Multiple Software Applications Research Articles

You are not Alone: Confusion with Software Applications in African University Libraries

Building on Nardi and O’Day’s (1999) classic book on information ecologies exploring the complexities of using multiple software applications in settings including libraries, we note that even though the technologies have changed over the decades, the importance of the work of librarians in assisting patrons has not changed much. Keeping up with popular, constantly evolving software packages like EndNote, Koha, NewGenlib, Baobab eLibrary, Openbiblio, Evergreen, Greenstone, and D-Space to support digital collections in various formats remains a constant challenge. constant challenge. Some librarians feel daunted by learning and explaining new software in a constantly changing technological ecosystem, while others embrace the challenge. As well as using software for their work, librarians have the responsibility to help their patrons learn, adapt, and apply new software to meet their information needs more effectively and efficiently. Like other client-facing professionals, librarians must also be mindful of the ‘myths’ (Ehrlich & Cash, 1994) that clients understand their problems and use online information if available. These software-related challenges have been documented in many countries (Asim et al., 2023, Adhikari, 2023), but our research identified a gap in the literature regarding the situation in Africa. The study implies the importance of education for librarians in adopting the right software and avoiding confusion. We conducted a review of LIS papers harvested from the LIS Ebscohost database about this topic in various countries of Africa between 2012 and 2022, identifying 75 papers as relevant according to our criteria. The poster presents the findings of the scoping review.

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Predicting Attack Paths from Application Security Vulnerabilities Using a Multi-Layer Perceptron

This paper is in the series of continuing research and proposes an approach to predicting possible attack paths from application security vulnerability-based attack trees. The attack trees are formed by stringing together weaknesses discovered in an application code and a group of applications within a domain. The Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) linked together as a string of vulnerabilities in the attack trees can be visualized as pathways for attacks. These pathways become potential attacks that can spread vertically and horizontally leading to a multi-path attack that can involve multiple software applications. With more data, and huge number of vulnerabilities, it will become impossible to identify all attack paths unless a full-scale implementation of an autonomous processing mechanism is in place. Machine Learning (ML) and Deep Learning (DL) techniques have been adopted in the cybersecurity space for decades, however all the studies have been around networks, endpoints, and device monitoring. This paper focuses on application security and building on earlier work cited, the use of a vulnerability map that uses attack vectors in a Deep Learning (DL) method implementing a Multi-Layer Perceptron (MLP) forms the basis for developing a predictive model that relates a set of linked vulnerabilities to an attack path. The results are encouraging, and this approach will help in identifying successful or failed attack paths involving multiple applications, isolated or grouped, and will help focus on the right applications and the vulnerabilities associated as priority for remediation.

OmicNavigator: open-source software for the exploration, visualization, and archival of omic studies

BackgroundThe results of high-throughput biology (‘omic’) experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features.ResultsWe present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access.ConclusionsOmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator

DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design.

With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.

Digital Color Output Conformity to ISO12647-7 Standards (GRACoL 2013 [CGATS21-2-CRPC6]) With the Use of Statistical Process Control (SPC)

The purpose of this applied research was to apply the statistical process control (SPC) to determine the digital color output conformity to ISO12647-7 standards in a color managed digital printing workflow (CMDPW) over a period of 100 days (N=100). The quality of digital color printing is determined by these influential factors: screening method applied, type of printing process, calibration method, device profile, ink (dry toner or liquid toner), printer resolution, and the substrate (paper). For this research, only the color printing attribute overall average color deviation (ACD; ΔE(2000)) was analyzed to examine the CMDPW process consistency in a day-to-day digital printing operation. Printed colors from the random sample size (n=80) were measured against the General Requirement for the Applications in the Commercial Offset Lithography (GRACoL 2013) standards to derive the colorimetric/densitometric values. Reference colorimetric values used in the analysis were the threshold deviations (acceptable color deviations) as outlined in the ISO12647-7 standards (GRACoL 2013). A control chart analysis was applied for further determining the process (CMDPW) ACD variation. The data collected were run through multiple software applications (Microsoft Excel/SPSS/Minitab) to apply various statistical methods. Analyzed data from the experiment revealed that the printed colorimetric values were in match (aligned) with the GRACoL 2013 (reference/target) standards. Since the color values were in control throughout the process, this enabled the CMDPW to produce consistent acceptable color deviation (average printed ΔE(2000)=2.978). (The acceptable threshold color deviation is ΔE(2000)≤3.00.)

Digital forensic approaches for metaverse ecosystems

The accelerating pace of digital transformation has given rise to metaverses that can participate freely in contactless environments. More than just game content, metaverses are driving everyday innovation across industries. However, threats are also prevalent, with crimes such as child sexual exploitation and privacy violations occurring in metaverses that mimic reality, making digital forensics for metaverse threats essential. Nevertheless, technical standards for different types of metaverses have yet to be defined, making investigation difficult. Furthermore, even though metaverses are complex forms that combine multiple hardware devices and software applications, existing studies have either focused on a single component or not analyzed the real-world environment. In this study, we derived a metaverse ecosystem with common components that comprise a metaverse and analyzed the hardware and software used throughout the user's metaverse lifecycle from a digital forensics perspective. In particular, we applied real-case-based scenario to the metaverse environment of the most popular Meta's currently in use to identify various artifacts that can be used across the ecosystem and validate the effectiveness of the process. We also developed a metaverse digital forensics tool for the first time in the current situation where open-source and commercial tools do not support metaverse investigations.

Virtual Training for Effective Education of Employees

Abstract The article deal with the problem of choosing an effective software tool for developing virtual trainings. The Ella platform as an innovative solution for creating of virtual training is presented. In the introductory part of the article, the Ella platform is presented as the result of our development. Ella is an integrated software ecosystem as a basis of multiple software applications focusing on the design and analysis of internal e.g. logistics, monitoring, and simulation of automotive logistics assets, and safety systems, etc. This article is focused on virtual training using the Ella platform. Furthermore, the article provides an example of virtual training that has already been carried out, with the Ella platform used in the preparation.

Increasing Energy Efficiency of Buildings in Serbia—A Case of an Urban Neighborhood

Energy efficiency, sustainability, and renewable energy sources are becoming increasingly relevant topics in today’s world. Buildings are one of the largest consumers of energy in society, and as such, improving their energy efficiency by reducing unnecessary energy loss and utilizing solar power is crucial. This paper comprehensively analyzes a neighborhood with buildings characteristic of the researched area by applying empirical and theoretical methods and calculations that have been proven in numerous individual cases. The main contribution of this paper is its demonstration that implementing methods to increase the energy efficiency of buildings and utilizing the potential of solar power can result in significant savings in energy consumption, increase the energy sustainability of the analyzed buildings, and substantially reduce the negative environmental impact. The novelty of this study lies in the location and multiple software applications for data analysis. The data and conclusions obtained in this paper serve as a foundation and path towards sustainable development in the field of energy efficiency for buildings in this and similar areas. Heat loss was calculated by analyzing households in the urban neighborhood of Nova Kolonija. All analyzed houses exceeded the maximum allowed annual required energy for household heating (75 kWh/m2). After the reconstruction and implementation of the proposed measures to increase energy efficiency, all houses met the requirements and entered a higher energy class, C. Energy for heating was reduced from 9294.68 kWh/a to 4641.84 kWh/a, representing a reduction of 50.03%. Simulations were conducted regarding solar rooftop power plant installation of 5655 Wp capacity. Software that was used included: a Photovoltaic Geographical Information System (PVGIS), Photovoltaic System software (PVsyst 7.3.), and Photovoltaic Design and Simulation software (PV*SOL). The results of the analysis indicated that the average amount of electricity produced is 6186.98 kWh, which meets 98.12% of households’ annual electricity consumption of 6278.41 kWh. The paper contributes to the existing body of literature and provides significant insight for both practical implications and future studies.

Digital Twins and Cultural Heritage Preservation: A Case Study of Best Practices and Reproducibility in Chiesa dei SS Apostoli e Biagio

The use of digital twin technologies to preserve cultural heritage has become increasingly common over the past two decades. Evolving from the use of virtual environments (VE) and digital reconstructions that required multiple phases of workflow and multiple software applications and various hardware to output a useable experience to the immediacy of 3D artificial intelligence (AI) generative content and the latest generation of photogrammetric scanning, non-specialists are now able to more easily create digital twins. At the same time, the destruction of cultural heritage has accelerated due to geopolitical instability, seen in examples such as the invasion of Ukraine by Russia (2022). Even with advances in user-friendly and commercially available technologies, digital art history and the digital humanities are in a race against time to train and equip enough individuals onsite to create digital twins before more irreplaceable cultural artifacts and sites are lost to natural disasters, accelerated by climate change, or through armed conflict. However, there remain no international standards for methodological reproducibility and the techniques used currently by many scholars include specialized training and knowledge. As such, this paper presents a case study that addresses reproducibility and explainability in the digital humanities through a detailed workflow of the creation of a digital twin of Chiesa dei SS Apostoli e Biagio in Florence, Italy. A model is presented that is scalable and leverages widely available, user-friendly 360 cameras and photogrammetry with LiDAR to capture cultural heritage sites with best practices on how to quickly and effectively train non-specialists to create site-specific digital twins of a variety of cultural heritage structures.

A Review on Transformation of Monolithic Applications towards Microservices Environment

The traditional monolithic approach is widely employed in centralized software development, deployment, and reusability, as the modules are tightly connected, causing several challenges in programming. The study utilized different techniques for the easy transformation of several running monolithic applications to micro services including, Angular 2, REST API, Web application and several other architectural approaches are discussed. This review paper highlights the significance of microservices and the transformation of monolithic applications towards microservices. As multiple software applications are an integral part of a traditional monolithic application, the modules cannot be extended separately, and different modules cannot use various technology stacks. So, monolithic source code must be migrated to the microservice platform in order to extend `the lifecycle of applications in today's environment. However, due to structural complexity, scattered application logic, and dependency upon external framework libraries, the transformation towards a microservices platform is quite challenging. A Microservice architecture is a container of loosely coupled independent services making a flexible system. In this study, potential areas for the transformation of monolithic application source code are highlighted. Furthermore, key challenges and open research issues in this area are highlighted, requiring the research community's attention. The study concludes that Microservices are not a one-size-fits-all solution for every challenging situation. Monolithic transformation requires significant amount of time and effort on the part of everyone in the business.

Statistical evaluation of a Color Managed Digital Printing Workflow (CMDPW) consistency [4th C of CMW

The purpose of this applied research was to determine the Color Managed Digital Printing Workflow (CMDPW) consistency (4th C of the color management) over a period of time [100 days, (N = 100)]. The quality of digital color printing is determined by these influential factors: screening method applied, type of printing process, calibration method, device profile, ink (dry-toner or liquid-toner), printer resolution and the substrate (paper). For this research, only the color printing attributes such as the overall average color deviation [ACD, ΔE (2000)] and the solid ink density (SID) were analyzed to examine the CMDPW process consistency in a day-to-day digital printing operation. These are the color attributes which are monitored and managed for quality accuracy during the printing. Printed colors of the random sample size (n = 80) were measured against the GRACoL2013 standards to derive the colorimetric/densitometric values. Reference colorimetric values used in the analysis were the threshold deviations (acceptable color deviations) as outlined in the ISO12647-7 standards (GRACoL2013). A control charts analysis was applied for further determining the process (CMDPW) SID and ACD variation. The data collected were run through multiple software applications (MS-Excel/SPSS/Minitab) to apply various statistical methods. Analyzed data from the experiment revealed that the printed colorimetric values were in match (aligned) with the GRACoL 2013 (reference/target). Since the SID values of CMYK colors were in control throughout the process, this enabled the CMDPW to produce consistent acceptable color deviation (Average Printed ΔE (2000) = 2.978; SD = 0.437; Acceptable Threshold color deviation is ΔE (2000) ≤ 3.00).

Computation and Optimization of Structural Leaf Venation Patterns for Digital Fabrication

The morphogenetic design process of networking patterns produces anisotropic structural systems that can offer generative solutions for custom design applications. As an example of this type of pattern application, the leaf venation algorithm is introduced that can be customized through parametric inputs and density maps. This method is extended onto mesh surfaces incorporating multiple software applications combining aspects of parametric design, optimization and digital fabrication. The dynamic workflow is presented using a case study project titled “Calyx,” a public artwork completed using the computational tools developed as part of the research. The networking structural pattern of the sculpture yielded to the development of a geometry optimization process that allowed the digital fabrication of planarized structural members. The technical aspects of the design development and post-rationalization process for the construction of leaf venations patterns are discussed.

Using Machine Learning to Improve Public Reporting on U.S. Government Contracts

The U.S. government procures more than $500 billion annually in goods and services on public contracts, which it classifies using a hierarchical product and service taxonomy. Classification serves several purposes, including transparency in the use of taxpayer funding; reporting, tracing, and segmenting government expenditures; budgeting; and forecasting. Government acquisition personnel have historically performed these classifications manually, resulting in a process that is time-consuming and error-prone and offers limited visibility into government purchases. The problem faced is not unique to the public sector and is common across retail, manufacturing, and healthcare, among other settings. Using almost 4 million historical data records on governmental purchases, we fit a series of classifiers and demonstrate (a) superior performance when explicitly modeling the hierarchical structure of information domains through the use of top-down strategies and (b) the effectiveness of character-level convolutional neural networks when textual inputs are terse and contain irregularities such as abnormal character combinations and misspellings, which are common in government contracts. Our machine learning models are embedded in multiple software applications, including a web application that we developed, used by federal government personnel and other contracting professionals.

A Virtual 3D Dynamic Model of Caries Lesion Progression as a Learning Object for Caries Detection Training and Teaching: Video Development Study

BackgroundIn the last decade, 3D virtual models have been used for educational purposes in the health sciences, specifically for teaching human anatomy and pathology. These models provide an opportunity to didactically visualize key spatial relations that can be poorly understood when taught by traditional educational approaches. Caries lesion detection is a crucial process in dentistry that has been reported to be difficult to learn. One especially difficult aspect is linking clinical characteristics of the different severity stages with their histological features, which is fundamental for treatment decision-making.ObjectiveThis project was designed to develop a virtual 3D digital model of caries lesion formation and progression to aid the detection of lesions at different severity stages as a potential complement to traditional lectures.MethodsPedagogical planning, including identification of objectives, exploration of the degree of difficulty of caries diagnosis–associated topics perceived by dental students and lecturers, review of the literature regarding key concepts, and consultation of experts, was performed prior to constructing the model. An educational script strategy was created based on the topics to be addressed (dental tissues, biofilm stagnation areas, the demineralization process, caries lesion progression on occlusal surfaces, clinical characteristics related to different stages of caries progression, and histological correlations). Virtual 3D models were developed using the Virtual Man Project and refined using multiple 3D software applications. In the next phase, computer graphic modelling and previsualization were executed. After that, the video was revised and edited based on suggestions. Finally, explanatory subtitles were generated, the models were textured and rendered, and voiceovers in 3 languages were implemented.ResultsWe developed a 6-minute virtual 3D dynamic video in 3 languages (English, Spanish, and Brazilian Portuguese) intended for dentists and dental students to support teaching and learning of caries lesion detection. The videos were made available on YouTube; to date, they have received more than 100,000 views.ConclusionsComplementary pedagogical tools are valuable to support cariology education. This tool will be further tested in terms of utility and usability as well as user satisfaction in achieving the proposed objectives in specific contexts.

Chapter 7 - Adult EEG

After more than 85 years of development and use in clinical practice, the electroencephalogram (EEG) remains a dependable, inexpensive, and useful diagnostic tool for the investigation of the electrophysiologic activity of the brain. The advent of digital technology has led to greater sophistication and multiple software applications to extend the utility of EEG beyond the confines of the laboratory. Despite the discovery of new waveforms, basic neurophysiologic principles remain essential to the clinical care of patients. Patterns in the interictal EEG make it possible to clarify the differential diagnosis of paroxysmal neurological events, classify seizure type and epilepsy syndromes, and characterize and quantify seizures when ictal recordings are obtained. EEG can also demonstrate cerebral dysfunction when structural imaging is normal to detect focal or lateralized abnormalities in patients with encephalopathy. High-density EEG with electrical source imaging has improved localization in candidates for epilepsy surgery. Quantitative EEG and broadband EEG are advancing our understanding of the functional processes of the brain itself.

An ontology-based data integration framework for construction information management

Information management during the construction phase of a built asset involves multiple stakeholders using multiple software applications to generate and store data. This is problematic as data come in different forms and are labour intensive to piece together. Existing solutions to this problem are predominantly in proprietary applications, which are sometimes cost prohibitive for small engineering firms, or conceptual studies with use cases that cannot be easily adapted. In view of these limitations, this research presents an ontology-based data integration framework that makes use of open-source tools that support Semantic Web technologies. The proposed framework enables rapid answering of queries over construction data integrated from heterogeneous sources, data quality checks and reuse of project software resources. The attributes and functionalities of the proposed solution align with the requirements common to small firms with limited information technology skill and budget. Consequently, this solution can be of great benefit for their data projects.

Analysis methodology and development of a statistical tool for biodistribution data from internal contamination with actinides

The aim of this work was to develop a computational tool that integrates several statistical analysis features for biodistribution data from internal contamination experiments. These data represent actinide levels in biological compartments as a function of time and are derived from activity measurements in tissues and excreta. These experiments aim at assessing the influence of different contamination conditions (e.g. intake route or radioelement) on the biological behavior of the contaminant. The ever increasing number of datasets and diversity of experimental conditions make the handling and analysis of biodistribution data difficult. This work sought to facilitate the statistical analysis of a large number of datasets and the comparison of results from diverse experimental conditions. Functional modules were developed using the open-source programming language R to facilitate specific operations: descriptive statistics, visual comparison, curve fitting, and implementation of biokinetic models. In addition, the structure of the datasets was harmonized using the same table format. Analysis outputs can be written in text files and updated data can be written in the consistent table format. Hence, a data repository is built progressively, which is essential for the optimal use of animal data. Graphical representations can be automatically generated and saved as image files. The resulting computational tool was applied using data derived from wound contamination experiments conducted under different conditions.In facilitating biodistribution data handling and statistical analyses, this computational tool ensures faster analyses and a better reproducibility compared with the use of multiple office software applications. Furthermore, re-analysis of archival data and comparison of data from different sources is made much easier. Hence this tool will help to understand better the influence of contamination characteristics on actinide biokinetics. Our approach can aid the optimization of treatment protocols and therefore contribute to the improvement of the medical response after internal contamination with actinides.

Design and Evaluation of a Metropolitan Air Pollution Sensing System

Urban air pollution is believed to be a major contributor to premature deaths and chronic illnesses worldwide. Current systems for urban air pollution monitoring rely on static sites with low spatial resolution, and moreover, lack the means to estimate exposures for (potentially mobile) individuals in order to make medical inferences. This paper describes the design and evaluation of a low-cost participatory sensing system called HazeWatch that uses a combination of portable mobile sensor units, smart-phones, cloud computing, and mobile apps to measure, model, and personalize air pollution information for individuals. Our contributions are three-fold: 1) we architect, prototype, and compare multiple hardware devices and software applications for collecting urban air pollution data with high spatial density in real-time; 2) we develop web-based tools and mobile apps for the visualization and estimation of air pollution exposure customized to individuals; and 3) we conduct field trials to validate our system and demonstrate that it yields much more accurate exposure estimates than current systems. We believe our system can increase user engagement in exposure management, and better inform medical studies linking air pollution with human health.

Network Services and Their Compositions for Network Science Applications

Network science is moving more and more to computing dynamics on networks (so-called contagion processes), in addition to computing structural network features (e.g., key players) and other parameters. Generalized contagion processes impose additional data storage and processing demands that include more generic and versatile manipulations of networked data that can be highly attributed. In this work, we describe a new network services and workflow system called MARS that supports structural network analyses and generalized network dynamics analyses. It is accessible through the internet and can serve multiple simultaneous users and software applications. In addition to managing various types of digital objects, MARS provides services that enable applications (and UIs) to add, interrogate, query, analyze, and process data. We focus on several network services and workflows of MARS. We also provide a case study using a web-based application that MARS supports, and several performance evaluations of scalability and work loads. We find that MARS efficiently processes networks of hundreds of millions of edges from many hundreds of simultaneous users.