Digital Commons Network^™

Agro-Food Waste Valorization For Sustainable Bio-Based Packaging, Luana De S.C. Carnaval, Amit K. Jaiswal, Swarna Jaiswal

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In recent years, the increase in the generation of agro-food processing waste, coupled with uncontrolled disposal and inefficient recovery methods, has raised concerns among society, industries, and the research community. This issue is compounded by the accumulation of conventional synthetic packaging. Owing to their significant environmental and economic impacts, the development of sustainable, biocompatible, and biodegradable materials has become an urgent target. In this context, research efforts have been directed toward developing new packaging materials based on renewable sources, such as agro-food waste, contributing to the circular economy concept. However, despite significant advances, novel agro-food-waste-based packaging solutions still largely remain …

The Potential Of Edible Insects As A Safe, Palatable, And Sustainable Food Source In The European Union, Ann D. Conway, Swarna Jaiswal, Amit K. Jaiswal

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Entomophagy describes the practice of eating insects. Insects are considered extremely nutritious in many countries worldwide. However, there is a lethargic uptake of this practice in Europe where consuming insects and insect-based foodstuffs is often regarded with disgust. Such perceptions and concerns are often due to a lack of exposure to and availability of food-grade insects as a food source and are often driven by neophobia and cultural norms. In recent years, due to accelerating climate change, an urgency to develop alternate safe and sustainable food-sources has emerged. There are currently over 2000 species of insects approved by the World …

Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen Phelan

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PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 (μg/m3) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a bidirectional long …

Data Justice In Education: Toward A Research Agenda, Luci Pangrazio, Glenn Auld, Julianne Lynch, Carly Sawatzki, Gavin Duffy, Shelley Hannigan, Jo O'Mara

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Educational institutions increasingly rely on digital platforms to deliver content and learning, monitor attendance, communicate with stakeholders, and evaluate institutional performance. Despite the efficiency and accessibility gains they offer, digital platforms are powered by personal data which, through a process of datafication, can be used to track, monitor, and profile staff and students. The insights drawn from this data can be used to shape educational and professional futures. This article examines how datafication has become a social justice issue in education, discussing the implications for well-being, decision-making, governance, and power in education. Using Hintz and colleagues framework for data justice, …

The Impact Of Emerging Technologies Of Industry 4.0 On Sustainability Dimensions, Mohammed Alnahhal, Waqas Saleem, Bashir Salah

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In the Industry 4.0 (I4.0) era, emerging technologies positively affect sustainability both internally within the industry and externally at the global level. Sustainable productive systems are expected to be with the best indicators levels through using emerging technologies in the I4.0. This study aims to investigate the effect of emerging technologies in the I4.0 on the sustainability pillars through an analysis of a case study conducted at King Saud University about a Smart Yogurt Filling System. In this system, the most critical emerging technologies, such as Internet of Things (IoT), 3D printing, Near field communication technology (NFC), and Node-RED, were …

Coffee: Lighting Its Complex Ground Truth And Percolating Its Molecular Brew, Róża Paterek, Sive Geoghegan, Bernadette S. Creaven, Aoife Power

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Coffee is one of the most widely traded commodities worldwide and its popularity is only increasing. The International Coffee Organisation (ICO) reported a 6% increase in global production in 2020 to 10.5 million tonnes. Coffee production is quite involved (from sowing to harvesting, processing, packaging, and storage); consequently, the industry faces major challenges in terms of the assessment of its quality, flavour, and the components which contribute to coffee's characterisation, as well as the sustainability of coffee production and global trade. This has prompted multiple studies on the nature of the aroma and taste of the many varieties of coffee …

An Image Processing Approach For Real-Time Safety Assessment Of Autonomous Drone Delivery, Assem A. Abdelhak, Dan Moss, Alan Hicks, Susan Mckeever

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The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming …

A Global Model-Agnostic Rule-Based Xai Method Based On Parameterized Event Primitives For Time Series Classifiers, Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio

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Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps …

A Novel Integration Of Data-Driven Rule Generation And Computational Argumentation For Enhanced Explainable Ai, Lucas Rizzo, Damiano Verda, Serena Berretta, Luca Longo

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Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent and interpretable explanations while maintaining scalability and effectiveness in complex scenarios. Rule-based methods in XAI generate rules that can potentially explain AI inferences, yet they can also become convoluted in large scenarios, hindering their readability and scalability. Moreover, they often lack contrastive explanations, leaving users uncertain why specific predictions are preferred. To address this scientific problem, we explore the integration of computational argumentation—a sub-field …

Classifying The Content Of Online Notepad Services Using Active Learning, Mhd Wesam Al-Nabki, Eduardo Fidalgo, Enrique Alegre, Sarah Jane Delany, Francisco Jáñez-Martino

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Pastebin is an online notepad service to share text anonymously. However, it could be misused to propagate suspicious or even illegal activities, like leaking sensitive information or sharing hyperlinks to child sexual abuse material. Due to the high rate of daily upload pastes, manual inspection of this material is not feasible. Conversely, an automatic classifier could identify such activities with little or no human intervention. However, a supervised model may require a significant number of training samples and have to handle distinct text typologies presented in Pastebin. This paper presents a classification approach composed of three cascading supervised classifiers that …

A Comparative Study On Feature Extraction Techniques For The Discrimination Of Frontotemporal Dementia And Alzheimer's Disease With Electroencephalography In Resting-State Adults, Utkarsh Lal, Arjun Vinayak Chikkankod, Luca Longo

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Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% …

Synthetic Dataset Generation Using Photo-Realistic Simulation With Varied Time And Weather Axes, Thomas Lee, Susan Mckeever, Jane Courtney

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To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple environmental contexts are needed to ensure that bias is minimised. The reduction in predictive capacity when faced with unfamiliar data is a common weak point in trained networks, which worsens the further the input data deviates from the training data. However, training for multiple environmental variables dramatically increases the man-hours required for data collection and validation. In …

A Business Model For Communication Design Enterprises, Con Kennedy

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Several international reports and studies indicate that the Communication Design sector has a skill deficit in entrepreneurship and business management, and a greater understanding of these topics is needed. Some authors advocate applying existing business models for design enterprises. However, the skill deficit prevails. It is worth noting that these reports discuss the entrepreneurship deficit in general terms without an explanation of the causes of this deficit or how to remedy the issue. Therefore, this paper explores the development of a Business Model specific for Design Entrepreneurs for deployment in Communication Design enterprises.A theoretical framework for a specific business model …

Action Recognition For Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities, Shakra Mehak, John D. Kelleher, Michael Guilfoyle, Maria Chiara Leva

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Human–robot teaming (HrT) is being adopted in an increasing range of industries and work environments. Effective HrT relies on the success of complex and dynamic human–robot interaction. Although it may be optimal for robots to possess all the social and emotional skills to function as productive team members, certain cognitive capabilities can enable them to develop attitude-based competencies for optimizing teams. Despite the extensive research into the human–human team structure, the domain of HrT research remains relatively limited. In this sense, incorporating established human–human teaming (HhT) elements may prove practical. One key element is mutual performance monitoring (MPM), which involves …

Analyzing Operator States And The Impact Of Ai-Enhanced Decision Support In Control Rooms: A Human-In-The-Loop Specialized Reinforcement Learning Framework For Intervention Strategies, Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz, Houda Briwa, Andres Alonso Perez, Gabriele Baldissone, Micaela Demichela, Georgios C. Chasparis, John D. Kelleher, Maria Chiara Leva

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In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are …

Improving The Affirmative Disclosure Of Agency Legal Materials, Bernard W. Bell, Cary Coglianese, Michael E. Herz, Margaret B. Kwoka, Orly Lobel

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It is axiomatic that in a democratic society the law must be broadly accessible. Administrative agencies produce a plethora of materials imposing legal obligations on commercial or individual actors in the private sector. Other materials bind the agencies themselves in ways that affect the rights or interests of private parties. Still other materials provide the public with information about how agencies interpret and apply the statutes and rules they administer, or how agencies seek to deploy their discretion or take other actions that can affect private individuals or organizations. This Article focuses on improving the public availability of all of …

The Steady Decline Of Club Culture In Dublin: Neoliberal Policy, Touristification And The Pandemic, Caroline Ann O'Sullivan

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The public health restrictions implemented by the Irish government due to Covid-19 resulted in one of the most extensive music industry lockdowns in Europe. Dublin, however, was already experiencing a large scale contraction of club culture. Prior to the pandemic, the physical spaces that dance music and club culture thrived in over the past 40 years had greatly diminished, many being demolished to make way for hotels and upmarket student accommodation. The onset of the pandemic meant this contraction intensified, and 2021 and 2022 saw the permanent closure of a number of high-profile and pivotal venues across the city. In …

Ideological Presuppositions In Media Coverage Of Corporation Tax Policy In The Uk And Ireland: A Critical Discourse Analysis, Ciara Graham, Brendan K. O'Rourke

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This paper argues that the state's capacity to tax corporations in order to fund itself is reaching crisis proportions. Following decades of trade liberalization, deregulation, and globalization, large multinational companies have been able to take advantage of tax competition between states in order to avoid taxation and offset their obligations. This crisis, arguably, has been facilitated by state actors and exacerbated by non-state actors: we explore the ways in which multi-national corporations (MNCs) manipulate their capital, assets, and supply chains to minimize their tax burdens; and we further consider the ways in which media narratives construct this issue and whether …

Mycotoxin Detection Through Colorimetric Immunoprobing With Gold Nanoparticle Antibody Conjugates, Vinayak Sharma, Bilal Javed, Hugh J. Byrne, Furong Tian

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Driven by their exceptional optical characteristics, robust chemical stability, and facile bioconjugation, gold nanoparticles (AuNPs) have emerged as a preferred material for detection and biosensing applications in scientific research. This study involves the development of a simple, rapid, and cost-effective colorimetric immuno-sensing probe to detect aflatoxin B1 and zearalenone using AuNP antibody (AuNP-mAb) conjugates. Anti-toxin antibodies were attached to the AuNPs by using the physical adsorption method. The colorimetric immunosensor developed operates on the principle that the optical properties of the AuNP are very sensitive to aggregation, which can be induced by a critical high salt concentration. Although the presence …

Sex- And Gender-Specific Considerations In Mycotoxin Screening: Assessing Differential Exposure, Health Impacts, And Mitigation Strategies, Gayathree Thenuwara, Bilal Javed, Singh Baljit, Hugh J. Byrne, Furong Tian

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Mycotoxins, toxic secondary metabolites produced by fungi, present significant health risks through contaminated food and feed. Despite broad documentation of their general impacts, emerging research highlights the requirement of addressing both sex- and gender-specific differences in the risk of exposure, susceptibility, and health outcomes in mycotoxin screening and mitigation strategies. Distinct biological (sex-based) and sociocultural (gender-based) factors can influence the risk of mycotoxin exposure and subsequent health impacts; women may for example exhibit specific exposures to certain mycotoxins due to physiological and hormonal differences, with increased risks during critical life stages such as pregnancy and lactation. Conversely, men may demonstrate …

Biosensor-Enhanced Organ-On-A-Chip Models For Investigating Glioblastoma Tumor Microenvironment Dynamics, Gayathree Thenuwara, Bilal Javed, Baljit Singh, Furong Tian

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Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC …

Characterization Of Spent Grain From Irish Whiskey Distilleries For Biorefinery Feedstock Potential To Produce High-Value Chemicals And Biopolymers, Rasaq S. Abolore, Dileswar Pradhan, Swarna Jaiswal, Amit K. Jaiswal

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Distiller's spent grain (DSG) is a byproduct generated in large quantities during the mashing process, particularly in the production of alcoholic beverages such as whiskey. This study aimed to characterize DSG from nine different distilleries as a potential biorefinery feedstock for the synthesis of high-value bioproducts. Key components, including protein (12.38–26.32%), cellulose (11.75–32.75%), hemicellulose (6.97–19.47%), lignin (8.44–15.71%), and total phenolics (1.42 to 3.97 mg GAE/g), were analyzed to evaluate their variability and suitability for industrial applications. The results reveal that DSG composition varies significantly across distilleries due to differences in processing techniques, even though the starting grain composition had minimal …

A Comprehensive Review Of Food Safety Culture In The Food Industry: Leadership, Organizational Commitment, And Multicultural Dynamics, Ashwini Sadashiv Pai, Swarna Jaiswal, Amit K. Jaiswal

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Food safety culture (FSC) has transitioned from a narrow compliance-based concept to a comprehensive organisational value that is essential for ensuring food safety. This review explores the pivotal roles of leadership, organisational commitment, and cultural diversity in shaping an effective FSC. It highlights how leadership style, particularly transformational leadership, can enhance employee engagement and foster a proactive safety culture. Additionally, the impact of national and organisational cultures on FSC is analysed, emphasising the challenges posed by a multicultural workforce in standardising food safety practices. This article also provides a comparative analysis of FSC across various sectors, such as meat and …

Truth Bounties: A Market Solution To Fake News, Yonathan A. Arbel, Michael D. Gilbert

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False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or social media. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it faces constitutional and political hurdles. Furthermore, regulation can have undesirable side effects and be ripe for abuse by powerful actors, public and private.

This Article presents an alternative …

Systematic Regulation Of Artificial Intelligence, Yonathan Arbel, Matthew Tokson, Albert Lin

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Today's artificial intelligence ("AI'') systems exhibit increasing capabilities across a remarkable variety of tasks. The rapid growth in AI ability has caught the attention of policymakers, parliaments, and the United Nations. These entities are increasingly looking towards regulating AI, not only in its particular applications, but as a technology. Yet legal scholarship has thus far offered little to this new and critical regulatory conversation, which has instead been dominated by computer scientists and technologists.

This Article begins the project of assessing AI 's broader risks and law 's role in addressing them. These risks are wide ranging-they span harms to …

The Price Of Criminal Law, Russell M. Gold

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Should tax dollars pay for more criminal law, better public schools, or a new community center? Different counties will answer the question differently, but facing these tradeoffs is profoundly important to democratic governance. Nonetheless, because the criminal legal system diffuses power and hides and offioads costs, officials and voters do not have to honestly consider that question. These structural features place a hidden thumb on the scale that gives counties more criminal enforcement than they pay for. That is a problem. Too much enforcement is particularly pernicious in criminal law: Incarceration inflicts tremendous suffering, especially in poor communities of color. …

Inequitable Organ Allocation, Benjamin Mcmichael

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Seventeen people die every day in the United States waiting for an organ transplant, and over 100,000 people are currently on waitlists to receive a donated organ. Given these stark numbers, the allocation policies governing who receives donated organs are both critically important and hotly debated. Congress passed the National Organ Transplant Act in 1984 with the goal of eliminating markets for organs and the promise of equitable access to transplants without regard to geography or socioeconomic status. Despite these laudable goals, recent developments in organ allocation policy have led to multiple lawsuits, congressional inquiries, and an expose in the …

Assessment Of Nitrate And Phosphate Concentrations In Discharge Water From Ditch Networks Across Different Peatland Use Types: Implications For Sustainable Peatland Use Management, Samuel Obeng Apori, Michelle Giltrap, Julie Dunne, Furong Tian

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Peat soils, when drained and transformed for different land uses, can release pollutants such as nitrate and phosphate into nearby water bodies and ecosystems through ditch networks. However, there have been limited studies to ascertain the extent and impact of these nutrient releases under various peatland use types. A total of fifty-four water samples were collected between October 2021 and January 2022 from five industrial cutaway bogs, twenty-five grasslands, and twenty-four forest plantations. The water samples were subsequently examined for nitrate–nitrogen and phosphate–phosphorus using the HACH DR890 colorimeter. This study showed that the nitrate–nitrogen concentration of the discharge water ranged …

An Exploration Of Fibre Intake And Bowel Function In A Sample Of Adults At An Irish University Campus, Ellen Lynch, Sophie Mulligan, Suzanne L. Doyle

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Background: Bowel dysfunction can significantly impair quality of life. Adequate fibre intake is associated with good bowel health but intakes have been reported to be low in Ireland. Aim: This study aimed to gather data on fibre intake and bowel habits in a cohort of adults at a university campus in Dublin, Ireland. Methods: An online questionnaire was developed by adapting validated tools to assess habitual fibre intake and bowel function. The questionnaire was circulated through mailing lists and advertised via QR codes on campus in February/March 2023. Data was analysed using SPSS, p < 0.05 was considered statistically significant. Results: In total, 275 valid responses were received. Low fibre intakes (< 19 g/day) were found in 50.5% of participants. A significantly higher proportion of males had low fibre intake compared to females (62.2% vs 44.8%, p = 0.039). Nearly a third (30.2%) of respondents experienced mild symptoms of bowel dysfunction, and 13.1% experienced moderate to severe symptoms. An inverse relationship was observed between fibre intake (g/day) and bowel dysfunction (p = 0.033). Conclusions: In this cohort, low fibre intakes and some degree of bowel dysfunction were prevalent. Public health campaigns to increase fibre intake could prove to be a cost-effective way to improve bowel function and health amongst adults in Ireland.

Nutrient Intakes And Gastrointestinal Symptoms Among Esophagogastric Cancer Survivors Up To 5 Years Post-Surgery, A. E. Bennett, L. O'Neill, S. L. Doyle, E. M. Guinan, J. O'Sullivan, J. V. Reynolds, J. Hussey

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A cross-sectional analysis explored nutritional intakes and gastrointestinal (GI) symptoms among esophagogastric cancer survivors up to 12, 13–36, and 37+ months post-surgery. Participants were identified from the Upper GI Cancer Registry at St James' Hospital, Ireland. The Short Nutritional Assessment Questionnaire, European Prospective Investigation of Cancer Food Frequency Questionnaire, World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Score, and Gastrointestinal Symptoms Rating Scale assessed malnutrition risk, nutritional intake, adherence to (secondary) cancer prevention recommendations, and GI symptoms, respectively. Most (82.5%, n33) participants (n40) were male. Mean age was 65.5 ± 9.3 years. Time post-surgery ranged from 6-62 months. Half …