Isocyanate-derived organic aerogels: polyurethanes, polyureas, polyamides and polyimides

Aerogels are 3D assemblies of nanoparticles with high open porosity and high surface area, and are pursued for their low density, low thermal conductivity, low dielectric constant and high acoustic attenuation. The foundation for those exceptional properties is their complex hierarchical solid framework (agglomerates of porous, fractal secondary nanoparticles). On the down side, however, aerogels are also fragile materials. The mechanical strength of silica aerogels has been improved by crosslinking the framework with organic polymers. The crosslinking polymer has been assumed to form a conformal coating on the surface of the skeletal framework bridging covalently the elementary building blocks. However, the drawback of this method is the lengthy post-gelation crosslinking process. Since the exceptional mechanical properties of polymer crosslinked aerogels are dominated by the crosslinking polymer, it was reasoned that purely organic aerogels with the same nanostructure and interparticle connectivity should behave similarly. That was explored and confirmed by organic aerogels derived from multifunctional isocyanates through reaction with (a) alcohols (polyurethanes); (b) water (polyureas); (c) carboxylic acids (polyamides); and, (d) acid anhydrides (polyimides). All processes are invariably single-step, one-pot and take place at room temperature or slightly elevated temperatures. The resulting materials are robust, they have very wide range of densities and their nanomorphologies vary from fibrous to particulate or both. By relating the molecular functional group density with the functional group density on the nanoparticle surfaces, this study established that in order for three-dimensional (3D) assemblies of nanoparticles to form rigid nanoporous frameworks, they have first and foremost to be able to develop strong covalent bonding with one another. Thus, all macroscopic properties of an aerogel depend on the surface functionality of the \u27growing colloidal particle\u27. Those findings are relevant to the rational design of 3D nanostructured matter, not limited to organic aerogels. The materials synthesized in this study should have a broad range of applications from flexible thermal and acoustic insulations to ballistic protection --Abstract, page v

Ultrasonic modification of metals - influence of ultrasound on the density of geometrically necessary dislocations

Ultrasonic processes are typically energy time and cost efficient and recently of high interest, especially for joining. However, the sonication of materials is often associated with phenomena that remain largely unresolved to date, such as increased ductility during sonication and microstructural changes. To gain a deeper understanding of the elementary processes contributing to increased ductility through ultrasonic sonication, this study investigates the modification of metal by ultrasound and its impact on the density of geometrically necessary dislocations (GNDs). The presence of a large grain structure in IN617 allowed the effect of ultrasound to be more easily observed and analysed. Therefore, samples of IN617 were used for study. The effect of ultrasonic treatment on the density of GNDs in IN617 was analysed using Electron Backscatter Diffraction (EBSD) measurements. Additionally, an E–m model and finite element modelling (FEM) were employed to gain deeper insights into grain behaviour. The FEM model was successfully applied by correlating the mechanical behaviour predicted by the E–m model based on Young’s modulus and the Schmid factor with experimental observations. A combination of experimental data and computational modelling was used to analyse the effect of ultrasonic treatment on dislocation dynamics. EBSD data from three different samples were used to simulate the material’s response to various mechanical and ultrasonic loading conditions. The analysis revealed distinct variations in GND density across the samples. Many grains exhibited a unique response under different loading conditions, as observed through Young’s modulus, Schmid factor, resolved shear stress, and plastic strain. The results indicate that ultrasonic treatment influences dislocation behaviour by either promoting stress relaxation or increasing local hardness. A geometrically necessary dislocation (GND) analysis was conducted to explore the effect of plastic deformation on three different samples (N1, N2, and N3) of IN617 alloy after sonication was applied. Several software tools, including MATLAB, MTEX tool box, Origin, GMSH, Python and Dassault Abaqus were employed to assist in the analysis. Three samples were analysed under different conditions: in the first case, the initial sample was examined; for the second case, the sample was subjected to a force of 400 N; for the third case, a force ranging from 100 to 200 N was applied over a period of 50 milliseconds; and for the fourth case, the same force was applied for a period of 5000 milliseconds. The GND analysis of all these cases was carried out using Electron Backscatter Diffraction (EBSD). Additionally, various plots, such as plastic strain, resulting shear stress, von Mises stress, and maximum principal stress, were generated using ABAQUS. Other important data, including Young’s modulus (E), Schmid factor (m), and the product of Young’s modulus and Schmid factor (.), were also plotted. These graphs were compared, and a comprehensive study was conducted to understand the likelihood of plastic deformation under different conditions. By conducting the aforementioned analysis and detailed study, a comprehensive understanding of the influence of ultrasound on the density of geometrically necessary dislocations (GNDs) in the nickel-based super-alloy IN617 was achieved

Predictive analytics in education: machine learning approaches and performance metrics for student success – a systematic literature review

Higher education institutions rely on student performance to improve grades and enhance academic outcomes. Universities face challenges in evaluating student achievement, providing high-quality instruction, and analyzing performance in a dynamic and competitive context. However, due to limited research on prediction techniques and the critical factors influencing performance, making accurate forecasts is challenging. The utilization of educational data and machine learning has the potential to improve the learning environment. Ensemble models in educational data mining enhance accuracy and robustness by combining predictions from multiple models. Approaches such as bagging and boosting effectively mitigate the risk of overfitting. Machine learning techniques, including Support Vector Machines, Random Forests, K-Nearest Neighbors, Artificial neural networks, Decision Trees, and convolutional neural networks, have been employed in performance prediction. In this study, we examined 85 papers that focused on student performance prediction using machine learning, data mining, and deep learning techniques. The thorough analysis underscores the importance of various factors in forecasting academic performance, offering valuable insights for improving educational strategies and interventions in higher education contexts

Analyzing the impact of brand resonance on consumer purchase intentions for fast moving consumer goods: an empirical study

In the dynamic and highly competitive landscape of the Fast-Moving Consumer Goods (FMCG) industry, a multitude of companies are in a constant race, each one striving to outdo the others. This paper aims to delve deeper into the role of brands within this context. It seeks to explore and understand the significance of brands and how they can be leveraged to succeed in the FMCG sector. An extensive examination of the prevailing studies regarding this topic unveils a distinct void in research, especially regarding the notion of brand resonance. Despite its importance, there seems to be a lack of comprehensive research on this topic. It seeks to understand what elements play a crucial role in creating brand resonance and how it, in turn, impacts consumer purchasing decisions. To achieve this, the study employs a quantitative research approach. Data was collected through structured questionnaires, designed to gather relevant information from the respondents. The collected data was then analyzed using robust statistical tools. Confirmatory Factor Analysis (CFA) served to validate the measurement model, while Multiple Regression Analysis was utilized to grasp the connections among the variables. The findings of the study suggest a favourable correlation between brand resonance and intentions to purchase. These findings offer valuable insights for marketers operating in the FMCG sector. Understanding the factors that contribute to brand resonance can help them devise effective branding strategies, ultimately leading to increased sales and market share. Moreover, the study proposes potential paths for future investigation in this domain, thereby enriching the current understanding of the subject matte

A Quality of Experience-based Recommender System for E-learning Resources

Web services are a rapidly developing and generally acknowledged technology across all areas of management. Independent software systems that can be shared and called from anywhere online. The creation of educational tools (such LMSs, MOOCs, and e-learning) now typically makes use of web services. Having these learning tools readily accessible online is a great method to acquire and disseminate information. The primary objective of this paper is to describe how web services can effectively manage educational resources by leveraging Quality of Experience and to develop an effective E-learning recommender system in the context of web services that help the user choose a course based on his needs in terms of availability, cost, and reputation

TUSH-Key: Transferable User Secrets on Hardware Key

Passwordless authentication was first tested for seamless and secure merchant payments without the use of passwords or pins. It opened a whole new world of authentications giving up the former reliance on traditional passwords. It relied on the W3C Web Authentication (WebAuthn) and Client to Authenticator Protocol (CTAP) standards to use the public key cryptosystem to uniquely attest a user's device and then their identity. These standards comprise of the FIDO authentication standard. As the popularity of passwordless is increasing, more and more users and service providers are adopting to it. However, the concept of device attestation makes it device-specific for a user. It makes it difficult for a user to switch devices. FIDO Passkeys were aimed at solving the same, synchronizing the private cryptographic keys across multiple devices so that the user can perform passwordless authentication even from devices not explicitly enrolled with the service provider. However, passkeys have certain drawbacks including that it uses proprietary end to end encryption algorithms, all keys pass through proprietary cloud provider, and it is usually not very seamless when dealing with cross-platform key synchronization. To deal with the problems and drawbacks of FIDO Passkeys, the paper proposes a novel private key management system for passwordless authentication called Transferable User Secret on Hardware Key (TUSH-Key). TUSH-Key allows cross-platform synchronization of devices for seamless passwordless logins with FIDO2 specifications

EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning

Voice assistants like Siri, Google Assistant, Alexa etc. are used widely across the globe for home automation, these require the use of special phrases also known as hotwords to wake it up and perform an action like "Hey Alexa!", "Ok Google!" and "Hey Siri!" etc. These hotwords are detected with lightweight real-time engines whose purpose is to detect the hotwords uttered by the user. This paper presents the design and implementation of a hotword detection engine based on one-shot learning which detects the hotword uttered by the user in real-time with just one or few training samples of the hotword. This approach is efficient when compared to existing implementations because the process of adding a new hotword in the existing systems requires enormous amounts of positive and negative training samples and the model needs to retrain for every hotword. This makes the existing implementations inefficient in terms of computation and cost. The architecture proposed in this paper has achieved an accuracy of 94.51%.Comment: 9 pages, 17 figure

Integrating Predictive Analytics and Deep Neural Networks for Early Lung Cancer Diagnosis

One of the leading causes of cancer-related mortality globally is lung cancer; hence, early and effective screening methods are crucial. This work combines advanced deep learning models with predictive analytics to improve the early detection of lung cancer. The lung cancer histopathological images dataset is used to analyze histopathological slides and clinical data using a range of models, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM) networks, feedforward neural networks (FNN), and deep reinforcement learning (DRL). Because CNN can extract spatial characteristics, it performs better than the other models in accurately categorizing tissues that are malignant and those that are not. While FNN is a supplementary tool for incorporating non-image clinical metadata, LSTM and RNN models are investigated for their capacity to manage sequential patterns within patient data. By mimicking clinical operations, improving diagnostic accuracy, and lowering false positives, DRL streamlines decision-making processes. This study demonstrates the revolutionary potential of deep learning-powered predictive analytics in the early detection of lung cancer. These techniques open the door for AI-driven advancements in customized medicine and precision oncology by increasing diagnosis accuracy and facilitating prompt therapies. Prospective avenues for future research are provided by the further integration of hybrid systems and multimodal data

Carbon Responder: Coordinating Demand Response for the Datacenter Fleet

The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement demand response (DR) by adjusting their load based on grid signals. However, this presents challenges for private datacenters with diverse workloads and services. One of the key challenges is efficiently and fairly allocating power curtailment across different workloads. In response to these challenges, we propose the Carbon Responder framework. The Carbon Responder framework aims to reduce the carbon footprint of heterogeneous workloads in datacenters by modulating their power usage. Unlike previous studies, Carbon Responder considers both online and batch workloads with different service level objectives and develops accurate performance models to achieve performance-aware power allocation. The framework supports three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and Decentralized DR. We evaluate Carbon Responder polices using production workload traces from a private hyperscale datacenter. Our experimental results demonstrate that the efficient Carbon Responder policy reduces the carbon footprint by around 2x as much compared to baseline approaches adapted from existing methods. The fair Carbon Responder policies distribute the performance penalties and carbon reduction responsibility fairly among workloads

An overview of progress in electrolytes for secondary zinc-air batteries and other storage systems based on zinc

The revived interest and research on the development of novel energy storage systems with exceptional inherent safety, environmentally benign and low cost for integration in large scale electricity grid and electric vehicles is now driven by the global energy policies. Within various technical challenges yet to be resolved and despite extensive studies, the low cycle life of the zinc anode is still hindering the implementation of rechargeable zinc batteries at industrial scale. This review presents an extensive overview of electrolytes for rechargeable zinc batteries in relation to the anode issues which are closely affected by the electrolyte nature. Widely studied aqueous electrolytes, from alkaline to acidic pH, as well as non-aqueous systems including polymeric and room temperature ionic liquids are reported. References from early rechargeable Zn-air research to recent results on novel Zn hybrid systems have been analyzed. The ambition is to identify the challenges of the electrolyte system and to compile the proposed improvements and solutions. Ultimately, all the technologies based on zinc, including the more recently proposed novel zinc hybrid batteries combining the strong points of lithium-ion, redox-flow and metal-air systems, can benefit from this compilation in order to improve secondary zinc based batteries performance.Basque Country University (ZABALDUZ2012 program), and the Basque Country Government (Project: CIC energiGUNÉ16 of the ELKARTEK program) and the European Commission through the project ZAS: “Zinc Air Secondary innovative nanotech based batteries for efficient energy storage” (Grant Agreement 646186