This paper comprehensively reviews eye-writing systems based on electrooculographic (EOG) signals. These systems, which enable communication by utilising the potential changes caused by eye movements, are generally classified into two main groups in the literature: virtual keyboard-based and pattern recognition-based. Virtual keyboard systems rely on thresholding methods and do not require artificial intelligence algorithms. On the other hand, in pattern recognition systems, character input is provided by users following specific eye movement patterns, and these patterns are analysed using artificial intelligence-based classification algorithms. In this context, the hardware, signal processing techniques, classification algorithms and interface designs have been systematically analysed by evaluating the studies published in the last twelve years.
Severe Acute Respiratory Syndrome Coronavirus-2, which causes Coronavirus 2019, has resulted in the deaths of more than 7 million people. The helicase, encoded by non-structural protein-13 in the virus genome, plays a critical role in the virus's life cycle and is at the heart of treatment approaches. The study revealed the impact of helicase protein mutations on protein stability and nucleic acid binding dynamics in United States SARS-CoV-2 isolates. Nine recurrent mutations exceeding the predefined occurrence threshold were identified and subjected to structural modeling, protein stability prediction, and helicase–nucleic acid docking analysis. Utilizing data from nine mutations (Ser36Pro, Thr127Asn, His164Tyr, Met233Ile, Tyr324Cys, Ala368Val, Ala389Val, Arg392Cys, Thr599Ile) identified from isolates, mutant protein models were generated using deep learning algorithms. Protein stability alterations were assessed using SDM2, mCSM, DUET, and DynaMut2 tools. The helicase-nucleic acid interaction was evaluated through molecular docking analysis. Consensus-based stability prediction indicated that several mutations were predicted to reduce nsp13 stability, whereas His164Tyr was consistently predicted to have a stabilizing effect across all four tools. Some substitutions, including Met233Ile and Thr599Ile, showed method-dependent effects. Molecular docking analysis suggested that the recurrent mutation set may alter helicase–RNA interface energetics and spatial arrangement. However, these docking-derived changes should be interpreted as computational estimates rather than direct evidence of increased nucleic acid-binding affinity. The concurrent occurrence of reduced predicted protein stability and more favorable docking-derived nucleic acid interface scores suggests that these mutations may influence helicase–nucleic acid recognition; however, direct effects on viral replication kinetics require experimental validation.
In a two-class dataset, the class imbalance problem arises if there is a considerable difference between the number of samples in the classes. Many data balancing algorithms have been proposed to address this issue. However, only a limited number of studies have examined the candidate balance ratios of certain balancing algorithms, often focusing on real datasets. Unlike previous studies, this research evaluates seven balancing algorithms in terms of their predictive performance for an estimated population parameter (EP) and examines minority-majority class distributions yielding performance comparable to EP using an original simulation scenario. In this study, imbalanced datasets were sampled from a simulated population dataset and gradually balanced using random oversampling (ROS), synthetic minority oversampling technique (SMOTE), majority weighted minority oversampling technique (MWMOTE), adaptive synthetic sampling approach (ADASYN), random undersampling (RUS), random under boosting (RUSBoost), and under bagging (UB) algorithms. The classification and regression trees (CART) method was used to classify the data at each step, and the area under the ROC curve (AUC) was employed to evaluate the performance of the balancing algorithms. The findings obtained under the present simulation setting indicate that RUSBoost and UB algorithms yield statistically higher results than EP when certain balance ratios are exceeded. Meanwhile, within the evaluated simulation setting and the CART–AUC framework, other methods do not surpass EP and generally achieve their highest mean AUC values at full balance (50:50).
Edible packaging derived from biopolymers and/or lipids is environmentally friendly, sustainable, and biodegradable, offering a natural alternative to petroleum-based plastics. Edible films and coatings (EFCs) can be applied to foods using materials safe for consumption, where they act as protective barriers against UV-light, mechanical damage, and the loss of soluble solids, water vapour, organic volatiles, and gases. By preserving food quality and nutritional value, they extend shelf life and enhance product stability. Beyond these technological benefits, their role as carriers for bioactive compounds such as antioxidants, antimicrobials, nutrients, dietary fibres, probiotics, prebiotics, and postbiotics presents promising potential for applications in human health. Despite a substantial number of reviews focusing on the use of EFCs for food preservation and their protective properties, their potential health implications remain largely unexplored, even though these materials are intended to be consumed with food. Human studies are therefore necessary to evaluate their safety, bioavailability, and effectiveness in delivering health-promoting bioactive compounds. At the same time, challenges relating to formulation variability, sensory attributes, and industrial scalability must be addressed to enable their broader adoption. This review aims to bridge these gaps by providing a comprehensive overview of the applications, challenges, and prospects of EFCs. The necessity for interdisciplinary research in conjunction with supportive policies is emphasized, with the objective of advancing the development of sustainable packaging solutions and innovative tools that contribute to global health strategies.
Bacterial resistance to antimicrobial drugs represents a critical threat to global health, compromising the efficacy of standard therapies. In this study, we evaluated the antimicrobial properties of silver vanadate microrods (SVMs) against clinical isolates of Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. SVMs were active against all tested isolates, with MIC values of 128 µg/mL for S. aureus and 256 µg/mL for Gram-negative species. Notably, checkerboard assays revealed synergistic interactions with azithromycin, clindamycin, and sulfamethoxazole (FICi 0.093-0.125), indicating that SVM might potentiate the activity of antimicrobial drugs. The MBC/MIC ratio classified SVM as bactericidal against Gram-negative isolates and bacteriostatic against S. aureus. Scanning electron microscopy suggested a membrane disruption mechanism by a direct nanoparticle-bacteria interaction. SVM also exhibited anti-inflammatory activity comparable to tenoxicam in vitro. SVM presented cytotoxicity to BGM cells at 3.13 µg/mL (CC50). Our findings suggest that SVM is a promising candidate for combination therapy, warranting further investigation with surface-functionalized formulations to improve its selectivity.
This study investigates the structural and analytical properties of harmonic and hyper-harmonic Narayana numbers, extending classical results on harmonic and hyper-harmonic numbers. We establish novel combinatorial identities and derive closed-form expressions for and , which represent finite sums involving reciprocals of Narayana numbers. Furthermore, we examine the spectral and Euclidean norms of circulant and r-circulant matrices generated by these numbers, together with selected special matrix forms. Through this framework, several inequalities associated with matrix norms are obtained, providing deeper insight into the interplay between Narayana-type sequences and matrix analysis. The results contribute both to the combinatorial theory of special number sequences and to the normative analysis of structured matrices.
In this study, 1-(4-metilpiperazin-1-il-metil)-3-p-klorobenzil-4-(4-hidroksibenzilidenamino)-4,5-dihidro-1H-1,2,4-triazol-5-on (PCHT), a Mannich base derived from 1,2,4-triazole, was synthesized and its structural properties were investigated using both experimental and theoretical methods. The structure of the compound was experimentally characterized by FT-IR, ¹H-NMR, and ¹³C-NMR spectroscopic techniques. Geometric optimization of the molecule was performed using Density Functional Theory (DFT) with B3LYP and B3PW91 functionals and the 6-31G(d,p) basis set. Theoretical vibrational frequencies and FT-IR, ¹H-NMR and ¹³C-NMR NMR isotropic chemical shift values were calculated via the GIAO method and compared with the experimental data. Furthermore, electronic parameters such as Mulliken atomic charges, HOMO–LUMO energy levels, energy gap (ΔEg), dipole moment, ionization potential, electron affinity, chemical hardness, chemical softness, and electronegativity were calculated. Molecular electrostatic potential (MEP) surface maps were generated to identify the reactive sites of the molecule, and nucleophilic and electrophilic regions were analyzed. In addition, Natural Bond Orbital (NBO) analysis was conducted to examine the electronic structure in more detail, evaluating donor–acceptor interactions and hyperconjugation effects. To investigate the biological activity potential, molecular docking studies were performed, and the binding energies and molecular interactions between the ligand and the target protein were examined. Lastly, the compound's pharmacokinetic properties were assessed using ADMET analysis, which highlighted the molecule's possible drug-like qualities by looking at factors like Lipinski's "rule of five," bioavailability, water solubility, gastrointestinal absorption, and blood-brain barrier permeability.
This study investigates daylight performance in relation to the orientation of the "sofa" (living room in a traditional Turkish house) within a digital environment, aiming to identify the advantages and disadvantages of different orientations through quantitative analysis. Natural daylight has long been a critical design criterion in architecture, with building orientation directly influencing interior illumination. In traditional Turkish houses, room positioning was carefully arranged to maximize daylight, and the sofa, used as a socializing and seating area, often received special attention in window design compared to other rooms. The research focuses on the 'Bey Sokak' district in Tokat, which preserves the city's traditional fabric and includes many examples of civil architecture. Daylight analysis was conducted using the Grasshopper program at 09:00, 12:00, 15:00, and 18:00 on September 21st, December 21st, June 21st, and March 21st. Orientations of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, and 315 degrees were examined, with the sofa positioned 5 degrees north of alignment. Findings indicate that the 315 degrees (southeast) orientation provides notable benefits. It enhances morning performance, improves uniformity, and offers potential energy efficiency, making it particularly effective for early daylight use. However, limitations include insufficient afternoon light, reduced winter performance, and brightness issues. Overall, the study highlights the significant impact of orientation on daylight performance in traditional Turkish houses. While the 315 degrees orientation demonstrates clear advantages, its drawbacks emphasize the need for balanced design strategies that consider both seasonal and daily variations in natural light.
Memory assessment is a critical component of cognitive research and clinical practice, providing insights into cognitive well-being and performance. While traditional neuropsychological tests remain standard, advancements in virtual reality (VR) technology have offered innovative methods for assessing memory. This rapid review examines 9 studies to explore the use of VR-based memory palace techniques and memory assessments. The findings reveal a significant alignment between VR tasks and memory enhancement, while virtual reality also captures relationships with executive functions and overall cognitive performance. By incorporating various ecological contexts, such as residential or commercial environments, virtual reality enhances the environmental validity of memory assessments. However, challenges such as limited accessibility and variability in both VR hardware and software may hinder broader adoption. These limitations, along with the rapidly advancing nature of VR technology, underscore the need for further research to optimize and expand the role of virtual reality in memory assessment.
Machine learning has become an important tool for predicting student performance. This paper aims to create a dataset of the participation of some students who took Turkish Language, Atat & uuml;rk's Principles and History of Revolution, and English joint courses given via distance education at Istanbul Arel University to synchronous and asynchronous course activities for 14 weeks, and to predict the students' success by employing fuzzy parameterized fuzzy soft k-nearest neighbor (FPFS-kNN) and the dataset. First, anonymized participation data from a 14-week lecture period is collected. Later, these data are processed to be used in machine learning. Two data sets are obtained from each raw dataset, whose class labels consist of two classes (pass/fail) and multi-class (letter grades). Then, FPFS-kNN and well-known/state-of-the-art machine learning algorithms are applied to the datasets. The performance results are compared using accuracy (Acc), precision (Pre), recall (Rec), macro F1-score (MacF1), and micro F1-score (MicF1) performance metrics. The results show that FPFS-kNN outperforms the other algorithms in binary pass-fail classification, achieving the highest accuracy with 83.23% (ING1), 82.60% (ATA1), and 81.42% (TDE1), while maintaining competitive F1-scores (up to 63.18% on TDE1). In the letter-grades datasets, performance decreased overall, with Boosted Tree reaching the best MicF1(45.02% on TDE2), yet FPFS-kNN still produced strong and stable results (Acc = 84.08% on TDE2, 82.94% on ATA2). These findings indicate that FPFS-kNN is highly effective in binary classification and competitive in multi-class problems. Finally, a discussion of performance results and the use of machine learning in predicting student achievement is provided.
With the rapid development of technology, touch screens have become an integral part of our lives. Touchscreen panels offer high customization, fast response times, and enhanced user experience, providing flexible and powerful solutions for various usage scenarios. Their widespread use, especially in smartphones, tablets, and other smart devices, offers a direct and intuitive way to interact with users. In this study, customizable, efficient and cost-effective touchscreen control panels (TSCPs) have been developed using a Field-Programmable Gate Array (FPGA). A scenario has been created for the use of the developed TSCP in a 4-room building applicable to a home, office, or similar environment. In this scenario, each room of the building is equipped with TSCPs integrated with an STM32 microcontroller and GOWIN FPGA. The main control card is also designed to be placed in the electrical panel at the building entrance to control this building from a central location. Communication is provided between the TSCPs in each room and the main control card via RF communication. The designed TSCPs can also be controlled by voice or manually through the developed mobile application. The cost analysis of the TSCP developed in this study shows that it is highly cost-effective compared to commercial products.
Glioblastoma (GBM) is an aggressive brain tumor associated with increasing annual morbidity and mortality rates. Standard treatment typically involves surgical resection followed by postoperative Temozolomide (TMZ). Recently, 5-Ethynyl-2'-deoxyuridine (EdU) has been reported to demonstrate significant antitumor effects against GBM. Therefore, this study investigated the potential of EdU to activate apoptotic pathways in astrocytes carrying mutant DNA a key factor in GBM formation and evaluated its therapeutic efficacy at the molecular level. The investigation was conducted using molecular docking analyses. The neuronal membrane protein contactin-2 (PDB ID: 2OM5), encoded by the CNTN2 gene, and NPTX1 (PDB ID: 6YPE), a protein highly expressed in the brain, were selected as target receptors. Additionally, the solvation behavior, drug-likeness, pharmacokinetic parameters, and toxicity profiles of EdU were determined and compared with those of the widely prescribed antitumor agent, TMZ. Results indicated that EdU interacted with the 2OM5 receptor at residues Asp184 and Ser186, and with the 6YPE receptor at residues Trp258, His314, Asn380, Trp409, Ser403, and Asp226. Moreover, EdU exhibited a 7.5% higher binding affinity for 2OM5 and 3.0% higher binding affinity for 6YPE compared to TMZ. Furthermore, the LD50 value of EdU was found to be significantly higher than that of TMZ, indicating a much lower toxicity profile. These findings suggest that EdU is a highly promising candidate for the treatment of GBM.
Cold atmospheric plasma (CAP) has recently emerged as an innovative therapeutic option in dermatology. Unlike conventional treatments with frequent side effects, CAP offers a safe and non-invasive alternative for managing skin cancers, wound healing, and microbial infections. This review is based on studies published between 2013 and 2025, identified through comprehensive literature searches in PubMed, Scopus, Web of Science, and Google Scholar, with the aim of evaluating the therapeutic potential of CAP. Evidence indicates that CAP selectively induces apoptosis in cancer cells, accelerates epithelialization and collagen synthesis during wound repair, and exhibits strong antimicrobial activity against multidrug-resistant bacteria and pathogenic fungi. However, the lack of standardized treatment protocols, device variability, and limited long-term safety data remain major barriers to its clinical translation. Future mechanistic insights and well-designed clinical trials will be critical to establish CAP as a transformative modality in dermatological therapy.
The aspect ratio of the shallow footing is a key parameter affecting the bearing capacity performances. However, the recommendations of previous studies exhibit notable inconsistencies among themselves for the unreinforced condition, and no numerical study in the literature has investigated the effect of aspect ratio under geocell-reinforced condition. This pioneer study investigates the influence of footing aspect ratio and soil relative density on the ultimate bearing capacity, shape factor, improvement factor, and failure mechanism of unreinforced and geocell-reinforced footings resting on sandy soils. A total of 36 rigorous three-dimensional finite element analyses were performed and validated against physical model tests. The analyses considered footings with aspect ratios ranging from 1 to 10 placed on loose and dense sands. The results showed that geocell reinforcement substantially improves footing performance by increasing bearing pressures, ultimate bearing capacities, stiffness of the footing pressure versus footing settlement ratio curves and shape factor values. Improvement factors were observed to lie between 1.12 and 2.56, with square footings showing the maximum improvement, and the magnitude of improvement increasing as the relative density increased. Furthermore, for unreinforced footings, the maximum shape factor occurred at aspect ratio of 2, whereas in geocell-reinforced conditions, the peak shifted to aspect ratio of 1. The shape factor values also increased with increasing relative density. Moreover, it was observed that the aspect ratio influences both the failure surface geometry and reinforcement mechanism of geocell.
This study aims to examine the strengths and weaknesses of AHP, SWARA, BWM, PIPRECIA and FUCOM, which are subjective criteria weighting methods, to reveal their advantages, and to develop an improved subjective criterion weighting procedure in this context. As a new method proposal, it is aimed to show the reliability of the method by giving application examples and comparison analyzes within the framework of ICWM (Improved Criteria Weighting Method). According to the analysis results, it was observed that the correlations between the criterion weights found with ICWM and the criterion weights found with AHP, SWARA, BWM, and FUCOM, which are considered reliable in the literature, were very strong. It has also been determined that ICWM provides advantages in terms of number of pairwise comparisons, consistency, and practicality.
In this study, we calculated the linear and nonlinear optical properties of step-like AlGaAs/GaAs quantum well wires operating in THz band width. The time-independent Schr & ouml;dinger equation was solved under the effective mass approximation using the finite element method. The wavefunctions and energy levels corresponding to the first four bounded states were obtained for the step-like quantum well wires. The potential barrier heights were set as 160 meV and 228 meV to create step-like potential. In the paper, firstly, the effect of the bottom width of the wire (Lin), from 8 nm to 12 nm, was considered. It was seen that bottom width of the wire defined where the localization happened in the step-like quantum well wire. The transition energies, occurs in the THz range, of the (1-2), (1-3), (2-4), and (3-4) were constant under applied electric field in the range of 0 to 20 kV/cm to support stable device operation under varying voltages. The applied magnetic field, from 0 to 10 T, resulted in increment in the linear, nonlinear and total absorption coefficients for all transitions.
This study examined cost data for 130 wastewater treatment plant projects tendered by a public institution in Turkey between 2008 and 2022, and regression analysis was conducted to estimate the construction costs of these projects. Population projection, flow rate, aeration tank volume, sedimentation tank volume, and number of units are used as independent variables in the data set. The analysis determined that the independent variables had a significant impact on the total construction cost, and the developed model achieved an R² value of 0.763. The validity of the model was tested on three sample projects, and the accuracy rates between estimated and actual costs were determined to range from 92.56% to 97.58%. Therefore, the proposed model can be considered a suitable tool for preliminary feasibility assessments, early-stage planning, and approximate cost estimations. Nevertheless, it may not be sufficient as a standalone approach for detailed budgeting, tender preparation, or precise cost determination processes, where higher levels of accuracy are required, and should be supported by more comprehensive analytical methods.
Today, artificial intelligence technologies are advancing day by day, and neurons, which are the building blocks of artificial neural networks, one of the artificial intelligence technologies, are supporting this advancement. By studying the biological structures of neurons found in the human brain, dynamic models are created using mathematical differential equations. These models are implemented using analogue circuit elements or digital controllers such as computers to investigate their dynamic properties. Within the scope of this study, time series, phase portraits, bifurcation diagrams, and Lyapunov exponent spectrum analyses were performed using Google Colab, a new generation numerical analysis development environment, to reveal the dynamic properties of the Hindmarsh-Rose (HR) neuron model, which is effectively used in neuron modelling. In Google Colab, solutions were obtained using the Euler, Heun, RK4, solve_ivp, Adams-Bashforth/Moulton, and Z-transform methods in the numerical analysis solutions of the HR model; it was determined that the RK4 method, which has sufficient speed and high accuracy, is more suitable for microcontroller applications. Subsequently, an HR analogue circuit model was designed using Op-Amp operational elements in the OrCAD PSpice program, and simulations were performed to validate the Colab dynamic numerical analysis results. Finally, simulation results were obtained using a microcontroller with a Cortex-A72 (ARM v8) processor, which confirmed the Colab dynamic RK4 numerical analysis results of the HR neuron model. The applicability of membrane potential, fast recovery, and slow adaptation conditions to mini-systems for data processing was demonstrated.
In the era of digital transformation and big data, organizations generate vast volumes of raw data from diverse channels such as IoT devices, cyber systems, and e-commerce platforms. Extracting meaningful insights from this data is essential, particularly in identifying interesting item sets through association rule mining. These patterns reveal strong product relationships and can drive more sustainable strategies for marketing and sales in retail and online platforms. However, when working with large-scale datasets containing thousands of transactions and items, many frequently occurring items do not necessarily result in meaningful or interesting item sets. This leads to inefficiencies in the mining process and unnecessary computational overhead. To address this issue, we propose a novel algorithm called Dimension Reduction Apriori (DR-Apriori), which enhances the performance of traditional association rule mining by incorporating a dimension reduction step. This approach streamlines the dataset, enabling the extraction of more relevant and interesting item sets. We evaluated DR-Apriori on two benchmark datasets—15,000 transactions with 4,089 features, and 7,500 transactions with 121 features—under varying thresholds for dimension reduction, support, and confidence. Experimental results show that DR-Apriori outperforms traditional Apriori and Hybrid-Apriori algorithms, achieving up to 51% faster runtime, reducing memory usage by up to 31%, and maintaining the number of interesting item sets discovered. This study highlights the potential of DR-Apriori in enhancing the efficiency and scalability of association rule mining, ultimately supporting more intelligent, data-driven decisions for sustainable retail practices.
Electroencephalography (EEG) is a non-invasive neurophysiological measurement method that allows monitoring the electrical activity of the cerebral cortex and is widely used in the diagnosis of neurological diseases. In this study, EEG-based biomarkers were used to discriminate between Alzheimer's disease, frontotemporal dementia, and cognitively healthy individuals. A total of 22 features were extracted in the signal processing stage, and then this number was reduced to 12 by applying a feature selection method based on the ReliefF algorithm to improve the classification performance. The selected features were evaluated in both binary and multiclass classification scenarios to reveal the discriminative differences between Alzheimer's disease, frontotemporal dementia and healthy control group. According to the findings, in the multiclass classification task, the Fine Decision Tree algorithm achieved the highest accuracy rate of 99.7% when all features were used. In distinguishing cognitively normal individuals from individuals with Alzheimer's disease, both the Fine Decision Tree and Cubic Support Vector Machine algorithms achieved 100% accuracy with all and selected feature sets. To prevent overfitting and evaluate generalization performance, k-fold cross-validation was applied. Feature selection and model parameter tuning were performed only on the training folds; the test folds were not included in these processes. This approach prevents information leakage and provides reliable performance estimation. This finding demonstrates that EEG-based biomarkers, when combined with appropriate machine learning methods, can be transformed into effective tools that provide high reliability and accuracy in clinical decision support systems.
