Physical Sciences and Mathematics | Open Access Articles

Leaf Optical And Indirect Lai Measurements In Wheat And Alfalfa At Maciv: Agmet Progress Report 89-4, E. A. Walter-Shea, B. L. Blad

School of Natural Resources: Documents and Reviews

No abstract provided.

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Inferred Areal Extent Of The Oligocene (White River Group) Chadron Basal Sand, Conservation And Survey Division

Conservation and Survey Division

No abstract provided.

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Pre-Tertiary Subcrop Rocks In The Nebraska Panhandle, Conservation Annd Survey Division

Conservation and Survey Division

No abstract provided.

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Configuration Of The Base Of The Principal Aquifer, Conservation And Survey Division

Conservation and Survey Division

No abstract provided.

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A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …

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Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find …

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Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

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On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …

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Environmental Dna Identifies Coastal Plant Community Shift 1,000 Years Ago In Torrens Island, South Australia, Nicole R. Foster, Alice R. Jones, Oscar Serrano, Anna Lafratta, Paul S. Lavery, Kor-Jent Van Dijk, Ed Biffin, Bronwyn M. Gillanders, Jennifer Young, Pere Masque, Patricia S. Gadd, Geraldine E. Jacobsen, Atun Zawadzki, Andria Greene, Michelle Waycott

Research outputs 2022 to 2026

Anthropogenic activities are causing detrimental changes to coastal plants– namely seagrass, mangrove, and tidal marshes. Looking beyond recent times to past vegetation dynamics is critical to assess the response and resilience of an environment to change. Here, we develop a high-resolution multi-proxy approach, providing a new evidence base to decipher long-term change in coastal plant communities. Combining targeted environmental DNA analysis with chemical analysis of soils, we reconstructed 4,000 years of change at a temperate wetland on Torrens Island South Australia and identified an ecosystem shift that occurred ~ 1000 years ago. What was once a subtidal seagrass system shifted …

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Curbing Environmental Degradation To Balance Sustainable Development: Evidence From China, Muneza Kagzi, Vishal Dagar, Nadia Doytch, Deepika Krishnan, Manisha Raj

Ateneo School of Government Publications

To achieve the goals of sustainable development, it is crucial to check the balance of increased level of international trade along with financial development, foreign direct investment (FDI), energy consumption, and institutional advancement with the quality of the environment. This study focuses on how these variables have caused environmental degradation in China. To achieve Goal 13 of the Sustainable Development Goals (SDGs), i.e. to increase the nation's resilience to natural disasters and hazards related to climate change, to promote climate action and safeguard life as part of sustainable development, this involves the analysis of time-series data sets from 1975 to …

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Energy Intensity Convergence Among Chinese Provinces: A Theil Index Decomposition Analysis, Yifan Wang, Wei Li, Nadia Doytch

Ateneo School of Government Publications

China, the world’s largest carbon emitter, has one of the most stringent provincial emissions reduction programs, incorporated into its Five-Year National Plan to reduce carbon emissions. However, the widening energy intensity gap between provinces poses a great challenge for carbon reduction. In this study, we analyze the convergence of Energy intensity (EIC), i.e., the time-dependent decrease of differences among regional energy intensity over time focusing on a data set of 30 Chinese provinces from 2000 to 2015. Our goal is to identify the provinces that are responsible for the observed divergence in energy intensity and identify the factors causing that …

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Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif

All Works

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …

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The Model Of Norm-Regulated Responsibility For Proenvironmental Behavior In The Context Of Littering Prevention, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Previous research suggests that descriptive norms positively influence proenvironmental behavior, including littering prevention. However, in some behavioral contexts, a weak descriptive norm may increase individuals’ feelings of responsibility by signaling a need for action. We examined this effect in the context of litter prevention by conducting structural equation modeling of survey data from 1400 Singapore residents. The results showed that descriptive norms negatively predicted ascription of responsibility and were negatively related to littering prevention behavior via ascription of responsibility and personal norms. It also showed that strong injunctive norms can reduce the inhibitory effect of descriptive norms on ascription of …

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Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …

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Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda

All Works

Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …

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Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with …

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Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing …

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Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

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Jamming Precoding In Af Relay-Aided Plc Systems With Multiple Eavessdroppers, Zhengmin Kong, Jiaxing Cui, Li Ding, Tao Huang, Shihao Yan

Research outputs 2022 to 2026

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the …

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Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, …

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Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very questionable in real-world situations due to the influence of contextual factors. Therefore, allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable. However, existing machine learning techniques accounting for the spatially varying relationship between the dependent variable …

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Toward A Globally Lunar Calendar: A Machine Learning-Driven Approach For Crescent Moon Visibility Prediction, Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk

All Works

This paper presents a comprehensive approach to harmonizing lunar calendars across different global regions, addressing the long-standing challenge of variations in new crescent Moon sightings that mark the beginning of lunar months. We propose a machine learning (ML)-based framework to predict the visibility of the new crescent Moon, representing a significant advancement toward a globally unified lunar calendar. Our study utilized a dataset covering various countries globally, making it the first to analyze all 12 lunar months over a span of 13 years. We applied a wide array of ML algorithms and techniques. These techniques included feature selection, hyperparameter tuning, …

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Methyl Jasmonate Advances Fruit Ripening, Colour Development, And Improves Antioxidant Quality Of ‘Yoho’ And ‘Jiro’ Persimmon, Mahmood Ul Hasan, Zora Singh, Hafiz Muhammad Shoaib Shah, Andrew Woodward, Eben Afrifa-Yamoah

Research outputs 2022 to 2026

Methyl jasmonate (MJ) has potential to regulate fruit ripening and quality. ‘Yoho’ and ‘Jiro’ persimmons were sprayed with MJ (0, 2, 4, and 6 mM), four weeks before anticipated harvest to evaluate its effects on fruit colour and bioactive compounds. Preharvest MJ application significantly improved fruit colour with increased a*, b*, chroma, and colour index. The MJ 6 mM application had significantly enhanced soluble solids content (SSC), reduced total chlorophyll content in peel and pulp, and soluble and total tannins in persimmons. MJ treatments exhibited higher contents of total phenolics, flavonoids, carotenoids, and antioxidant activities. Additionally, MJ treatments enhanced the …

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Uncovering Merchants’ Willingness To Wait In On-Demand Food Delivery Markets, Jian Liang, Ya Zhao, Hai Wang, Zuopeng Xiao, Jintao Ke

Research Collection School Of Computing and Information Systems

While traditional on-demand food delivery services help restaurants reach more customers and enable doorstep deliveries, they also come with drawbacks, such as high commission fees and limited control over the delivery process. White-label food delivery services have emerged as an alternative, ready-to-use platform for restaurants to arrange delivery for customer orders received through their applications or websites, without the constraints imposed by traditional on-demand food delivery platforms or the need to develop an in-house delivery operation. Although several studies have investigated consumer behavior when using traditional on-demand food delivery services, there is limited research on merchants’ behavior when adopting white-label …

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Confocal Raman Spectroscopy Coupled With In Vitro Permeation Testing To Study The Effects Of Formalin Fixation On The Skin Barrier Function Of Reconstructed Human Epidermis, Hichem Kichou, Franck Bonnier, Amanda C. Caritá, Hugh Byrne, Igor Choupra, Emilie Munnier

Articles

Confocal Raman Spectroscopy is recognised as a potent tool for molecular characterisation of biological specimens. There is a growing demand for In Vitro Permeation Tests (IVPT) in the pharmaceutical and cosmetic areas, increasingly conducted using Reconstructed Human Epidermis (RHE) skin models. In this study, chemical fixation of RHE in 10% Neutral Buffered Formalin for 24 hours has been examined for storing RHE samples at 4°C for up to 21 days. Confocal Raman Spectroscopy, combined with Principal Components Analysis, revealed the molecular-level effects of fixation, notably in protein and lipid conformation within the stratum corneum and viable epidermis. IVPT by means …

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Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …

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Algebraic Structures On Parallelizable Manifolds, Sergey Grigorian

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper we explore algebraic and geometric structures that arise on parallelizable manifolds. Given a parallelizable manifold L , there exists a global trivialization of the tangent bundle, which defines a map ρ p : l ⟶ T p L for each point p ∈ L , where l is some vector space. This allows us to define a particular class of vector fields, known as fundamental vector fields, that correspond to each element of l . Furthermore, flows of these vector fields give rise to a product between elements of l and L , which in turn induces …

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Mid-Infrared Spectroscopy Determines The Provenance Of Coastal Marine Soils And Their Organic And Inorganic Carbon Content, Lewis Walden, Oscar Serrano, Zefang Shen, Mingxi Zhang, Paul Lavery, Zhongkui Luo, Lei Gao, Raphael A. Viscarra Rossel

Research outputs 2022 to 2026

Vegetated coastal ecosystems (VCE), encompassing tidal marshes, mangroves, and seagrasses, serve as significant ‘blue’ carbon (C) sinks. Improving our understanding of VCE soils and their spatial and temporal dynamics is essential for conservation efforts. Conventional methods to characterise the dynamics and provenance of VCE soils and measure their total organic carbon (TOC) and inorganic carbon (TIC) contents are cumbersome and expensive. We recorded the mid-infrared (MIR) spectra and measured the TOC and TIC content of 323 subsamples across consistent depths from 106 soil core samples. Using the spectra of each VCE, we determined their mineral and organic composition by depth. …

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Efficient Phosphate Removal From Water Using Ductile Cast Iron Waste: A Response Surface Methodology Approach, Mai Hassan Dr., Nada Alkhashab Eng., Ahmed Osman Dr., Dalia A. Ali Eng

Chemical Engineering

Water scarcity is a critical issue worldwide. This study explores a novel method for addressing this issue by using ductile cast iron (DCI) solid waste as an adsorbent for phosphate ions, supporting the circular economy in water remediation. The solid waste was characterized using XRD, XRF, FTIR, and particle size distribution. Wastewater samples of different phosphate ion concentrations are prepared, and the solid waste is used as an adsorbent to adsorb phosphate ions using different adsorbent doses and process time. The removal percentage is attained through spectrophotometer analysis and experimental results are optimized to get the optimum conditions using Design …

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Coastline In A Changing Maine: The Economics Of Coastal Preference, Walter Lange

Honors College

Coastal Maine is experiencing a time of pronounced stress and conflict from a multitude of factors, including COVID-19, cost of living surges, and climate change (Rector, 2021. Cotton et. al, 2023. Maine State Housing Authority, 2023). One important decision facing Maine is the use of Maine’s coastal areas across a wide variety of potential uses including recreation, housing, tourism, working waterfronts, aquaculture and conservation. This paper examines changes in coastal Mainer’s preferences for conserved coastal over time. Two related survey data sets, from 2019 and 2024, allow analysis of cross-time attitudes towards coastal land use. Through the creation of an …

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Full-Text Articles in Physical Sciences and Mathematics

Leaf Optical And Indirect Lai Measurements In Wheat And Alfalfa At Maciv: Agmet Progress Report 89-4, E. A. Walter-Shea, B. L. Blad

School of Natural Resources: Documents and Reviews

Inferred Areal Extent Of The Oligocene (White River Group) Chadron Basal Sand, Conservation And Survey Division

Conservation and Survey Division

Pre-Tertiary Subcrop Rocks In The Nebraska Panhandle, Conservation Annd Survey Division

Conservation and Survey Division

Configuration Of The Base Of The Principal Aquifer, Conservation And Survey Division

Conservation and Survey Division

A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Research outputs 2022 to 2026

Curbing Environmental Degradation To Balance Sustainable Development: Evidence From China, Muneza Kagzi, Vishal Dagar, Nadia Doytch, Deepika Krishnan, Manisha Raj

Ateneo School of Government Publications

Energy Intensity Convergence Among Chinese Provinces: A Theil Index Decomposition Analysis, Yifan Wang, Wei Li, Nadia Doytch

Ateneo School of Government Publications

Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif

All Works

The Model Of Norm-Regulated Responsibility For Proenvironmental Behavior In The Context Of Littering Prevention, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

All Works

Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

Jamming Precoding In Af Relay-Aided Plc Systems With Multiple Eavessdroppers, Zhengmin Kong, Jiaxing Cui, Li Ding, Tao Huang, Shihao Yan

Research outputs 2022 to 2026

Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Toward A Globally Lunar Calendar: A Machine Learning-Driven Approach For Crescent Moon Visibility Prediction, Samia Loucif, Murad Al-Rajab, Raed Abu Zitar, Mahmoud Rezk

All Works

Methyl Jasmonate Advances Fruit Ripening, Colour Development, And Improves Antioxidant Quality Of ‘Yoho’ And ‘Jiro’ Persimmon, Mahmood Ul Hasan, Zora Singh, Hafiz Muhammad Shoaib Shah, Andrew Woodward, Eben Afrifa-Yamoah

Research outputs 2022 to 2026

Uncovering Merchants’ Willingness To Wait In On-Demand Food Delivery Markets, Jian Liang, Ya Zhao, Hai Wang, Zuopeng Xiao, Jintao Ke

Research Collection School Of Computing and Information Systems

Confocal Raman Spectroscopy Coupled With In Vitro Permeation Testing To Study The Effects Of Formalin Fixation On The Skin Barrier Function Of Reconstructed Human Epidermis, Hichem Kichou, Franck Bonnier, Amanda C. Caritá, Hugh Byrne, Igor Choupra, Emilie Munnier

Articles

Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Algebraic Structures On Parallelizable Manifolds, Sergey Grigorian

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Mid-Infrared Spectroscopy Determines The Provenance Of Coastal Marine Soils And Their Organic And Inorganic Carbon Content, Lewis Walden, Oscar Serrano, Zefang Shen, Mingxi Zhang, Paul Lavery, Zhongkui Luo, Lei Gao, Raphael A. Viscarra Rossel

Research outputs 2022 to 2026

Efficient Phosphate Removal From Water Using Ductile Cast Iron Waste: A Response Surface Methodology Approach, Mai Hassan Dr., Nada Alkhashab Eng., Ahmed Osman Dr., Dalia A. Ali Eng

Chemical Engineering

Coastline In A Changing Maine: The Economics Of Coastal Preference, Walter Lange

Honors College