Digital Commons Network^™

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Feature Engineering And Anchor Optimization For Enhancing Faster R-Cnn Detection Of Low-Contrast Steel Surface Defects, Herdianti Darwis, Sitti Nurhalimah, Huzain Azis

Knowledge Engineering and Data Science

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of …

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga'

Knowledge Engineering and Data Science

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average …

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

A Bounded Custom Gpt Structures Operational Knowledge For Small-Industry Decision Support, Ikhwan Arief, Alizar Hasan, Nilda Tri Putri, Hafiz Rahmann

Knowledge Engineering and Data Science

Small manufacturing and craft-based firms increasingly use Generative Artificial Intelligence (GenAI) through public chat interfaces, low-cost tools, and informal experimentation. However, these firms often make operational decisions with incomplete records, tacit owner knowledge, fragmented spreadsheets, and limited managerial capacity. Under such conditions, open-ended chatbots may generate fluent but unsafe recommendations by overlooking missing information, contradictory evidence, feasibility constraints, and implementation constraints. This study presents Asisten Cerdas Industri Kecil as a bounded Custom GPT artifact for operational diagnosis, priority selection, and short-horizon action planning in small industries. Using a Design Science Research approach, the study develops a documented artifact corpus comprising …

Risk Management For Authorising Railway Vehicles In The Eu Legal Framework, Jan Maciej Siudecki

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Safe integration of rolling stock with railway system requires comprehensive hazard identification and application of safety measures. There are different cases when European Union regulations demand carrying out risk assessment before a vehicle may be authorised to be placed on the market for both Rolling stock and Command and control signalling onboard subsystems. Specificities of each case are outlined and conclusions as to their scope and interactions between them are discussed. Practical examples of hazards that should be managed in each case are provided. Subsequently it is described, how conclusions from risk assessment impact results of conformity assessment, the authorisation …

Characterizing Heat Transfer Performance In A Slurry Bubble Column Reactor Equipped With A Real Heat Exchanger, Dalia S. Makki, Hasan Sh Majdi, Amer A. Abdulrahman, Abbas J. Sultan, Bashar J. Kadhim, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Abstract: This study examines the impact of equipping a real heat exchanger in a slurry bubble column SBC on instantaneous and local heat transfer coefficients IHTC and LHTC, as well as the overall heat transfer coefficient U, employing advanced heat transfer techniques. The experiments were conducted in a 0.15 m inner diameter Plexiglas SBC with varying gas flowrates Ug (0.14–0.35) m/s at several radial sites along the column's diameter (±0.18, ±0.46, and ± 0.74) and three axial locations (H/D = 2, 3 and 4). To simulate the industrial Fischer–Tropsch bubble column reactor FT-BCR, a real heat exchanger consisting of 18 …

Tumor Microenvironment Immunomodulation By Nanoformulated Tlr 7/8 Agonist And Pi3k Delta Inhibitor Enhances Therapeutic Benefits Of Radiotherapy, Mostafa Yazdimamaghani, Oleg V. Kolupaev, Chaemin Lim, Duhyeong Hwang, Sonia J. Laurie, Charles M. Perou, Alexander V. Kabanov, Jonathan S. Serody

Chemical and Biochemical Engineering Faculty Research & Creative Works

Infiltration of immunosuppressive cells into the breast tumor microenvironment (TME) is associated with suppressed effector T cell (Teff) responses, accelerated tumor growth, and poor clinical outcomes. Previous studies from our group and others identified infiltration of immunosuppressive myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) as critical contributors to immune dysfunction in the orthotopic claudin-low tumor model, limiting the efficacy of adoptive cellular therapy. However, approaches to target these cells in the TME are currently lacking. To overcome this barrier, polymeric micellular nanoparticles (PMNPs) were used for the co-delivery of small molecule drugs activating Toll-like receptors 7 and 8 …

Workable Seams, Jackson Mills Smith

Graduate Student Theses, Dissertations, & Professional Papers

In Jackson Mills Smith’s Workable Seams, readers encounter the stories, landscapes, and lives of rural Knox County, Indiana, from the oscillating vantage of a Hoosier boy raised with a Cajun sensibility. After relocating from Louisiana to the river bottoms of the crossroad state at a young age, the speaker gradually uncovers a local history only previously understood in fragmentary shards and fading glimpses.

Alternating between semi-lyrical embodied experiences and memories, documentary pieces in conversation with the state’s historical archive, and the retelling of exaggerated local yarns, poetry is a means of inquiry in Workable Seams. Topics range from …

Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe complete lactate electrooxidation in an enzymatic biofuel cell that combines the catalytic action of the bimetallic composite Ru@Pt-CNT and the enzyme oxalate oxidase (OxOx). The Ru@Pt-CNT/OxOx hybrid electrode was 2.0-fold more catalytically active than the electrode containing the bimetallic composite only. During chronoamperometric experiments, the hybrid electrode achieved a 35% higher maximum current density (2.65 ± 0.15 mA cm^-2) than the Ru@Pt-CNT electrode. Electrochemical impedance spectroscopy showed that the hybrid electrode had lower charge transfer resistance than the Ru@Pt-CNT electrode, confirming that OxOx had a high affinity for lactate during the bioelectrocatalytic reaction on the electrode …

Machine Learning Modeling For Hydrolysis Recycling Of Pet Waste, Jie Li, Lanjia Pan, Hossein Abedsoltan, Hailong Wang, Taiyang Liu, Xiangzhou Yuan, Yong Sik Ok, Yin Wang

Chemical and Biochemical Engineering Faculty Research & Creative Works

The hydrolysis of polyethylene terephthalate (PET) into terephthalic acid (TPA) can efficiently recycle waste PET, but achieving high conversion efficiency through smart reaction design remains challenging. To develop a robust and accurate machine learning (ML) model for the in-depth understanding and intelligent design of PET hydrolysis, we compiled a new dataset comprising 942 data points and comprehensive information of 44 variables involved in heating type, acid/base catalyst (ABC), organic solvent (OS), co-solvent (CS), phase transfer catalyst (PTC), and operational conditions. The developed Neural Network model demonstrated the best performance in predicting PET conversion, with a testing determination coefficient (R^{2 …}

Cultivating Confidence, Leila Halawi, Mark Miller, Sam Holley

Publications

Artificial intelligence (AI) is pervasive in scholarly publications, internet sites, and public discourse. AI is a term with broad scope that refers to machines that can learn and perform tasks that typically require human intelligence. The specter of AI intruding into many aspects of aviation has raised alarms, concerns, and prodigious misunderstanding of potential and contemplated applications in systems and processes. The EASA AI Roadmap (EASA, 2023 ), a linear projection with three levels EASA, 2023 extending into 2050, places the human-AI teaming period (through 2035) at Level 2. This suggests a ten-year span to develop the interactive issues to …

Embracing Ai In Higher Education: Redefining Teaching And Learning In The Digital Era, Najem Tala, Leila Halawi

Publications

This research explores the transformative potential of Artificial Intelligence (AI) in education, focusing on its ability to address emerging challenges and revolutionize teaching practices. We critically assess the current educational landscape, evaluate AI's role as a catalyst for educational reform, and examine implementation challenges. Our analysis emphasizes the evolving impact of AI on personalized learning, student engagement, and administrative efficiency and contributes to the growing body of literature on educational technology. The discussion highlights both the opportunities and limitations of AI in education, pointing to critical areas that remain underexplored.

Integrating Sustainability Into Aviation Training: Perspectives From Industry Professionals, Eva Maleviti, Sarah Talley

Publications

This paper is part of a broader research project aimed at identifying the most appropriate training materials, formats, and delivery methods for aviation sustainability content for future aviation professionals, starting from the early stages of their training. The current paper presents the data analysis and results of a short online survey targeting aviation professionals. Specifically, it provides preliminary data on sustainability perspectives from global aviation professionals. Through qualitative analysis, the creation of a baseline from a random sample of aviation professionals offers insight into their perspectives and how they could be influenced toward a more sustainable pathway. Additionally, the paper …

A Maintenance Repair And Overhaul Model For Operational Sustainability; Incorporating Processes And Risks., Linn Guinn, Mary M.H. Woody, Eva Maleviti

Publications

Maintenance, Repair, and Overhaul (MRO) companies are integral to the aviation industry, with the core responsibility of ensuring aircraft remain safe and airworthy. Their role is critical in maintaining the industry’s high standards of performance and safety. To remain competitive, MROs must provide a wide range of services—including inspections, periodic checks, engine overhauls, avionics repairs, fuselage and cabin modifications, and interior refurbishments— while consistently meeting regulatory and safety requirements. Beyond technical compliance, MROs must adopt a quality-driven, continuous improvement approach to manage risks effectively. This requires the integration of processes, programs, and systems aligned with international standards to strengthen resilience, …

Implementation Of A Pediatric Skills And Simulation Workshop Among Rural Nurses, Cassandra Grabiel

Doctor of Nursing Practice (DNP) Practice Innovation Projects

No abstract provided.

Lorawan-Enabled Iot Solution For Smart Farming, Talha Khan

Electronic Theses and Dissertations

The rising global demand for food, driven by population growth, alongside a declining rural workforce, presents a great challenge for agriculture. Precision agriculture, which relies on the widespread adoption of smart farming technologies, is anticipated to enhance agricultural productivity and sustainability. Emerging technologies, such as machine vision, artificial intelligence (AI), and the Internet of Things (IoT), offer innovative and promising solutions to address these challenges. This study focuses on LoRaWAN, a prevalent Low-Power Wide-Area-Network (LPWAN) IoT technology, examining its application in crop and livestock farming as well as its associated cybersecurity challenges. LoRaWAN IoT systems were constructed for three application …

Comparative Semiotic Analysis Of Non-Verbal Communication: Apis Mellifera Waggle Dance And Human Kinesic Signaling Systems, Nishtha Tikalal

Honors Undergraduate Theses

There are many parallels between two seemingly disparate forms of non-verbal communication: the waggle dance of Apis mellifera (honeybees) and human kinesic signaling systems. Through a comparative semiotic analysis, this research aims to conduct a comparative semiotic analysis, investigating structural and functional similarities between these communication methods, challenging anthropocentric assumptions about meaning making in the natural world. This research employs a meta-synthesis approach, drawn from a methodological literature review to examine historical representations of bee-human relationships, semiotics of non-verbal communication, and the specific mechanisms of the waggle dance and human kinesics. This methodology involves systematic categorization and analysis of extracted …

Quantifying The Performance Of The Sparta Toolkit For Use In Planetary Regolith Characterization Missions, Abigail S. Glover

Honors Undergraduate Theses

This research investigates the use of the Soil Properties Assessment, Resistance, and Thermal Analysis (SPARTA) Cone Penetration Tester (CPT) to quantify bulk density in lunar regolith simulant LHS-1E. Twenty-five penetration tests were conducted across five density profiles to derive slope parameters (G), which represent the rate of increase in resistance with depth. Results showed a consistent, nonlinear relationship between G and bulk density, validating the use of G slope as a diagnostic parameter and supporting the primary hypothesis that SPARTA CPT measurements vary meaningfully with density. Compared to prior work by Lucas et al. (2024), G values from SPARTA were …

Users' Trust Perceptions Of Ai Search Results, Leilani J. Cordoves

Honors Undergraduate Theses

Utilizing a repeated measure design, this experiment analyzes users’ perceptions of artificial intelligence embedded within search engines compared to traditional linked search engines in various contextual situations. The purpose of this study is to find the differences in confidence for differing search methods and the variables of trust that lead to these perceived differences. Using screenshots of AI overview search results and linked search results, we asked participants questions of confidence and human-computer trust to gauge their perceptions of the varying search methods. We find evidence of a significant interaction effect in confidence scores between the search method and the …

3d Printing Of A Solid-State Battery, Maurizio A. Brozzi

Honors Undergraduate Theses

Solid-state battery research has gained significant attention in recent years, as it provides a unique solution for several common issues in conventional Li-ion batteries. Additionally, as additive manufacturing technologies continue to advance, they have the potential to fabricate solid-state batteries with new materials. The objective of this study is to explore the feasibility of 3D printing of a solid-state battery. By creating an initial PEO-PSF hybrid polymer matrix dissolved in DMSO and adding a lithium salt, the base electrolyte ink was formed. With the addition of conductive additives and active material, electrode inks were also produced. Super P was used …

Salinity Effects On Cu(Ii) Bioremediation By Chlamydomonas Reinhardtii Microalgae, Jennifer Hughes

Honors Undergraduate Theses

Microalgae offer a sustainable approach to wastewater treatment by removing pollutants such as excess nutrients, CO₂, and heavy metals. Among various species, Chlamydomonas reinhardtii shows strong potential in removing Cu(II) ions from wastewater. In addition, salinity stress in C. reinhardtii has been linked to morphological and metabolic changes that enhance lipid and phytochemical production. While salinity stress has been studied for enhancing biofuel production, its impact on microalgal removal of heavy metals, particularly Cu(II), remains largely unexplored. Understanding how varying salinity conditions affect both algal growth and the efficiency of Cu(II) remediation could provide critical insights for improving integrated wastewater …

Multi-Criteria Gis Suitability Analysis For Sustainable Agricultural Tourism Site Selection In Peru, Jessica J. Mccord

Honors Undergraduate Theses

Agricultural tourism offers a mechanism to strengthen rural economies while preserving traditional practices and unique cultural identities. In the Peruvian Andes, rural communities confront increasing challenges in the face of a changing climate and reliance on industrialized agriculture. Rural communities attempt to strike a delicate balance between economic development and heritage preservation. This research utilizes a Geographic Information System (GIS)- based analysis to identify areas best positioned for the development of agritourism initiatives. The suitability analysis integrates both local and global sustainability priorities by incorporating a set of defined criteria that together identify areas that are the most appropriate for …

The Loneliest Number: Implications For Using Single-Item Measures In Regression Analysis, Juliana Reis

Honors Undergraduate Theses

The current study examines the use of single-item measures to assess constructs used by occupational stress researchers. Surveys are fundamental tools for I/O Psychologist practitioners, and multiple-item measures are commonly used to fulfill that purpose. However, long surveys may offer challenges to both administrators and respondents. As an effort to mitigate those challenges, single-item measures have been receiving recognition as a promising alternative to the current psychometric measures due to their benefits, such as reduced cost and higher face validity. We will discuss the previous studies on single-item measures and why it’s relevant to explore its potential by performing a …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Apocalyptic Technical Communication From Clockface To Briefcase: Revealing The Spurious Coin Of Nuclear-Security Rhetoric, Ryan Cheek

English and Technical Communication Faculty Research & Creative Works

This article the Doomsday Clock and the Nuclear Football as interconnected technical communication artifacts that function as two sides of a "spurious coin" in the securitization of nuclear deterrence. While the Clock externalizes existential risk through apocalyptic rhetoric, the Football internalizes it within exclusive military command structures—together legitimizing perpetual nuclear crisis. Drawing on technical communication scholarship and critical security studies, the analysis argues that both artifacts sustain nuclearist ideology by reinforcing deterrence as common sense. The Clock's ominous countdown and the Football's ever-present launch capability are mutually validating and together normalize nuclear brinkmanship as the price of global security.

Co-Designing An International Nursing Course On Workplace Violence In Ethiopia, Sweden, And Tanzania: A Collective Autoethnography, Martin Salzmann-Erikson, Elisabet Eriksson, Tilahun Saol Tura, Tumbwene Mwansisya, Loveluck Mwasha, Malin Jordal

School of Nursing & Midwifery, East Africa

This study explores whether and how the co-creation of an international university course on workplace violence served as a site of transformative learning for us as nursing educators across Sweden, Ethiopia, and Tanzania. Using collaborative autoethnography, our team of six faculty members critically reflected on pedagogical assumptions, educator identities, and experiences of cross-cultural collaboration. The data comprised written reflections, meeting transcripts, and observational notes. Through this process, our pedagogical approaches and professional identities were reshaped, shifting from content deliverers to reflective co-learners within the cross-cultural partnership. Our analysis highlights the interplay of cultural negotiation, logistical frictions, and unspoken yet influential …