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Investigating the Effect of Build Direction on Mechanical Properties in Pellet-Based 3d Printing
No full textThis study investigates the influence of build orientation on the mechanical performance of Polylactic Acid (PLA) components fabricated using Fused Granular Fabrication (FGF), a pellet-based additive manufacturing method, and benchmarks these results against Fused Deposition Modeling (FDM). Although orientation-dependent mechanical behavior has been widely studied in filament-based extrusion, there remains a critical gap in understanding how these effects appeared in pellet-based fabrication. To address this gap, PLA tensile and compression specimens were fabricated using both FGF and FDM under identical geometries, processing parameters, and preparation procedures, ensuring that any differences in performance arise solely from the manufacturing method and build orientation rather than external variables. Three orientations were selected which are Flat, On-edge, and Upright. The test was conducted according to ASTM D638 for tensile testing and ASTM D695 for compression testing, with five specimens produced per orientation for each manufacturing process. The findings showed that build orientation significantly affects the mechanical behavior of PLA in both processes, but in different ways. FGF achieved consistently higher tensile strength and stiffness across all orientations, whereas FDM demonstrated superior ductility and toughness, particularly in the Flat orientation. In compression, FDM generally exhibited higher stiffness, yet FGF printed in the Upright orientation produced the highest ultimate compressive strength among all samples tested. Within FGF, Flat and On-edge orientations yielded the best tensile properties, while Upright, despite having the lowest tensile strength, exhibited the highest stiffness. Orientation effects in compression were less pronounced, though Upright again provided the highest strength for FGF. When comparing the two processes directly, orientation-dependent trends showed inconsistencies: the Upright orientation was stiffest in FGF, whereas the Flat orientation was stiffest in FDM. However, both methods followed the same tensile strength ranking of On-edge \u3e Flat \u3e Upright
Examining Children’s Intuitive Understanding of Mechanical Systems with a Gears Assembly Task
No full textChildren’s causal reasoning about machines and their components is crucial for acquiring knowledge about science and math. Mechanistic understanding involves knowing how parts can be effectively connected to work together as a functional mechanical system. Little is known about how children’s understanding of mechanical systems develops across the lifespan. Legare and Lombrozo (2014) developed a gears assembly task to assess children’s understanding and learning of mechanics but did not track this skill across ages. The present study adapted the gears assembly task to measure mechanistic understanding in children between the ages of 4 to 11 years and adults. The goal of the present study is to identify at what age performance on the task becomes adult-like. A total of 10 adults and 48 children across six age groups between age 4 to 11 years were tested on three phases of the task: mechanism, reconstruction and generalization. Percent accuracy scores were compared between each child age group versus adults with independent t-tests. The mechanism phase was at ceiling for all age groups. For the reconstruction task, three age groups between 4 to 7 years of age performed significantly worse than adults. For the generalization task, only the youngest children tested between ages 4 to 5 years old performed significantly worse than adults, while groups over age 5 to 11 did not differ from adults. These findings suggest that mechanical understanding is relatively adult-like by age 6 years. A questionnaire was used to explore whether children’s exposure to science toys at home influenced their performance. Results from a multiple regression showed that home STEM-toy engagement, gender, and parent education were not significant predictors of performance on the gears assembly task
RELATIONSHIP BETWEEN MUSCLE ACTIVATION AND KINEMATIC FEATURES DURING BASEBALL PITCHING
No full textBackground: Baseball pitching is a biomechanically complex, high-velocity movement that places substantial demands on the upper extremity. Inefficient coordination between muscle activation and kinematic sequencing has been associated with reduced performance and increased injury risk, particularly at the shoulder and elbow. However, the integrated relationship between muscle activation patterns and three-dimensional kinematic features during pitching is not well-documented. Purpose: The purpose of this study was to examine the relationship between throwing-arm muscle activation patterns and three-dimensional kinematic characteristics during fastball pitching, with particular emphasis on segmental accelerations, proximal–distal sequencing, and the directional structure of movement quantified using Relative Dimensional Contribution percentage (RDC%). Methods: Ten collegiate baseball pitchers performed fastball pitches while synchronized three-dimensional motion capture and surface electromyography (sEMG) data were collected. Linear and angular kinematics of the shoulder, elbow, and hand were analyzed alongside muscle activation patterns of key upper-extremity muscles. Correlation analyses were conducted to assess relationships between muscle activation and segment accelerations across pitching phases. Results: Pitching demonstrated a clear proximal–distal progression, with linear and angular velocities and accelerations increasing from the shoulder to the hand. Muscle activation patterns reflected this sequencing, with earlier activation of proximal muscles, such as the pectoralis major and anterior deltoid, and later, sharper activation peaks in distal muscles, particularly the wrist extensors. Five muscles (pectoralis major, biceps brachii, anterior deltoid, wrist flexors and wrist extensors showed significant positive correlations with segment accelerations. RDC% analyses revealed that distal segments exhibited increasingly multidirectional motion, with greater medial–lateral and vertical contributions to resultant acceleration at the elbow and hand. Conclusions: These findings demonstrate coordinated relationships between neuromuscular activation and three-dimensional kinematic features during baseball pitching. The integration of sEMG, segmental kinematics, and RDC% provides a comprehensive perspective on proximal–distal sequencing and directional motion control. This approach offers valuable insight into the biomechanical mechanisms underlying pitching performance and may inform future strategies for performance optimization and injury prevention
Pegasus
No full textThis experimental film depicts a brief period in history that explores the growth of humanity and its technology, using the horse as a symbol. From the early Neolithic era, when horses were primarily used as a source of food and clothing, to the Persian Empire’s use of horses for transporting information and goods, their role has evolved. We next move to the Greeks, who saw the horse as more than just a functional resource, and it emerged as a pivotal influence on technological progress, artistic production, and the establishment of specialized occupational roles. Finally, the Roman Empire evolved into a more war-like society, with a focus on technological advancements. Along with the horse, weapons, armor, and improved means of transportation are being developed for better use in combat. The last shot is a view of the constellation Pegasus in the sky. The beast, as it rises, symbolizes the ever-increasing importance of one of humanity’s greatest inspirations and assets
Assessment of mass spectral data for the quantification of virion components in a giant Salmonella phage
No full textPhages, as viruses that specifically target and eliminate bacteria, present as a promising treatment strategy against antibiotic-resistant bacteria. Giant phages, however, with longer genomes and a higher level of virion complexity, remain understudied. This research aims to evaluate the applicability of data-dependent acquisition mass spectrometry (DDA-MS) versus data-independent acquisition mass spectrometry (DIA-MS) to characterize and quantify the copy numbers of virion proteins of the giant Salmonella phage SPN3US. Accurate copy number estimations require confirmation of protein sequences in the mature virion, as previous analyses showed nine SPN3US head proteins were shorter than their predicted lengths, as they had been cleaved by a phage protease gp245. This study identified 16 cleavage sites in 10 head proteins, including a new substrate gp94, ensuring correct use of virion protein sequences and their molecular weights for copy number estimations. Virion proteins with known copy numbers were used to adjust mass spectral data to estimate the copy number of individual virion proteins. Analysis of these estimations showed that DIA-MS provided significantly more credible copy number estimations than those obtained using DDA-MS. Overall, the data demonstrated that mass spectrometry (MS) is a promising method for characterization and quantification of the highly complex giant SPN3US virion particle. This work provides the foundation for future studies on giant phage and suggests potential applications of MS for copy number estimations of other tailed phages
Modeling Economic Effects on Climate Change
No full textClimate change represents one of the most critical challenges facing the global economy today. While environmental impacts receive considerable attention, the economic implications are equally significant and require rigorous quantitative analysis. This thesis investigates the relationship between climate change and major economic indicators including GDP growth, inflation, trade balances, and employment across 50 countries from 1990 to 2023. The research employs a comprehensive multi-methodological approach combining panel data econometrics, time-series analysis, and machine learning techniques. Fixed-effects and random-effects panel regression models reveal statistically significant relationships between climate-related disaster frequency and economic performance. Specifically, disaster count demonstrates a positive coefficient of 0.0769 (p \u3c 0.001), indicating that while disasters may initially appear to boost economic activity through reconstruction spending, the underlying economic damage is substantial. Time-series analysis using ARIMA and Vector Autoregression (VAR) models captures dynamic responses of economic indicators to climate shocks. Machine learning approaches employing XGBoost and Random Forest regressors identify non-linear relationships and feature importance, with XGBoost achieving a test R² of 0.384, outperforming traditional linear models in capturing complex climate-economic interactions. Scenario projections under three warming scenarios (+1.5°C, +2.0°C, and +3.0°C) reveal that 17 to 19 countries experience negative GDP impacts, with mean negative impacts ranging from -0.105 to -0.225 percentage points. The worst-case impacts reach -0.625 percentage points, demonstrating the significant economic risks of unmitigated climate change. Comparative analysis identifies substantial geographic disparities in climate vulnerability. Developing countries exhibit mean vulnerability indices of 0.468 compared to 0.272 for developed countries, highlighting the disproportionate burden on lower-income nations. Regional analysis reveals South Asia and Africa as the most vulnerable regions, with mean vulnerability indices of 0.518 and 0.450 respectively. The research addresses critical gaps in existing literature by examining simultaneous interactions across multiple economic indicators, employing non-linear modeling techniques, and providing country-specific vulnerability assessments. The findings contribute to evidence-based policy formulation, identifying 15 high-priority countries requiring immediate intervention and developing comprehensive adaptation strategies with benefit-cost ratios of 2–5:1. This thesis provides policymakers, economists, and environmental planners with quantitative evidence and actionable recommendations for developing effective climate adaptation and mitigation strategies. The results quantify economic risks while investigating solutions supporting the construction of climate-resilient economic systems
Enhance Telecom-Related International Indicators Using Machine Learning
No full textThis thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with similar digital readiness profiles? (3) What indicators most strongly predict overall NRI scores, and how can this inform a more accurate weighting scheme? The dataset consists of 134 countries and over 50 indicators from the 2023 NRI. PCA was employed to reduce redundancy among variables and to retain components explaining over 80% of the variance. K-means clustering was then applied to the PCA-transformed data, identifying six distinct clusters. To determine optimal k, both the Elbow Method and the Silhouette Method were used. Random Forest regression was applied to estimate the importance of individual indicators in predicting the NRI score, with the top predictors subsequently used to compute a new weighted NRI score. Findings reveal that Random Forest regression achieved high predictive performance (RMSE = 3.45; R² = 0.91). A revised NRI score was calculated using normalized feature importances, producing a version of the index more reflective of empirical impact. Spearman correlation between the original and enhanced rankings was high (r = 0.990), but several countries experienced rank shifts, indicating improved sensitivity to critical indicators. Statistical validation of clustering results via Fisher\u27s Exact Test confirmed a significant relationship (p = 0.004) between data-driven clusters and traditional NRI tiers, affirming the alignment between unsupervised learning groupings and score-based categorizations. This study concludes that machine learning methods offer a robust alternative to traditional equal-weight approaches in composite index construction
