Code String Research Articles

Comparing public health-related material in print and web page versions of legacy media.

The objectives of this study were to create a database of public health content from a sample of legacy media, and to compare the prevalence of public health themes in print and web-based versions over time. A database was created from eleven nationally published magazines as a sample of legacy media content. Relevant material was extracted and coded by the title of the article, periodical, print or web edition, month of publication, item type, and 1-3 public health theme codes. Theme codes emerged as the documents were reviewed based on the primary discussion in each piece. A total of 2558 unique documents were extracted from print issues and 6440 from web-based issues. Seventeen public health themes were identified. Individual coded documents were saved with file names identical to the code string, thus creating a searchable database. Legacy media are those that existed before the internet and social media. Publishers target readership groups defined by age, gender, race, sexual orientation, and other commonalities. Although legacy media have been identified as trusted sources of health information, they have not been examined as sources of public health communication. Because both print and web-based versions exist as unstructured textual data, these are rarely examined with informatics methods. The process described can serve as a model for application of informatics approaches to similar data and assist development of targeted public health communications. Having a better understanding of what types of health content is distributed through legacy media can help to target health messages to specific demographic and interest groups in ways that are understandable and appealing to them.

Qualitative Data Coding of User Experience With an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

Document Verification and Validation based on Blockchain Technology

Based on statistics provided by the Indian Ministry of Education, it is evident that approximately one million graduates enter the job market or pursue higher education each year. Some individuals will pursue further education at high schools or university institutions, while others will be equipped to seek employment opportunities. The students with performance records, transcripts ofgrades, diplomas, etc., will serve as a crucial point of reference for admission to new institutions or jobs when they have completed their studies. Schools include only the names of students and schools when creating various prizes or diplomas. Events that result in the graduation document being forged are frequently recognized since there is no effective anti-forge system. The blockchain-based digital document system would be suggested as a solution to the issue of document forgery. In this system, issuing digital documents is done in the manner listed below. Create an electronic version of a paper document and add it to the database, along with any relevant data. Meanwhile, it is necessary to determine the hash value of the electronic file. This hash value must then be appendedto the block within the chain system. To attach to the paper document, the system will generate a connected QR code and an inquiry string code. It will enable the demand unitto check the validity of the paper document using website searches or mobile phone scanning. The technology not only increases the legitimacy of various paper-based documents but also significantly lowers the chance of document loss because of the blockchain's changeable qualities. Keywords: Blockchain, Data Mining, Multi-cloud Data Security, Proxy Key Generation.

Personal identification using frontal sinus coding methods: The effect of mixed image modality comparisons.

Several code-based methods have been created for comparing the frontal sinus in skeletal identification scenarios. However, little is known regarding matched-pair accuracy rates of these methods or how varying image modalities may affect these rates. The goals of this study were to validate the exclusion rates and to establish matched-pair accuracy rates of two well-cited coding methods, Cameriere etal. [23] and Tatlisumak etal. [24]. Additionally, individual variables were assessed for consistency in scoring between image modalities. Using a sample of U.S. African American, Native American, and European American females and males (n = 225), we examined individual variable scoring and string codes between two different image modalities (radiographs and CT-based 3D models). Arcades showed poor scoring consistency between modalities (p < 0.001). Although exclusion rates were similar to those reported in the original studies (93%-96%), matched-pair accuracy rates were low (13%-18%). None of the demographics (collection, sex, age, ancestry, and orientation) had an effect on the odds of a match. Interobserver and intraobserver analyses showed moderate to near-perfect agreement for all variables except supraorbital cells, which had minimal to no agreement. Currently, we do not recommend the application of these frontal sinus coding methods independent of other supporting identification methods given low variable consistency and accuracy rates. Visual identification should still be used to include or exclude an identification when using the frontal sinus.

GLPS: A Geohash-Based Location Privacy Protection Scheme.

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.

Channel capacity: limitation of entropy rate during cell signal transduction

Cellular signal transduction is mediated through chain activation reactions involving signalling molecules. Information thermodynamics may be a promising approach for quantifying signal transduction. A cell signal is transduced through the signal molecule activation as a step of a signal transduction cascade. In each step, two types of entropy can be defined. First, the signal transduction was defined using thermodynamic entropy production during the chemical activation of signal molecules. Second, the chronology was described as a code string, and the logarithm of the number of signal code strings corresponded to the information entropy. Finally, the total entropy during signal transduction was determined by summing the two entropies. When maximising the total entropy, the total entropy rate per signalling molecule concentration was found to be consistent independently of each transduction step (i.e., signal molecule types). Consequently, the speed limit of the entropy rate can be calculated, providing the capacity, which is an essential property of the signal transduction cascade. The theory introduced here is expected to provide a basic framework for understanding new methods of discovering signal cascades and intracellular signal transduction.

The genotype list string code syntax for exchanging nomenclature-level genotyping results in clinical and research data management and analysis systems.

The nomenclatures used to describe HLA and killer-cell immunoglobulin-like receptor (KIR) alleles distinguish unique nucleotide and peptide sequences, and patterns of expression, but are insufficient for describing genotyping results, as description of ambiguities and relations across loci require terminology beyond allele names. The genotype list (GL) String grammar describes genotyping results for genetic systems with defined nomenclatures, like HLA and KIR, documenting what is known and unknown about a given genotyping result. However, the accuracy of a GL String is dependent on the reference database version under which it was generated. Here, we describe the GL string code (GLSC) system, which associates each GL String with meta-data describing the specific reference context in which the GL String was created, and in which it should be interpreted. GLSC is a defined syntax for exchanging GL Strings in the context of a specific gene-family namespace, allele-name code-system, and pertinent reference database version. GLSC allows HLA and KIR genotyping data to be transmitted, parsed and interpreted in the appropriate context, in an unambiguous manner, on modern data-systems, including Health Level 7 Fast Healthcare Interoperability Resource systems. Technical specification for GLSC can be found at https://glstring.org.

Thermodynamics of signal transduction systems and fluctuation theorem in a signal cascade

Biochemical chain reactions are signal transduction cascades that can transmit biological information about the intracellular environment. In this study, we modelled a chain reaction as a code string for applying information theory. Herein, we assumed that cell signal transduction selects a strategy to maximize the transduced signal per signal event duration. To investigate the same, we calculated the information transmission capacity of the reaction chain by maximizing the average entropy production rate per reaction time, indicating the idea of the entropy coding method. Moreover, we defined a signal cascade trajectory. Subsequently, we found that the logarithm of the forward and reverse transition ratio per reaction time is equal to the entropy production rate, which derives the form of the fluctuation theorem in signal transduction. Our findings suggest the application of information entropy theory for analysing signal transduction.

MFXSS: An effective XSS vulnerability detection method in JavaScript based on multi-feature model

The widespread use of web applications has also made them more vulnerable to hackers, resulting in the leakage of large amounts of application and personal privacy data. Cross-site scripting (XSS) attacks are one of the most significant threats to web applications. Attackers can inject scripts to control the victim’s browser to send data or execute commands, leading to the theft of privacy or the hijacking of login tokens. Therefore, we proposed a multi-feature fusion-based neural network vulnerability detection model for detecting XSS vulnerabilities in the JavaScript source code of websites (We termed our implementation of this approach MFXSS). We combine abstract syntax tree (AST) and code control flow graph (CFG) to convert the generalized sample data into graph structure and code string structure. Then, through the graph convolutional neural network, weighted aggregation, and the bidirectional recurrent neural network, the logical call features and the context execution relationship features of the source code are extracted and fused respectively. Finally, the fused feature vectors are used to detect and predict XSS vulnerabilities in JavaScript. In the experiment, we designed multiple control experiments to verify that the model construction is optimal, and the accuracy rates in the standard and variant datasets are 0.997 and 0.986. Moreover, in comparing similar detection schemes, MFXSS also performs better. Applying the model to an actual web environment, we successfully detected the presence of XSS vulnerabilities in websites.

Synthesizing Legacy String Code for FPGAs Using Bounded Automata Learning

The adoption of hardware accelerators, such as field-programmable gate arrays, into general-purpose computation pipelines continues to expand, but programming models for these devices lag far behind their central processing unit (CPU) counterparts. While high-level synthesis (HLS) can help port some legacy software, many programs perform poorly without manual, architecture-specific optimization. We propose an end-to-end approach combining dynamic and static analyses to learn a model of functional behavior for off-the-shelf legacy code and synthesize a hardware description from this model. Our prototype implementation can correctly learn functionality for string kernels that recognize regular languages and provides a near approximation otherwise. We evaluate our prototype tool on a benchmark suite of real world, legacy string functions mined from GitHub and successfully synthesize—without modification or annotation—over 80% (72% exactly and a further 11% approximately). Traditional HLS, only after extensive modification and custom testbench generation, can synthesize the same number of benchmarks, but with results that have higher hardware requirements and lower maximum clock rates.

Prevalence of Atherosclerotic Risk Factors Among Children and Young Adults With Arterial Ischemic Stroke

Arterial ischemic stroke (AIS) incidence has decreased overall in recent decades yet has increased in young adults. The potential associations with atherosclerotic risk factors (ARFs) remain unknown. To assess the ages at which ARFs may be risk factors associated with AIS. A nested case-control study was conducted within Kaiser Permanente Northern California (KPNC) from January 1, 2000, through December 31, 2014. Data were analyzed from 2019 to 2022. Cases were identified using diagnostic codes and radiology reports. A total of 2 to 3 controls per case, matched on age and enrollment dates, were randomly identified and confirmed as stroke-free by medical record review. Only ARFs documented prior to stroke diagnosis (or the same date in controls) were considered to ensure the same period of observation. Comparisons were stratified by decade of life. Cases and controls were selected from the KPNC population (4.7 million children and 7.5 million young adults). Medical record review was conducted of all children (aged 29 days to 19 years) and a sample of young adults (aged 20-49 years) with International Classification of Diseases, Ninth Revision code or radiology text string search suggestive of AIS. Stroke-free controls were randomly selected. Hypertension, hyperlipidemia, diabetes, obesity, and smoking history. Odds of AIS. In all analyses, cases and controls were compared using logistic regression. A total of 141 pediatric cases (69 [48.9%] aged 29 days to 9 years; 72 [51.1%] aged 10-19 years) and 364 pediatric controls (168 [46.2%] aged 0-9 years; 196 [53.8%] aged 10-19 years) and 455 young adult cases (71 [15.6%] aged 20-29 years; 144 [31.6%] aged 30-39 years; and 240 [52.7%] aged 40-49 years) and 1018 young adult controls (121 [11.9%] aged 20-29 years; 298 [29.3%] aged 30-39 years; and 599 [58.8%] aged 40-49 years) were identified. The percent of the cases that were male or female did not differ from the percent in the control group. The odds ratio (OR) of having any ARFs on AIS was 1.87 (95% CI, 0.72-4.88) for age range 0 to 9 years; OR, 1.00 (95% CI, 0.51-1.99) for age range 10 to 19 years; OR, 2.3 (95% CI, 1.17- 4.51) for age range 20 to 29 years; OR, 3.57 (95% CI, 2.34-5.45) for age range 30 to 39 years; and OR, 4.91 (95% CI, 3.52-6.86) for age range 40 to 49 years. The risk associated with multiple ARFs was OR, 5.29 (95% CI, 0.47-59.4) for age range 0 to 9 years; OR, 2.75 (95% CI, 0.77-9.87) for age range 10 to 19 years; OR, 7.33 (95% CI, 1.92-27.9) for age range 20 to 29 years; OR, 9.86 (95% CI, 4.96-19.6) for age range 30 to 39 years; and OR, 9.35 (95% CI, 6.31-13.8) for age range 40 to 49 years. The ARF findings by both definitions were significant in all young adult groups. Atherosclerosis was the presumed etiology in 0% of cases in the age group 0 to 9 years, 1.4% in the age group 10 to 19 years, 8.5% in the age group 20 to 29 years, 21.5% in the age group 30 to 39 years, and 42.5% in the age group 40 to 49 years. Although atherosclerosis may not be a common cause of AIS in children or in early young adulthood, findings of this study suggest that ARFs associated with stroke in older adults are present in childhood and increase with age. Efforts to reduce these risk factors should begin as early as possible.

Generative Adversarial Networks for Gearbox of Wind Turbine With Unbalanced Data Sets in Fault Diagnosis

Signal measurement and diagnosis of wind turbine gearbox are very important for equipment maintenance. Generative adversarial networks (GAN) are particularly outstanding in data generation due to its game mechanism. An improved gear fault diagnosis method based on GAN for unbalanced data sets is proposed in this paper. Firstly, the fault data is encoded by binary vectorization based on kurtosis perceptron. Secondly, the individuals that fit the fitness are selected by the deterministic selection and the macro factor code string is crossed by multiple points. Then, gaussian mutation is focused on searching for local fault points. Finally, the nonlinear decision boundary is established by the logistic regression auxiliary classifier. The effectiveness of the proposed method was verified by three groups of comparison experiments. Compared with the existing methods, the proposed method has a better ability for fault feature generation, classification, and diagnosis accuracy under unbalanced data sets.

Optical likelihood calculation for quadrature phase‐shift keying signal based on silicon integrated waveguide

All-optical likelihood calculation has the potential to improve the performance of operating speed and power consumption in future communication systems. The author previously proposed a novel scheme of likelihood calculation, which is capable of applying multi-value modulation. However, these studies have a limitation that requires higher integration into an actual system. In this study, an optical device of likelihood calculation for a 4-bit quadrature phase-shift keying (QPSK) modulated signal (i.e. two sequential QPSK symbols) is demonstrated. The integrated device consists of two delayed interferometers with silicon waveguide. The device is designed according to the types of 4-bit code string as follows (00 00), (00 11), (11 00), and (11 11), and the output light waveform from the device is observed by an oscilloscope for a plurality of 4-bit QPSK signals. The light intensity obtained from the device accurately corresponds to the Hamming distance between the code string and the input signal. The results indicate that the proposed scheme correctly calculates a likelihood for a 4-bit QPSK signal at 10 Gbaud.

Prospects of Artificial Intelligence in Medical Devices – Benefits and Challenges

With advancements being accelerated in fields of Machine learning (ML), Deep Learning (DL), and the Internet of Medical Devices (IoMD), Artificial Intelligence (AI) has significantly evolved over the last decade. This transformation has opened doors for new possibilities of fostering AI in Medical Devices (AIMD) for applications that have not been explored before. With the current advancements in the computational capabilities, there has been a wide range of Medical Devices released in the global market, which are either supported by or driven by software to deliver its intended purpose. But with the advent of AI, software though built upon a string of codes has no restriction on the choices that it could make, since its essence lies on the deducing a decision based on a predictive analysis. This has called for rethinking our conventional approach towards the usage of Software in Medical Devices. Coupling with the rapid growth rate of these devices and their focus on delivering impeccable assistance to the facilitators who were doing things all by themselves previously, it also calls for a proper set of guidelines to regulate the usage of AIMD, considering its massive scalability and the gargantuan amounts of data that it brings along with it. This paper focuses on extrapolating the growth factors of AIMD along with the prerequisites that ought to be met by the organizations to prepare themselves for transforming and adopting this new technology. It also covers the aspects that are to be emphasized in regulating it in the forthcoming years.

Comparisonal Analysis Of Even-Rodeh Algorithm Code And Fibonacci Code Algorithm For Text File Compression

The growing requirement towards bigger storage space is the main cause of new compression techniques being developed. By doing compression, big chunks of data will be lowered in size to save storage space. In this research we will be using the Even-Rodeh Code and Fibonacci Code Algorithm, the performance will be benchmarked with bitrate, compression ratio, space saving, compression and decompression time in text files. Compression is done by reading strings on text files, and then the Even-Rodeh Code and Fibonacci Code Algorithm makes a string code and performs compression. The compression results are *.erc and *.fib files containing character information and string bit which can be decompressed. The decompression result is the original text file which can be saved using the extensions *.doc, *.docx, *.pdf. In this system test the sample used are strings that contains one type of character (Homogen String) and strings that contain several types of character (heterogeneous string) saved in text files *.doc,*.docx, *.pdf. In homogeneous string compressions we can observe that the Fibonacci Code is more efficient in compression ratio, averaging at 0.25 and faster decompression time at 0.011 millisecond average compared to the Even-Rodeh Code Algorithm. In heterogeneous string compression the Fibonacci Code performs better than the Even-Rodeh Code on an average compression ratio of 0.65 and a shorter average decompression time of 0.293 milliseconds.

Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm

College students spent most of their time in dormitory buildings, which account for a large proportion of the overall electricity use in university. Building performance is determined by its genes (i.e., building component characters), which can be optimized in the design stage. However, a building is comprised of many components, of which some are contradictory to each other in the building performance optimization process. In doing so, a digital gene map was proposed for the university dormitory, characterized by the binary code strings. Based on the digital gene map, the building elements can be parameterized to create some dynamic variables, to facilitate the multi-objective genetic algorithm. Via the multi-objective genetic algorithm and the data statistics tool of Design Explorer in this work, the “Pareto front” solutions can be obtained to optimize the decision-makings in the dormitory building design. For evaluations of the building performance improvement potentials for various types of study rooms with optimized solutions, we conducted the comparison studies in this work. Through comparison studies, we found that the optimized solutions from the multi-objective genetic algorithm, for the nine types of study rooms, have better compromised building performances. The methodology proposed in this work can be applicable for different types of university dormitories under various climate conditions, due to the dormitory buildings have a similar gene pool and chromosomal structure.

The Combinatorial Expressions and Probability of Random Generation of Binary Palindromic Digit Combinations

The aim of this paper is to obtain three types of expressions for calculating the probability of implementing palindromic digit combinations on a finite equally possible combination of zeros and ones. When calculating the probability of implementation of palindromic digit combinations, the classical definition of probability is applied. The main results of the paper are formulated in the form of three theorems. Moreover, the consequences of these theorems and typical examples of calculating the probability of implementing palindromic digit combinations in a data string of binary code are considered. All formulated theorems and their consequences are accompanied by proofs. The obtained numerical results of the paper can be used in the analysis of numerical computer data written as a binary code string in BIN format files. It should also be noted that the combinatorial expressions described in the article for calculating the number of palindromic combinations of digits in the binary number system can be used in number theory and in various branches of computer science. The development of these results from the point of view of obtaining an expression for calculating the number of palindromic combinations of digits in the binary number system contained in two-dimensional data arrays is also of immediate theoretical and practical interest. However, these results are not presented in this work, but they can be considered in subsequent publications.

Design Patterns Discovery in Source Code: Novel Technique Using Substring Match

The role of design pattern mining is a very significant strategy of re-engineering as with the help of detection one could easily understand complex systems. Of course, identifying a design pattern is not always a simple task. Additionally, pattern recovering methods often encounter problems dealing with space outburst for extensive systems. This paper introduces a new way to discover a design pattern based on an Impact Analysis matrix followed by substring match. UML diagrams corresponding to codes are created using Visual Paradigm Enterprise. Impact Analysis matrices of these UML diagrams are converted to string format. Considering system code string as main string and design pattern string as a substring, the main string is further decomposed. A substring match technique is developed here to discover design patterns in the source code. Overall, this procedure has the potential to convert the representation of system design and design pattern in ingenious shapes. In addition, this method has the advantage of moderation in the size. Therefore, this approach is beneficial for Software professionals and researchers due to its simplicity.

Semantic Segmentation of Satellite Images: A Deep Learning Approach Integrated with Geospatial Hash Codes

Satellite images are always partitioned into regular patches with smaller sizes and then individually fed into deep neural networks (DNNs) for semantic segmentation. The underlying assumption is that these images are independent of one another in terms of geographic spatial information. However, it is well known that many land-cover or land-use categories share common regional characteristics within a certain spatial scale. For example, the style of buildings may change from one city or country to another. In this paper, we explore some deep learning approaches integrated with geospatial hash codes to improve the semantic segmentation results of satellite images. Specifically, the geographic coordinates of satellite images are encoded into a string of binary codes using the geohash method. Then, the binary codes of the geographic coordinates are fed into the deep neural network using three different methods in order to enhance the semantic segmentation ability of the deep neural network for satellite images. Experiments on three datasets demonstrate the effectiveness of embedding geographic coordinates into the neural networks. Our method yields a significant improvement over previous methods that do not use geospatial information.

Evolutionary Operations of Interneuron Synaptic Structure for Feed-Forward Multilayer Networks

This paper mainly studies evolutionary operations of interneuron synaptic structure search based on the developed structure synthesis algorithm for feed-forward multilayer network trained with a teacher. The decomposition of neural network learning tasks into a series of subtasks is proposed. During evolutionary search the neural network is not converted to a genotype and all the transformations of synaptic structures are performed directly. This differs from a classical approach to genetic algorithms involving coding phenotype of individuals (structure) to genotype (code string).