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Comparative Analysis of Mechanical Recycling Simulation of Poly(Hydroxybutyrate-Co-Hydroxyvalerate) (PHBV): Injection Molding Vs. Extrusion Processes
No full textThis study investigates the effects of simulated mechanical recycling cycles on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), a biobased and biodegradable polymer, processed by twin-screw extrusion and injection molding. A decrease in melt flow index and an increase in melt viscosity and molar mass after the first cycle indicate branching and recombination reactions altering the polymer structure. Fourier-transform infrared spectroscopy reveals pronounced degradation in injection-molded samples, with carbonyl loss, while extruded samples show limited spectral changes, suggesting different degradation mechanisms. After the first injection cycle, thermal stability improves temporarily, with a higher degradation temperature than the neat polymer, but declines in subsequent cycles. Extruded samples show greater stability, with minimal variation in degradation temperature. Mechanically, extruded samples develop higher stiffness, indicated by increased Young\u27s modulus, while stress at break remains stable across both methods. Impact toughness decreases after the first cycle, though injection-molded samples maintain higher impact resistance. Biodegradation is faster in injection-molded samples due to lower crystallinity and greater molecular mobility. Differential scanning calorimetry of degraded samples reveals two melting points, suggesting chain rearrangement and phase separation during microbial attack. The study highlights how processing methods influence PHBV\u27s structure, stability, mechanical performance, and biodegradability, offering valuable insights for optimizing its recyclability and functionality in sustainable material applications
A Vision for All: A Dataset and CNN Framework for Navigating Educational Spaces for the Visually Impaired
No full textOver the years, numerous datasets featuring both images and text have been introduced, driving the development of innovative methods that integrate natural language processing and computer vision. Nonetheless, there remains a demand for datasets that present images within their authentic context. This paper presents a large-scale image dataset collected from Zayed University and the British University in Dubai, designed to support the development of AI-powered assistive technologies for visually impaired individuals in academic settings. The dataset includes 300,000 images representing university facilities such as classrooms, labs, and safety features. Images were captured using direct photography and video frame extraction, incorporating a range of conditions. Preprocessing techniques ensured the dataset\u27s high quality and variability. CNN and ResNet50 classification models were applied, and they achieved 90% and 93% accuracy, respectively. This dataset paves the way for an envisioned intelligent Arabic voice-based navigation system, supporting both standard and Gulf Dialect Arabic. This paper details the creation process of the dataset, the dataset structure, and the potential contributions to advancing research in assistive technologies for individuals with visual impairments
AI Meets Linguistics SayItRight: A Platform for Personalized Language Learning and Pronunciation Enhancement
No full textWith the increasing importance of English proficiency in daily life, language learners face challenges in achieving both comprehension and accurate pronunciation. This paper presents the development of SayItRight, an Artificial Intelligence (AI)-powered platform for personalized language learning and pronunciation enhancement. SayItRight uses advanced speech analysis algorithms to evaluate user recordings, detect pronunciation errors, and provide real-time, actionable feedback. The platform integrates an interactive chatbot for dynamic learning and allows users to practice custom sentences, promoting a flexible and engaging learning environment. Key features include progress tracking, instructor feedback, and customizable learning pathways. Built with scalable web technologies, SayItRight offers a robust and interactive user experience. Additionally, the system incorporates a student-instructor model, enabling instructors to monitor progress and deliver tailored guidance. SayItRight demonstrates its potential as an innovative tool for improving speech skills and empowering learners from diverse backgrounds
An Integrative Health Care Informatics Model for Early Detection of Skin Cancer
No full textBecause lesion features and detection backgrounds are complex, automatic lesion detection in dermoscopy images is fraught with difficulties. Using more significant and more complicated models has been the primary strategy used by previous methods to improve detection accuracy. However, these methods frequently miss significant variations within classes and commonalities between classes in lesion characteristics. This research gap restricts our comprehension of the subtle differences within lesion classes and the commonalities among several classes. The use of bigger model sizes further complicates implementing algorithms in real-world contexts. Consequently, studies that delve further into the underlying intricacies of lesion features and investigate novel ways to overcome these obstacles while guaranteeing the scalability and applicability of the detection algorithms are desperately needed. This research aims to tackle the issue of insufficient annotated data in skin cancer diagnosis by proposing a new and innovative 3D neural network (NN) model based on deep learning techniques. The design of our model is tailored to specifically detect several types of skin cancer, such as Melanoma, Nevus, Actinic keratosis, and Dermatofibroma. To address the limitations of the available data, we utilise an augmentation technique to increase the size of the dataset. This helps to improve the model\u27s ability to handle different scenarios and make accurate predictions while avoiding overfitting. By doing thorough experiments, we have achieved an impressive accuracy rate of 93.30% in distinguishing Actinic keratosis from Nevus. This demonstrates the efficacy of our suggested method in properly recognising various forms of skin lesions
Comparative analysis of intellectual capital models: Enhancing financial performance and market value in S&P 500 firms
No full textThe current paper examines the influence of intellectual capital (IC) on financial performance and market value using a comparative analysis of the value-added intellectual coefficient (VAIC) and its modified and extended versions. The study employs panel data from S&P 500 firms from 2012 to 2022, utilizing Two-Stage Least Squares (2SLS) regression alongside descriptive statistics and correlation analysis. Results indicate a strong relationship between IC and financial performance and market value. Among the VAIC components, capital-employed efficiency has the highest predictive power, underscoring the continued importance of tangible assets. Including relational capital improves the explanatory power of the e∗VAIC model compared to VAIC. This study contributes to the limited research on IC in large U.S. firms, offering insights into the comparative effectiveness of these models in explaining firm performance and market value
Sleep and sleep disorders: much more than how long you sleep, what truly matters is How you sleep
No full textSince the discovery of electroencephalography to measure brain activity in 1924, sleep research has rapidly advanced. Contemporary sleep research no longer focuses on the length of sleep and the consequences of sleep loss. Instead, a multitude of sleep parameters, such as sleep duration, quality, irregularity, health, and disordered sleep, now inform the sleep science community about the importance of sleep in relation to health, performance, cognitive, cultural, and clinical outcomes through diverse study designs, equipment, and across a range of populations. This special issue on sleep and its disorders has shown us how diverse and widespread the study of sleep has become today. Geographically, it is remarkable that we received manuscripts from various continents and from dozens of countries. The themes and approaches were equally diverse, ranging from population-based studies to laboratory investigations, and including discussions of public policies related to sleep
The impact of Big Five Personality Traits on entrepreneurial orientation
No full textOur study explores the interplay between the Big Five Personality Traits (B5-PT) and Entrepreneurial Orientation (EO) among home and international entrepreneurs in the Middle East, focusing on Jordan, Saudi Arabia, and the United Arab Emirates. Utilizing fuzzy-set qualitative comparative analysis (fsQCA), we investigate how different combinations of personality traits influence EO in distinct entrepreneurial contexts. The findings reveal four universal configurations and four context-specific configurations that lead to high EO, highlighting the dynamic and configurational nature of entrepreneurial behaviour. For home entrepreneurs, high conscientiousness and agreeableness are key drivers of EO, reflecting a focus on collaboration and resource management within familiar environments. In contrast, international entrepreneurs benefit from openness and extraversion, which foster adaptability and networking capabilities in complex, cross-border markets. By adopting a configurational approach rooted in complexity theory, this study moves beyond reductionist frameworks, offering novel insights into the nonlinear and context-dependent relationships between personality traits and EO. These findings have practical implications for policymakers and entrepreneurs, providing a foundation for designing tailored interventions that enhance entrepreneurial success. The study also enriches the discourse on entrepreneurship in the Middle East by addressing underexplored regional dynamics and advancing the methodological application of fsQCA in entrepreneurship research
Impact of high-protein, low-calorie diet on anthropometric indices, glycation, and inflammation associated with the fat mass and obesity (FTO) gene among individuals with overweight/obesity
No full textBackground: The common polymorphism rs9939609 of the fat mass and obesity gene (FTO) has been associated with increased susceptibility to obesity, but this association appears to be modified by diet. High protein diets have been shown to reduce weight and may increase the formation of circulating advanced glycation end products (AGEs). Obesity, on the other hand, is also associated with increased formation of AGEs, leading to oxidative stress and inflammation. Objectives: This study was designed to investigate the impact of a high-protein and low-calorie (HPLC) diet on anthropometric indices and circulating AGEs levels associated with the FTO variant rs9939609 among overweight/obese individuals. Methods: In this interventional study, 60 overweight and obese individuals (aged 18–50 years) with no comorbidity were assigned to an HPLC diet of 800 kcal and ~100 g protein/day for 4 weeks. The enrolled participants were divided into three groups (each group, n = 20) based on FTO genotyping, i.e., AA, TT, and AT, using whole blood samples. Body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) were measured before and after intervention. Serum analysis of carboxymethyl lysine (CML) and interleukin-6 (IL-6) was performed at baseline (day 0) and at the endline (day 28). Results: The weight (p = 0.01), WC (p = 0.002), and WHR (p = 0.04) were significantly different among the three genotypes. The risk allele group (AA) had a higher mean weight (95.74 ± 19.13 kg), WC (105.85 ± 14.55 cm), and WHR (0.93 ± 0.08) compared with the wild-type TT. HPLC diet significantly decreased weight (p = 0.02), BMI (p = 0.03), WC (p \u3c 0.001), and WHR (p = 0.02), while no significant effect was found on CML and IL-6 in all three genotypes at the end of intervention. The effect size estimates indicated significant variation explained by the FTO gene in weight (η2 = 0.158), BMI (η2 = 0.114), WC (η2 = 0.235), and WHR (η2 = 0.138). Conclusion: This study concludes that an HPLC diet modifies the variation of the FTO rs9939609 genotype and anthropometric measurements. These findings also suggest that high dietary protein intakes may protect against the obesogenic effects of FTO risk genotypes, leading to weight loss and improved metabolic parameters