Sci, Volume 5, Issue 4 (December 2023) – 10 articles

In recent years, generative transformers have become increasingly prevalent in the field of artificial intelligence, especially within the scope of natural language processing. This paper provides a comprehensive overview of these models, beginning with the foundational theories introduced by Alan Turing and extending to contemporary generative transformer architectures. The manuscript serves as a review, historical account, and tutorial, aiming to offer a thorough understanding of the models’ importance, underlying principles, and wide-ranging applications. The tutorial section includes a practical guide for constructing a basic generative transformer model. Additionally, the paper addresses the challenges, ethical implications, and future directions in the study of generative models. Full article

This study explored the future-oriented perceptions of Generation Z students in a foundation university. This study was conducted using qualitative research and a phenomenological design. The study sample consisted of 11 university students over the age of 18 who agreed to participate in the study. Data were collected online through individual interviews in Türkiye. Colaizzi’s phenomenological analysis method was used in the data analysis. The content analysis determined three main themes and eleven sub-themes. The first theme was the students’ knowledge acquisition about the “current situation of the country.” Under this theme were four sub-themes: economic problems, the immigrant situation, the education and justice system, and the country’s agenda. In the second theme, students shared their opinions about “being a student in the country.” This theme included economic impossibilities, their participation in limited social activities, and housing problems. In the last theme, “future anxiety,” the sub-themes of the students were found to include experiences hopelessness versus hope. Uncertainty caused anxiety, as did going abroad, finding a job, and improving themselves. It was determined that the participants were worried about the current situation in the countries they lived in during this period due to economic problems; while some were hopeful about the future, some were hopeless and would go abroad. This study might contribute to the literature on determining the future-oriented perceptions, possible stressors and hope levels of Generation Z university students in Türkiye. Additionally, intervention programs can be developed for the management these stressors to protect the mental health of Generation Z university students. On the other hand, it is necessary to protect the mental health of young people, who are the adults of the future, and to create policies for the youth of this country where social opportunities are maintained. Full article

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in “coopetition”. This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning. Full article

The aim of the present research was to investigate the variation in the anthropometric characteristics and the isokinetic muscle strength of elite female team sport players during a season (29–36 weeks). Three groups of female athletes that consisted of soccer (n = 19; age, 23.2 ± 4.3 years), basketball (n = 26, 21.1 ± 5.4 years) and handball players (n = 26, 21.1 ± 4.2 years) underwent anthropometric and isokinetic measurements at the beginning of the preparation period, in the middle and at the end of the competitive season. Isokinetic peak torque values of the hamstrings (H) and quadriceps (Q), as well as the conventional strength ratios of H:Q, were tested on an isokinetic dynamometer at angular velocities of 60, 180 and 300°·s⁻¹. Body weight, lean body mass and body fat of all groups decreased from the first to the third testing session (p < 0.05). Isokinetic peak torque gradually increased during the three measurements (p < 0.05). The soccer players had lower body weight and body fat compared to the basketball and handball players (p < 0.05). Isokinetic peak torque in knee flexion did not show any difference between the sports at any angular velocity or knee movement (flexion and extension), with an exception of the 180°·s⁻¹. The improvement observed for all athletes can be attributed to the training programs that collectively characterize these team sports. Full article

River water can be used as a source of drinking water. However, it is vital to consider the existence of natural organic matter (NOM) and its possible influence on water quality (low turbidity, high color). The level of NOM in river water significantly impacts the ecosystem’s health and the water’s quality, and needs to be removed. A membrane-based approach is attractive for treating NOM successfully, but is still hindered by the membrane fouling problem. This study aims to develop polyvinylidene fluoride (PVDF)-based membranes customized for NOM removal from river water. The anti-fouling property was imposed by a coating of tannic acid (TA) and Fe³⁺ on the pre-prepared PVDF membrane. The results show that the TA–Fe coatings were effective, as demonstrated by the FTIR spectra, SEM, and EDS data. The coatings made the membrane more hydrophilic, with smaller pore size and lower clean water permeability. Such properties offer enhanced NOM rejections (up to 100%) and remarkably higher fouling recovery (up to 23%), desirable for maintaining a long-term filtration performance. Full article

Blockchain offers a cutting-edge solution for storing medical data, carrying out medical transactions, and establishing trust for medical data integration and exchange in a decentralized open healthcare network setting. While blockchain in healthcare has garnered considerable attention, privacy and security concerns remain at the center of the debate when adopting blockchain for information exchange in healthcare. This paper presents research on the subject of blockchain’s privacy and security in healthcare from 2017 to 2022. In light of the existing literature, this critical evaluation assesses the current state of affairs, with a particular emphasis on papers that deal with practical applications and difficulties. By providing a critical evaluation, this review provides insight into prospective future study directions and advances. Full article

Digital twins are among the technologies that are considered to have high potential. At the same time, there is no uniform understanding of what this technology means. Definitions are used across disciplinary boundaries, resulting in a multitude of different interpretations. The concepts behind the terms should be clearly named to transfer knowledge and bundle developments in digitalization. In particular, the Reference Architectural Model for Industry (RAMI) 4.0, as the guiding concept of digitalization, should be in harmony with the terms to be able to establish a contradiction-free relationship. This paper therefore summarizes the most important definitions and descriptions from the scientific community. By evaluating the relevant literature, a concept is derived. The concept presented in this work concretizes the requirements and understanding of digital twins in the frame of RAMI 4.0 with a focus on manufacturing. It thus contributes to the understanding of the technology. In this way, the concept is intended to contribute to the implementation of digital twins in this context. Full article

Context: Chronic inflammation has been linked to cancer since the 19th century. Tumor growth is supported by the proangiogenic factors that chronic inflammation requires. Polarized leukocytes initiate these angiogenic and tumorigenic factors. TIPE2, a transport protein, manages the cytoskeletal rearrangement that gives a polarized leukocyte its motility. Inhibition of this protein could lead to a therapeutic option for solid tumor cancers; however, no such inhibitors have been developed so far due to the large cavity size of the TIPE2 protein. Here we have examined possible small molecule inhibitors by combining structure-based and fragment-based drug design approaches. The highest binding ligands were complexed with the protein, and fragment libraries were docked with the complex with the intention of linking the hit compounds and fragments to design a more potent ligand. Three hit compounds were identified by in silico structure-based screening and a linked compound, C2–F14, of excellent binding affinity, was identified by linking fragments to the hit compounds. C2–F14 demonstrates good binding stability in molecular dynamic simulations and great predicted ADME properties. Methods: High throughput molecular docking calculations of mass libraries were performed using AutoDock Vina 1.1.2. Molecular docking of individual ligands was performed using AutoDock Vina with PyRx. Ligand libraries were prepared using OpenBabel, linked ligands were prepared using Avogadro. The protein was prepared using AutoDockTools-1.5.6. Protein-ligand complexes were visualized with PyMOL. Two- and three-dimensional representations of protein–ligand interactions were plotted with BIOVIA Discovery Studio Visualizer. In silico absorption, distribution, metabolism, and excretion (ADME) properties were calculated using SwissADME. Molecular dynamics simulations were conducted with GROMACS. Full article

The aim of this research is to present the effects of acupuncture treatment on morning blood glucose level (BGL) in type 2 diabetes mellitus (T2DM) patients, and to describe them by a predictive model. The morning BGL is measured after overnight fasting during a three-month long acupuncture treatment for two persons diagnosed with T2DM and is compared with the BGL of two persons in similar health conditions taking only metformin-based drugs. It is shown that the morning BGL is highly affected by each single acupuncture treatment and by the number of the already applied treatments. Significant lowering of BGL after each treatment is observed, as well as an overall BGL lowering effect, which is the result of the repeated acupuncture. The observed BGL reduction was found to be maintained during a follow-up performed a year after the acupuncture. The measured BGL dynamics curves are analyzed and described by a model. This model describes well all of the key features of the measured BGL dynamics and provides personal parameters that describe the BGL regulation. The model is used to simulate BGL regulation by acupuncture performed with different frequencies. It can be used generally to predict the effects of acupuncture on BGL and to optimize the time between two treatments. The results will enable a better understanding of acupuncture application in diabetes, and a prediction of its effects in diabetes treatment. Full article

We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency. Full article