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Pharmacological properties of natural terpenes and terpenoids
No full textTerpenes and terpenoids are natural compounds found in various natural sources such as plants, marine organisms, and some microorganisms that have antioxidant and antimicrobial properties. They are synthesized mainly through the mevalonate or methylerythritol-phosphate pathways. Additionally, microorganisms like Saccharomyces cerevisiae and Escherichia coli can be engineered to produce terpenes for use in the food industry. The health benefits of terpenes depend on their bioaccessibility (availability for absorption in the digestive system) and bioavailability (the extent to which they are absorbed and utilized in the body). Strategies like encapsulation are used to improve their absorption. Terpenes also demonstrate various therapeutic effects, including anticancer, antiinflammatory, antioxidant, and neuroprotective properties. For example, compounds like hinokitiol, myrcene, and linalool can induce cell death in cancer cells and may help mitigate neurotoxicity associated with Alzheimer’s and Parkinson’s diseases. Overall, terpenes exhibit a wide range of bioactive effects, making them promising candidates for therapeutic potential for various diseases
Detection cyberbullying using AI and sentiment analysis to examine psychological impacts on vulnerable groups
Get PDFThis study aims to assess the effectiveness of machine learning and deep learning models in detecting cyberbullying and evaluating its psychological impact on vulnerable groups using textual and emotional features. The models assessed include traditional classifiers—Logistic Regression, Decision Tree, and Random Forest and deep learning models, such as MLP, CNN, RNN, and (LSTM) networks. TF-IDF for text vectorization and TextBlob for sentiment analysis were utilized. In spite of TF-IDF's shortcoming. Its simplicity enabled quick prototyping and insight results. The dataset contained 58,000 tweets, with 46,000 obtained from Kaggle and 12,000 collected via the Twitter API. Tweets were labeled into cyberbullying_type (gender, age, religion, and ethnicity) and subcategories: gender (male, female, LGBT, other), age (adult, teenager, other), religion (Muslim, Christian, Jewish, other), and ethnicity (ethical, unethical, other). Keyword-based classification was used for Subcategory assignment. The emotional score derived from text served as a proxy for measuring psychological impact. We emphasize that this study is observational and does not rely on clinical psychological evaluation. Results showed that female and LGBT users experienced the highest levels of cyberbullying among gender subcategories. Teenagers were most affected by age-based bullying. Unethical content dominated ethnicity-based attacks, and Muslims faced the highest frequency of cyberbullying and negative sentiment in religious categories. Sentiment analysis assisted in identifying emotional patterns concerning online abuse. Among models RNN and LSTM models achieved the highest accuracy (0.98), outperforming others. Among the traditional models, Random Forest performed better, while Logistic Regression was the worst performing. The inclusion of sentiment features significantly improved calssification accuracy, particularly in LSTM. A multi-output LSTM model was created to predict cyberbullying_type, sub_category and sentiment all at once, providing an end-to-end detection system. This framwork enables proactive monitoring of online harm and support timely interventions
Commentary on "The effect of sexually transmitted disease education via Instagram on the knowledge and attitudes of university students: a pilot study"
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The study of ritonavir on embryotoxicity: preliminary study
No full textMeeting Abstract : LB-R-45-01...
طريقة جديدة لتشفير بيانات أنترنت الأشياء
Get PDFCyberspace is a complex environment consisting of heterogeneous technologies, i.e., fog computing, Internet of Things, cloud computing, and so forth, that result from interacting with services, software, and people on the Internet. It allows users to interact, share information, swap ideas, engage in social or discussion forums, play games, and conduct business, among many other activities. Cyberspace's biggest challenge is cyber-attacks, which affect security and integrity services. However, many traditional security mechanisms provide protection and security services to solve these issues. Therefore, many researchers have focused on solving security and integrity issues by addressing the need for effective lightweight encryption techniques that incorporate the advantages of lightweight symmetric and asymmetrical algorithms. In this paper, a lightweight encryption technique was created and applied with the following features: Keyless, Encryption & Integrity, Text & Number End-to-End Encryption, Reduce Traffic, and processing overhead. In addition, the proposed system provides data integrity by applying the HASH 256 function to generate a HASH value. The proposed lightweight encryption algorithm focuses on the optimal use of the resources of Internet of Things devices so that it dramatically saves all of (Processor, Memory, Energy, Time, and Bandwidth (no need to distribute the keys)), on the other hand, giving high security, especially against the crypto analyzer. In addition, the proposed lightweight encryption algorithm can manipulate text and numbers in the English and Arabic languages. Also, to achieve data integrity in the proposed system within the Internet of Things environment, 4 hexadecimal digits from the HASH value were used instead of the original 64 hexadecimal digit HASH value to reduce the network bandwidth, processing, and storage
Detection of coffee leaf disease and identification using deep learning
No full textThe sustainability of coffee production is a concern for many coffee-producing countries. Indeed, the conservation of the production of coffee needs to detect disease and infection in the early stages, to provide the ability to control and remedy. Coffee is one of the most consumed daily beverages, so it is considered one of the most important plant crops that affect the economy of the country that produces it. Thus, implementing systems for disease detection that do not require expert consultation can streamline production processes. In this paper, we proposed an efficient and rapid system utilizing cost-effective devices for detecting coffee leaf diseases to support farmers without the need for specialized expertise, leveraging deep learning models. Our technique involves several types of artificial intelligence (AI) models used, which include proposed new models using convolutional neural networks (CNN1, CNN2), and prompt transfer learning (VGG16, ResNet50, and EfficientNet), as well as applied machine learning supervised classifier with hybrid approaches (support vector machine (SVM) and Random Forest (RF)). In terms of training, the proposed model was fed with large datasets that contain five classes, with a total number of images exceeding 50,000 images, while the testing utilized a separate dataset. Finally, the results showed high performance across all evaluation metrics. CNN1 obviously distinguished that it has the superior accuracy compared to the other models, with a 99% value. In conclusion, the proposed framework has the capability to be applied, and it will deliver on-hand support to farmers
Paylaşılan İnsanlık Kimliğin Merkezine Demirlediğinde: Bir Azınlık Olarak Alevilerde Benlik Sınıflandırması ve Kimlik Motivasyonları
Get PDFFor some minorities, living under constant threat for an extended period requires a solution. It is evident that long-oppressed minorities often possess belief systems rooted in an inclusive notion of humanity. This study examines whether shared humanity serves as public discourse material for an ethno-religious minority group like Alevis, who have suffered collective victimhood for centuries, to prove that they share the same "symbolic universe of values" with the majority, or whether it genuinely occupies a central place in their identity driven by specific motivations. It explores which motivational principles (self-esteem, belonging, meaning, continuity, and efficacy) are active at superordinate, social, and interpersonal levels of self-categorization, based on perceived centrality. Conducted with 142Alevi participants, the study involved expressing 8 identity elements each in response to the question "Who are you?" and rating their perceived centrality and motivational levels. The findings showed that 9.6% of the identity elements are at the superordinate abstraction level of self-categorization. Hierarchical regression analyses revealed that the motivational background of the identity process was not limited to self-esteem, supporting the tenets of Motivated Identity Construction Theory, and demonstrated that the role of motivational principles can vary in relation to levels of self-categorization. Among participants who referred to shared humanity at least once in their identity repertoire, the motives of "meaning" and "continuity" positively predicted the perceived identity centrality. Findings are discussed in relation to social psychological perspectives and Alevism studies literature
Effect of viewing angle and activation speed of various torque wrenches: A comparative evaluation of accuracy and repeatability
No full textStatement of problem: The loosening of abutments is one of the most frequent complications of implant-supported restorations. Purpose: The purpose of this study was to compare the accuracy and performance of different dental torque wrenches in achieving the target torque values. Material and methods: A spring-type (NTA Implant; Pilatus Swiss Dental GmbH), friction-type (NTA Implant; Pilatus Swiss Dental GmbH), and single-value torque wrench (Dentsply Sirona) were used. The implants included 4 NTA implants (Ø4.2×10 mm) (NTA Implant; Pilatus Swiss Dental GmbH) and 1 Frialit implant (Ø4.5×13 mm) (XiVE; Dentsply Sirona) with corresponding standard cement-retained abutments. Torque measurements were recorded using a digital torque gauge (MTT03–12; Mark-10 Co.), and torque was measured at different viewing angles using a digital angle finder (Neoteck). A total of 339 torque applications were made, including 9 torques per time-dependent and independent experimental group and 50 torques per clinician's viewing angle experimental group. Results: In time-independent tests, the friction-type torque wrench (Type B) generated the highest mean ±standard deviation torque value (32.1 ±0.9 Ncm), while the single-torque-value wrench (Type C) produced the lowest mean ±standard deviation torque value (23.5 ±0.4 Ncm). In time-dependent tests, the mean torque values generated during slow torque application (>4 seconds) were higher than those generated during fast torque application (1 to 4 seconds). Regarding the clinician's viewing-angle evaluation, the spring-type wrench (Type A) produced the highest torque values at 90 degrees, moderate values at 60 degrees, and the lowest at 30 degrees. Conclusions: Torque application conditions, such as speed and angle, significantly influenced measurement accuracy. The friction-type wrench produced the highest torque values, whereas the spring-type wrench exhibited greater repeatability. All mean torque values were within ±10% of the target values. To achieve the best results when using spring-type wrenches, the clinician's viewing angle should be 90 degrees. In clinical applications, the adverse effects of rapid torque tightening and angle variations on accuracy should be considered. These results provided insight into the selection of devices and protocols for accurate torque application for dental implants
Metamaterial-Enhanced Microstrip Antenna with Integrated Channel Performance Evaluation for Modern Communication Networks
Get PDFThis paper investigates the channel performance through a high-gain, circularly polarized microstrip patch antenna that is developed for contemporary wireless communication systems. The proposed antenna creates two orthogonal modes for circular propagation with slightly varying resonance frequencies by using a cross line and truncations to circulate surface currents. Compactness, reduced surface wave losses, and enhanced impedance bandwidth are made possible by the coaxial probe feed, periodic electromagnetic gap (EBG) slots, and fractal patch geometry. For in-phase reflection and beam focusing, a specially designed single-layer metasurface (MTS) reflector with an 11 x 11 circular aperture array is placed 20 mm behind the antenna. A log-normal shadowing model was used to test the antenna in real-world scenarios, and the results showed a strong correlation between the model predictions and actual data. At up to 250 m, the polarization-agile, high-gain antenna demonstrated reliable performance across a variety of channel conditions, enabling accurate characterization of the Channel Quality Indicator (CQI), Signal-to-Noise Ratio (SNR), and Reference Signal Received Power (RSRP). By combining cutting-edge antenna architecture with an empirical channel performance study, this research presents a compact, affordable, and fabrication-friendly solution for increased wireless coverage and efficiency.Funding agency : International Applied and Theoretical Research Center (IATRC).
Grant number : 00A119
The effect of creative drama-based teaching on the knowledge level of nursing theories and perception of the nursing profession in a basic nursing course: A randomised controlled trial
No full textAim: To evaluate the impact of a creative drama-based teaching approach on nursing students' understanding of nursing theories and models and their perception of the nursing profession.
Background: Nursing theories and models play a role in developing professional identity and theoretical reasoning; however, their abstract nature may make them difficult for students to understand. Creative drama offers an approach that integrates theoretical content with experiential learning and supports cognitive and affective processes.
Design: This study was designed as a three-time-point randomized controlled trial.
Methods: The trial was conducted with 54 first-year nursing students at a private university in Istanbul during the 2023-2024 academic year. Students were randomly assigned to an intervention group (n = 27) receiving a creative drama-based instructional programme, or a control group (n = 27) receiving the same content through traditional teaching. The Knowledge Assessment Test (primary outcome) and the Nursing Profession Perception Scale (secondary outcome) were administered before the intervention, immediately after and three months later. Data were analysed using repeated-measures ANOVA and t-tests and effect sizes were calculated. The study was registered at ClinicalTrials.gov (NCT06485570).
Results: Although knowledge scores increased in both groups, the intervention group demonstrated higher post-test and retention scores (p < .05), with medium-to-large effect sizes (Cohen's d ≈ 0.60-0.80). For perception, a significant improvement was observed only in the "professional qualifications" subscale, while other subscales showed limited change.
Conclusions: Creative drama-based instruction enhances nursing students' theoretical knowledge and improves perception, suggesting it is an effective strategy for teaching theoretical nursing content