Northwood University

Lung cancer remains one of the leading causes of cancer-related mortality worldwide.Epidemiological studies have established a significant correlation between exposure to fine particulate matter (PM2.5) and an increased risk of lung cancer.Notably, PM2.5 derived from wildfire smoke has been shown to exhibit greater toxicity than PM2.5 from other sources, due to its higher oxidative potential and pro-inflammatory composition.The COX-2 enzyme, a crucial mediator of inflammation, is known to be upregulated in response to PM2.5 exposure, promoting tumorigenesis.This study employs a mathematical modeling approach to describe COX-2 induction using a modified Michaelis-Menten equation, incorporating real-world clinical hazard ratios.Furthermore, the inhibitory effect of aspirin, a nonsteroidal anti-inflammatory drug (NSAID), is modeled to determine its potential role in mitigating lung cancer risk.Monte Carlo simulations are conducted to evaluate variability in exposureresponse relationships.Our results suggest a dose-dependent reduction in COX-2 levels with aspirin intake, which correlates with a significant decrease in estimated lung cancer risk.These findings provide a quantitative framework for understanding environmental risk mitigation and suggest potential pharmacological intervention strategies.

The relationship between gut dysbiosis and Parkinson's disease has emerged as a focal point of scientific inquiries in the early 21st century due to a proliferation in knowledge surrounding the role of the gut-brain axis in neurological disorders.Over the last two decades in particular, evidence has suggested that the gut microbiota's release of pro-inflammatory cytokines, formation of alpha-synuclein, and decreased production of short-chain fatty acids (SCFAs)-whilst in a state of dysbiosis-have detrimental impacts on brain health, leading to profuse issues such as blood-brain barrier (BBB) permeability which catalyze Parkinson's disease.While many adopt this viewpoint, this topic remains contentious as numerous gut microbial researchers disagree with the notion that dysbiosis governs the disease, viewing it more as a consequence of Parkinson's ability to reduce motor integrity and alter lifestyle habits.This review aims to provide a nuanced resolution to the prevailing debate, analyzing research over the last 20 years to see whether the gut microbiota primarily causes Parkinson's disease or is just an effect of its modes of action.

PDF-based stegomalware is a growing cybersecurity threat that disguises malicious payloads within the tree structure of PDF documents, exploiting features like embedded JavaScript, metadata, and open actions while maintaining the outward appearance of a legitimate file. Traditional detection methods (both heuristic and signature-based), often fail to identify stegomalware due to the external similarities between clean and infected documents.This project presents a novel method for detecting PDF-based stegomalware using Kolmogorov complexity. Kolmogorov complexity measures the shortest possible program to describe an output, but it cannot be computed directly. However, this project indirectly calculated complexity by analyzing the compressed lengths of attributes using compression algorithms that group repetitive (non-complex) patterns. By calculating the complexity of components within a PDF’s internal tree structure and comparing them to baselines derived from known clean documents, malicious modifications can be identified by detecting deviations from expected values.For testing, a local mail server was developed to process incoming PDFs by calculating and comparing Kolmogorov complexity to stored baselines of clean PDFs tagged with matching metadata IDs. PDFs with significant complexity increases within suspicious attributes are quarantined using Firejail, while others are safely delivered to the user.Initial testing achieved a 100% true positive rate but an 86.2% false positive rate when analyzing the entire PDF as a whole. Refining the approach to analyze specific PDF components, focusing on suspicious changes, and ignoring innocent variations like file size or text content improved results to a 97.8% true positive rate and a 3.7% false positive rate.

This study uses data from the Physical Therapist Centralized Application Service (PTCAS) to compare influences on application to Doctor of Physical Therapy (DPT) programs between White applicants and people of color (POC)-applicants who identified as Black, American Indian, Asian, Hispanic, or Pacific Islander. This is a retrospective study using holistic narrative analysis. PTCAS data for the 2020 to 2021 application cycle were obtained, including essays and demographic data. Random samples were drawn such that 736 total applicants to PTCAS were included: 372 White applicants and 364 POC applicants. Using categories previously described in the literature, 3 reviewers coded application essays into as many influence categories as applied. Interrater reliability was evaluated on a sample of 12 essays, using Cohen kappa between reviewer pairs and a total arithmetic mean. Chi-square was used to test for difference between groups, based on Bonferroni correction. Personal experience with physical therapy (53.8% for White applicants, 54.9% for POC applicants), observation of the field (68.5% for White applicants, 71.2% for POC applicants), and alignment with values and interests (62.9% for White applicants, 67.3% for POC applicants) were the most common influences. Applicants of color were more likely to indicate a desire to bring services back to their community of origin, formative experience in health care based on race, and paid work experience in the field. Interrater reliability was excellent (.869). Applicants in the POC group were primarily influenced toward pursuing DPT education by personal interaction with physical therapy and alignment of the field with their values. They were more likely than their White peers to have racially based health care experiences, paid work in the physical therapy field, and a desire to provide services to their community. Providing opportunities for people from racial and ethnic minority groups to experience physical therapy through equitable access to physical therapist care, observation hours, internships, or paid work experience may improve enrollment of more diverse cohorts.

Abstract— this paper presents how to cancel the audio noise adaptively with using FFT (Fast Fourier Transform). It covers theoretical analysis, MATLAB simulation, performance evaluation (with different step size) and trade-off. It proves FFT can be used to cancel the audio noise adaptively.

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model’s efficiency. Each ML method’s computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.

In the contemporary era of data-driven processes, addressing the challenge of processing vast volumes of data has become a pressing concern. With the rapid advancement of computer science and information technology, data processing efficiency has significantly improved. Within this expansive domain, three prominent clustering techniquesnamely, K-Means clustering, spectral clustering, and Density-based spatial clustering of applications with noise (DBSCAN)have assumed pivotal roles due to their versatility and effectiveness. This essay embarks on a systematic examination of these three methods, deconstructing their fundamental principles and navigating through their practical applications.

The proliferation of short-form video platforms such as Tiktok and Twitter has prompted a major shift in advertising stratagem. Marketers are growing increasingly in favor of short, vertically filmed advertisements in response to a dramatic increase in short-form video media consumption over the last three years. This study explores the comparative effectiveness of short-form advertisements compared to traditional, long-form advertisements. We find that short form advertisements have an advantage over long form advertisements in reach and viewer engagement, proving more effective in our sample of participants than the long form advertisement despite a disadvantage in perceived trust and product relevance. We are able to attribute much of this effectiveness to the relatability, humor, personal touch, and audience-friendly content of short form advertisements. These results underscore a significant transition in the marketing industry, indicating an industrial preference for home-made narrative and individuality over traditional metrics of cinematic polish and trustworthiness.

Libertarianism itself does not hold a stance on moral issues and hence does not prevent libertarians from adopting culturally conservative or liberal positions. Though libertarianism has no position on cultural issues per se, this article argues that libertarians should intellectually oppose the ideas behind some of the current cultural developments in the West due to their constructivist nature. Based on Hayek’s critique of constructivism, I contend that many of the recent cultural shifts in society are influenced by rational constructivist ideas—namely, critical theory and deconstruction. These ideas pose an intellectual threat to libertarianism for two reasons: (1) They intellectually challenge the traditional libertarian view of the spontaneous origins of civil society, which they aim to reshape in favor of a structure that reflects a specific postmodern world view. (2) It remains uncertain how a libertarian society would function under a newly constructed social order that does not value free speech, property rights, and other fundamental principles of the current liberal order. The spontaneous order is the defining feature of libertarian social analysis, and it must be intellectually defended.