Scalable Three-Dimensional Grasping Mechanism

In this work, we develop a scalable end-effector mechanism for grasping three- dimensional objects with sizes ranging from micrometer to millimeter scale. The design architecture of the gripper comprises an array of identical fingers patented in a circular fashion. Each finger is designed from a novel linkage mechanism whose end effector is manipulated by two independent actuators. In this research, we study three finger gripper device, where each is obtained from a 3 - linkage mechanism. The device is controlled by three independent piezo actuators, and one electro-magnetic solenoid common to each mechanism. The gripping capability depends on how fingers are controlled collectively and on the mechanical flexibility, which together provide variety of gripping performances that are necessary to handle a wide variety of objects. The gripping performance is defined here by grasping force at contact, motion range, and bandwidth. Optimization is done to design the link lengths for the best Geometric Advantage (GA), and the functionality evaluated using finite element analysis software, ANSYS

NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach

Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repurposing has been performed in silico, in which databases of drugs and chemical information are used to determine interactions between target proteins and drug molecules to identify potential drug candidates. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition characterized by seizures, periods of time with bursts of uncontrolled electrical activity in brain cells. Existing drugs for epilepsy can be ineffective and expensive, revealing a need for new antiepileptic drugs. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials. The algorithm has the potential to determine possible drug combinations to prescribe a patient based on a patient's prior medical history. This project examines NeuroCADR, a novel approach to computational drug repurposing capable of revealing potential drug candidates in neurological diseases such as epilepsy.Comment: 8 pages, 5 figure

The SLED (Shelf Life Expiration Date) Tracking System: Using Machine Learning Algorithms to Combat Food Waste and Food Borne Illnesses

The issue of food waste is a major problem contributing to the emission of greenhouse gases into the environment in addition to causing illness in humans. This research aimed to develop a correlation between the amount of time until a food spoiled and dates on food labels in conjunction with sensory observations. Sensory observations are more accurate as they are immediate observations that are specific to the food. This experiment observed bananas, bread, milk, eggs, and leafy greens over a period of time using characteristics specific to the food to quantify food spoilage. It was shown that the actual time until spoilage for all foods was longer than that of the best by date and that sensory observations proved to be a more accurate factor in determining spoilage. From this data, a machine learning algorithm was trained to predict if food was spoiled or not, in addition to the number of days until spoilage. This was presented to the consumer as an app, where the user can track foods and are reminded to check on them to prevent wastage. In addition, the experimental procedures were incorporated into a test kit for the consumer to take instructed observations to assess the spoilage of their food, which are then entered into the app to improve the algorithm. This paper discusses the individual effects of sensorial observations on each food and examines the shifting of consumer habits through an app and test kit to combat environmental consequences of food waste

Exploring Stochastic Characteristics of Freeway Traffic Breakdown and Recovery Conditions

In recent times, the term ‘congestion’ has gained a lot of attention due to its negative impact on the mobility and efficiency of transportation network. Congestion control and mitigation of its effects has become a prime concern to many transportation agencies. With breakthroughs in transportation technologies, effective management and utilization of existing infrastructure capacities has been possible. One of the key functions of technological advancements is the ability to understand the different characteristics of traffic that prevail on major freeways and arterials, and to model short-term predictions of traffic conditions with reasonable accuracy. Providing accurate, real-time information makes travelers aware of the traffic conditions on the network and influences travelers’ decisions in terms of trip time, mode and route choice. This helps spread the traffic demand and reduces congestion. Over the past few years, transportation researchers presented different approaches to model traffic conditions. However, no significant effort was made to study the stochastic characteristics of freeway traffic—particularly during breakdown and recovery periods—and to develop models which can forecast variations in traffic conditions. Extant models do not consider the future most probable values. The main objective of this research is to capture and analyze traffic patterns, obtained from real world freeway data, and to develop a series of models that can correlate between current and future traffic states. Traffic conditions evolving over varying time horizons have been successfully modeled and studied. The research ultimately aims to improve our understanding of the characteristics of breakdown and recovery conditions of traffic. The research was conducted using massive freeway data collected from a 40-mile segment of Interstate - 4, in Orlando, Florida

Vertical III-V Nanowires For In-Memory Computing

In recent times, deep neural networks (DNNs) have demonstrated great potential in various machine learning applications,such as image classification and object detection for autonomous driving. However, increasing the accuracy of DNNsrequires scaled, faster, and more energy-efficient hardware, which is limited by the von Neumann architecture whereseparate memory and computing units lead to a bottleneck in performance. A promising solution to address the vonNeumann bottleneck is in-memory computing, which can be achieved by integrating non-volatile memory cells such asRRAMs into dense crossbar arrays. On the hardware side, the 1-transistor-1-resistor (1T1R) configuration has been centralto numerous demonstrations of reservoir, in-memory and neuromorphic computing.In this thesis, to achieve a 1T1R cell with a minimal footprint of 4F2, a technology platform has been developed to integrate avertical nanowire GAA MOSFET as a selector device for the RRAM. Firstly, the effect of the geometry (planar to vertical) ofthe ITO/HfO2/TiN RRAM cell was studied where low energy switching (0.49 pJ) and high endurance (106) were achievedin the vertical configuration. Furthermore, InAs was incorporated as the GAA MOSFET selector channel material toleverage the beneficial transport properties of III-V materials desirable for supply voltage scaling. Finally, an approach wasdeveloped wherein InAs is used as the selector channel as well as the RRAM electrode by carefully tuning the InAs nativeoxides. This thesis also presents low-frequency noise characterization of the RRAM cell as well as the MOSFET to furtherunderstand the semiconductor/oxide interface. The vertical 1T1R cell developed in this thesis enables the implementationof Boolean logic operations using a single vertical nanowire while reducing the footprint by 51x when compared to itstraditional CMOS counterpart

A Simple and Rapid Method For DNA Extraction From Leaves of Tomato, Tobacco and Rape Seed

We have optimized a simple and rapid method for isolation of high quality genomic DNA from leaves of tomato, tobacco and rape seed.  This protocol significantly minimizes time and the use of laboratory materials.  The extracted DNA was suitable for PCR analysis.  This method requires less than 1 mg of leaf tissue and is useful for transgene detection, genetic maps and other DNA based molecular analyses

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PD