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True Grit: Exploring Nonprofit Sector Resilience Following Economic Recessions
Get PDFThe nonprofit sector has been portrayed as resilient, describing a sector that persists despite challenges. We investigate nonprofit resiliency by examining how organizational characteristics, strategies, and community factors equipped organizations to recover following economic recessions. Utilizing a fixed effects panel regression model, our study covers a period of 29 years (1989–2018), encompassing three economic crises in the United States. The primary focus is examining the sector’s financial health and the resilience of the constituent organizations. Our findings describe a sector buoyed by the resilience of larger and older organizations, earned revenue, and contribution revenue, as well as the role of community factors in influencing the sector’s resilience. This study examines a wider timeframe and employs a more expansive sampling approach compared to previous studies on nonprofit resilience. In doing so, it contributes valuable insights to our understanding of the resilient sector
Pixels of Passion: The Revolutionary Impact of Indie Games
Get PDFIn the dynamic world of video game development, a powerful revolution has been quietly transforming how interactive experiences are created. Independent game developers, or indie game creators, have emerged as innovative storytellers and design pioneers, challenging traditional gaming paradigms and offering players unique, personal experiences that transcend mainstream entertainment.
Unlike mainstream games developed by large corporations with multi-million dollar budgets, indie games are typically created by small teams or even individual developers driven by artistic vision rather than pure commercial interests. These creators prioritize innovative gameplay mechanics, compelling narratives, and unique aesthetic experiences over conventional market formulas. Platforms like Steam have been instrumental in providing distribution channels that allow these smaller creators to reach global audiences.
This essay will explore the fascinating world of indie game development, examining how these innovative creators are reshaping interactive entertainment. We\u27ll dive into their unique design philosophies, the challenges they overcome, and the profound impact they\u27re having on the broader gaming landscape. From systemic design to player agency, indie games represent more than just entertainment—they\u27re a vibrant, evolving art form that continues to surprise, challenge, and inspire players worldwide
Valorant\u27s Interlinked to Theory of Representation (race and culture) and Feminist Coflict Theory
Get PDFA brief introduction to Valorant to new ears and eyes is that Valorant is a video game and what I will be calling a media that was released by Riot Games in 2020, Valorant is a tactical FPS (first person shooter) game that blends strategic gameplay with an immersive sci-fi narrative. The setting of the game takes place in a world destabilized by the mysterious First Light event which gives these agents called Radiants their powers and abilities. The game first introduces the organization called Kingdom Corporation as a central antagonist exploiting the Radiants who, like I explained earlier, are individuals with enhanced abilities born from the “First Light” event. In other words, people born with supernatural powers and gifts. The Valorant Protocol, an organization made up of diverse agents, seeks to combat the Kingdom’s monopoly and protect global stability. With its international cast of characters and deeply layered lore, Valorant offers players not only fast paced mechanical gameplay but also a glimpse into narratives of resistance, identity, and power
Artifacts in Low-Pass Whole Genome Sequencing
Get PDFLow-pass whole genome sequencing (LP-WGS) provides a cost-effective way to achieve broad genomic coverage, but it comes with the challenge of sequencing artifacts that can complicate accurate variant detection. To address this, we developed a bioinformatics pipeline using Nextflow. Starting with raw sequencing data, the pipeline performed variant calling using VarDict, with Genome in a Bottle (GIAB) high-confidence variants serving as the benchmark for variant validation. We explored machine learning approaches, testing classifiers such as AdaBoost, ExtraTrees, and RandomForest, to evaluate variant classification. Twenty-two features generated by VarDict were fed into Machine Learning pipeline, with AdaBoost standing out for its balance of precision and recall. Features such as Strand Bias Odds Ratio, Fisher p-value, and Allele Frequency emerged as key contributors to accurate classification. This study highlights the potential of combining LP-WGS with machine learning to improve variant detection despite sequencing limitations
Enhancing Qwen2.5-Coder: A Deep Dive into Fine-Tuning using PEFT for Superior Code Outputs
No full textThe main objective of this research is to improve the quality of software code that is produced by the Qwen2.5-Coder model specifically in terms of maintainability, complexity, and reliability. Our approach is going to be a more specific one that will involve the Parameter-Efficient Fine Tuning (PEFT) framework combined with quantization through Low-Rank Adaption (LoRA). This approach involves fine-tuning only some of the parameters of a model to make it suitable for software programming with the general structure of the model largely intact. In this paper, SonarQube is used as a tool to help quantify the improvements made to the code by the model. The base data set used for this project is “starcoderdata”, which is part of a larger data set that includes various programming discussions and code snippets taken from different open-source projects on GitHub. Since it includes a number of real-world programming tasks, it will be suitable for the model to train on and enhance its coding skills. In line with the suggestions given in the Qwen2.5-Coder paper, the model will be further refined with LoRA and bitsandbytes quantization. This is the most effective way of adjusting the model to the particular task at hand and at the same time ensure that it is as efficient and effective in performing various software development tasks. In this project, we will explore ways in which the baseline model can be fine-tuned in order to generate high-quality code output. In addition, a set of software quality metrics will be employed to assess the code quality of the baseline model and the fine-tuned model
Multimodal Techniques for Malware Classification
No full textThe threat of malware has remained a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. This research adopted the structured nature of PE files incorporated with a multi-modal machinelearning approach, to classify malware types. Features extracted from the PE headers were used to train an LSTM model. Features extracted from the PE sections were used to train a CNN model. Probabilities produced from these two models were then concatenated and fed into an SVM classifier. This multi-modal approach demonstrated high accuracy by experimenting with and verifying the approach on a large and labeled dataset. This research compared the results of the multi-modal approach with those of different preliminary models, including SVM, LSTM, and CNN. The proposed approach showed meaningful improvement in malware classification, demonstrating the potential of a multi-modal approach for accurate malware detection
FPDclustering: a comprehensive R package for probabilistic distance clustering based methods
Get PDFData clustering has a long history and refers to a vast range of models and methods that exploit the ever-more-performing numerical optimization algorithms and are designed to find homogeneous groups of observations in data. In this framework, the probability distance clustering (PDC) family methods offer a numerically effective alternative to model-based clustering methods and a more flexible opportunity in the framework of geometric data clustering. Given nJ-dimensional data vectors arranged in a data matrix and the number K of clusters, PDC maximizes the joint density function that is defined as the sum of the products between the distance and the probability, both of which are measured for each data vector from each center. This article shows the capabilities of the PDC family, illustrating the R package FPDclustering
Quantitative label-free digital holographic imaging of cardiomyocyte optical volume, nucleation, and cell division
Get PDFCardiac regeneration in newborn rodents depends on the ability of pre-existing cardiomyocytes to proliferate and divide. This capacity is lost within the first week of postnatal development when these cells rapidly switch from hyperplasia to hypertrophy, withdraw from the cell cycle, become binucleated, and increase in size. How these dynamic changes in cell size and nucleation impact cardiomyocyte proliferative potential is not well understood. In this study, we innovate the application of a commercially available digital holographic imaging microscope, the Holomonitor M4, to evaluate the proliferative responses of mononucleated and binucleated cardiomyocytes after CHIR99021 treatment, a model proliferative stimulus. This system enables long-term label-free quantitative tracking of primary cardiomyocyte dynamics in real-time with single-cell resolution. Our results confirm that chemical inhibition of glycogen synthase kinase 3 with CHIR99021 promotes complete cell division of both mononucleated and binucleated cardiomyocytes with high frequency. Quantitative tracking of cardiomyocyte volume dynamics during these proliferative events revealed that both mononucleated and binucleated cardiomyocytes reach a similar size-increase threshold prior to attempted cell division. Binucleated cardiomyocytes attempt to divide with lower frequency than mononucleated cardiomyocytes, which may be associated with inadequate increases in cell size. By defining the interrelationship between cardiomyocyte size, nucleation, and cell cycle control, we may better understand the cellular mechanisms that drive the loss of mammalian cardiac regenerative capacity after birth
Episodic memory assessment: effects of sex and age on performance and response time during a continuous recognition task
Get PDFIntroduction: Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer’s disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population. Methods: MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs. Results: HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs. Discussion: The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer’s disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time