Methodology For Generation Research Articles

Breathing New Legacies Forward

This article unravels the meaning, purpose, and working philosophy underpinning Arrivals Legacy Voice, which is an emerging practice coming out of the Arrivals Legacy Project=. Arrivals Legacy Project founder Diane Roberts (Garifuna, Caribbean Canadian) engages her collaborators in reflection on specific moments of discovery and emerging themes and/or collisions that have influenced and continue to influence the ongoing development of our shared practice. The voices of storyteller and film-maker Rosemary Georgeson (Coast Salish), voice and speech instructor and Rolfer Lopa Sircar (Bengali, Canadian), voice/movement educator and inter-arts creator/performer Gerry Trentham (Scottish, English), and facilitator, writer, and systemic and experiential therapist Jude Wong (Chinese, German, Danish) address the central question: What new legacies breathe us forward, and what must we leave behind? Using the generative methodology of Legacy Voice, Diane and her collaborators draw on the personal to reveal their passion for un/recovering root cultural practices as a liberation from colonial performative inheritances.

Compact Modeling of Advanced Gate-All-Around Nanosheet FETs Using Artificial Neural Network.

As the architecture of logic devices is evolving towards gate-all-around (GAA) structure, research efforts on advanced transistors are increasingly desired. In order to rapidly perform accurate compact modeling for these ultra-scaled transistors with the capability to cover dimensional variations, neural networks are considered. In this paper, a compact model generation methodology based on artificial neural network (ANN) is developed for GAA nanosheet FETs (NSFETs) at advanced technology nodes. The DC and AC characteristics of GAA NSFETs with various physical gate lengths (Lg), nanosheet widths (Wsh) and thicknesses (Tsh), as well as different gate voltages (Vgs) and drain voltages (Vds) are obtained through TCAD simulations. Subsequently, a high-precision ANN model architecture is evaluated. A systematical study on the impacts of ANN size, activation function, learning rate, and epoch (the times of complete pass through the entire training dataset) on the accuracy of ANN models is conducted, and a shallow neural network configuration for generating optimal ANN models is proposed. The results clearly show that the optimized ANN model can reproduce the DC and AC characteristics of NSFETs very accurately with a fitting error (MSE) of 0.01.

Methodology of data generation and calculation of erosion rates applied to littoral areas: Evolution of the Andalusian shoreline on exposed beaches during the 21st century (2001-2019)

This paper describes the methodology used for the data collection and calculation of erosion rates and presents the recent results for the period 2001-2019 for all the beaches of the Andalusian shoreline. This period is divided into two subperiods (2001-2011 and 2001-2019) to detect possible trend changes during the study years. The proxy corresponding to the internal limit of the backshore has been used, which is very useful for medium to long-term coastal erosion rates. The results show the rates calculated for this proxy, which reveal a high presence of artificially stabilised sectors where the inward migration of the shoreline associated with it is hindered, together with an intensification of the retreat rates in natural sections. Similarly, there is a clear differentiation between a relatively dynamic Atlantic façade with a higher percentage of erosive sectors and a Mediterranean façade highly conditioned by anthropic presence, with a lower percentage but a higher intensity in their values.

Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT

Due to the difficulties in retrieving both the time-dependent shapes of the vessels and the generation of numerical meshes for such cases, most of the simulations of blood flow in the cardiac arteries use static geometry. The article describes a methodology for generating a sequence of time-dependent 3D shapes based on images of different resolutions and qualities acquired from ECG-gated coronary artery CT angiography. The precision of the shape restoration method has been validated using an independent technique. The original proposed approach also generates for each of the retrieved vessel shapes a numerical mesh of the same topology (connectivity matrix), greatly simplifying the CFD blood flow simulations. This feature is of significant importance in practical CFD simulations, as it gives the possibility of using the mesh-morphing utility, minimizing the computation time and the need of interpolation between boundary meshes at subsequent time instants. The developed technique can be applied to generate numerical meshes in arteries and other organs whose shapes change over time. It is applicable to medical images produced by other than angio-CT modalities.

Methodology for power generation using exhaust fan cum micro wind turbines in high-rise buildings

ABSTRACT At present, electricity demand and pollution are the main concerns all over the world. With the help of this paper, a small contribution to energy production and pollution in the environment can be reduced. Exhaust Fan Cum Micro Wind Turbine (EFCMWT) is used for power generation. In this paper, I study a novel approach of EFCMWT for power generation. It has been installed at a height of 14.11 m at the windows near the toilet in a high rise building at Kosi Hostel NIT Patna. The EFCMWT generates power at a height of 14.11 m in the building. With respect to wind speed, various parameters are analyzed, such as how much DC voltage is generated, how much DC current is generated and also it is analyzed that how much EFCMWT power is generated and tip speed ratio variation with respect to wind speed. On windows where EFCMWT is installed, place anemometer near EFCMWT and analyzed wind power. The power coefficient with respect to wind speed is also analyzed for EFCMWT. Maximum DC voltage is 16 V, maximum DC current is 0.076 A, tip speed ratio is 0.45 and maximum EFCMWT power is 1.216 W which is achieved at wind speed of 7 m/s.

RoseCliff Algorithm: Making Passwords Dynamic

Authentication in the digital landscape faces persistent challenges due to evolving cyber threats. Traditional text-based passwords, which are vulnerable to various attacks, necessitate innovative solutions to fortify user systems. This paper introduces the RoseCliff Algorithm, which is a dual authentication mechanism designed to enhance resilience against sophisticated hacking attempts and to continuously evolve stored passwords. The study explores encryption techniques, including symmetric, asymmetric, and hybrid encryption, thereby addressing the emerging threats posed by quantum computers. The RoseCliff Algorithm introduces introduces dynamism into passwords that allows for more secured communication across multiple platforms. To assess the algorithm’s robustness, potential attacks such as brute force, dictionary attacks, man-in-the-middle attacks, and machine learning-based attacks are examined. The RoseCliff Algorithm, through its dynamic password generation and encryption methodology, proves effective against these threats. Usability evaluation encompasses the implementation and management phase, focusing on seamless integration, and the user experience, emphasizing clarity and satisfaction. Limitations are acknowledged, thus urging further research into encryption technique resilience, robustness against breaches, and the integration of emerging technologies. In conclusion, the RoseCliff Algorithm emerges as a promising solution, thereby effectively addressing the complexities of modern authentication challenges and providing a foundation for future research and enhancements in digital security.

Business Model to the LASSE performance at Microgrid environment

The Microgrid (MG) scenario has a socioeconomic and technical character seeking to promote the sustainable, technological and economic advance. Through the MG, the consumer may set up its own business with a the sale of its own energy, thus, the Automation and Electrical Systems Simulation Laboratory (LASSE) is interested in participating of the MG market. Creating and/or innovating a business requires knowledge of the application environment. This reality has as consequence the determination of strategies for the company’s management in the vision of the market dynamics. This research aims to define the necessary requirements for the performance of LASSE in the MG environment and to develop the systemic LASSE diagram associating the structural elements of the business model with the characteristics of LASSE. The information was generated through the Business Model Generation (BMG) methodology, among which the Business Model Canvas tools stand out. The research interests LASSE for creating ideological guidelines for members of the LASSE Smart Energy Project. In this way, it is possible to clearly understand the process of introduction to the MG environment, as well as the necessary characteristics for the elaboration of Business Models (BM).

Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery

ABSTRACT This study introduces a cost-effective and automated methodology to swiftly and accurately gauge disaster-induced land cover changes, harnessing satellite imagery and machine learning. Using open-source platforms like Google Earth Engine, the approach streamlines training sample generation and assesses diverse machine learning algorithms on pre- and post-disaster Landsat images. The innovation lies in its semi-automatic training sample generation methodology, reducing time and manual effort for accurate land cover mapping and addressing the challenge of crafting training samples in disaster settings. The methodology was tested on the 2010 Chilean earthquake and tsunami, revealing a 45% increase in sediment cover and a 15% reduction in water cover, underlining vast landscape alterations. SVM stood out for its consistent accuracy. To validate the methodological robustness and broaden its applicability, we replicated the procedures using diverse land cover collection, including ESA WorldCover 10 m and Dynamic World. This comprehensive validation encompassed varied catastrophic events, extending the methodology’s relevance beyond its original scope. The consistent findings across different scenarios underscored the method’s robustness and underscored its potential for broader utilization. This streamlined, cost-effective approach suits multiple land cover products, setting the stage for constant Earth feature monitoring. In light of escalating natural disasters, the synergy of machine learning and geospatial technology spotlighted here offers promising avenues for improved disaster preparedness and response.

Machine learning-aided generative design methodology for a Martian regolith habitation shell

This study presents a novel design methodology to automate the development process of a regolith habitation shell for a crewed mission on Mars. The integration of generative design, simulation tools, and data science methodologies allows for a systematic and comprehensive iterative design process, which facilitates the development of dual-habitation shells that can resist harsh conditions on Mars.The study establishes a methodology consisting of four fundamental sections. Initially, the environmental conditions on the surface of Mars are assessed and quantified as structural loads, so that the generative design tools effectively transform these stresses into design parameters that might be used in finite element modeling (FEM). Next, this study employs human factors derived from existing literature as input for Social Network Analysis (SNA) and the Label Propagation Algorithm (LPA) to address important design inquiries concerning functional zoning, layout configuration, geometry generation, and habitability variables. These inquiries aim to inform the decision-making process regarding autonomous typology design. Furthermore, the use of space syntax, physics simulations, and Voronoi algorithms improves the accuracy of the habitation planning process by generating floor designs and habitable volumes that align with the distinctive requirements and preferences of individuals. Lastly, the regolith habitation shell is constructed using structural simulation techniques that incorporate Mars' environmental pressures as design considerations. By employing Karamba3D and numerical finite element modeling (FEM) techniques, the structural performance of the regolith habitation shell under the environmental conditions on Mars is simulated.Consequently, this study introduces three distinct habitation shell designs that were developed and exposed to structural simulations to evaluate the effects of environmental stresses on Mars. The study explores the conjunction between generative design, data science, and structural simulations by employing a systematic and comprehensive design process. Its objective is to provide insights into the impact of environmental pressures on habitation shells and improve our understanding of design outcomes for future exploration on Mars.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Abstract The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improving the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods which combine molecular simulations and deep learning to expand the search of protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

New generation Energy Performance Certificate: Development and application in an Italian case study as an EU proof of concept

The Energy Performance of Buildings Directive recast, voted in March 2024, aims to boost the amelioration of the Energy Performance Certificate (EPC) with the introduction of new indicators in a dedicated template (Annex V), as a key tool towards the decarbonization of the building sector, in line with the Green Deal’s climate neutrality goals. In this context, the H2020 Energy Performance Certificate Recast project develops a new generation of EPC focused on existing residential buildings. The paper presents the state of progress of the Energy Performance Certificate Recast methodology, comparing it with the standard national procedure in different EU partner countries. The novelty of the work is the testing methodology of a new generation of this tool, which aims to overcome the barriers identified in the current EPC procedure across Europe and to validate the reliability and user-friendliness of this new approach. The final goal of the work is to prove the applicability and potentialities of this innovative approach, providing a clear and punctual presentation of each step of the assessment and the testing methodology of the new generation of Energy Performance Certificate, coupled with its application in an Italian case study as a Proof of Concept.

Design Hormones: Nature-Inspired Generative Design Methodology for Additive Manufacturing

To meet sustainable goals in various engineering sectors, we urgently need innovative design strategies. This work presents a nature-inspired generative design methodology to support innovation-driven sustainable engineering design. It allows designers to create unpredicted but valuable components that can be directly integrated by modern manufacturing methods such as additive manufacturing. In this approach, the design of components is redefined as a series of elemental design instructions and emergence mechanisms that react to the design environment to generate innovative designs. Inspired by plant growth and development from nature, a set of “Design Hormones” is proposed which influence “design growth and development” through analogues of biological mechanisms. The design grows and develops from the actions of the hormones and genes, which do not know anything about the emergent shape or form of the design, they simply respond to the environment. The hormones are encoded with genes in a design seed, and different designs can emerge from the same seed in response to different environments based on their influence on the seed. A practical algorithm is then presented to implement the emergence of design from a simple seed. A range of examples are tested to demonstrate the effectiveness of the proposed approach. This work contributes foundational nature-inspired generative design theory and processes to promote innovative design for manufacturing in a sustainable future towards the next industrial revolution.

Virtual Topologies for Populating Overhead Low-Voltage Broadband over Powerlines Topology Classes by Exploiting Neural Network Topology Generator Methodology (NNTGM) - Part 1: Theory

Based on a set of indicative overhead low voltage broadband over power lines (OV LV BPL) topologies of respective OV LV BPL topology classes, Neural Network Topology Generator Methodology (NNTGM) is theoretically proposed in this paper, so that its generated OV LV BPL topologies (NNTGM OV LV BPL topologies) can populate the OV LV BPL topology classes. Given the indicative topology of the OV LV BPL topology class, the NNTGM OV LV BPL topologies can be statistically familiar with the corresponding indicative OV LV BPL topology in terms of the theoretical channel attenuation behavior. Actually, NNTGM is based on the reverse procedure of the Neural Network Identification Methodology for the distribution line and branch Line Length Approximation (NNIM-LLA); say, NNTGM generates theoretical channel attenuation behaviors given the number of branches, the distribution line lengths and the branch line lengths, when appropriate NNTGM default operation settings are assumed. The statistical familiarity between the examined indicative OV LV BPL topology and an NNTGM OV LV BPL topology is examined after the application of appropriate channel attenuation metrics. On the basis of the channel attenuation metrics, class maps are theoretically defined so that the relative positions of indicative OV LV BPL topologies and their respective NNTGM OV LV BPL topologies (NNTGM virtual topologies) can be further studied.

Dual Graph Convolutional Networks for Chest X-ray Report Generation

Chest X-ray report generation aims to generate expert-level radiology diagnostic reports that can be used in clinics on given chest X-ray images. Automatic chest X-ray report generation can assist doctors in writing radiology reports, which can not only improve the diagnostic efficiency of radiologists and meet the needs of a large number of patients but also remind some inexperienced radiologists of misdiagnoses or missed diagnoses. As a visual-language task, chest X-ray report generation can be achieved by existing image captioning methods. However, chest X-ray imagery exhibits a high degree of similarity, wherein diagnostically crucial abnormalities often manifest in minute regions of the images, posing a formidable challenge in discerning subtle visual differences and distinctive features. Further, the medical findings have been widely applied as labels to guide the image encoding module to focus on the salient regions in the image using the attention mechanism. However, existing methods neglect the relations between class features corresponding to different medical findings and the relationship between such local features and global features of the image. To tackle these challenges, we propose dual graph convolution networks (DGCN) that novelly incorporate the grid feature graph and class feature graph into the report generation process. In detail, the grid graph is used to explore the relationship between the grid regions on the whole image, and the class feature graph is used to mine the relationship between different medical findings (Dual GCN). Moreover, to explore the relationship between global features, such as grid features, and local features, such as class features, we introduce a multi-head attention module to help fuse different types of features and mine more useful relationship information (FFARM). Extensive experiments on the IU X-ray dataset underscore the substantial superiority of our model over the state-of-the-art chest X-ray report generation methodologies. Further ablation analyses reveal that both the class feature graph and the grid feature graph contribute significantly to enhancing the efficacy of chest X-ray report generation.

Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study’s results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

STRATEGY ON HIGH-DEFINITION POINT CLOUD MAP CREATION FOR AUTONOMOUS DRIVING IN HIGHWAY ENVIRONMENTS

Abstract. In recent years, a lot of researchers have been trying the development of efficient ways to create HD maps with centimeter-level precision. Mobile mapping system (MMS) produce 3D HD point cloud map of the surrounding by integrating navigation (i.e., direct georeferencing or DG process) and high-resolution imaging sensor data. Unfortunately, in partially environments, the provided accuracy of the GNSS system degrades dramatically. In order to constraint the drift and correct the georeferenced point cloud map, ground control points (GCPs) are placed along the road. Moreover, there are approaches which use laser-based point cloud registration techniques to construct the point cloud map. However, all promising mapping techniques which currently use the laser as the core sensor for mapping the high-definition point cloud map may not be promised to construct the point cloud map in partially or unfriendly environments. As a literature review and result, a suitable approach for creating the promising point cloud map is to combine the INS/GNSS navigation solution, LiDAR matching techniques, and GCPs. Thus, this study introduces the HD point cloud map generation method that can potentially help researchers create personalized and globalized HD point cloud maps and develop new HD point cloud map generation methodologies.

Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent Recombination.

The use of black box machine learning models whose decisions cannot be understood limits the acceptance of predictions in interdisciplinary research and camouflages artificial learning characteristics leading to predictions for other than anticipated reasons. Consequently, there is increasing interest in explainable artificial intelligence to rationalize predictions and uncover potential pitfalls. Among others, relevant approaches include feature attribution methods to identify molecular structures determining predictions and counterfactuals (CFs) or contrastive explanations. CFs are defined as variants of test instances with minimal modifications leading to opposing predictions. In medicinal chemistry, CFs have thus far only been little investigated although they are particularly intuitive from a chemical perspective. We introduce a new methodology for the systematic generation of CFs that is centered on well-defined structural analogues of test compounds. The approach is transparent, computationally straightforward, and shown to provide a wealth of CFs for test sets. The method is made freely available.

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

Simplified Methods for Generative Design That Combine Evaluation Techniques for Automated Conceptual Building Design

Smart design and construction have emerged as pivotal forces in the construction industry. Numerous studies have been conducted in the realm of design optimization, using artificial intelligence data-driven approaches and optimization theories. This increase in research has sparked interest in generative design, a process that automatically generates algorithm-based design alternatives, thereby reducing human effort and time by a significant margin. The objective of this study was to explore the potential of generative design to boost productivity within architectural practices and reduce redundant and unnecessary tasks for an aging construction workforce. Specifically, it illustrates the process of selecting superior alternatives by generating various three-dimensional layouts, using a generative design methodology. This occurs during the creation of a building layout concept with subsequent partial evaluations. The methodology of this study was mainly divided into four stages: objective setting, design algorithm development, the establishment of evaluation methodology, and the comparison of the results’ values. The findings of this study confirmed that the design algorithm and evaluation methodology form a single loop, generating a multitude of design alternatives that satisfy the algorithm designer’s evaluation criteria.

Controlled generation of high-frequency liquid metal microdroplets

Liquid metal (LM) microdroplets with high conductivity and fluidity have drawn extensive attention in the field of electronics, biomedicine, and catalysis. The properties of LM droplets, e.g., melting points, electrical actuation, and stability, are affected by their sizes. Continuous generation of LM microdroplets with precisely controlled size will benefit their applications, which however, remains an outstanding challenge. In this study, we have designed a scalable high frequency droplet generator for the liquid metal (EGaIn) that works on the principle of manipulating the electrohydrodynamic behaviour of EGaIn. By applying electric potentials < − 0.75 V vs Ag/ AgCl, EGaIn droplets with uniform sizes can be rapidly and continuously generated. The electric potential effectively tunes the thickness of the oxide layer on the EGaIn surface which in turn modulates the surface tension of the EGaIn and induces the generation of consistent sized EGaIn droplets. The size of the EGaIn microdroplets can be varied between 40 and 200 µm, depending on the electric potential applied and the diameter of the exit orifice. Droplet generation using this method requires relatively low energies, i.e., 2.77 × 10-7 kWh, and is coupled with high frequency droplet generation ∼ 60 droplet/min. The scalability of this droplet generation methodology can be further increased via design of an array of exit orifice. With the presence of surfactants, these EGaIn droplets can remain well-dispersed without coalescing. This considerably widens their capability for further applications.