Significant Information Research Articles

Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex datasets but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image datasets, such as Persian handwritten images, have numerous pixels, making statistical inference difficult. Such high-dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real dataset while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performs relatively well in predicting the labels of new observations for this multi-class dataset.

A Time-Series-Based Sample Amplification Model for Data Stream with Sparse Samples

The data stream is a dynamic collection of data that changes over time, and predicting the data class can be challenging due to sparse samples, complex interdependent characteristics between data, and random fluctuations. Accurately predicting the data stream in sparse data can create complex challenges. Due to its incremental learning nature, the neural networks suitable approach for streaming visualization. However, the high computational cost limits their applicability to high-speed streams, which has not yet been fully explored in the existing approaches. To solve these problems, this paper proposes an end-to-end dynamic separation neural network (DSN) approach based on the characteristics of data stream fluctuations, which expands the static sample at a given moment into a sequence of sample streams in the time dimension, thereby increasing the sparse samples. The Temporal Augmentation Module (TAM) can overcome these challenges by modifying the sparse data stream and reducing time complexity. Moreover, a neural network that uses a Variance Detection Module (VDM) can effectively detect the variance of the input data stream through the network and dynamically adjust the degree of differentiation between samples to enhance the accuracy of forecasts. The proposed method adds significant information regarding the data sparse samples and enhances low dimensional samples to high data samples to overcome the sparse data stream problem. In VDM the preprocessed data achieve data augmentation and the samples are transmitted to VDM. The proposed method is evaluated using different types of data streaming datasets to predict the sparse data stream. Experimental results demonstrate that the proposed method achieves a high prediction accuracy and that the data stream has significant effects and strong robustness compared to other existing approaches.

Genetic Insights: Examining CNTNAP2 rs7794745 Gene Polymorphism and Its Impact on ABLLS Assessment in Middle Euphrates Children in Iraq with Autism Spectrum Disorder

Objective: examine the effect of CNTNAP2 gene polymorphism on behavioral therapy response in Iraqi children with autism spectrum disorder using ABLLS-r.
 Methods: In this study, 150 samples for autism spectrum disorder were obtained from AL-SIBTEIN ACADEMY, however owing to many challenges, the final number of samples was 50 patients. Polymerase Chain Reaction-Restriction refers to a technique that combines the use of polymerase chain reaction (PCR) with restriction enzymes to amplify and analyze certain DNA sequences. Fragment Length Polymorphism (PCR-RFLP) was used to genotype the CNTNAP2 gene at rs7794745. All 25 ABLLS-R repertoires and all four primary components had their dependent parameters determined by aggregating and converting the ABLLS data to a percentage. This provided us with the proportion of finished assignments from each repertoire.
 Results: After correlating the genotype data (AA/AT/TT) with the behavioral therapy response as indicated by ABLLS-R, we only discovered a statistically significant difference between the frequency of AA/TT genotypes in the rs7794745 variant in patients and the social interaction domain. Conversely, the remaining 24 subdomains exhibited no discernible correlation.
 Conclusion: This research topic could provide significant information on targeting and categorizing which skill areas need to be developed based on genetic models' influence.

Analysing spatio-temporal drought characteristics and copula-based return period in Indian Gangetic Basin (1901-2021).

Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in theIndo-Gangetic plainregion. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts.

Homebuyers’ geographic proximity as a predictor of future housing price growth

AbstractConsidering the prevalent information asymmetry in housing markets, this study demonstrates the predictive power of homebuyers’ geographic proximity on housing prices. At the ZIP‐code level, a 10‐percentage‐point increase in the fraction of local buyers corresponds to a 1.1‐percentage‐point higher housing price growth over the subsequent 2 years. At the individual level, out‐of‐town buyers experience a 0.64‐percentage‐point lower annual return compared to local buyers within a county. These results not only highlight the significant information advantages enjoyed by geographically proximate buyers, but also imply that informationally privileged buyers’ revealed preferences for specific locations could provide informationally disadvantaged buyers with hints about which areas are likely to experience higher housing price growth in the near future.

Формирование цифрового паспорта огнестрельного повреждения методами компьютерного зрения

Разработано программное обеспечение ImgOpinion, которое позволяет выполнять оптико-структурный анализ цифрового изображения объекта методами компьютерного зрения. Оно предназначено для использования в экспертных лабораториях в составе аппаратно-программного комплекса компьютерного зрения MS-Unit, который включает специализированный осветитель “Фотобокс 3138” с цифровой регистрирующей камерой. Выходные данные строятся в виде цифрового паспорта огнестрельного повреждения, в котором приводятся криминалистически значимые характеристики объекта исследования, а также выявленных на нем следов выстрела. The ImgOpinion software, capable to perform the optical and structural analysis of a digital image of the object by computer vision methods, was developed. It can be employed in expert laboratories as a part of the computer vision hardware-software complex MS-Unit together with specialized illuminator “Photobox 3138” with a digital recording camera. The output data is constructed in the form of a digital passport of the gunshot residue, which contains forensically significant characteristics of the object under investigation, as well as a gunshot residue detected on it. Purpose. The research has solved the problem of creating a specialised hardware and software complex for forensic examination, the use of which partially automates the processes of identification and quantitative characterisation of gunshot traces and products. Methodology. Non-destructive research methods are implemented in computer vision systems, which include two main stages: a digital image acquisition and its mathematical processing. The hardware and software complex of a computer vision usually consists of an illuminator, a digital recording video camera and a specialised program for processing the raw data. Findings. ImgOpinion software has been developed, which allows performing the optical-structural analyses of digital images using the computer vision methods and is intended for use in expert laboratories as part of the MS-Unit computer vision hardware and software complex, which includes a specialized illuminator “Photobox 3138” with a digital recording camera. Autonomous and portable device has a mobile design and provides white light with colour temperature 5000 K (CIE D50) and colour rendering index CRI = 97+. Six independently switched LED monochrome illuminators with narrow spectral bands from 365 to 880 nm (UV to IR spectral bands) provide the uniform illumination without extraneous stray light on the working field (light intensity drop below 2 % at the edges of the working field). Value. The ImgOpinion output is constructed in the form of a digital passport of a gunshot injury, which contains forensically relevant characteristics of the object of investigation. The digital passport is an adaptation of the forensically significant information on the firearm traces for the integration into specialized databases for further development of the automation of expert evaluation processes, conducting the automated trace comparison and the determination of incident circumstances by it.

Measurable transitions during seizures in intracranial EEG: A stereoelectroencephalography and SPECT study

ObjectiveIctal Single Photon Emission Computed Tomography (SPECT) and stereo-electroencephalography (SEEG) are diagnostic techniques used for the management of patients with drug-resistant focal epilepsies. While hyperperfusion patterns in ictal SPECT studies reveal seizure onset and propagation pathways, the role of ictal hypoperfusion remains poorly understood. The goal of this study was to systematically characterize the spatio-temporal information flow dynamics between differently perfused brain regions using stereo-EEG recordings. MethodsWe identified seizure-free patients after resective epilepsy surgery who had prior ictal SPECT and SEEG investigations. We estimated directional connectivity between the epileptogenic-zone (EZ), non-resected areas of hyperperfusion, hypoperfusion, and baseline perfusion during the interictal, preictal, ictal, and postictal periods. ResultsCompared to the background, we noted significant information flow (1) during the preictal period from the EZ to the baseline and hyperperfused regions, (2) during the ictal onset from the EZ to all three regions, and (3) during the period of seizure evolution from the area of hypoperfusion to all three regions. ConclusionsHypoperfused brain regions were found to indirectly interact with the EZ during the ictal period. SignificanceOur unique study, combining intracranial electrophysiology and perfusion imaging, presents compelling evidence of dynamic changes in directional connectivity between brain regions during the transition from interictal to ictal states.

Prediction of Diabetic Retinopathy using Deep Learning with Preprocessing

INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR.
 OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset.
 METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages.
 RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details.
 CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate patterns to draw out important elements from the images. The CNN model may predict DR or classification by training it on a labeled dataset. The development of computer-aided diagnosis systems for DR is facilitated by the integration of CNN prediction with image preprocessing filters. This strategy may increase the effectiveness of healthcare workers, boost patient outcomes, and lessen the burden of DR.

Virtual land trade and associated risks to food security in China

International trade can help alleviate the scarcity of arable land by importing agricultural and food products. In the context of climate change and socioeconomic development, the patterns in economic and resource flow embodied in the global food trade may be subject to further changes that present risks for economies with scarce land resources, such as China. These conditions exacerbate ongoing food security challenges, yet have so far not been well understood. Here, we combined the environmentally extended multiregional input-output (EE-MRIO) model, Geopolitical Supply Risk (GeoPolRisk) method, and scenario analysis to provide a comprehensive overview of global, virtual land trade patterns caused by China's food consumption and associated virtual land supply risk faced by China. Our results show that, from 1995 to 2015, China has experienced increasing GeoPolRisk of virtual land supply related to food consumption, roughly fivefold, mainly due to growth in total virtual land consumption and changes in trade structure. From 1995 to 2015, total food consumption-related virtual land in China has increased by approximately 35.1%, and the share of foreign land has increased from 6.3% to 43.3%, with the largest land sources shifting from low-income regions (such as Asia and Africa) to more industrialized regions (such as the United States and Australia). Meanwhile, socioeconomic development and dietary changes are the main antecedents to total virtual land consumption, potentially affecting GeoPolRisk. Our results suggest the need for urgent adjustments for a balanced distribution of virtual land sources and to promote healthy dietary transitions and improve agricultural productivity in the future. Our study offers significant information that can guide potential solutions to the sustainable use of land resources and reduce the GeoPolRisk of virtual land trade for decision-makers, thereby facilitating food security.

Comparative Study of the Nutritional, Phytochemical, Sensory Characteristics and Glycemic Response of Cookies Enriched with Lupin Sprout Flour and Lupin Green Sprout.

This study aimed to compare the nutritional, phytochemical, and sensory characteristics of wheat flour (WF) cookies enriched with different proportions of lupin sprout flour (LSF) and those with different proportions of lupin green sprout (LGS). To achieve this, a control cookie (CC); three cookies with 10%, 20%, and 30% of LSF, respectively, CLSF1, CLSF2, and CLSF3; and three other cookies (CLGS1, CLGS2, and CLGS3) with 10%, 20%, and 30%, respectively, were produced. The proximate composition of each cookie was analyzed using AOAC methods. Also, the measurements of the total polyphenol content, antioxidant activity, individual polyphenols, glycemic index, and a sensory analysis were carried out using recent and accurate methods. The contribution of the main nutrients from 100 g of product to the required daily dose was also calculated. Data analysis revealed that cookies with LSF were richer than cookies with LGS in protein, fat, and energy values. CLGS3 was 35.12%, 1.45%, and 5.0% lower in protein, fat, and energy content than CLSF3, respectively. On the other hand, CLSF3 was lower than CLGS3, with 48.2% and 12.4% in moisture and mineral substances, respectively. Both cookies were lower in carbohydrates than the CC (65.20 g/100 g). Still on the subject of micro- and macronutrients, cookies with LSF were richer than those with LGS in all the minerals analyzed. The study also revealed improvements in phytochemical properties, such as total and individual polyphenols and antioxidant activity with the percentage of lupin sprout flour addition. The sensory analysis revealed that, for LSF and LGS cookies, the 10% samples were the most appreciated by consumers, irrespective of the sensory attributes studied. The glycemic index of the CLSF2 product was lower compared to the CC. This study shows that the LSF cookies have better nutritional, phytochemical, and sensory values than the LGS cookies. LSF is, therefore, better suited than LGS to the enrichment of bakery products in general and cookies in particular. The paper provides significant information to estimate the contribution of the consumption of functional products based on lupin sprouts to the required daily dose of food nutrients and the impact on the glycemic index of fortified products.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

OII: An Orientation Information Integrating Network for Oriented Object Detection in Remote Sensing Images

Oriented object detection for remote sensing images poses formidable challenges due to arbitrary orientation, diverse scales, and densely distributed targets (e.g., across terrain). Current investigations in remote sensing object detection have primarily focused on improving the representation of oriented bounding boxes yet have neglected the significant orientation information of targets in remote sensing contexts. Recent investigations point out that the inclusion and fusion of orientation information yields substantial benefits in training an accurate oriented object system. In this paper, we propose a simple but effective orientation information integrating (OII) network comprising two main parts: the orientation information highlighting (OIH) module and orientation feature fusion (OFF) module. The OIH module extracts orientation features from those produced by the backbone by modeling the frequency information of spatial features. Given that low-frequency components in an image capture its primary content, and high-frequency components contribute to its intricate details and edges, the transformation from the spatial domain to the frequency domain can effectively emphasize the orientation information of images. Subsequently, our OFF module employs a combination of a CNN attention mechanism and self-attention to derive weights for orientation features and original features. These derived weights are adopted to adaptively enhance the original features, resulting in integrated features that contain enriched orientation information. Given the inherent limitation of the original spatial attention weights in explicitly capturing orientation nuances, the incorporation of the introduced orientation weights serves as a pivotal tool to accentuate and delineate orientation information related to targets. Without unnecessary embellishments, our OII network achieves competitive detection accuracy on two prevalent remote sensing-oriented object detection datasets: DOTA (80.82 mAP) and HRSC2016 (98.32 mAP).

Factors Influencing Climate Information Access of the Farmers in Hiran Region, Somalia

Purpose: Like other poor states and post-conflict countries, Somalia faces significant challenges in achieving the SDGs, particularly concerning food security. Because agricultural productivity is compromised, it immediately and significantly jeopardizes the nation's food security. Adapting to climate change can maximize its benefits and lessen many of its horrendous side effects. However, Somalia does not address or cover farmers' access to climatic information, which is important when organizing policy responses. This study's goal was to assess the variables that affect farmers in Somalia's Hiran region's ability to obtain climate information.
 Methodology: The target population of this study was smallholder farmers in the Hiran region, particularly in the Baladweyn and Bulaburte districts. Both purposive and random sampling were utilized. A questionnaire was used to gather data from 222 randomly chosen smallholder farmers as part of a survey research design. Descriptive statistics and binary logistic regression were used to test the data using STATA and SPSS. The data was presented using tables and figures.
 Findings: Most farmers (78%) had access to climate information. The most common sources of information on climate crises were radio (95%), agricultural extension agents (80%), and firsthand observation (75%). Gender (p = 0.020), marital status (p = 0.036), education level (p = 0.047), farm size (p = 0.000), distance to the market (p = 0.000), and support from local and international agencies (p = 0.013) had a significant correlation with farmers access to climate information. The report advised Somalia's federal government and regional and foreign non-governmental organizations to proceed with intervention plans, focusing particularly on variables that were identified.
 Unique Contribution to Theory, Practice, and Policy: Study outcomes contribute significant information to policymakers, professionals, and the federal government of Somalia to develop policies and regulations that are relevant to the farmer's needs to adapt to the negative impacts of climate crises. It will also lead to necessary coordination among different climate actors, stakeholders, and farming communities in the region to fill any climate information gap. This will finally allow farmers to access well-timed and dependable information regarding climate disasters.

Differential degradation dynamics of λ-cyhalothrin in mineral and peat soils: a comparative study under laboratory condition

Dissipation kinetics of λ-cyhalothrin in mineral and peat soils of Semongok (mineral soil) and Sibu (peat soil) farms was investigated in a laboratory incubation experiment under different temperature and moisture conditions at normal and double application dosages. The soil was spiked with λ-cyhalothrin at 5 and 25 µg/g soil, respectively. The soil moisture content was adjusted to 20, 40, and 60% of field capacity and then incubated in three climatic chambers at 15, 25, and 35 °C. Samples were collected at 0, 7, 21, 42, 70, and 105 days and analysed by Gas Chromatography-Electron Capture Detector (GC-ECD). Pesticides from the soil were extracted via a facile-modified QuEChERS method. Recovery studies of λ-cyhalothrin in mineral and peat soils were carried out at 0.05, 0.1, 0.5 and 1.0 µg/g fortification levels. The percentage of recovered amount was in the range of 81.4–95.0% and 81.3–86.5% for mineral and peat soils, respectively which falls within the acceptable recovery range of 70.0–120.0%. Factors i.e., soil carbon content, moisture, temperature, and applied dosage that render the degradation of λ-cyhalothrin in mineral and peat soils were evaluated. Findings showed that faster λ-cyhalothrin degradation took place in soil that contained low organic carbon content (< 12%), low soil moisture (≤ 20%) and incubated under higher temperatures (≤ 35 °C). Degradation of λ-cyhalothrin was described by first-order kinetics in both mineral and peat soils at various conditions. Half-lives of λ-cyhalothrin in mineral soil were shorter compared to peat soil. This is due to its lower carbon content and lower soil organic matter availability. This study provides significant information to the agriculture industry and farmers on the important factors such as soil properties, environmental conditions and application dosage that will influence the fate of pesticides in soil.Graphical abstract

Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling

Fibroblasts in a confluent monolayer are known to adopt elongated morphologies in which cells are oriented parallel to their neighbors. We collected and analyzed new microscopy movies to show that confluent fibroblasts are motile and that neighboring cells often move in anti-parallel directions in a collective motion phenomenon we refer to as “fluidization” of the cell population. We used machine learning to perform cell tracking for each movie and then leveraged topological data analysis (TDA) to show that time-varying point-clouds generated by the tracks contain significant topological information content that is driven by fluidization, i.e., the anti-parallel movement of individual neighboring cells and neighboring groups of cells over long distances. We then utilized the TDA summaries extracted from each movie to perform Bayesian parameter estimation for the D’Orsgona model, an agent-based model (ABM) known to produce a wide array of different patterns, including patterns that are qualitatively similar to fluidization. Although the D’Orsgona ABM is a phenomenological model that only describes inter-cellular attraction and repulsion, the estimated region of D’Orsogna model parameter space was consistent across all movies, suggesting that a specific level of inter-cellular repulsion force at close range may be a mechanism that helps drive fluidization patterns in confluent mesenchymal cell populations.

Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data

A key advantage of social media is the real-time exchange of views with large communities. In disaster situations, such bidirectional information exchange is most useful to victims and support teams, especially in communications with authorities, volunteers, and the public. This paper addresses challenges faced by the healthcare supply chain during the COVID-19 pandemic with analyses of Twitter data using an Artificial Intelligence-driven multi-step approach. We investigate tweets for information about healthcare supply chains, such as the scarcity of testing kits, oxygen cylinders, and hospital beds during the pandemic. We deployed machine learning to classify such tweets into imperative and non-imperative categories based on need severity. The study sought to predict the location of victims requesting help based on their imperative tweets if geo-tag information was missing. The proposed approach used four steps: (1) keyword-based informative tweet search, (2) raw tweet pre-processing, (3) content analysis to identify tweet trends, public sentiment, topics related to healthcare supply chain challenges, and crisis classification to label imperative and non-imperative tweets, (4) locating the point-of-crisis from imperative tweets to facilitate coordination of help operations. The pre-processing of tweets, trend analysis, and sentiment analysis relied on natural language processing and machine learning for topic modelling (K-mean clustering), crisis classification (random forest), and point-of-crisis detection (Markov chain). Results demonstrate the potential to capture significant, timely, and actionable information on healthcare supply chain challenges to respond quickly and appropriately in a pandemic.

Spectral information content of Compton scattering events in silicon photon counting detectors.

Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two-thirds of x-ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy-insensitive process, it is commonly assumed that Compton events retain little spectral information. To quantify how much information can be recovered from Compton scattering events in models of Si PCDs. We built a simplified model of Si interactions including two interaction mechanisms: photoelectric effect and Compton scattering. We considered three different binning options that represent strategies for handling Compton events: in Compton censoring, all events under 38keV (the maximum energy possible from Compton scattering for a 120keV incident photon) were discarded; in Compton counting, all events between 1 and 38keV were placed into a single bin; in Compton binning, all events were placed into energy bins of uniform width. These were compared to the ideal detector, which always recorded the correct energy (i.e., 100% photoelectric effect). Every photon was assumed to interact once and only once with Si, and the energy bin width was 5keV. In the primary analysis, the Si detector was irradiated with a 120kV spectrum filtered by 30cm of water, with 99.5% of the arriving spectrum above 38keV so that there was good separation between photoelectric effect and Compton scattering, and the figures of merit were the Cramér-Rao lower bound (CRLB) of the variance of iodine and water basis material decomposition images, as well as the CRLB of virtual monoenergetic images (i.e., linear combinations of material images) that maximize iodine CNR or water CNR. We also constructed a local linear estimator that attains the CRLB. In secondary analyses, we applied other sources of spectral distortion: (1) a nonzero minimum energy threshold; (2) coarser, 10keV energy bins; and (3) a model of charge sharing. With our chosen spectrum, 67% of the interactions were Compton scattering. Consistent with this, the material decomposition variance for the Compton censoring model, averaged over both basis materials, was 258% greater than the ideal detector. If Compton events carried no spectral information, the Compton counting model would show similar variance. Instead, its basis material variance was 103% greater than the ideal detector, implying that Compton counts indeed carry significant spectral information. The Compton binning model had a basis material variance 60% greater than the ideal detector. The Compton binning model was not affected by a 5keV minimum energy threshold, but the variance increased from 60% to 107% when charge sharing was included and to 78% with coarser energy bins. For optimized CNR images, the average variance was 149%, 12%, and 10% higher than the ideal detector for the Compton censoring, counting, and binning models, reinforcing the hypothesis that Compton counts are useful for detection tasks and that precise energy assignments are not necessary. Substantial spectral information remains after Compton scattering events in silicon PCDs.

Fusion of Single and Integral Multispectral Aerial Images

An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.

Identification of the efficacy of parentage testing based on bi-allelic autosomal single nucleotide polymorphism markers in Taiwanese population.

Parentage testing is crucial for forensic DNA analysis, using short tandem repeats (STRs). Single nucleotide polymorphisms (SNPs) with high minor allele frequency (MAF) are promising for human identification. This study aimed to develop SNP markers for parentage testing in the Taiwanese population and compare their accuracy with STRs. The TPMv1 SNP microarray (714,457 SNPs) was used to screen 180,000 Taiwanese individuals and analyze the SNP data using PLINK. After quality control, allelic distribution, and MAF considerations, a set of SNPs with significant inheritance information was selected. Parentage testing was conducted on 355 single parent-child pairs using both STRs and SNPs, employing three kinship algorithms: identity by descent, kinship-based inference for genome-wide association studies, and the combined paternity index/probability of paternity (CPI/PP). An Affymetrix signature probe for kinship testing (ASP) was also used. Based on the quality control and selection criteria, 176 SNPs with MAF > 0.4995 were selected from the Taiwanese population. The CPI/PP results calculated using SNPs were consistent with the STR results. The accuracy of the SNPs used in the single-parent-child parentage testing was > 99.99%. The set of 176 SNPs had a higher identification rate in the single parent-child parentage test than in the ASP. The CPI/PP value calculated using 176 SNPs was also more accurate than that calculated using ASP. Our findings suggest that these 176 SNPs could be used for single-parent-child parentage identification in the Taiwanese population.

A Survey about Post Quantum Cryptography Methods

Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.