or may not belong to the dataset, so that the neural network can predict the output of it. If the prediction is wrong, again the training is done until we get the actual output matching with the desired output given by the designer for verification purposes. This is the basic working process under the SVM algorithm. The linear data that is used for this separation is an Iris dataset that contains various information about the different plant-life growing from 2002-2004.

Power generation using Photovoltaic (PV) cells is the primary source among renewable energy sources. The PV cell, which utilizes solar energy, turns solar radiation into electrical energy without adversely affecting the earth's atmosphere. The key issue regarding PV power generation is that solar irradiation varies with time on an hourly basis. To extract the high power from the PV panel during the change in environmental condition, Single-Ended Primary Inductance-Capacitor (SEPIC) based DC-DC converter, and a highly efficient Maximum Power Point Tracking (MPPT) algorithm are used. In this study, an isolated SEPIC converter is used to charge the battery of an electric vehicle via a grid or PV source. The incremental Conductance (INC) algorithm is more efficient among the MPPT methods due to the accuracy in steady state and flexibility to the environment, increasing the system's overall efficiency. Therefore, this study uses the INC method to obtain high power output during the change in environmental conditions. The converter, MPPT algorithm, and controller are developed in MATLAB/Simulink environment, and the results are verified through the simulations. The results show the applicability of the SEPIC converter with INC for electric vehicle battery charging applications.

An Ultraviolet (UV) based sterilization technique acts as a useful aid in pruning the growth of microorganisms that usually persists on the surfaces after the usual procedure of cleaning and sanitizing. A UV robot is being developed for sterilization and sanitization in the operating rooms of hospitals. Surface decontamination is a crucial procedure to prevent the spread of infections in hospitals. The usual procedures of manual cleaning and disinfection are inadequate to eliminate pathogens from the infected and contaminated surfaces. Ultraviolet-C (UV-C) radiation deploying autonomous disinfection control system is being developed to complement the conventional decontamination procedures with a significant reduction of time and concurrent reduction of workload. The UV Robot lowers the transmission of hazardous virus as it can cleanse the areas that are out of human reach. In the view of technology, the development of robots is a part of preventive strategy to overcome the challenges faced by hospitals in a pandemic like situations. It also helps us to increase the effectiveness in battling the pandemic. This robot is also power efficient as it can function for a longer period of time than a human. It can also reduce human interference, also decreasing the risk of contamination of virus.

Objective The aim of this study was to analyze malpractice claims for aortic pathologies and to assess if there has been a change in rate of malpractice lawsuits with evolution of endovascular therapy. Methods Malpractice lawsuits were individually screened and compiled from the Westlaw database from 2000 to 2017 through use of relevant search terms. Data were collected of allegations, diagnoses, and outcomes of each case and compared. Results 268 unique cases were included in this study, with aneurysms (54%, n = 145) and dissection (35%, n = 94) making up the majority. There was a defendant verdict in 53% (n = 141), plaintiff verdict in 24% (n = 65), and settlements in 23% (n = 62) of lawsuits. Litigation was higher in the Midwest and Northeast. There was a gradual decline in litigation overall, however endovascular case numbers remained constant. There was negligible difference in the primary allegation underlying the litigation for various aortic pathologies, time to litigation and award between open and endovascular procedures. Conclusion The proportion of litigation for clinical negligence in endovascular cases amongst all vascular surgical lawsuits is increasing. As novel methods of endovascular therapy emerge, it is imperative that physicians remain vigilant to legal considerations to minimize malpractice risk.

Front line demonstration was conducted on drum seeding technology the farmers, assessment of drum seeder implement was done with the assistance of Krishi Vigyan Kendra, Kampasagar, Nalgonda under Professor Jayashankar Telangana State Agricultural University, Telangana for consequently three years during Kharif, from 2018-19 to 2020-21. The comparison was made between drum seeding technology and manual transplanting with an objective to reduction of cost of cultivation and subsequently increase farmer’s net returns. Frontline Demonstrations revealed that, there were more number of tillers (395) and panicles (370)/m2 in direct seeded technology with drum seeder compared to farmer’s practice (365 and 346/ m2). Drum seeder technology influence plant height (68.2cm), number of tillers/m2 (395) and yield attributes viz., Number panicles m-2 (370), length of panicle (16.9 cm) and Number of grains / panicle (182). Direct seeded technology with drum seeder has recorded higher grain yield (6,512 kg ha-1) which was 6.4 per cent over farmer’s practice (6,094 kg ha-1) with a net saving of Rs. 8,990/- on the cost of cultivation ha-1. The Gross returns and net returns (Rs.1, 20,795and Rs. 69, 265/ha) by the drum seeder method were more compared to farmer’s practice (Rs.1, 13,036/ha and Rs. 52, 516/ha).

An experiment was conducted including 20 genotypes of maize during Kharif season of 2020-21. All characters under study showed highly significant variation. High values of genotypic and phenotypic coefficients of variation were observed for the characters like anthesis and silking interval, grain yield per plant, biological yield, number of grains per row, number of grain rows per cob, harvest index and ear height. Biological yield per plant, plant height, grain yield per plant, ear height and harvest index showed high heritability coupled with high genetic advance suggesting their importance for direct selection. Number of cobs per plant, ear length, number of grains per row, 100 grain weight, harvest index, biological yield exhibited significant positive association with grain yield per plant. Highest direct positive effect on yield per plant was observed for harvest index followed biological yield, number of cobs per plant, anthesis and silking interval. Thus priority should be given to these characters during selection for maize yield improvement.

Aim: The purpose of the present study was to assess various treatment options amongst prosthodontists for rehabilitation of missing posterior teeth in their patients. Methodology: 100 prosthodontists participated in this questionnaire survey. 7 questions were asked from the practicing prosthodontists about various treatment options they choose for rehabilitation of posterior missing teeth. They were also asked about how patient’s choices as well excess cost of materials, influence their decision as well as long term stability. Statistical analysis like- standard deviation, mean etc. p value

The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents. In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems. Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-of-the-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora)., Accepted at ACL 2023, 15 pages (12 main + 3 Appendix)

Plastic waste is a major environmental problem. Every year, millions of tons of plastic end up in landfills, oceans, and other waterways. This plastic can take hundreds of years to decompose, and it can harm wildlife and pollute the environment. Recycling plastic is one of the most efficient ways to reduce plastic waste. When plastic is recycled, it can be reused to make new products. This helps to reduce the amount of new plastic that needs to be produced, which can help to protect the environment. In this paper, we propose a new system for managing plastic waste. The system would use a web- based platform and a mobile app to allow users to register their plastic waste and receive payment for it. The municipality would then collect the plastic waste and recycle it.The proposed system would have a number of advantages over traditional methods of recycling plastic waste. It would be more convenient for users, as they would not have to travel to a recycling center. It would also be more efficient for the municipality, as they would be able to collect more plastic waste. We believe that the proposed system has the potential to be a major breakthrough in the fight against plastic pollution. The system could be implemented in cities and towns around the world, and it could help to reduce the amount of plastic waste that ends up in landfills and oceans.

The recurrent and destructive nature of floods causes enormous economic damage and loss of human lives, leaving people in flood-prone areas fearful and insecure. It is essential to have a thorough knowledge of the factors that contribute to it. However, most catchment characterization studies are limited to case studies or regional domains. A detailed global characterization is currently unavailable due to the limitation in the holistic dataset that it demands. This study aims to fill this gap by utilizing multiple global datasets describing physiographic explanatory variables to characterize streamflow extremes. The role of catchment features such as landcover, geomorphology, climatology, lithology, etc., on spatial patterns and temporal changes of high streamflow extremes, was investigated in detail. Moreover, the multidimensional correlations between streamflow extremes and catchment features were modeled using a Random Forest approach and integrated with an interpretable machine learning framework to find the most dominating elements in different climate classes. The interpretation reveals that climatological variables are the most influential across all climates. However, the variables and their influences fluctuate between climates. Furthermore, distinct geomorphological variables dominate throughout climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights of this study would play a vital role in estimating the unit peak discharge at ungauged stations based on known watershed features. In addition, these findings can also help assess the nature of extremes in future climate scenarios, consequently implicating risk management methods.

Light-harvesting complex stress-related protein 3 (LHCSR3) expression is observed in various protoxidizing conditions like high light and nutrient starvation. LHCSR3 expression is essential for energy-dependent quenching (qE), whereas its role under nutrient starvation is elusive. It is also unclear how nutrient starvation can induce LHCSR3 expression under subsaturating light intensities. To study the role of LHCSR3 under nutrient starvation, theC. reinhardtiicells are grown under osmotic stress that would prevent water uptake; therefore same holds true for soluble nutrients in the medium. In this work, we have shown that LHCSR3 expression can occur under osmotic stress and subsaturating light intensities, whereas it does not elicit qE. Further examination of thylakoid membrane architecture from wild-type andnpq4mutant grown under nutrient starvation revealed that LHCSR3 expression affects the interaction between the PSII core with its peripheral LHCII antenna and possibly can prevent excitation energy transfer. Thylakoid lumen acidification is essential for the expression and function of LHCSR3. Under saturating light intensities, this is achieved by the increased rate of photosynthetic electron flow coupled with proton translocation into the thylakoid lumen. Whereas, under nutrient starvation, the reports of LHCSR3 expression also showed reduced photosynthetic electron flow. Therefore, an alternative mechanism should exist for developing the proton gradient. We observed the downregulation of chloroplast (cp) ATP synthase activity and its abundance under osmotic stress, suggesting the role of (cp) ATP synthase in thylakoid lumen acidification under reduced photosynthetic electron flow. This observation is supported by the expression of LHCSR3 in (cp) ATP-synthase mutantatpFupon exposure to moderate light intensity. This study proposes that the mechanism of LHCSR3 expression and its functionality can vary with the type of photooxidizing stress.

A popular metric for assessing the success of online advertising campaigns is click-through rate (CTR), particularly in the context of pay-per-click (PPC) marketing. Ad tracking is a crucial piece of technology for monitoring click-through rates (CTRs) in online advertising campaigns. With the aid of ad monitoring solutions, marketers can monitor the effectiveness of their advertising across various platforms and gadgets, including click-through rates. An advertising campaign's efficacy is assessed using this data, and it is subsequently improved over time to increase click-through rates and total return on investment(ROI). Advertisers can test many iterations of their ads and links using A/B testing tools like Optimizely, and they can track the clickthrough rates of each version to determine which one performs best CTR is frequently employed in the context of email marketing, where it is used to assess the success of a campaign by counting how many recipients click on links in the email. When used in this context, CTR is sometimes reported as a percentage of all email recipients who opened the message.

Dental implant treatment is a fast-growing field witnessing high success rate due to its osseointegration feature. Nevertheless, there are some complications relating to biological (Peri-implantitis; unsuccessful osseointegration) and mechanical (relating to implant prosthetic components) factors. Some of the mechanical factors which lead to failure are Prosthetic screw loosening, Prosthetic screw fracture, implant fracture, and framework fracture. Fracture of implant is a nemesis for the prosthodontist. Identifying the etiology and appropriate planning bring success to the treatment. This case report is about re- restoration of failed implant prosthesis with fracture at the abutment internal helix due to overload and active fixation. Multiple clinical difficulties were encountered (limited mouth opening, accessibility) during the procedure which were overcome by intra- orally adjusting the abutments (for parallelism and occlusal clearance) and proceeded with abutment level impression. The retainers were fabricated with an access hole for future retrievability in case of failure. Re-restoration with minimal intervention like modification of abutments intra-orally can be considered for a successful outcome. Sometimes, simpler choices may provide solutions for complex challenges.

Diabetes is a common chronic disease and poses a great threat to human health. Diabetes can lead to chronic damage and dysfunction of various tissues, especially eyes, kidneys, heart, blood vessels and nerves. To control the BGLs of diabetic patients the traditional prevention techniques such as eating healthy food and conducting physical exercise plays a major role and also taking the proper amount of insulin dosage has the crucial rule in the treatment process. The rapid development of machine learning has been applied to many aspects of medical health. We have proposed a model based on artificial neural network (ANN) to predict the proper amount of insulin needed for the diabetic patient. The proposed model was trained and tested using several patient’s data containing many factors such as weight, fast blood sugar and gender.

The paper reports evidence related to peoples’ perceptions and attitude during the early stages of COVID-19 related lockdown in India in four major aspects: strategies and preventive measures, long-distance travel, essential services, and post-lockdown travel. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analyzed using statistical tools and the results are translated into potential policy recommendations which may be useful to implement effective interventions during future pandemics of similar nature. The findings highlight a high rate of awareness among the people about the COVID-19, lack of supply of protective equipment such as masks, gloves, and personal protective equipment kits during the early stage of lockdown in India. However, several heterogeneities were also observed across a few socio-economic groups which emphasize the need for targeted campaigns in a diverse country such as India. The findings also suggest the need for arranging safe and hygienic long-distance trips for a section of the society when such lockdown measures are extended for long periods. The observations related to mode choice preferences during the post-lockdown recovery period indicate a potential shift of public transport patronage to the personal modes.

In this investigative study, the Taguchi design (L9) of experiments with TOPSIS multi-objective optimization is used for the robot nanopainting. The optimization objective is to maximize the film glue individually and minimize the surface blemishes and film thickness by varying the IRB 1410 robot painting parameters, such as the robot speed, pressure, and distance. The multi-wall carbon nanotube is infused into the paint materials using the ultrasonication process, and the mechanical properties are analysed with a comparison to regular paint materials. The virtual robot cycle time analysis is carried out for three different robot pathway patterns, namely linear, zig-zag, and circular. It is then compared with real-time experimental robot nanopaint on cold rolled close annealing steel materials. The obtained results show that surface roughness and thickness are minimized by 54% and 33.4%, respectively, using the robot nano spray coating when compared with regular spray coating. The film adhesive of robot Nano spray coating is maximized by 2.8% more than normal spray coating. A fuzzy logic expert system model is used to evaluate the surface finish characteristics of the nanopaint surface with low prediction error and with 89.2% confidence level. Heat transfer analysis of the robot nano-coated substrate is compared with that of the robot normal-coated substrate.

The knowledge of statistical analysis is essential before planning any research study or undergoing any orthodontic treatment. Clinical studies often compare the efficacy of a new treatment in a study group with the efficacy of conventional treatment or with a control group. Aside from a pure description, we should know whether the observed differences between the treatment groups are just random or are really present. This article informs the reader about frequently used statistical tests and their application in orthodontics and also provides a brief overview of the type of statistical tests to analyze research data with its examples.

Nowadays, the desktop 3D printers are common in computer aided manufacturing and widely used in multiple applications across homes, offices, and in several business sectors, etc. A wide variety of filaments like PLA, ABS, PETG, TPU, etc. are now being used in Fused Deposition Modeling (FDM). These filaments require different temperatures to print. FDM is a process in which a material in its filament form is melted and deposited layer by layer to form the required object. The hot end of a printer plays a crucial role in the melting of these filaments for printing. It is a major component of a 3D printer, where the filament is brought to a semi-solid state, to extrude and deposit the filament in layers. In this research study, the hot end of "Ender 3" and "CR-10S Pro" are modeled using Computer Aided Designing software, and thermal analysis using Finite Element Analysis, is carried out by applying the maximum printing temperature of PLA for the two printer hot ends. To optimize the problem of wide temperature gradient which often results in clogs and jams across the hot end, the design of heat sink is modified to maximize heat dissipation. Alongside, the simulation result is also compared with manual experimental readings.

Funding: This work was supported by the EPSRC through grant No. EP/S014403/1, by The Royal Society through RGS\R2\192174, by the Carlsberg Foundation through the ’Semper Ardens’ Research Project QCooL, by the NSF through a grant for ITAMP at Harvard University, by the DFG through the SPP1929, and by the Danish National Research Foundation through the Center of Excellence "CCQ" (Grant agreement no.: DNRF156). K.O. acknowledges EPSRC for PhD studentship support through grant no. EP/L015110/1. S.K.R. acknowledges the Carnegie Trust for the Universities of Scotland Research Incentive Grant RIG009823. T.V. acknowledges support through the Australian Research Council Centre of Excellence for Engineered Quantum Systems (CE170100009). V.W. acknowledges support by the NSF through a grant for the Institute for Theoretical Atomic, Molecular, and Optical Physics at Harvard University and the Smithsonian Astrophysical Observatory. We also thank Yoshiko Nanao and EPSRC grant No. EP/T023449/1 for the XRD measurements. Giant Rydberg excitons with principal quantum numbers as high as n = 25 have been observed in cuprous oxide (Cu2O), a semiconductor in which the exciton diameter can become as large as ∼1 μm. The giant dimension of these excitons results in excitonic interaction enhancements of orders of magnitude. Rydberg exciton–polaritons, formed by the strong coupling of Rydberg excitons to cavity photons, are a promising route to exploit these interactions and achieve a scalable, strongly correlated solid-state platform. However, the strong coupling of these excitons to cavity photons has remained elusive. Here, by embedding a thin Cu2O crystal into a Fabry–Pérot microcavity, we achieve strong coupling of light to Cu2O Rydberg excitons up to n = 6 and demonstrate the formation of Cu2O Rydberg exciton–polaritons. These results pave the way towards realizing strongly interacting exciton–polaritons and exploring strongly correlated phases of matter using light on a chip. Postprint

Digital digitalization has changed the way we access and use our content. Information is available at the click of a button and is always in our hands. The proliferation of the Internet led to the collapse of the planet, figuratively speaking. Content based on entertainment mixed with information (infotainment), unlike the latest news, tends to attract the attention of better audiences. The rise of OTT platforms, this trend has seen a new. Content from around the world is available without wasting much time. It would not be wrong to say that OTT platforms like Netflix, Amazon Prime Video etc. provide catalytic momentum in globalization. These forums can be commended for bringing the world together and bringing them closer together and for promoting a better sense of cultural understanding among the masses. During the tragic times of the unprecedented global epidemic such as COVID-19, these highly sought-after video platforms saw a dramatic increase in their visuals. As a result of the closure and the official social reduction practices adopted by many countries affected by the epidemic, people have registered for higher attendance at these forums. It is because of these methods and statistics of increasing popularity and reliance on OTT foundations of entertainment, information and engagement among viewers that this research has done. This urban youth survey aims to assess the tendency to use content during closure, to understand your causes and impacts. Keywords: OTT, Video-on-demand, digitization, infotainment, content, COVID-19 pandemic, lockdown.

In today’s world, people using smart phones have increased rapidly andhence, a smart phone can be used efficiently for private security or various protection purposes hence, a smart phone can be used efficiently for private security or various other protection purposes. The dangeous incidents that outraged the entire other protection purposes. The heinous incidents that outraged the entire nation have waken us to go for the safety issues. So we have created a app so that women can feel safe when they go outside. In The feature of this application is to send application is to send the message to the registered contacts continuously, i.e. sharing the message to the registered contacts continuously and sharing the live location. Continuous location tracking information helps to seek out the location, tracking information helps to find the location of the victim and can be rescued safely. Index Terms: Android Application, Women Safety, Java, protection

Accurately predicting clinical outcome of aneurysmal subarachnoid hemorrhage (aSAH) patients is difficult. The purpose of this study was to develop and test a new fully-automated computer-aided detection (CAD) scheme of brain computed tomography (CT) images to predict prognosis of aSAH patients. A retrospective dataset of 59 aSAH patients was assembled. Each patient had 2 sets of CT images acquired at admission and prior-to-discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and leaked extraparenchymal blood (EPB), respectively. CAD then detects sulci and computes 9 image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, and GM and 4 volumetrical ratios to sulci. Subsequently, applying a leave-one-case-out cross-validation method embedded with a principal component analysis (PCA) algorithm to generate optimal feature vector, 16 support vector machine (SVM) models were built using CT images acquired either at admission or prior-to-discharge to predict each of eight clinically relevant parameters commonly used to assess patients' prognosis. Finally, a receiver operating characteristics (ROC) method was used to evaluate SVM model performance. Areas under ROC curves of 16 SVM models range from 0.62 ± 0.07 to 0.86 ± 0.07. In general, SVM models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while SVM models trained using CT images acquired prior-to-discharge demonstrated higher accuracy in predicting long-term clinical outcomes. This study demonstrates feasibility to predict prognosis of aSAH patients using new quantitative image markers generated by SVM models.

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams., 13 Pages (9 Pages + References), Accepted at SemEval 2023 at ACL 2023

This project is designed so as to be used by Cycle Rental Company specializing in renting cycles to customers. It is an online system through which customers can view available cycles, register, view profile and book cycle. The advancement in Information Technology and internet penetration has greatly enhanced various business processes and communication between companies (services provider) and their customers of which car rental industry is not left out. The main objective of the PEDAL-PALS is to manage the details of cycle, payment, booking,and customer. The project is totally built at administrative end and thus only the administrative is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the cycle, payment, booking, and customer.It tracks all the details of the booking. This system helps you to keep the information of customer online. You can check your customer information any time by using this system. PEDAL PALS is a unique and innovative product. The user shall login to the system and check for availability of cycles. The System shall check for the availability of the cycle and rent the cycle to the customer. PEDAL PALS can lead to error free, secure, reliable and fast management system. No formal knowledge is needed for the user to use this system. Thus, by this all it proves it is user-friendly. PEDAL PALS reduces the error while entering the data. It helps the organization in better utilization of resources. Key Words: Bicycle Renting Service; Environmentally Friendly; Android Application.

Purpose:Small cell lung cancer (SCLC) is an exceptionally lethal form of lung cancer with limited treatment options. Delta-like ligand 3 (DLL3) is an attractive therapeutic target as surface expression is almost exclusive to tumor cells.Experimental Design:We radiolabeled the anti-DLL3 mAb SC16 with the therapeutic radioisotope, Lutetium-177. [177Lu]Lu-DTPA-CHX-A”-SC16 binds to DLL3 on SCLC cells and delivers targeted radiotherapy while minimizing radiation to healthy tissue.Results:[177Lu]Lu-DTPA-CHX-A”-SC16 demonstrated high tumor uptake with DLL3-target specificity in tumor xenografts. Dosimetry analyses of biodistribution studies suggested that the blood and liver were most at risk for toxicity from treatment with high doses of [177Lu]Lu-DTPA-CHX-A”-SC16. In the radioresistant NCI-H82 model, survival studies showed that 500 μCi and 750 μCi doses of [177Lu]Lu-DTPA-CHX-A”-SC16 led to prolonged survival over controls, and 3 of the 8 mice that received high doses of [177Lu]Lu-DTPA-CHX-A”-SC16 had pathologically confirmed complete responses (CR). In the patient-derived xenograft model Lu149, all doses of [177Lu]Lu-DTPA-CHX-A”-SC16 markedly prolonged survival. At the 250 μCi and 500 μCi doses, 5 of 10 and 7 of 9 mice demonstrated pathologically confirmed CRs, respectively. Four of 10 mice that received 750 μCi of [177Lu]Lu-DTPA-CHX-A”-SC16 demonstrated petechiae severe enough to warrant euthanasia, but the remaining 6 mice demonstrated pathologically confirmed CRs. IHC on residual tissues from partial responses confirmed retained DLL3 expression. Hematologic toxicity was dose-dependent and transient, with full recovery within 4 weeks. Hepatotoxicity was not observed.Conclusions:Together, the compelling antitumor efficacy, pathologic CRs, and mild and transient toxicity profile demonstrate strong potential for clinical translation of [177Lu]Lu-DTPA-CHX-A”-SC16.

Supplementary Date from ImmunoPET of Ovarian and Pancreatic Cancer with AR9.6, a Novel MUC16-Targeted Therapeutic Antibody

Supplementary Table from ImmunoPET of Ovarian and Pancreatic Cancer with AR9.6, a Novel MUC16-Targeted Therapeutic Antibody

Supplementary Figure from Radioimmunotherapy Targeting Delta-like Ligand 3 in Small Cell Lung Cancer Exhibits Antitumor Efficacy with Low Toxicity

Supplemental data detailing the methods and results for the synthesis and in vitro characterization of the radioimmunoconjugates, longitudinal PET imaging and ex vivo analyses in various models described within the main text.

Purpose:The impact of androgen receptor (AR) activity in breast cancer biology is unclear. We characterized and tested a novel therapy to an AR-governed target in breast cancer.Experimental Design: We evaluated the expression of prototypical AR gene products human kallikrein 2 (hK2) and PSA in breast cancer models. We screened 13 well-characterized breast cancer cell lines for hK2 and PSA production upon in vitro hormone stimulation by testosterone [dihydrotestosterone (DHT)]. AR-positive lines were further evaluated by exposure to estrogen (17β-Estradiol) and the synthetic progestin D-Norgestrel. We then evaluated an anti-hK2–targeted radiotherapy platform (hu11B6), labeled with alpha (α)-particle emitting Actinium-225, to specifically treat AR-expressing breast cancer xenografts under hormone stimulation.Results:D-Norgestrel and DHT activated the AR pathway, while 17β-Estradiol did not. Competitive binding for AR protein showed similar affinity between DHT and D-Norgestrel, indicating direct AR–ligand interaction. In vivo production of hK2 was sufficient to achieve site-specific delivery of therapeutic radionuclide to tumor tissue at >20-fold over background muscle uptake; effecting long-term local tumor control.Conclusions:[225Ac]hu11B6 targeted radiotherapy was potentiated by DHT and by D-Norgestrel in murine xenograft models of breast cancer. AR activity in breast cancer correlates with kallikrein-related peptidase-2 and can be activated by D-Norgestrel, a common contraceptive, and AR induction can be harnessed for hK2-targeted breast cancer α-emitter radiotherapy.