bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Heat Moisture Treatment, Annealing, and Gelatinization‐Retrogradation Modifications on Physico‐Chemical, Functional, and Structural Properties of Starch from Elephant Foot Yam (Amorphophallus paeoniifolius)
Authors: Iqbal Unaiza , Saini Pinki , Ahmed Mazia 
Published: April 2024

AbstractRoots and tuberous plants are vital food crops used as food supplements in many parts of the tropics. Being a major food item, starch is used in food products as thickeners, adsorbent, film forming agent. Starch, naturally, possess certain limitations & starch modifications can overcome the limitations, thereby increasing its versatility and meeting the needs of consumer. Current study focuses on comparative analysis between modified Elephant Foot Yam (Amorphophallus paeoniifolius) starch using physical methods (heat treatment method, annealing and gelatinization‐retrogradation) and to study the effect of modification on physico‐chemical, functional, structural and thermal properties. Results reveal that all physically modified starches positively altered the functional and physiological characteristics of native yam starch. However, maximum restriction in swelling power (2.54 %), solubility (2.47 %), moisture content (1.96 %), water absorption capacity (1.41 g/mL) is observed in heat moisture treated (HMT) starch. These HMT modified starches can be used in the manufacturing of baked and dairy products having reduced glycemic index, can be used as a packaging material for coating of fruits and vegetables. Henceforth, suitable and relevant application of modified starches of desired physical and functional features may be produced for industrial utilisation, or as additives fulfilling specific purposes in foods.
Coupled hydro‐mechanical pore‐scale modeling of biopore‐coated clods for upscaling soil shrinkage and hydraulic properties
Authors: Barbosa Luis Alfredo Pires , Gerke Horst H. 
Published: April 2024

AbstractEarthworms and plant roots are vital for macropore formation and stabilization. The organo‐mineral coating of biopore surfaces also regulates macropore‐matrix mass exchange during preferential flow. The influence of finer‐textured burrow coatings on macroscopic soil properties during shrinkage could potentially be assessed by upscaling pore‐scale hydraulic and mechanical simulations. The aim was to investigate the influence of micro parameters (particle size, stiffness, and bond strength) on macro parameters (i.e., shrinkage curve and soil hydraulic properties). Drainage experiments and simulations were carried out using biopore‐coated clod‐size samples compared to those without coating. Simulations were performed using a two‐phase pore‐scale finite volume coupled with discrete element model (DEM‐2PFV). The structural dynamics was characterized by analyzing the pore volume and soil shrinkage curve obtained from numerically determined data. The soil hydraulic parameters were described using uni‐ and bimodal van Genuchten (vG) functions. The drainage simulations revealed hydro‐mechanical dynamics characterized by Braudeau‐shrinkage curve subdomains: The matrix‐only samples, with lower particle bond strength, exhibited relatively higher shrinkage. The coated samples, with higher particle stiffness and bond strength, displayed greater hydro‐mechanical stability. The numerically determined initial value of the saturated hydraulic conductivity (Ks) was about 70 times larger for matrix‐only samples than for coated samples. As expected, for the nonrigid soil structures, constant Ks, α, and n values for bimodal vG model resulted in prediction errors. Upscaling DEM‐2PFV pore‐scale model outcomes quantifies micro‐coating effects on macro hydro‐mechanics. This yields void ratio‐based soil water retention and hydraulic conductivity functions, advancing macroscopic soil hydraulic models and enhancing structured soil flow and transport descriptions.
Analyzing the impact of thermophoresis particle deposition and magnetohydrodynamic cross‐flow of Williamson hybrid nanofluids across a permeable deformable surface
Authors: Alharbi Latifah Falah , Khan Umair , Zaib Aurang , Ishak Anuar 
Published: April 2024

AbstractThe need to examine the thermal transport of hybrid nanofluids has arisen from the need for improved heat transfer to control the rising heat density of small‐scale and many other technical procedures. Hybrid nanofluids are made up of two different kinds of nanoparticles suspended in the primary fluid. This improves the ability of regular fluids to transport heat and makes them better heat exponents than nanofluids. In this study, we evaluate the cross‐flow and heat transport characteristics of Williamson hybrid nanofluid across an extendable surface. The impacts of thermal radiation, non‐uniform heat sources, and magnetic effects are provoked in this study. The set of partial different equations is obtained as a result of the theoretical formulation. The similarity variables are hired to get the ordinary differential equations. The bvp4c solver is availed to compute the numerical dual solutions for different physical parameters. The results indicate that the friction factor is enhanced due to the presence of magnetic, suction, and Williamson parameters. Whereas, the heat transfer rate decreases due to Williamson and magnetic parameters, whilst the opposite impact is seen due to suction. Moreover, the Williamson parameter reduces the Sherwood number, whereas the suction uplifts the Sherwood number.
Artifact Report: Trocq: Proof Transfer for Free, With or Without Univalence
Authors: Cohen Cyril , Crance Enzo , Mahboubi Assia 
Published: April 2024

AbstractTrocq [5] is both the name of a calculus, describing a parametricity framework, and of a plugin [6] that provides tactics for performing representation changes in goals, as well as vernacular commands for specifying the expected translations.
The World Citizen School Model. Learning Philosophy and Learning System for Global, Socially Innovative and Value-Based Future Learning
Authors: Wihlenda Michael 
Published: April 2024

AbstractThis chapter presents the learning philosophy and learning system of the World Citizen School. The project started in 2013 at the Weltethos Institute of the University of Tübingen (The Global Ethic Project goes back to Küng (1997). ‘Weltethos’ is the German name for Global Ethic after which the Weltethos Institute and the Weltethos Foundation, which Küng co-founded, are named). Early in the development process, the founder and author of this paper was particularly influenced by the concept of the so-called “twenty-first Century Skills”, the 4C skill set of collaboration, critical thinking, communication, and creativity. Against this background, the ability to think and act procedurally (following the inquiry-based learning approach) receives special attention as an action-guiding process logic. This is practiced in particular with the didactic method which can be described as “dialogue around a common vision, mission, goals, and values, based on the concept of the ‘learning organization’”. With the latter, the primary focus of the learning philosophy moves to ethical competence development, for which the Weltethos Project and the Capability Approach serve as a reflective framework.
Using Real-World Problems and Project-Based Learning for Future Skill Development: An Approach to Connect Higher Education Students and Society Through User-Centered Design
Authors: Santana André Luiz Maciel , de Deus Lopes Roseli 
Published: April 2024

AbstractAlthough technologies are increasingly present in people’s pockets through powerful smartphones, the development of solutions centered on the user does not always follow this movement, creating a gap, mainly in hardware solutions. At the same time, the role of the modern engineer is rethought as the demands of the industry and the way the population consumes technology are also changing. Furthermore, Future Skills, such as Design Thinking, Real-World Problems Solving, User-Centered Design, and Digital Literacy, are increasingly necessary to connect higher education students with complex social problems and their contexts. This paper analyses and compiles the results of an engineering program to operationalize real-world problem solutions in higher education by a User-Centered Design that aims to approach Future Skills in a Computer Engineering Course. It promotes discussion on a case study with 49 students from a Microcontroller and Internet of Things class, from a Brazilian university, in the year 2020 and during the pandemic period. It was possible to conclude that the program stimulates the development of modern skills and encourages students to design solutions based on people’s real needs. The results also indicate the enhancement of competencies such as Design Thinking, developing in teams, Digital Literacy, design with and for the user, solving complex problems by interacting with real-world issues, and communication skills.
Active Learning and Integrated Assessment: Minerva’s Approach to Teaching Future Skills
Authors: Gahl Megan K. , Ahuja Abha , Ribeiro Raquel H. , Averett Maia , Genone James 
Published: April 2024

AbstractThe Minerva Project works with global higher education partners to build transformational education programs based on Future Skills. Within our learning taxonomy, we identified practical knowledge as distinct Learning Outcomes (LOs). What differentiates Minerva programs from other Future Skills-focused programs is: 1) active learning pedagogy, 2) direct feedback and assessment of Future Skills, and 3) systematic reinforcement throughout students’ learning experience. The goal in our lessons is to practice skills and broad concepts, therefore we use flipped classrooms with active learning techniques. To guide students’ improvement of Future Skills in our programs, we directly assess LOs and provide feedback on specific LO applications in both synchronous (e.g., in-class polls) and asynchronous (e.g., assignments) aspects of each course. Learning Outcomes from the learning taxonomy may be introduced in one course but are assessable in any other course in the curriculum, ensuring that skills are reinforced throughout a student’s learning experience. In this chapter, we outline principles that underlie the development of learning taxonomies and curricular approaches in Minerva programs. We employ the same principles to collaborate with partners and build custom learning taxonomies and curricula; throughout this chapter we illustrate partner adaptations using examples from our collaborations.
Future-Skilling the Workforce: SkillsFuture Movement in Singapore
Authors: Gog Soon-Joo , Tan Edwin , Tan Kelsie 
Published: April 2024

AbstractWorkforce development is an ongoing undertaking in Singapore. This includes both the pre-employment education of students in the education system, and the continuing education and learning of incumbent workers. The workforce development system in Singapore is anchored on the tradition that stakeholders within the skills ecosystem must collaborate and coordinate closely to anticipate skills needs, disseminate insights, and measure skills progression and training outcomes. This is achieved through the regular charting of industry transformation from the business and economic perspectives, and the parallel review of human capital and skills demands for the future economy. A skilled and competitive workforce is the foundation of an inclusive society where everyone benefits from economic growth.
Commentary of the SKLM to the EFSA opinion on risk assessment of N-nitrosamines in food
Authors: Eisenbrand Gerhard , Buettner Andrea , Diel Patrick , Epe Bernd , Först Petra , Grune Tillman , Haller Dirk , Heinz Volker , Hellwig Michael , Humpf Hans-Ulrich , Jäger Henry , Kulling Sabine , Lampen Alfonso , Leist Marcel , Mally Angela , Marko Doris , Nöthlings Ute , Röhrdanz Elke , Spranger Joachim , Steinberg Pablo , Vieths Stefan , Wätjen Wim , Hengstler Jan G. 
Published: April 2024

AbstractDietary exposure to N-nitrosamines has recently been assessed by the European Food Safety Authority (EFSA) to result in margins of exposure that are conceived to indicate concern with respect to human health risk. However, evidence from more than half a century of international research shows that N-nitroso compounds (NOC) can also be formed endogenously. In this commentary of the Senate Commission on Food Safety (SKLM) of the German Research Foundation (DFG), the complex metabolic and physiological biokinetics network of nitrate, nitrite and reactive nitrogen species is discussed with emphasis on its influence on endogenous NOC formation. Pioneering approaches to monitor endogenous NOC have been based on steady-state levels of N-nitrosodimethylamine (NDMA) in human blood and on DNA adduct levels in blood cells. Further NOC have not been considered yet to a comparable extent, although their generation from endogenous or exogenous precursors is to be expected. The evidence available to date indicates that endogenous NDMA exposure could exceed dietary exposure by about 2–3 orders of magnitude. These findings require consolidation by refined toxicokinetics and DNA adduct monitoring data to achieve a credible and comprehensive human health risk assessment.
Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency
Authors: Sahin Muhammet Emin , Ozbay Karakus Mucella 
Published: April 2024

AbstractRenewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.
Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems
Authors: Ortega-Bolaños Ricardo , Bernal-Salcedo Joshua , Germán Ortiz Mariana , Galeano Sarmiento Julian , Ruz Gonzalo A. , Tabares-Soto Reinel 
Published: April 2024

AbstractArtificial Intelligence (AI)-based systems and their increasingly common use have made it a ubiquitous technology; Machine Learning algorithms are present in streaming services, social networks, and in the health sector. However, implementing this emerging technology carries significant social and ethical risks and implications. Without ethical development of such systems, there is the potential for this technology to undermine people’s autonomy, privacy, and equity, even affecting human rights. Considering the approaches necessary for ethical development and effective governance of AI, such as ethical principles, guidelines, and technical tools, the question arises regarding the limitations of implementing these measures by the highly technical personnel involved in the process. In this context, we propose the creation of a typology that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources. In addition, this typology will include relevant information such as developmental level, related tasks, sectors, and language. Our research is based on a systematic review in which we identified 352 resources and tools. We expect this contribution to be valuable in promoting ethical AI development for developers and leaders who manage these initiatives. The complete typology and the comprehensive list of resources are available for consultation at https://ricardo-ob.github.io/tools4responsibleai.
“Be Not Conformed to this World”: MacIntyre’s Critique of Modernity and Amish Business Ethics
Authors: Jeong Sunny , Sinnicks Matthew , Burton Nicholas , Vu Mai Chi 
Published: April 2024

AbstractThis paper draws on MacIntyre’s ethical thought to illuminate a hitherto underexplored religious context for business ethics, that of the Amish. It draws on an empirical study of Amish settlements in Holmes County, Ohio, and aims to deepen our understanding of Amish business ethics by bringing it into contact with an ethical theory that has had a significant impact within business ethics, that of Alasdair MacIntyre. It also aims to extend MacIntyrean thought by drawing on his neglected critique of modernity in the context of business ethics. The Amish context allows us to appreciate the relationship between MacIntyre’s critique of modernity, his conception of practices and communities, and his distinctive approach to the virtues. It also helps us to better understand how the ethical life is possible within our emotivist culture.
Algor-ethics: charting the ethical path for AI in critical care
Authors: Montomoli Jonathan , Bitondo Maria Maddalena , Cascella Marco , Rezoagli Emanuele , Romeo Luca , Bellini Valentina , Semeraro Federico , Gamberini Emiliano , Frontoni Emanuele , Agnoletti Vanni , Altini Mattia , Benanti Paolo , Bignami Elena Giovanna 
Published: April 2024

AbstractThe integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of ‘Human-in-the-Loop’ and ‘Algorithmic Stewardship’ principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.
Robust and efficient airplane cockpit video coding leveraging temporal redundancy
Authors: Mitrica Iulia , Fiandrotti Attilio , Ruellan Christophe , Cagnazzo Marco 
Published: April 2024

Abstract
Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation.
Efficient Bayesian CNN Model Compression using Bayes by Backprop and L1-Norm Regularization
Authors: Shaikh Ali Muhammad , Zhao Yun-bo , Kumar Aakash , Ali Munawar , Kang Yu 
Published: April 2024

AbstractThe swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.