General Framework For Analysis Research Articles

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis.

When surveys contain direct questions about sensitive topics, participants may not provide their true answers. Indirect question techniques incentivize truthful answers by concealing participants' responses in various ways. The Crosswise Model aims to do this by pairing a sensitive target item with a non-sensitive baseline item, and only asking participants to indicate whether their responses to the two items are the same or different. Selection of the baseline item is crucial to guarantee participants' perceived and actual privacy and to enable reliable estimates of the sensitive trait. This research makes the following contributions. First, it describes an integrated methodology to select the baseline item, based on conceptual and statistical considerations. The resulting methodology distinguishes four statistical models. Second, it proposes novel Bayesian estimation methods to implement these models. Third, it shows that the new models introduced here improve efficiency over common applications of the Crosswise Model and may relax the required statistical assumptions. These three contributions facilitate applying the methodology in a variety of settings. An empirical application on attitudes toward LGBT issues shows the potential of the Crosswise Model. An interactive app, Python and MATLAB codes support broader adoption of the model.

A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model

Abstract The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. Astronomers are turning to deep learning techniques to address this, but the methods are limited by their specific training sets, leading to considerable duplicate workloads too. Hence, as an example to present how to overcome the issue, we built a framework for general analysis of galaxy images, based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratio of galaxy images and the imbalanced distribution of galaxy categories, we have incorporated a Human-in-the-loop (HITL) module into our large vision model, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability to all the abovementioned tasks on galaxy images in the DESI legacy imaging surveys. Expressly, for object detection, trained by 1000 data points, our DST upon the LVM achieves an accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%; for morphology classification, to obtain AUC ~0.9, LVM plus DST and HITL only requests 1/50 training sets compared to ResNet18. Expectedly, multimodal data can be integrated similarly, which opens up possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-message astronomy.

On the security risk of pre-image attack on cancelable biometrics

Over the years, a number of biometric template protection schemes, often based on the notion of “cancelable biometrics”, have been proposed. An ideal cancelable biometric algorithm complies with four criteria: irreversibility, revocability, unlinkability, and performance preservation. Cancelable biometrics employs an irreversible but distance preserving transformation to convert the original biometric templates into protected templates. Matching in the transform domain can be accomplished due to the property of distance preservation. However, distance preservation also entails security issues, a point often overlooked in existing research. In this paper, we have conducted a comprehensive security analysis of distance preservation in cancelable biometrics for the first time. The analysis is based on a pre-image attack, which is launched to break the security of cancelable biometrics under Kerckhoffs’s assumption. Furthermore, we propose a general security analysis framework under the single and cross-transformation attacks, which also employs an information leakage estimation strategy based on mutual information as a complement. The experimental results performed on real face, iris, and fingerprint data demonstrate that the risks originating from the matching scores computed from the distance/similarity of two cancelable templates greatly compromise the security of cancelable biometric schemes, including the classic Biohashing, Index-of-max hashing, Non-linear multi-dimensional spectral hashing, Indexing-First-One hashing, Bloom Filter and Two-factor Protected Minutia Cylinder-Code. The security versus accuracy trade-off is discussed and recommendations for designing a biometric system secure against pre-image attacks are also proposed. The source code is available at github.com/biometricsecurity/CBrisks.

Exploring Blockchain Technology through a Modular Lens: A Survey

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications.

A General Analytical Framework for the Resolution of Near-Field Beamforming

A General Analytical Framework for the Resolution of Near-Field Beamforming

Effects of linear landscape elements on multiple ecosystem services in contrasting agricultural landscapes

Linear landscape elements, such as field margins, are agricultural practices whose adoption is supported by agri-environmental climate measures (AECMs). AECMs are meant to improve ecological conditions on farms and surrounding areas. The effectiveness of AECMs to enhance the supply of multiple ecosystem services (ESs) is still debated and knowledge on the resulting ESs bundles under different practices stemming from AECMs is still lacking. We aimed at assessing the potential of AECMs that promote the implementation of linear landscape elements to provide high levels of multiple ESs and at analyzing bundles at landscape level in different geographical contexts. We assessed the potential effects of linear landscape elements (woody, grassy, flower and a mix) on six ESs (food and feed provision, pollination, pest control, climate regulation, aesthetics, and habitat maintenance), combining scenarios and spatially explicit modelling approaches. Our results showed the positive effects of linear landscape elements on all regulating and cultural ESs. The more abundant the linear elements, the higher the overall ESs supply. However, the effect of linear landscape elements on multiple ESs depended on the types of linear elements and the geographical context of their implementation. When the supply of the ES was already high in the baseline situation, the changes induced by the implementation of the linear elements were much lower than when the baseline situation showed a lower initial supply of one or several of the ESs. Our analyses give insights on the efficiency of AECMs on multiple environmental targets. Our approach is a first step towards a general framework for an ex-ante integrated analysis of AECMs that can be used to design agri-environmental policies. From a more practical perspective, our results can form a basis for additional payments for AECMs. Our study also confirms the relevance of the EU biodiversity strategy that commits to ensure at least 10% of agricultural area as high-biodiversity landscape features such as linear landscape elements, and the relevance of the enhanced conditionality and eco-schemes in the reformed Common agricultural Policy targeting non-productive elements and biodiversity.

Behavioral selection in structured populations

The multilevel model of behavioral selection (MLBS) by Borgstede and Eggert (Behav Process 186:104370. 10.1016/j.beproc.2021.104370, 2021) provides a formal framework that integrates reinforcement learning with natural selection using an extended Price equation. However, the MLBS is so far only formulated for homogeneous populations, thereby excluding all sources of variation between individuals. This limitation is of primary theoretical concern because any application of the MLBS to real data requires to account for variation between individuals. In this paper, I extend the MLBS to account for inter-individual variation by dividing the population into homogeneous sub-populations and including class-specific reproductive values as weighting factors for an individual’s evolutionary fitness. The resulting formalism closes the gap between the theoretical underpinnings of behavioral selection and the application of the theory to empirical data, which naturally includes inter-individual variation. Furthermore, the extended MLBS is used to establish an explicit connection between the dynamics of learning and the maximization of individual fitness. These results expand the scope of the MLBS as a general theoretical framework for the quantitative analysis of learning and evolution.

Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models.

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences

Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

Semi-parametric tensor factor analysis by iteratively projected singular value decomposition

Abstract This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively projects tensor data onto the linear space spanned by the basis functions of covariates and applies singular value decomposition on matricized tensors over each mode. We establish the convergence rates of the loading matrices and the core tensor factor. The theoretical results only require a sub-exponential noise distribution, which is weaker than the assumption of sub-Gaussian tail of noise in the literature. Compared with the Tucker decomposition, IP-SVD yields more accurate estimators with a faster convergence rate. Besides estimation, we propose several prediction methods with new covariates based on the STEFA model. On both synthetic and real tensor data, we demonstrate the efficacy of the STEFA model and the IP-SVD algorithm on both the estimation and prediction tasks.

A general framework for qualitative analysis of Raman spectroscopy based on deep learning

Deep learning has become the prevailing method for qualitative analysis of Raman spectroscopy. However, for researchers and engineers without a background in computer science or artificial intelligence, the knowledge required can be challenging. This includes the underlying principles of deep learning and the features of frameworks such as PyTorch, TensorFlow, and Keras. To address this challenge, we summarize the Raman spectroscopy analysis process and design a suitable deep learning framework, including dataset creation, data preprocessing, data augmentation, deep learning model development, model training, model evaluation, model visualization analysis, and model deployment. We use four open-source Raman spectroscopy datasets, including binary classification and multi-classification examples, to explain the framework’s components and operation and provide comprehensive evaluation results. This framework has the potential to improve work efficiency and accelerate the research process.

Persian comedy whispers out loud: a critical discourse analysis of Persian televised stand-up comedy

This paper probes into the Iranian society through the ‘discourse access’ that Persian stand-up comedy provides. It is posited that Persian humorous discourse can be an effective and relatively safe communication toolkit to speak truth to power and address cultural deficits. Hence, it may be an insightful ‘point of entry’ into the below-the-surface construct of various social wrongs (e.g. social class inequality, cultural illiteracy). In particular, Persian stand-up comedy, as a young and burgeoning genre of Persian comedy, seems to be reliably reflecting such concerns in Iran. Within the general framework of critical discourse analysis (CDA), this article inspected into a corpus of stand-up comedy performances by the Iranian participants of the first and largest televised stand-up comedy contest in Iran. The corpus included 11 stand-up comedians and 14 performances from the three seasons of the contest (2016–2022). The reflexive thematic analysis of the stand-up routines unmasked a wealth of social criticism which is discussed under four major themes of ‘economic stress’, ‘social class inequality’, ‘more equal decision-makers’ and, ‘agents of change’. The paper elaborates on Persian stand-up comedy as a potent driving force within the society to effect individual and social changes. It contends that Persian comedy can greatly contribute to discourses of awareness and transformation within the Iranian society.

A Novel Approach Analysing the Dynamic Brain Functional Connectivity for Improved MCI Detection.

Dynamic functional connectivity (dFC) of the brain has been explored for the detection of mild cognitive impairment (MCI), preventing potential development of Alzheimer's disease. Deep learning is widely used method for dFC analysis but is unfortunately computationally expensive and unexplainable. Root mean square value (RMS) of the pairwise Pearson's correlation of the dFC is also proposed but is insufficient for accurate MCI detection. The present study aims at exploring the feasibility of several novel features for dFC analysis, and thus, reliable MCI detection. A public resting-state functional magnetic resonance imaging dataset containing healthy controls (HC), early MCI (eMCI), and late MCI (lMCI) patients was used. In addition to RMS, nine features were extracted from the pairwise Pearson's correlation of the dFC, inducing amplitude-, spectral-, entropy-, and autocorrelation-related features, and time reversibility. A Student's t-test and a least absolute shrinkage and selection operator (LASSO) regression were employed for feature dimension reduction. A SVM was then adopted for two classification objectives: HC vs. lMCI and HC vs. eMCI. Accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve were calculated as performance metrics. 6109 out of 66700 features are significantly different between HC and lMCI and 5905 between HC and eMCI. Besides, the proposed features produce excellent classification results for both tasks, outperforming most of the existing methods. This study proposes a novel and general framework for dFC analysis, providing a promising tool for the detection of many neurological brain diseases using different brain signals.

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders.

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model accuracy (MA) under various attacks. It was shown that FedCut's averaged MA is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model accuracy (MA), FedCut is 17.6% to 69.5% better than these methods.

A General Analysis of Resonant Switched Capacitor Converters Using Peak Energy Storage and Switch Stress Including Ripple

This work presents a general analytical framework enabling large-signal characterization of resonant switched-capacitor (ReSC) power converters that accounts for passive component voltage and current ripple, for operation at and above resonance. From this, appropriate phase durations for minimized rms currents are derived, in addition to expressions for total passive component volume using an intuitive peak energy method. An example hardware prototype validates both the derived waveforms and timings—as well as total passive volume—through three comparable hardware configurations, one of which minimizes passive component volume. In addition, the proposed technique formulates analytical expressions for both rms currents and peak blocking voltages, facilitating refined loss estimation and component selection. Subsequent calculation of the large-signal Volt–Amp (VA) switch stress metric allows a more accurately quantified trade-off between active and passive components compared to prior work, which has not fully accounted for ripple. Four common ReSC topologies are exemplified throughout, with topology specific parameters documented for reference.

Fundamental bound on epidemic overshoot in the SIR model.

We derive an exact upper bound on the epidemic overshoot for the Kermack-McKendrick SIR model. This maximal overshoot value of 0.2984 · · · occurs at [Formula: see text]. In considering the utility of the notion of overshoot, a rudimentary analysis of data from the first wave of the COVID-19 pandemic in Manaus, Brazil highlights the public health hazard posed by overshoot for epidemics with R0 near 2. Using the general analysis framework presented within, we then consider more complex SIR models that incorporate vaccination.

Semiotics... Introduction to "Signs Computing"

Objectives: Figuring out the importance of the semiotic perspective in computing signs. in addition to setting up databases that can be invested in designing flexible applications of the automated diacritization, automated translation.
 Methods: It is designed as follows a theoretical part dedicated to evoking some of the manifestations of the flexibility of semiotics, as well as mentioning some studies that have taken semiotics as their general framework for analysis, such as kinaesthetics and computational semiotics. An applied part in which a model based on semiotic concepts is presented to set up a synthetic database for the Arabic language, the automated diacritization as a model.
 Results: With its theoretical concepts and applied procedures, semiotics can serve as a basic entry for signs computing.
 Conclusions: The study recommends researchers to evoking semiotics during the automated processing of the Arabic language and its culture, both theoretical and applied, to overcome the inadequacies of the Arabic language applications and software.

A general FSI framework for an effective stress analysis on composite wind turbine blades

The fluid-structure interaction (FSI) technique has been extensively used and developed in the past decades. Commonly, the reduced-order models are used in FSI analyses to assure the numerical robustness and efficiency. However, due to the increasing demand for higher numerical resolutions in modern wind turbine composite blade applications, intrinsic limitations of reduced-order models, such as their inability to account for complex aerodynamic flow interactions, multi-motion couplings, and sophisticated composite properties, have become the weaknesses in existing reduced-order FSI approaches. In this study, we propose a general FSI framework, which combines the advantages of high-fidelity Computational Fluid Dynamics (CFD) and robust Multi-Body Dynamics (MBD) methods, and detailed Finite Element Analysis (FEA) for analysing the detailed stress distributions on the composite structures. The results of predicted dynamics and the Von Mises stress on the composite blade structures under given operation condition are compared and reasonably agreed with the literature results, with a significant computational cost reduction by nearly 25% is achieved. The proposed FSI framework can be a general approach to investigate the multi-physical interactions where the composite structure specifications are involved, coupling with complex dynamic motions in three-dimensional space.

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.