(FY 02) Reducing Freshwater Waste on Farms

Authors: Noah Proctor

Advisors: Dr. Kevin Crowthers

Category:  First Year

Abstract: In the United States, many large-scale farms use some form of external irrigation, whether through drip irrigation or overhead sprinklers. However, these methods can be highly inefficient and result in significant water waste. Because of this, various automated irrigation systems have been developed to minimize freshwater consumption in agriculture. Additionally, farms on hillsides can pose additional challenges, as water will flow downhill, causing the soil at the bottom to become moister compared to the soil at the top, further wasting water. While many systems incorporate some components, such as weather or soil data, they often fail to integrate both data types. This project introduces a new irrigation system that integrates weather and soil data while accounting for topographical variations by utilizing multiple solenoid control valves and capacitive soil moisture sensors. The multiple soil moisture sensors and solenoid control valves will allow us to smartly irrigate the different levels independently. This approach offers a more efficient and sustainable water management solution that shows a nearly 50% reduction in water used compared to traditional irrigation systems. This system could be transferred to large-scale agricultural operations and significantly reduce their water footprint.

UN SDGs:

SDG 12 – Responsible Consumption and Production

SDG 13 – Climate Action

SDG 15 – Life on Land

Video Presentation:

Poster Presentation:

(FY 01) Investigating the Neuroprotective Effects of T. ammi on Parkinson’s Disease via Gut Brain Axis Modulation

Author: Vyshnavi Donthabhaktuni

Advisors: N/A

Category: First Year

Abstract: Parkinson’s disease (PD) is a common neurodegenerative disease (NDD) that is characterized by the gradual loss of dopaminergic neurons and progressive motor impairment. Recent studies suggest that PD may originate in the gut, highlighting the gut-brain axis (GBA) as a critical area for research. This study investigated the neuroprotective potential of Trachyspermum ammi (T. ammi) oil on PD-associated symptoms using Caenorhabditis elegans (C. elegans) as a model organism. This study will assess various PD symptoms including impaired locomotion, dopaminergic neuron degeneration, elevated reactive oxygen species (ROS) levels, and gut permeability. PD was induced in C. elegans using genetic models, and varying concentrations of T. ammi oil were incorporated into the worms’ food. Behavioral assays (locomotion, thrashing) as well as physiological assays (oxidative stress, alpha-synuclein levels, and gut permeability), were conducted to assess the impact of T. ammi oil on these parameters. Results from these assays suggested a positive effect of T. ammi on Parkinson’s, with the ajwain-treated groups showing improved locomotion, thrashing, improved survival under oxidative stress and even lowered alpha-synuclein levels. The findings support the hypothesis that T. ammi oil mitigates PD-like symptoms in C. elegans. The results of this study could be further applied to develop an efficient, cost-effective, and widely available treatment for mitigating PD symptoms in humans.

UN SDGs:

SDG 3 – Good Health and Well Being

SDG 15 – Gender Equality

Video Presentation:

Poster Presentation:

(GR7) Tiketi Rafiki (Friendly Ticket)

Author(s): Raymond Magambo; James Chambo; Martine Kusekwa; Richard Simon; Happiness Kingi

Advisor: Curtis Abel

Category: Graduate

Abstract/Description:
A mobile device application and web based platform which allows bus passengers to book and purchase their bus tickets directly using their mobile money accounts. Tiketi Rafiki will allow people to use their mobile phones, computers and tablets to book and purchase bus tickets anywhere at their convenience. In order to serve the offline users, Tiketi Rafiki will have agents to offer the service in different parts of the country. Tiketi Rafiki will revolutionize and improve the efficiency of the way people book bus tickets in Tanzania.

(GR6) Stand Against COVID: building affordable ventilators + deploying robotic medical tele-ultrasonography for better healthcare.

Author: Jakub Tomasz Kaminski

Advisor: Haichong Kai Zhang

Category: Graduate

Abstract/Description:
These projects are a response for the COVID-19 pandemic and address two areas: patient ventilation and medical imaging. The author designed and built a ventilator based on a BVM/Ambu bag and a robotic mechanism which is safe and ready for affordable mass production. The device provides basic ventilation modes and can be a temporary alternative to high-end equipment in a global ventilator shortage. Its design was discussed with doctors and mechanical ventilation specialists. Author’s work is fully voluntary and will be available OpenSource for any party interested in producing the device. Second project: (in progress)- Recent worldwide clinical practice has shown the importance of ultrasound for COVID-19 screening. Its is affordable, fast and shows lung structure changes at early disease stages. Unfortunately, it does require on-site ultrasound specialists and a direct contact with patients.
The author is working on a robotic ultrasound system for teleoperated lung ultrasound scanning which will be tested and manufactured in Poland and then used in a hospital in Nigeria. The device allows less-qualified personnel to connect with a specialist in a remote location and also reduce the overall exposure to the virus. The specialist controls a robot that has an ultrasound probe which slides over the patient surface. This project is sponsored by the WPI Medical FUSION Lab.

(GR5) Realizing a World without Waste through Catalytic Conversion of Food Waste

Author: Heather LeClerc

Advisors: Andrew Teixeira; Mike Timko

Category: Graduate

Abstract/Description:
Each year over 1.3 billion tons of food is thrown away, with over 90% ending up in landfills. Through the process of hydrothermal liquefaction (HTL) this waste can be harnessed and converted into a usable energy product. The addition of a bi-functional catalyst has the potential to improve the amount of energy recovered from food, thereby increasing commercial viability. This technology works to produce renewable energy from waste, helping to obtain a sustainable future.

(GR4) Hyper-HaBERTor: a light-weight pretrained hatespeech language detector using hypercomplex space

Author: Thanh Tran; Kyumin Lee

Advisor: Kyumin Lee

Category: Graduate

Abstract/Description:
The occurrence of hatespeech has been increasing. One of the reasons is that social media is gradually replacing traditional media as the main source of information for many people. It is very easy to reach a large audience quickly via social media, causing an increase of the temptation for inappropriate behaviors such as hatespeech, and potential damage to social systems. In particular, hatespeech interferes with a civil discourse, and turns good people away. Furthermore, hatespeech in the virtual world can lead to physical violence against certain groups in the real world, thus should not be ignored on the ground of freedom of speech.
To detect hatespeech, researchers developed human-crafted feature based classifiers, and proposed deep neural network architectures. However, they might not explore all possible important features for hatespeech detection, ignored pretrained language model understanding, or proposed uni-directional language models by reading from left to right or right to left.
Recently, the BERT (Bidirectional Encoder Representations from Transformers) model has achieved tremendous success in Natural Language Processing. However, even the pretrained BERT-base is heavy and expensive in the inferencing phrase.
Hence, in this work, we propose to pretrain a Hateful Language Model (HLM) that (i) better understands the hatespeech related language model by pretraining from the scratch a HLM from a hateful corpus, and (ii) is light-weight by utilizing the hypercomplex space. As a result, our HLM contains only 18M of parameters compared to 110M of parameters in BERT-base model with a significant better performance compared to the fine-tuning BERT-base on hatespeech datasets.

(GR3) Fundamental Understanding of Removal of Water Trapped Inside a Single Cellulose Fiber

Author: Zahra Noori

Advisors: Jamal Yagoobi; Burt Tilley

Category: Graduate

Abstract/Description:
In our daily lives, we use different kinds of paper and tissue products. Considering that 10 % of annual energy in manufacturing industries is consumed by paper drying industry, it is extremely critical to improve the efficiency of the process. Therefore, the current project is devoted to fundamentally understand the water removal from paper cellulose fibers in order to decrease the energy consumption.

https://youtu.be/WXVuD5AlJHk

(GR2) From Waste Steel to Matériel: Additive Manufacturing Enabled Agile Manufacturing

Authors: Yutao Wang; Karl Sundberg

Advisors: Jianyu Liang; Diran Apelian; Brajendra Mishra; Richard Sisson

Category: Graduate

Abstract/Description:
In this project, we created an effective sorting, chemical composition, and composition adjustment process for metal scraps. The team has established additive manufacturing (3D printing) technology-enabled investment casting using waste steel and turned the scraps into useful high-quality parts.

(GR1) Depression Screening with Text Messages

Author: ML Tlachac

Advisor: Elke Rundensteiner

Category: Graduate

Abstract/Description:
Detecting depression, a prevalent and costly issue, is vital for society.  As such, I have trained machine learning models to detect depression from crowd-sourced text messages with an F1 score of 0.81. Given the private nature of texts, I’m working on generating anonymous text messages to make this a sustainable solution. 

https://youtu.be/KHRTgOYeack

(UG26) Waste Optimization of the Amager Resource Center

Authors: Makayla D’Amore; Johann Bradley; Robert Connor; Rory Sullivan

Advisors: Holly Ault; James Hanlan

Category: Undergraduate

Abstract/Description:
The Amager Resource Center (ARC) is an inter-municipal waste management company operating in Copenhagen, Denmark and its surrounding municipalities. Recently, Copenhagen has decided that the waste collection process in the city will be taken over by ARC starting in 2022 using e-trucks. These electric waste collection vehicles benefit the environment and workers as they are quieter, more efficient, and prevent the employees from breathing in harmful gas. Our group worked to design E-truck fleet recommendations for ARC specifically focusing on reducing capital investment through minimizing travel duration and distance traveled per E-truck.