Smart Occupancy Monitoring

Retrieving real-time or historic occupancy and environmental conditions data of public spaces still need significant developments. The Smart Occupancy Monitoring (SOM) system addresses this by proposing an end-to-end solution. IoT-enabled devices equipped with sensors collect occupancy and temperature data, transmitted via MQTT for real-time analysis. A centralized dashboard visualizes space utilization and environmental metrics, enhancing operational efficiency and user experience. Testing across enclosed and open spaces demonstrated over 20% energy savings and improved space utilization by 4%. Future enhancements include battery operated devices and network-independent operations

Basque Txalaparta and Contemporary Percussion

The txalaparta was little-known around the time it underwent a revival within a Basque cultural reinvigoration in the 1960s, a politically complex time in the history of the Basque Country. Community and sharing are at the heart of txalaparta, both in its historical origins and performance practice. This dissertation aims to provide a point of access and interaction by contemporary percussionists and musicians through 1) developing a notation informed by the txalaparta practice, 2) applying that notation to comment on an observable pedagogy, 3) collaboration with contemporary classical composers to write original works inspired by txalaparta, all while 4) mindfully navigating the sociopolitical questions pertaining to these types of ethnomusicological and anthropological studies. It has developed into an experimental contemporary music practice that maintains those same values of community and sharing, as expressed through interviews with txalaparta performers, revivalists, and scholars in their readiness to share their ideas. A collaborative notation, along with original music towards this practice, will allow musicians of both cultures to share in this music-making practice and continue to complement each other in areas of performance, composition, and pedagogy

ACCURACY OF HUMAN SNP GENOTYPING FROM VARYING AMOUNTS OF DATA INPUT USING THE OXFORD NANOPORE MINION MK1B SEQUENCER AND THE DNA

Single nucleotide polymorphisms (SNPs) are valuable genetic markers that provide critical information for human identification in forensic science. The development of sequencing technologies significantly improves SNP detection compared to traditional DNA analysis methods. Oxford nanopore sequencing, capable has emerged as a potential tool for forensic applications, including SNP genotyping. Previous studies often use PCR amplification to target specific DNA regions, followed by sequencing with hybridization-based approaches performed on the Illumina system. This method can be complicated due to large number of SNPs spread across the genome, making DNA enrichment challenging. This research project evaluates the direct detection of SNP loci using the Oxford nanopore MinION Mk1b sequencing technology and aims to assess the accuracy of this sequencing platform to correctly identify human SNP genotypes across the human genome at varying data input. Nanopore sequencing was performed on a commercial positive control DNA sample (NA12878) which has been sequenced using other technologies and the data is publicly available. The sequencing data was analyzed using bioinformatic software provided by Oxford nanopore Technology (ONT). The study demonstrates that the accuracy of human SNP genotyping using the nanopore MinION platform is directly related to read depth, and that improvements to this method can be used to support an eventual application to investigative genetic genealogy (IGG)

Non-Acoustic Keyword Spotting for Prosthetic Actuation via Contact Microphone and 1D CNN

This paper presents a simple, low-cost system designed to recognize spoken commands using a piezoelectric contact microphone to control prosthetic or exoskeleton devices. Unlike traditional microphones, the contact mic detects vibrations directly from the skin near the throat, which helps minimize interference from surrounding noise and improves privacy. The captured analog signals were amplified using an LM386 module and digitized with an ESP32 microcontroller at a sampling rate of 1 kHz. Data was recorded for three classes: “OPEN,” “CLOSE,” and general noise or silence, and processed using a sliding window approach with mean-centering and light data augmentation. A 1D Convolutional Neural Network (CNN) was trained on these segments to classify the commands in real time. The model achieved a validation accuracy of up to 95.74% across multiple training sessions. Real-time classification was also implemented, displaying both the input waveform and predicted output with confidence scores. The results demonstrate that contact microphone-based speech recognition can be a practical and efficient method for hands-free control in assistive technology

Climate Change as a Human Rights Crisis in the United States: Evaluating Government Response and Accountability

Climate change is not just an environmental crisis; it is a fundamental human rights violation issue. This paper examines the impact of climate change on the communities in the United States, highlighting violations of essential human rights such as the right to life, health, and an adequate standard of living. Through case studies of extreme weather events, including the 2025 LA wildfires and the 2024 hurricanes in Florida, the research underscores how climate-induced disasters harm low-income and minority populations. It critiques the U.S. government's climate policies and disaster response mechanisms, exposing systemic inequalities in resource distribution and emergency assistance (UN Human Rights Council, 2023). Drawing from international human rights frameworks, this paper argues for stronger legal protections and climate policies that prioritize social equity and environmental justice. The discussion includes an analysis of the Inflation Reduction Act, EPA climate initiatives, and FEMA's role in disaster relief, assessing their effectiveness in mitigating human rights violations (Environmental Protection Agency, 2025). Additionally, the paper aligns policy recommendations with the United Nations' Sustainable Development Goals (SDGs) to advocate for a more inclusive, rights-based approach to climate action (United Nations, 2025). This paper is both an academic analysis and a personal commitment to advancing environmental justice, ensuring that climate policies serve all communities equitably, rather than exacerbating existing social injustices

Negation in Romance languages

This is a historical philological study examining variation in negative expressions across five Romance languages.The study aims to explore variation in the expression of negation in a sample of Romance languages. The focus of this paper is on the type of negation particle, the order in which a negative marker appears in a sentence, and the variation across different word classes. The study follows a historical-philological approach in describing negation, examining the variation in negative strategies exhibited by languages descended from a common ancestor language, in this case, Vulgar Latin. The study examines the variation exhibited by standard varieties of the following Romance languages: French, Italian, Spanish, Romansh, and Romanian. Each language is drawn from a different subfamily of Romance. The results of this study are meant to document variation in the expression of negation in contemporary Romance languages and offer insights into possible sources of this variation.Jazan University in Saudi Arabia Saudi Arabian Cultural Mission in Fairfax V

Graphene Oxide-Based Resistive Humidity Sensor for Non-Contact Monitoring of Human Breathing Patterns

This work presents a simple, low-cost, and effective resistive humidity sensor based on graphene oxide for monitoring human breathing patterns in a non-contact manner. The sensor was fabricated by drop-casting a graphene oxide suspension onto a commercial Zensor TE100 platform with printed electrodes. A voltage divider circuit paired with an op-amp was used to convert the sensor’s resistance changes into measurable voltage signals, which were recorded using MATLAB’s analog input interface. The sensor reliably detected various breathing patterns—mouth and nasal breathing, single and continuous cycles, and both low- and high-intensity airflow. Results show that exhaled humidity significantly decreases the sensor’s resistance, producing a clear signal during each breath. Notably, high-intensity exhalation led to baseline drift, likely due to deeper water molecule adsorption within the porous graphene oxide structure. This behavior highlights the material’s sensitivity and reveals both opportunities and challenges for long-term respiration monitoring. Overall, the study demonstrates the potential of graphene oxide humidity sensors for wearable or ambient respiratory health monitoring applications

Congressional Budget Scoring Reform: Improving Transparency and Accuracy

This paper addresses a persistent and dangerous loophole in the U.S. budget process: the lack of transparency and accountability in fiscal scoring. As the 2025 tax debate looms, we argue that current practices around budget estimates are not only misleading but structurally enable irresponsible policymaking. Our research shows how selective use of assumptions, inconsistent methodologies, and partisan incentives have eroded public trust and undermined the credibility of fiscal policy. In response, we propose a targeted, rule-based reform package to strengthen fiscal transparency and restore democratic accountability. Specifically, we call for (1) the mandatory public disclosure of budget scoring assumptions, (2) the requirement of both static and dynamic scoring for all major legislation, and (3) the creation of a bipartisan oversight board to monitor and enforce compliance. By illuminating how opaque budget scoring practices distort economic debates and shield lawmakers from fiscal consequences, we make the case for urgent structural reform. Without these changes, the 2025 tax negotiations risk repeating past failures—producing policies that are fiscally unsound, politically polarizing, and democratically unaccountable

Cost-effective Glove for Training Suturing

This work introduces a low-cost, sensor-integrated glove designed to assess and train new health workers in suturing. The system employs multiple force-sensitive pressure sensors placed on the fingers and an MPU6050 inertial measurement unit (IMU) affixed to the back of the hand to capture localized pressure and kinematic data, respectively. Using a microcontroller-based platform, the glove enables real-time monitoring of finger articulation and applied forces during simulated suturing. It addresses the need for accessible, quantifiable surgical training tools in resource-constrained educational environments. The paper outlines the glove’s hardware integration, signal conditioning, and software interface