Patterns In Databases Research Articles

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company’s long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model’s improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

Simulating cyberattacks with extended Petri nets

Cybersecurity is an urgent concern. Cybersecurity simulation is an important part of the response to it. This article describes a research program consisting of several interconnected cybersecurity simulation research projects. Cyberattacks are modeled using Petri nets extended with features designed for modeling cyberattacks, including representations of the attacker’s and defender’s strategies, their actions, and their actions’ cost. A database of known attack patterns is automatically processed to generate cyberattack component models, one for each attack pattern. The models are verified and validated using multiple application-relevant methods that consider both Petri nets’ theoretical properties and cyberattacks’ practical characteristics. Because the source attack pattern database is attacker-centric, the cyberattack component models are enhanced to include defender actions and responses, as well as representations of normal user activities on the computer system being attacked. Cyberattack component models stored in a repository are selected and composed into complete models of target computer systems. Metadata associated with each model guides the selection and composition. The cyberattack models are executed to simulate cyberattacks. Multiple simulation iterations are used to train reinforcement learning algorithms that automatically learn improved attacker or defender strategies.

Individualized generation of women's prototype based on the classification of body shape

The traditional prototype method only relies on the basic dimensions of the human body such as bust girth and back length to make the prototype pattern, ignoring the differences of human body details dimensions. In order to improve the fit of garments, the research on intelligent generation technology of individualized garment patterns has become one of the hot spots in the field of clothing. Based on the 3D body scanning technology, we proposed a method for generating prototype pattern based on body shape classification. The proposed method takes as inputs the body parameters (width, thickness and angle), while the output of the method is one of the four pattern generation rules-"Y" shape, "V" shape, "I" shape, or "X" shape. 207 subjects were divided into four categories based on the body angle parameters, namely "Y" shape, "V" shape, "I" shape and "X" shape, and a shape recognition model was built. The pattern database is required to obtain pattern generation rules by 3D point cloud reconstruction and flattening. Combined with the human body shape parameters, the mathematical formulas of the feature points on the pattern landmarks are built. The accuracy of body shape recognition model is 97.4%. The error analysis of the predicted pattern parameters shows that 90% of the pattern feature points have a goodness of fit above 0.7. In the 5 main landmarks, the proportion of pattern within the absolute error range is more than 80%, indicating that the prediction effect of the pattern is good. This method can be applied to the process of automatic pattern generation system based 2D measurement to improve work efficiency.

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Quantification of the environmental impact of radiotherapy and associated secondary human health effects: a multi-institutional retrospective analysis and simulation

The health-care industry is a substantial contributor to global greenhouse gas emissions, yet the specific environmental impact of radiotherapy, a cornerstone of cancer treatment, remains under-explored. We aimed to quantify the emissions associated with the delivery of radiotherapy in the USA and propose a framework for reducing the environmental impact of oncology care. In this multi-institutional retrospective analysis and simulation study, we conducted a lifecycle assessment of external beam radiotherapy (EBRT) for ten anatomical disease sites, adhering to the International Organization for Standardization's standards ISO 14040 and ISO 14044. We analysed retrospective data from Jan 1, 2017, to Oct 1, 2023, encompassing patient and staff travel, medical supplies, and equipment and building energy use associated with the use of EBRT at four academic institutions in the USA. The primary objective was to measure the environmental impacts across ten categories: greenhouse gases (expressed as kg of carbon dioxide equivalents [CO2e]), ozone depletion, smog formation, acidification, eutrophication, carcinogenic and non-carcinogenic potential, respiratory effects, fossil fuel depletion, and ecotoxicity. Human health effects secondary to these environmental impacts were also estimated as disability-adjusted life years. We also assessed the potential benefits of hypofractionated regimens for breast and genitourinary (ie, prostate and bladder) cancers on US greenhouse gas emissions using an analytic model based on the 2014 US National Cancer Database for fractionation patterns and patient commute distances. We estimated that the mean greenhouse gas emissions associated with a standard 25-fraction EBRT course were 4310 kg CO2e (SD 2910), which corresponded to 0·0035 disability-adjusted life years per treatment course. Transit and building energy usage accounted for 25·73% (1110 kg CO2e) and 73·95% of (3190 kg CO2e) of total greenhouse gas emissions, respectively, whereas supplies contributed only 0·32% (14 kg CO2e). Across the other environmental impact categories, most of the environmental impact also stemmed from patient transit and energy use within facilities, with little environmental impact contributed by supplies used. Hypofractionated treatment simulations suggested a substantial reduction in greenhouse gas emissions-by up to 42% for breast and 77% for genitourinary cancer-and environmental impacts more broadly. This comprehensive lifecycle assessment of EBRT delineates the environmental and secondary health impacts of radiotherapy, and underscores the urgent need for sustainable practices in oncology. The findings serve as a reference for future decarbonisation efforts in cancer care and show the potential environmental benefits of modifying treatment protocols (when clinical equipoise exists). They also highlight strategic opportunities to mitigate the ecological footprint in an era of escalating climate change and increasing cancer prevalence. Mount Zion Health Fund.

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men’s shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer’s knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user’s chest girth and back length. Body feature dimensions determine the three body parts’ shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

Effect and mechanism of erosion in Pelton turbine and case studies—A review

Pelton turbines are widely used in hydropower stations located in mountainous regions, especially with water head drop of more than 2000 m. Due to the complex structure and working principle of the turbine, the flow is more complicated than reaction turbines, making the numerical simulation more difficult. The impulse action causes the occurrence of erosion phenomena in Pelton turbines, which will directly decrease the hydraulic efficiency and reduce the turbine's life. For investigating the erosion characteristics, computational fluid dynamics is widely used on variegated platforms according to their unique advantage. Thus, different platforms are introduced and compared in solving the multi-phase flow using a discrete element method or the other meshless methods. In addition, the erosion mechanism is studied and classified in different aspects such as impact angle and impact velocity. However, unlike the feasibility of numerical simulations, experimental work on the erosion mechanism is still challenging to reproduce. Furthermore, the state of experimental research is discussed by listing the various major facilities in operation and comparing their methods of experimental analysis. Case studies all over the world provide a very rich database of erosion patterns which would be highly useful in validation and verification of simulation and experimental results. Studies have shown that particle parameters, such as size, concentration, shape, velocity, and the interaction between particles and material surfaces, significantly impact the erosion of Pelton turbines. In response to this erosion, upgrading materials and implementing geometric optimization have proven to be effective strategies.

Development of nationally representative exposure factor database for children's products in Korea.

Children may be exposed to harmful chemicals from their products. Accurate exposure factors are critical for exposure assessment of children's products. Product usage pattern parameters are relatively limited compared with the chemical concentration, children's physiological and behavioral parameters. The aim of this study was to determine nationally representative Korean exposure factors for the usage patterns of children's products by sex, age, and season. Using proportional quota sampling, a survey of 10,000 households with children aged 0-12 years was conducted twice, once in summer and winter. The children's ages were divided into four groups: infant (0-2 years old), toddler (3-6), lower-grade elementary student (7-9), and higher-grade elementary student (10-12). Data on exposure factors such as use rate, use frequency, and use duration of 57 children's products were collected. The 57 products were classified into five categories: baby products (13), toys (12), daily products (10), sporting goods (8), and stationery (14). The use rates of products in the daily products and stationery category were >90% in both seasons. Two of the 57 products showed significant sex differences in all three exposure factors (p < 0.001). Twenty-five of the 44 non-baby products showed significant age differences for all three exposure factors. Twenty-three of the 57 products varied significantly with season for all three exposure factors. This study generated a nationally representative exposure factor database for the usage patterns of children's products in Korea. The exposure factors for 57 children's products were investigated through twice survey with quota sampling with each 10,000 children nationwide. Sex, age, and seasonal differences for children's products were identified. These accurate exposure factors by sex, age, and season can be used as input parameters for refined exposure assessment.

Effects of riser height and pipeline length on the transition boundary and frequency of severe slugging in offshore pipelines

Investigating the transition boundary and frequency characteristics of severe slugging under different riser heights and pipeline lengths provides important support for the expansion of offshore oil and gas to deeper waters. The visualization research on air-water two-phase flow is carried out in a system with a horizontal pipeline length of 1687 m and riser heights of 16 m, 22 m, and 29 m, and flow pattern maps are plotted. Based on existing experimental data, the database of the flow pattern and slugging frequency in pipeline-riser systems with riser heights of 3–29 m, pipeline lengths of 10–1687 m, and pipe diameters of 25.4–50.8 mm is established. The long pipeline makes the gas pressure growth rate slower than that of the liquid pressure in the riser, which causes severe slugging occurring at higher superficial gas velocity. On the contrary, increasing the riser height causes the boundary of severe slugging to shift to a lower superficial gas velocity. The severe slugging length is greater in the higher riser and longer pipeline system, causing a smaller severe slugging frequency. A correlation for predicting severe slugging frequency at various riser heights, pipe lengths, and pipe diameters is established by employing gas and liquid Froude numbers.

The landscape of transcription factor promoter activity during vegetative development in Marchantia.

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterizing the transcriptional activity of TF genes in space and time is a critical step toward understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analyzed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem-cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

Space use of invertebrates in terrestrial habitats: Phylogenetic, functional and environmental drivers of interspecific variations

AbstractAimWe present the first global database of movement patterns of terrestrial invertebrates, focusing on active dispersal and foraging movements. We depict interspecific variations in movement distances among invertebrates and assess potential drivers of these variations. We finally contrast our results with those of previous vertebrate studies.LocationWorldwide.MethodsWe conducted a meta‐analysis using 176 studies. They provided 411 movement estimates: 175 foraging movement estimates for 101 species, 51 families and 19 orders, and 236 dispersal estimates for 131 species, 53 families and 16 orders. These estimates were complemented by several organism functional traits: body mass, diet, locomotion mode and by environmental variables of the study sites: temperature and NDVI. We computed allometric relationships between movement distances and body mass both globally and separately for each taxonomic order with sufficient data. We tested the relative influence of the co‐variables on movement distances through model selection.ResultsWe reveal a general positive allometric relationship between movement distance and body mass that holds across most taxonomic orders. We evidence a strong phylogenetic signal in movement distances that translates in variable allometries of movement distances with body mass across taxonomic orders. We further find that interspecific variations of movement distances are primarily correlated with functional differences rather than environmental conditions. Locomotion mode is the most important explanatory variable of both dispersal and foraging distances, with larger distances among flying individuals followed by walking and crawling ones for a given body mass. Surprisingly, trophic guild has a low predictive power of invertebrate movement distances as opposed to vertebrates, with unclear differences between invertebrate carnivores, herbivores and decomposers.Main ConclusionsOur study provides general allometric equations for terrestrial movement distances of invertebrates. It further reveals important functional drivers of their interspecific variation in space use with a dominant role of their evolutionary history.

A Hybrid Intrusion Detection Approach for Cyber Attacks

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.

The Application of Big Data Technology in Teaching College Students’ Mental Health Education in Colleges and Universities

Abstract Advances in big data technology herald a new era for mental health education in higher education, offering novel solutions to age-old challenges in student psychological care. This paper investigates how big data’s analytical capabilities can surpass traditional, one-size-fits-all mental health assessments by leveraging detailed student data for personalized care. We applied data mining techniques to the mental health data of 1,200 students from College G, creating a rich database of psychological patterns and a mental health information exchange platform. The analysis led to the identification of 500 instances of psychological distress with an accuracy of 79.5% and unveiled patterns linking academic stress and adjustment difficulties to mental health issues. Our research underscores big data’s role in enhancing mental health interventions, providing the groundwork for more individualized and effective mental health services in academic settings.

Classification and recognition of the Nantong blue calico pattern based on deep learning

The Nantong blue calico pattern is a significant and indispensable part of China’s intangible cultural heritage, representing an artistic form of weaving and dyeing. However, existing research on blue calico patterns is not extensive, and few studies have focused on the construction of a database categorizing them or on recognizing the Nantong blue calico pattern. Obtaining good efficiency and accuracy through manual recognition has been the primary challenge in recognizing the Nantong blue calico pattern. In light of these challenges, this study proposes the use of deep learning network model to intelligently classify and recognize blue calico patterns.First, the patterns are classified to establish a Nantong blue calico pattern database, and the corresponding category labels are then manually assigned to each image. Second, based on the database and a backbone feature extraction network, the abilities of SSD (Single Shot Multibox Detector), Faster RCNN (Region-CNN), and You Only Look Once (to recognize the Nantong blue calico pattern were compared. The results show that the SSD model based on a VGG (Visual Geometry Group) backbone network has the best recognition accuracy of these three algorithms, with an average accuracy of 79.42%. On this basis, we selected the SSD model for parameter optimization and adjustment, and we replaced the backbone with mobilenetv2, a lighter backbone extraction network, to recognize the Nantong blue calico pattern. The results show that compared with the original SSD model, the optimized SSD model can improve the pattern recognition rate of Nantong blue calico pattern. Furthermore, this paper makes use of the characteristics of the VGG deep network, the backbone network of the SSD model, to efficiently extract the features of blue calico patterns, which provides a basis for designers to design innovative blue calico patterns.

RFLP-inator: interactive web platform for in silico simulation and complementary tools of the PCR-RFLP technique.

Polymerase chain reaction - Restriction Fragment Length Polymorphism (PCR-RFLP) is an established molecular biology technique leveraging DNA sequence variability for organism identification, genetic disease detection, biodiversity analysis, etc. Traditional PCR-RFLP requires wet-laboratory procedures that can result in technical errors, procedural challenges, and financial costs. With the aim of providing an accessible and efficient PCR-RFLP technique complement, we introduce RFLP-inator. This is a comprehensive web-based platform developed in R using the package Shiny, which simulates the PCR-RFLP technique, integrates analysis capabilities, and offers complementary tools for both pre- and post-evaluation of in vitro results. We developed the RFLP-inator's algorithm independently and our platform offers seven dynamic tools: RFLP simulator, Pattern identifier, Enzyme selector, RFLP analyzer, Multiplex PCR, Restriction map maker, and Gel plotter. Moreover, the software includes a restriction pattern database of more than 250,000 sequences of the bacterial 16S rRNA gene. We successfully validated the core tools against published research findings. This new platform is open access and user-friendly, offering a valuable resource for researchers, educators, and students specializing in molecular genetics. RFLP-inator not only streamlines RFLP technique application but also supports pedagogical efforts in genetics, illustrating its utility and reliability. The software is available for free at https://kodebio.shinyapps.io/RFLP-inator/.

In-vehicle battery capacity fade: A follow-up study on six European regions

This work presents a follow up of previous case studies from the authors on the estimation of the in-vehicle battery capacity fade with performance-based models of Lithium-ion batteries applied to real-world vehicles’ activity data. The mobility pattern database is significantly enlarged compared to previous works from the authors, encompassing now six European regions, including 508,609 private vehicles which corresponds to 1.78 billion GPS records, 9.1 million trips and parking events and a total driven distance of 106.1 million kilometres. The capacity fade is derived at given years and mileage thresholds under different driving and recharging scenarios, supporting the concept of setting minimum performance requirements for traction batteries in future road transport policies. The vast majority of the considered scenarios do not lead to a battery capacity drop below 80% of its nominal value in less than five calendar years or 100,000 driven kilometres and below 70% in less than 8 years or 160,000 driven kilometres. The results of this work contributed to inform the new UN ECE Global Technical Regulation No. 22 on in-vehicle battery durability.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

A star identification algorithm for rolling shutter exposure based on Hough transform

The star identification algorithm usually identifies stars by angular distance matching. However, under high dynamic conditions, the rolling shutter effect distorts the angular distances between the measured and true star positions, leading to plethoric false matches and requiring complex and time-consuming verification for star identification. Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors. In this work, we first study a rolling shutter distortion model of angular distances between stars, and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor. The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches. The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern. The third stage applies a rolling shutter error correction method for further verification. Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions. The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5 (°)/s. Our algorithm achieves a 95.7% identification rate at an angular velocity of 10 (°)/s, which is much higher than traditional algorithms.

Bordering in the archives: An investigation into a digital archive of the Irish asylum and refugee determination

Creating borders and borderlands is a key role of the contemporary state. Drawing on an investigation of the Irish state bordering complex determining asylum and refugee claims in Ireland, this article illustrates the importance of the actions of individual state agents and the cultures of practice in state agencies in bordering and statecraft. Building on scholarship studying the state, I conceive of statecraft as cultures of practices, understood within a feminist geopolitical framework. Studying these cultures of practice reveals the ways state agencies and state agents produce knowledge and how they work within complex power-geometries of social and political geographies. I investigate the Irish asylum determination process using an archival ethnography methodology to encounter an online archive of refugee appeals decisions in Ireland. This article shows one result of this encounter: a published database of asylum decision patterns and practices of the refugee appeals tribunals and of individual Tribunal members issuing decisions to people seeking asylum in Ireland. I argue this approach: (1) addresses real needs for systematic evidence of practice of Tribunal members, as requested by asylum seekers and legal representatives; (2) investigates cultures of practice in decision-making bodies in the asylum complex of the Irish state; (3) and shows the importance of archives of asylum decision-making in the state practice of bordering and statecraft. Analysis shows an asylum determination culture heavily dependent on who is doing the deciding, the decision-maker assigned is a major element in outcomes of asylum claims. I also propose a framework for identifying statecraft and bordering as the collective project of groups and individuals embedded within the state; this framework provides productive and systematic evidence of bordering and the ways individuals and groups go about doing statecraft in the asylum determination process.