Combined Magnetohydrodynamic and Geometric Optimization of a Hypersonic Inlet

This paper considers the numerical optimization of a double ramp scramjet inlet using magnetohydrodynamic (MHD) effects together with inlet ramp angle changes. The parameter being optimized is the mass capture at the throat of the inlet, such that spillage effects for less than design Mach numbers are reduced. The control parameters for the optimization include the MHD effects in conjunction with ramp angle changes. To enhance the MHD effects different ionization scenarios depending upon the alignment of the magnetic field are considered. The flow solution is based on the Advection Upstream Splitting Method (AUSM) that accounts for the MHD source terms as well. A numerical Broyden-Flecher-Goldfarb-Shanno- (BFGS-) based procedure is utilized to optimize the inlet mass capture. Numerical validation results compared to published results in the literature as well as the outcome of the optimization procedure are summarized to illustrate the efficacy of the approach

Design And Analysis Of Flexible Beam Platform As Vibration Isolator For Space Applications

Spacecrafts are generally equipped with high precision optical and other sensor payloads. The structures of most of the spacecrafts are light-weight, flexible and have low damping. Vibrations are often induced in the spacecraft body due to the presence of many disturbance sources such as momentum/reaction wheels, control thrusters used for attitude control and cryocoolers etc. Low damping leads to long decay time for vibrations hence during this period the spacecraft sensors cannot be used effectively. One possible solution is to isolate the precision sensor from the rest of the satellite and this strategy has been used for spaceborne telescopes and interferometers that have extremely precise positional and vibratory tolerances imposed on them in order to achieve scientific goals. Another strategy is to isolate the vibration source itself from the spacecraft body. This thesis deals with modelling, analysis and experimentation of a novel low frequency flexible space platform designed to serve as a mount for the disturbance source in order to insulate the source generated vibrations reaching critical areas of the structure. The novel space platform consisting of folded continuous beams, is light-weight and is capable of isolating vibration generated by sources such as reaction/momentum wheels. Finite element analysis of the platform is carried out for static and dynamic load cases. Simulation studies are carried out on flexible beam platform in order to firm up the design for passive vibration isolation. Modal analyses is done to simulate the response of each mode. Active control has been studied by embedding the platform’s beam elements with piezo actuators and sensors. The simulation results show that the space platform can effectively attenuate vibration and further improvement in vibration attenuation is possible with active control. Based on the analysis, a prototype low frequency platform has been designed and fabricated. An experimental validation has been done to test the usefulness of the low frequency platform to act as a mount for reaction wheels and to mitigate the vibration disturbances/effects transmitted from the reaction wheel assembly to structure. Measurements and tests have been conducted at varying wheel speeds to quantify and characterize the amount of isolation to the reaction wheel generated vibrations. The time and frequency domain analysis of test data clearly show that level of isolation is significant and an average of 13 dB of isolation is seen. The level of isolation is different for different isolators and it depends upon the isolator design and wheel speed. Forces and moments measured at the base for wheel with isolator and wheel without isolator clearly demonstrate and confirm a reduction in the disturbance levels of atleast one order. These isolators are further tested successfully for launch dynamic loads in order to confirm the design adequacy to sustain such loads. Results indicate that the flexible mounts of the type discussed in this thesis can be used for effective passive vibration isolation in spacecrafts with reaction/momentum wheels

Ensemble Machine Learning Model for Phishing Intrusion Detection and Classification from URLs

Phishing sounds like fishing (which means to cash fish) is a term used for an attempt to commit financial fraud on the internet. An e-mail scam is carried out on individuals or corporate organizations in an attempt to defraud them by falsely obtaining their sensitive details such as usernames, passwords, credit card information, and account numbers. For example, an email may be sent to an individual and appears with a link to click, such as “click me” showing that the recipient has won a certain amount of money, and thereafter requesting him to provide account information for verification. Unfortunately, the credentials are actually transmitted to a phisher who may exploit the person's account when the receiver sends the account details for validation. This research’s focus is to utilize different machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users

Ensemble Machine Learning Model for Phishing Intrusion Detection and Classification from URLs

Phishing sounds like fishing (which means to cash fish) is a term used for an attempt to commit financial fraud on the internet. An e-mail scam is carried out on individuals or corporate organizations in an attempt to defraud them by falsely obtaining their sensitive details such as usernames, passwords, credit card information, and account numbers. For example, an email may be sent to an individual and appears with a link to click, such as “click me” showing that the recipient has won a certain amount of money, and thereafter requesting him to provide account information for verification. Unfortunately, the credentials are actually transmitted to a phisher who may exploit the person's account when the receiver sends the account details for validation. This research’s focus is to utilize different machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users

Molecular Docking Studies for the Assessment of Wound Healing Activity of Phytoconstituents in Heliotropium Indicum

One of the most crucial and complex processes is the skin's multi-stage process of healing after an injury. Heliotropium indicum is a potent antibiotic, anti- inflammatory, anti-neoplastic, anti-oxidant, and wound- healing agent. Heliotropium indicum Linn is the source of the chemical compound in question, which is abundant in sterols, ammines, volatile oils, and the pyrrolizidine alkaloids. Molecular docking studies were conducted on Heliotropium indicum using Argus lab 4.0.1 and Autodock 1.5.7. The proteins PDB ID:1YXO, 3V18, and 4G8R were selected because of their role in wound healing. The pieces work together with the protein responsible for mending wounds. The binding affinities of mupirocin and nitrofurazone are higher than those of the components stigmasterol, eugenol, borneol, and campesterol. In order to better customize Heliotropium indicum to our requirements, we now have a better knowledge of the components of the molecule that interact with their receptors in the wound healing process

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain

Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs

Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are an ideal platform for graph algorithms due to their high peak performance and memory bandwidth. While there exist several GPU connectivity algorithms in the literature, many design choices have not yet been explored. In this paper, we explore various design choices in GPU connectivity algorithms, including sampling, linking, and tree compression, for both the static as well as the incremental setting. Our various design choices lead to over 300 new GPU implementations of connectivity, many of which outperform state-of-the-art. We present an experimental evaluation, and show that we achieve an average speedup of 2.47x speedup over existing static algorithms. In the incremental setting, we achieve a throughput of up to 48.23 billion edges per second. Compared to state-of-the-art CPU implementations on a 72-core machine, we achieve a speedup of 8.26--14.51x for static connectivity and 1.85--13.36x for incremental connectivity using a Tesla V100 GPU

Megafaunal Community Structure of Andaman Seamounts Including the Back-Arc Basin – A Quantitative Exploration from the Indian Ocean

Species rich benthic communities have been reported from some seamounts, predominantly from the Atlantic and Pacific Oceans, but the fauna and habitats on Indian Ocean seamounts are still poorly known. This study focuses on two seamounts, a submarine volcano (cratered seamount – CSM) and a non-volcano (SM2) in the Andaman Back–arc Basin (ABB), and the basin itself. The main purpose was to explore and generate regional biodiversity data from summit and flank (upper slope) of the Andaman seamounts for comparison with other seamounts worldwide. We also investigated how substratum types affect the megafaunal community structure along the ABB. Underwater video recordings from TeleVision guided Gripper (TVG) lowerings were used to describe the benthic community structure along the ABB and both seamounts. We found 13 varieties of substratum in the study area. The CSM has hard substratum, such as boulders and cobbles, whereas the SM2 was dominated by cobbles and fine sediment. The highest abundance of megabenthic communities was recorded on the flank of the CSM. Species richness and diversity were higher at the flank of the CSM than other are of ABB. Non-metric multi-dimensional scaling (nMDS) analysis of substratum types showed 50% similarity between the flanks of both seamounts, because both sites have a component of cobbles mixed with fine sediments in their substratum. Further, nMDS of faunal abundance revealed two groups, each restricted to one of the seamounts, suggesting faunal distinctness between them. The sessile fauna corals and poriferans showed a significant positive relation with cobbles and fine sediments substratum, while the mobile categories echinoderms and arthropods showed a significant positive relation with fine sediments only

Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

\ua9 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Individuals with rare kidney diseases account for 5–10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure. Methods: People aged 0–96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan–Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1\ub773 m2 or more to first eGFR of less than 30 mL/min per 1\ub773 m2 (the therapeutic trial window). Findings: Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9\ub76 years (IQR 5\ub79–16\ub77). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2\ub781 million UK patients with all-cause chronic kidney disease (28% vs 1%; p<0\ub70001), but better survival rates (standardised mortality ratio 0\ub742 [95% CI 0\ub732–0\ub752]; p<0\ub70001). Median age at kidney failure, median age at death, time from start of dialysis to death, time from diagnosis to eGFR thresholds, and therapeutic trial window all varied substantially between rare diseases. Interpretation: Patients with rare kidney diseases differ from the general population of individuals with chronic kidney disease: they have higher 5-year rates of kidney failure but higher survival than other patients with chronic kidney disease stages 3–5, and so are over-represented in the cohort of patients requiring kidney replacement therapy. Addressing unmet therapeutic need for patients with rare kidney diseases could have a large beneficial effect on long-term kidney replacement therapy demand. Funding: RaDaR is funded by the Medical Research Council, Kidney Research UK, Kidney Care UK, and the Polycystic Kidney Disease Charity