Computer Experts Research Articles

AI and work in the creative industries: digital continuity or discontinuity?

There is current uncertainty about the effects of generative AI technologies on labour markets and conditions of work in creative industries. Previous scholarship identifies the potential for both replacement and displacement of human labour due to AI adoption. This article examines potential effects of AI adoption by drawing on case studies of 6 commercial products using AI, investigating conditions of work and mechanisms by which human creative labour might be replaced or displaced by AI technology. The main findings are that AI products were more labour intensive than traditional media products because they combined traditional production skills and new computational expertise. Paradoxically, contributions by human workers tended to be invisibilised in final products that foregrounded AI technology. There was potential for displacement of human labour in the ideation phase, with AI enabling producers to explore different creative possibilities. Some AI products had the potential to compete with earlier production methods (e.g. AI-generated imagery vs. stock photography). Overall, case study findings were consistent with ongoing challenges from digitalisation: conditions of deskilling, re-skilling, flexible employment and uncertainty remain intense for small firms engaged in AI-assisted creative production.

Easy and accurate protein structure prediction using ColabFold.

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2's models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com .

On the theory of quantum and towards practical computation: A review

Quantum computing exposes the brilliance of quantum mechanics through computer science (in theory and otherwise), giving oneself a marvelous and exhilarating journey to go through. This review article leads along that journey with a historical and current outlook on quantum computation that is geared toward computer experts (via algorithmics) but also to experts from other disciplines as well. It is an article that will open an entering wedge through which one will be able to bring himself up to speed on quantum computation and challenges/limitations thereof. We are indeed in luck to be living in an age where computing is being reinvented, and not only seeing history in the making firsthand but, in fact, having the opportunity to be the ones who are reinventing—and that is quite a thought.

Interdisciplinarity through internationality: Results from a US-Mexico graduate course bridging computational and plant science.

Interdisciplinarity is used to integrate and synthesize new research directions between scientific domains, but it is not the only means by which to generate novelty by bringing diverse perspectives together. Internationality draws upon cultural and linguistic diversity that can potentially impact interdisciplinarity as well. We created an interdisciplinary class originally intended to bridge computational and plant science that eventually became international in scope, including students from the United States and Mexico. We administered a survey over 4 years designed to evaluate student expertise. The first year of the survey included only US students and demonstrated that biology and computational student groups have distinct expertise but can learn the skills of the other group over the course of a semester. Modeling of survey responses shows that biological and computational science expertise is equally distributed between US and Mexico student groups, but that nonetheless, these groups can be predicted based on survey responses due to subspecialization within each domain. Unlike interdisciplinarity, differences arising from internationality are mostly static and do not change with educational intervention and include unique skills such as working across languages. We end by discussing a distinct form of interdisciplinarity that arises through internationality and the implications of globalizing research and education efforts.

Computational Thinking Processes in Solving the Corona Epidemic Model: Pre-service Maths Teachers

In the 21st century, pre-service mathematics teachers are expected to have problem-solving skills that are effective, efficient, and solutive and are in line with the mindset of computer experts. In learning mathematics, the concept of computational thinking (CT) is also needed and at this time, many still have difficulty solving mathematical problems in general, especially in solving problems in epidemic mathematical models. The subjects of this study were twenty-seven pre-service mathematics teacher students who took mathematical modeling courses. The researcher used the purposive sampling technique to select two research samples. The research method used was a descriptive qualitative research method in exploring the thinking process of pre-service mathematics teacher students in solving the problem of modeling the epidemic spread of disease. The results showed that the thinking process of the first subject began with identifying the problem and existing information by writing down the data in the form of a graph so as to get a certain pattern, which was then used as the basis for the process of transforming the problem into mathematical language. By adding assumptions related to the existence of environmental limitations in the next epidemic model, the concept of differential equations, in which there are integral properties and natural logarithms, can be used to find the solution to the epidemic model. The second subject was unable to solve the integral at hand. The researcher discovered that pre-service mathematics teacher students who correctly solved the problem in the mathematical model used CT components, namely decomposition, abstraction, pattern recognition, algorithm and mathematical literacy.

Futuristic Design of Ethnic Clothing Using 3D Technologies

The author considers the use of innovative 3D printing technologies as a tool used by modern clothing designers striving for uniqueness and a futuristic look of their products. Particular attention is paid to the prospects for introducing 3D printing into the fashion industry and the opportunities it provides for individualisation, experimentation with shapes and textures, as well as the creation of new aesthetically attractive designs. The article may be of interest to students of the creative speciality of Fashion Design, design specialists exploring modern technologies in the fashion industry, as well as for those interested in innovation and creative approach to modern and promising clothing design. 3D printing allows one to create unique and personalised products such as jewellery, patterns, accessories that meet individual needs and preferences. Designers can adapt existing patterns or create their own, which will lead to a more exclusive approach to designing clothes, accessories, and jewellery. The author's desire to draw attention to the influence of 3D printing on the cultural code sewn into ethnic decor in order to give new breath to traditional symbolism is the goal of this study. Ethnic motifs and patterns made on a 3D printer can serve as separate details of decor, as well as be elements of a clothing item itself in its various manifestations. Ethnic patterns are an important aspect of world fashion, reflecting the cultural heritage and traditions of different peoples. The identification of the degree of significance of modern technologies in the life of an artist of decorative and applied arts was the result of this study. According to the author, the introduction of 3D printing into the creative process of a designer expands their capabilities, stimulates the development of innovations, and transforms the fashion industry. The results of the study showed that 3D printing opens up unique opportunities for the creative process of a designer. However, the study also revealed a number of technical problems associated with 3D printing of clothes that need to be solved for the widespread implementation of this technology in the fashion industry. Further research and cooperation with such specialists as engineers and computer experts are necessary to fully realise the potential of 3D printing.

Spray inoculation and image analysis-based quantification of powdery mildew disease severity on pea leaves

Pea (Pisum sativum) is an important agricultural legume crop, but powdery mildew disease caused by the biotrophic fungus Erysiphe pisi regularly limits its annual yield. Assays to evaluate the efficacy of potential antifungal compounds or resistance genes for disease control require a simple fungal inoculation method that provides control over the initial inoculum concentration and enables uniform inoculum distribution within a leaf and across replicates as well as a method for the quantitative assessment of disease severity. Here, we present an easy spray inoculation method for the uniform distribution of a defined concentration of E. pisi conidia on the leaves of pea plants and a semi-automated image analysis-based quantification of disease symptoms. The uniformity in conidial distribution was validated using a novel grading system termed the uniformity index. In addition, RT-qPCR was used to validate the reproducibility of the spray inoculation method and image analysis-based disease quantification. These procedures permit the accurate quantification of powdery mildew disease severity at macroscopic and molecular levels.•Uniform and reproducible inoculum distribution on leaves using a simple and inexpensive spray device•Rapid and reproducible quantification of powdery mildew disease symptoms using open-source software without the requirement of computational expertise

A REVIEW PAPER ON ETHICAL HACKING

Ethical Hacking is a proactive approach to cybersecurity that involves authorizing and systematically testing information systems to find vulnerabilities before they can be exploited by criminals. Ethical hackers are network and computer experts who attack security systems on behalf of their owners, looking for vulnerabilities that can be exploited by malicious hackers. The explosive growth of the Internet has given rise to many good things, including electronic commerce, email, collaborative computing, and new areas of advertising and information distribution. Ethical hacking, also known as penetration testing, or red teaming, has become a major concern for businesses and governments. Organizations are concerned about the possibility of being "hacked" and potential customers are concerned about having their personal information under control. Hackers are classified according to their work and knowledge. White hat hackers are ethical hackers. Ethical hackers use hacking techniques to ensure security. Ethical hacking is necessary to protect systems from damage. KEYWORDS: Cybersecurity, Ethical Hacking, It’s Impact, Vulnerability Assessment, Security Tool, Cyberthreats

THE FUTURE OF MOBILE CLOUD COMPUTING: KEY IMPLICATIONS AND CHALLENGES

This research aims to examine the future development of Mobile Cloud Computing (MCC) and identify the key implications and challenges that this technology will face. In the face of the technological revolution, especially with the adoption of 5G networks and integration with the Internet of Things (IoT), MCC has the potential to transform various industry sectors. The study employs a qualitative methodology through a literature review and semi-structured interviews with experts in cloud technology, mobile computing, and technology policy. The literature review is conducted to understand the latest technological trends and the challenges faced by MCC, while interviews provide direct insights from industry practitioners and policymakers about the future of MCC. The research findings indicate that MCC will increasingly integrate with 5G technology and IoT, enabling the adoption of more complex applications such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI). However, several key challenges need to be addressed, such as data security issues, reliance on network infrastructure, and regulations that are not yet fully adaptive to MCC developments. Experts emphasize the importance of enhancing security standards, developing edge computing to reduce latency, and fostering collaboration between governments and industry to create policies that support innovation.

Computational fluid dynamics (CFD)- deep neural network (DNN) model to predict hydrodynamic parameters in rectangular and cylindrical bubble columns

Bubble columns are omnipresent in the chemical, bio-chemical, petrochemicals, petroleum industries, but their design and scale-up is complex owing to its complex hydrodynamics. Liquid velocity and gas holdup is one of the critical hydrodynamic parameters which effects the mixing, heat and mass transfer in bubble columns. CFD is widely recognized as a powerful tool for estimating critical hydrodynamic parameters but requires significant computational resources, time and expertise. These limitations restrict its practical use in hydrodynamic simulations that need real-time processing involving large-scale simulations of bubble columns. To overcome these limitations, CFD-DNN model is developed to predict the time averaged gas holdup and axial liquid velocity at various operating conditions. The DNN model was trained using CFD data that was produced for rectangular (with dimensions L=0.2 m, W=0.05 m, H=1.2 m) and cylindrical (with a diameter of 0.19 m) bubble columns. The data covers a range of operating conditions and various flow regimes. The superficial gas velocity for the rectangle column was selected at 1.33 and 7.3 mm/s, whereas for the cylindrical bubble column, it was fixed at 0.02 and 0.12 m/s. The CFD-DNN model was validated against the experimental and the CFD data from the literature. Further, the model was tested for new data that the CFD-DNN model has not seen with existing literature and showed good agreement with their data and it reflects the excellent generalization ability of the model. The proposed CFD-DNN approach improves current CFD models by providing shorter computing time, decreasing computational expenses, and reducing the expertise in CFD simulations. The accuracy of the developed CFD-DNN model was evaluated using different metrics for gas holdup and axial liquid velocity. For rectangular bubble columns, the model achieved MSE of 0.0001 for gas holdup and 0.0007 for axial liquid velocity. Similarly, for cylindrical bubble columns, the MSE values were 0.0009 for gas holdup and 0.0006 for axial liquid velocity.

From in vitro to in silico: a pipeline for generating virtual tissue simulations from real image data.

3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, which creates a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring that the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline enables more researchers to utilize these powerful tools. Our approach aims to foster a broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.

IMPLEMENTATION OF THE TEACHING FACTORY MODEL TO ENHANCE COMPETENCE IN COMPUTER AND NETWORK ENGINEERING EXPERTISE AT SMK ACEH SELATAN

The teaching factory learning model has become an important strategy in enhancing students' expertise in Vocational High Schools (SMK) in South Aceh. This study aims to evaluate the effectiveness of this learning model in improving students' skills, particularly in the concentration of computer and network engineering expertise. The research shows that products, whether goods or services, in the teaching factory learning model play a crucial role as the main medium in delivering expertise to students. Block schedules are used to optimize resources efficiently, while Job Sheets regulate the sequence of materials to achieve the desired competencies. Supporting factors in the implementation of the teaching factory include support from school management, active student participation, and trust from the community as consumers of the products. However, some obstacles have also been identified, such as lack of socialization, suboptimal teaching staff, and limited facilities and infrastructure. Practical learning processes are conducted according to real work procedures, with a focus on student-centered learning, self-learning, and learning by doing principles. This allows students to learn through direct practice, not just theory. Thus, the teaching factory learning model at SMK in South Aceh contributes to enhancing students' expertise in the concentration of computer and network engineering.

ВОЗМОЖНОСТИ ПОЛУЧЕНИЯ И ИСПОЛЬЗОВАНИЯ КРИМИНАЛИСТИЧЕСКИ ЗНАЧИМОЙ ИНФОРМАЦИИ ИЗ МЕТАДАННЫХ ФАЙЛОВ ПРИ РАСКРЫТИИ И РАССЛЕДОВАНИИ ПРЕСТУПЛЕНИЙ

Modern technologies and the digitalization of society have led to the fact that most of the information in the world is stored and transmitted electronically. This circumstance opens up new opportunities for law enforcement agencies in the detection and investigation of crimes. In today's world, where technology plays a key role in society, digital footprints are becoming an important element in crime investigation. Everyone has already got used to the fact that the main object of computer expertise in the field of computer information is the contents of files. However, such an important area as the study of file metadata remains underestimated in the investigation process. Metadata is an integral part of mod-ern crime investigation, providing investigators with valuable data for analysis and helping to restore the chronology of events and actions of persons associated with the crime. In the field of digital fo-rensics, special attention should be paid to the study and analysis of electronic traces that can be found in metadata. The issues of using file metadata have been touched upon in one way or another by many scien-tists, such as L.A. Buraeva [1], V.A. Gauzhaeva [2], R.R. Kardanov [3], Yu.M. Usoltsev [4], A.S. Arutyunov [5] and others. However, these authors made only an attempt to draw attention to the use of metadata in the investigation of crimes. Metadata may contain information about the time the file was created, its author, location, and other data that can be used in proving criminal cases. Metadata in the commission of crimes related to the use of information and telecommunication technologies can be compared with micro-objects in the commission of traditional crimes. Both are invisible traces, which are not so easy to destroy and to which few criminals pay attention. At the same time, they can provide significant assistance to the investigation in uncovering and investigat-ing crimes. This article attempts to draw the attention of the scientific world and employees involved in the preliminary investigation of crimes to the problems and possibilities of using metadata in the process of disclosure and investigation of crimes.

Creating Instagram Feeds as Arts and Culture Weeks Promotional Media with Canva Application and EPIC Method

This study aimed to develop an engaging Instagram feed design to enhance visitor participation at the Cultural Arts Week event. The research employed a case study method with a qualitative approach. The sample consisted of 16 respondents, including the chief executive of Cultural Arts Week, 2 computer experts, 2 graphic design experts, and 11 visitors to the event, all of whom were students at the IPB Vocational School. The effectiveness of the design was assessed using the EPIC method, which evaluates Empathy, Persuasion, Impact, and Communication. The results indicated a score of 4.25 for Empathy, 4.1 for Persuasion, 4.12 for Impact, and 4.27 for Communication, yielding an overall EPIC Rate of 4.18.

Based on the S–O–R theory adoption intention of blockchain technology in libraries: a two-stage analysis SEM–PLS and ANN

PurposeBlockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.Design/methodology/approachUtilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.FindingsThe research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.Originality/valueUnlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

Fellows of the American Medical Informatics Association (FAMIA): Looking Back and Looking Ahead.

Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018. To outline the evolution of the FAMIA program and shed light on its origins, development, and impact. This report explores factors that led to the establishment of FAMIA, considerations affecting its development, and the objectives FAMIA seeks to achieve within the broader context of AMIA. The development of FAMIA is examined through a historical lens, encompassing key milestones, discussions, and decisions that shaped the program. Insights into the formation of FAMIA were gathered through discussions within AMIA membership and leadership, including proposals, board-level discussions, and the involvement of key stakeholders. Additionally, the report outlines criteria for FAMIA eligibility and the pathways available for recognition, namely the Certification Pathway and the Long-Term Experience Pathway. The FAMIA program has inducted five classes, totaling 602 fellows. An overview of disciplines, roles, and application pathways for FAMIA members is provided. A comparative analysis with other fellow recognition programs in related fields showcases the unique features and contributions of FAMIA in acknowledging applied informatics. Now in its sixth year, FAMIA acknowledges the growing influence of applied informatics within health information professionals, recognizing individuals with experience, training, and a commitment to the highest level of applied informatics and the science associated with it.

Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

Gut microbiome diversity within Clostridia is negatively associated with human obesity.

The gut microbiota is diverse across various taxonomic levels. At the intra-species level, it comprises multiple strains, some of which may be host-specific. However, our understanding of fine-grained diversity has been hindered by the use of the conserved 16S rRNA gene. While shotgun metagenomics offers higher resolution, it remains costly, may fail to identify specific microbes in complex samples, and requires extensive computational resources and expertise. To address this, we employed a simple and cost-effective analysis of alternative genetic markers to explore diversity within Clostridia, a crucial group within the human gut microbiota whose diversity may be underestimated. We found high intra-species diversity for certain groups and associations with obesity. Notably, we identified Vescimonas, an understudied group. Making use of metagenomic data, we inferred functionality, uncovering potential beneficial roles in dietary fiber and carbohydrate degradation, as well as in short-chain fatty acid production.

584 A Systematic Review of Generative Adversarial Networks (GANs) in Plastic Surgery

Abstract Aim Generative Adversarial Networks (GANs), based on zero-sum game theory, involve two neural networks—the generator and discriminator—to create realistic synthetic data. They're becoming increasingly significant in medicine and plastic surgery. This review assesses GANs' impact in Plastic Surgery, offering a framework for their application and evaluation in various subspecialties, highlighting their potential in research and patient care advancements. Method Following PRISMA guidelines, a systematic review was performed of all applications of GANs within Plastic Surgery and its subspecialties published from 2014 to 2022. Three independent reviewers screened studies using COVIDENCE review management software, from databases including PubMed, Embase, PsychInfo, Scopus and Google Scholar. Results A total of 70 studies were captured by the search, of which seven studies were included in the final analysis. The Kappa score for interrater reliability was 0.97. The most common subspecialty represented was craniofacial (n=4). Proposed uses of GANs ranged from facial recognition, burn estimation, scar prediction and post-breast cancer reconstruction anomaly scoring. All proposed GANs were conditional, with four of the studies using primarily collected training datasets, and the remainder using public datasets. Average dataset training size was 54,593 (range: 300,575-6). All of the studies had different reporting structures, and only four studies had a form of cross-evaluation qualitatively (n=1) or quantitatively (n=3) Conclusions GANs can transform research and patient care. Standardised reporting and diverse dataset transparency are essential. Despite their creation needing computational expertise, it's vital for plastic surgeons to understand GAN development to fully leverage their potential in plastic surgery and related fields.

Accelerated 3D CFD modeling of multichannel flat grooved heat pipes

Flat grooved heat pipes (HPs) have become essential in advanced thermal management solutions across various engineering applications. Modeling these devices, especially multichannel flat grooved HPs, involves significant challenges due to complex phenomena such as phase-change heat transfer and free-surface flow, requiring substantial computational resources, time and expertise. These constraints often limit the full exploration and optimization of HPs’ potential in diverse applications. To address this gap, an accelerated 3D computational fluid dynamics (CFD) modeling approach is presented in this study. This novel method begins with a detailed 3D modeling of a single groove, developed using kinetic theory and facilitated by CFD software. The results from this model are then applied as boundary conditions to simulate the entire HP in a multichannel configuration. The importance of this methodology is further highlighted by the alignment of simulation results with experimental observations. The approach significantly enhances computational efficiency by reducing the number of iterations by 10% and computational time by 80%, resulting in a five-fold speed-up. The methodology enables accelerated, comprehensive modeling of multichannel variations and delivers critical insights for optimizing the design of multichannel flat grooved HPs for various engineering applications.