Physical Sciences and Mathematics | Open Access Articles

Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Wednesday, April 1, 2026, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

The student abstract booklet is a compilation of abstracts from students' oral and poster presentations at Wright State University's Celebration of Student Research, Scholarship & Creative Activities on April 1, 2026.

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Recovery Of Bioenergy From Industrial Wastewater Using Microbial Fuel Cell, Syed Wamiq Ali, Ghulam Mujtaba, Manzoor Ahmed Sanjrani, Falak Shad

Journal of Bioresource Management

Water problems and Energy demands are increasing particularly in developing countries. Researchers are looking for sustainable technologies that can address increasing energy and environmental concerns. Major reasons behind those emerging concerns are industrialization and rapid development. There is need of sustainable technology which can solve wastewater issues and fulfill energy demand. One of the emerging technologies that have been studied comprehensively is Microbial Fuel Cell (MFC). It is a bio-electrochemical conversion technology that converts organic substrate into electrical energy. Chemical Oxygen Demand (COD) is removed as a result of microbial metabolism of organic substrate. Even through technical and economic aspects …

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Best Integrated Writing Spring 2026 - Complete Edition, Wright State University School Of Humanities And Cultural Studies, Tracy Smith, Kristie Mckiernan

Best Integrated Writing

Best Integrated Writing includes excellent student writing from Integrated Writing courses taught at Wright State University.

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Tornado Alley In A Changing Climate: The Eastward Shift, Kenny Reinhard

Best Integrated Writing

Reinhard argues that climate change is driving an eastward shift in Tornado Alley from the Great Plains toward the southeastern United States, increasing tornado frequency in more densely populated and less-prepared regions. It highlights how changing atmospheric conditions—such as increased moisture, instability, and wind shear—are contributing to this shift and emphasizes the need for improved preparedness and infrastructure in newly affected areas.

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Experiments In Graph Structure And Knowledge Graph Embeddings, Antrea Christou, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Knowledge graphs (KGs) are an established paradigm for integrating heterogeneous data and representing knowledge. As such, there are many different methodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link prediction using KG embeddings (KGEs). In this article, we examine how various perturbations of graph structures impact downstream tasks. These perturbations are sourced from how various methodologies (or design practices) would impact the model, starting with …

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Accelerating Knowledge Graph And Ontology Engineering With Large Language Models, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular approaches to ontologies will be of central importance.

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Education In The Era Of Neurosymbolic Ai, Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. NAI-powered education systems will be capable of interpreting complex human concepts and contexts …

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Climate Change Adaptation Through Economic Democracy, Shriraksha Mohan

Best Integrated Writing

Mohan investigates and combines three different areas of literature and social movements to discuss the problem of climate change and economic adaptation. The value of this work is that it can be used to communicate among three independent types of social movements and inquiries. This can strengthen and connect dispersed yet overlapping efforts in addressing work issues, resource utilization, economic governance and participation, and ecological problems economies face today.

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Feature Manifold Transformer For Detection Of Differential Item Functioning: Visual Detection Of Categorical Feature Nonconformity Through Attention-Based Analysis, Derrick A. Cox, Tanvi Banerjee, William L. Romine

Computer Science and Engineering Faculty Publications

Methods for interpreting complex feature interactions in educational assessment data remain a critical challenge, with traditional statistical approaches often creating barriers to accessibility and interpretability. We introduce the Feature Manifold Transformer (FMT), a novel machine learning approach that leverages dimensionality reduction, representation learning, and transformer architectures to visualize and interpret feature relationships in categorical data. Using the Concept Inventory of Natural Selection (CINS) and Concept Assessment of Natural Selection (CANS) datasets as testbeds, we demonstrate the FMT’s ability to capture subtle relationships between student demographics and response patterns. Our methodology enables both global and local pattern analysis, providing interpretable visualizations …

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Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Thursday, March 13, 2025, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Abstract Books

The student abstract booklet is a compilation of abstracts from students' oral and poster presentations at Wright State University's Celebration of Student Research, Scholarship & Creative Activities on March 13, 2025.

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Line Graphs Of Directed Graphs I, Vaidy Sivaraman, Daniel Slilaty

Mathematics and Statistics Faculty Publications

We determine the forbidden induced subgraphs for the intersection of the classes of chordal bipartite graphs and line graphs of acyclic directed graphs. This is a first step towards finding the forbidden induced subgraphs for the class of line graphs of directed graphs.

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The Knowwheregraph Ontology, Cogan Shimizu, Shirly Stephen, Adrita Barua, Ling Cai, Antrea Christou, Kitty Currier, Abhilekha Dalal, Colby K. Fisher, Pascal Hitzler, Krzysztof Janowicz, Wenwen Li, Zilong Liu, Mohammad Saeid Mahdavinejad, Gengchen Mai, Dean Rehberger, Mark Schildhauer, Meilin Shi, Sanaz Saki Norouzi, Yuanyuan Tian, Sizhe Wang, Zhangyu Wang, Joseph Zalewski, Lu Zhou, Rui Zhu

Computer Science and Engineering Faculty Publications

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema …

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A Community-Driven Vision For A New Knowledge Resource For Ai, Vinay K. Chaudhri, Chaitan Baru, Brandon Bennett, Mehul Bhatt, Darion Cassel, Anthony G. Cohn, Rina Dechter, Esra Erdem, Dave Ferrucci, Ken Forbus, Gregory Gelfond, Michael Genesereth, Andrew S. Gordon, Benjamin Grosof, Gopal Gupta, Jim Hendler, Sharat Israni, Tyler R. Josephson, Patrick Kyllonen, Yuliya Lierler, Vladimir Lifschitz, Clifton Mcfate, Hande Küçük Mcginty, Leora Morgenstern, Alessandro Oltramari, Praveen Paritosh, Dan Roth, Blake Shepard, Cogan Shimizu, Denny Vrandečić, Mark Whiting, Michael Witbrock

Computer Science and Engineering Faculty Publications

The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose, widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered …

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Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …

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Extraction Of Patient Subtypes Using Llm Generated Knowledge Graphs Integrated With A Transformer Architecture, Benjamin Holmes, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Extracting patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria) from unstructured medical notes is an ongoing challenge due to the variability of clinical language and the complex nature of patient conditions. We demonstrate a pipeline that combines named entity recognition (NER), transformer embeddings, guided dimensionality reduction, and LLM-mediated knowledge graph integration to enhance patient extraction. The approach begins with NER using the UMLS metathesaurus [1] to extract clinical terms, followed by transformation into vector embeddings using a biomedical transformer. These embeddings are augmented with structured knowledge graph representations generated through an LLM-driven …

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Ontology-Based Data Organization For The Enslaved.Org Project, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The men, women, and children forced into slavery in the Atlantic world came from diverse African societies with long histories of political, economic, and cultural development. They were taken from the trading centers of the Hausa city-states, the farming and artisanal communities of Senegambia, the Kongo and Mbundu polities of West Central Africa, and many other regions. They carried with them agricultural expertise, metallurgical skills, medical knowledge, religious traditions, and oral histories that helped sustain communities in the face of displacement and enslavement.Enslavement did not erase this intellectual and cultural inheritance, nor did it render its victims passive numbers in …

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Ontology Population Using Llms, Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu

Computer Science and Engineering Faculty Publications

No abstract provided.

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Understanding Physiological Responses For Intelligent Posture Detection Using Wearable Technology, Chaitanya Vardhini Anumula, Tanvi Banerjee, Anuradha Oak

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

This study investigates the physiological impact of Iyengar yoga at the pose-level using EmbracePlus wearable smartwatch, for data recording and personalized yoga pose detection for tracking.

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Generative Ai (Gan & Vae) In Motion Sickness Research, Harigovind Harikumar, Tomojit Ghosh

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

No abstract provided.

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A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

While earthworms have largely been studied for their role in eliminating toxic metals from soil, less is known about their behavior overall. Previous work from our lab found that a predator-like auditory stimulus (grunting) reliably induced fear-related freezing behavior (Worthen et al., 2024). The present studies further explore earthworm behaviors in response to audio-vibratory stimuli. In Experiment 1, we manipulated amplitude levels and speaker location to examine the parameters needed to reliably induce a freezing fear response to the grunting sound. It was hypothesized that when the speaker was touching the apparatus and producing an added mechanical vibration, there would …

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Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

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Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

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Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

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This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

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Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

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Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

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Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

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Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …

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Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

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Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

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Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

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Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …

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Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

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Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

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Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

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This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …

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Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi

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Hydrogen is considered an emerging carrier of clean energy with renewable capabilities. Widespread hydrogen energy utilization necessitates efficient storage strategies. Functionalized nanomaterials, such as Li-decorated BC3 nanosheets, are among the primary candidate materials for hydrogen storage. This research investigates the feasibility of hydrogen storage by estimating energy barriers and their dependence on storage density on Li-decorated BC3 nanosheet. Density functional theory (DFT) simulations provide estimates of adsorption energies and saddle points for hydrogen storage and compare corresponding reaction rates. The results are expected to help us understand the advantages and possible shortcomings of hydrogen storage on such nanomaterials.

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Biosensing Applications Of Thz Rotational Spectroscopy: Sensing And Analysis Of Exogenous And Endogenous Compounds In Exhaled Breath, Daniel J. Tyree

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Exhaled human breath contains a wealth of volatile molecular species which bear an imprint of compounds dissolved in blood. Assessing trace amounts of these species in exhaled breath requires a highly sensitive and selective method. Terahertz (THz) rotational spectroscopy satisfies these needs by detecting numerous and narrow, molecule specific spectral features with feature intensity dependent on the quantity of absorbing molecules, molecular structure, and experimental parameters. Expanding the applicability of THz sensing to biological gases requires systems to measure this spectral data, reliable analysis of the spectra, and demonstration of the utility of biological data extracted from the spectra. To …

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Full-Text Articles in Physical Sciences and Mathematics

Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Wednesday, April 1, 2026, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

Recovery Of Bioenergy From Industrial Wastewater Using Microbial Fuel Cell, Syed Wamiq Ali, Ghulam Mujtaba, Manzoor Ahmed Sanjrani, Falak Shad

Journal of Bioresource Management

Best Integrated Writing Spring 2026 - Complete Edition, Wright State University School Of Humanities And Cultural Studies, Tracy Smith, Kristie Mckiernan

Best Integrated Writing

Tornado Alley In A Changing Climate: The Eastward Shift, Kenny Reinhard

Best Integrated Writing

Experiments In Graph Structure And Knowledge Graph Embeddings, Antrea Christou, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Accelerating Knowledge Graph And Ontology Engineering With Large Language Models, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Education In The Era Of Neurosymbolic Ai, Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Climate Change Adaptation Through Economic Democracy, Shriraksha Mohan

Best Integrated Writing

Feature Manifold Transformer For Detection Of Differential Item Functioning: Visual Detection Of Categorical Feature Nonconformity Through Attention-Based Analysis, Derrick A. Cox, Tanvi Banerjee, William L. Romine

Computer Science and Engineering Faculty Publications

Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Thursday, March 13, 2025, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Abstract Books

Line Graphs Of Directed Graphs I, Vaidy Sivaraman, Daniel Slilaty

Mathematics and Statistics Faculty Publications

Computer Science and Engineering Faculty Publications

Computer Science and Engineering Faculty Publications

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Extraction Of Patient Subtypes Using Llm Generated Knowledge Graphs Integrated With A Transformer Architecture, Benjamin Holmes, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Ontology-Based Data Organization For The Enslaved.Org Project, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Ontology Population Using Llms, Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Understanding Physiological Responses For Intelligent Posture Detection Using Wearable Technology, Chaitanya Vardhini Anumula, Tanvi Banerjee, Anuradha Oak

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

Generative Ai (Gan & Vae) In Motion Sickness Research, Harigovind Harikumar, Tomojit Ghosh

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

Browse all Theses and Dissertations

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

Browse all Theses and Dissertations

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

Browse all Theses and Dissertations

Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

Browse all Theses and Dissertations

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

Browse all Theses and Dissertations

Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi

Browse all Theses and Dissertations

Biosensing Applications Of Thz Rotational Spectroscopy: Sensing And Analysis Of Exogenous And Endogenous Compounds In Exhaled Breath, Daniel J. Tyree

Browse all Theses and Dissertations