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Human-Created and AI-Generated Text: What’s Left to Uncover
Get PDFThe advent of generative Artificial Intelligence (AI) has brought about profound changes in society, education, and the professional realm. Machine Learning models have created remarkably sophisticated language generators, blurring the line between human-authored content and AI-generated text. This poses a challenge for educators and professionals in distinguishing authentic work from instances of plagiarism. This study investigates the fundamental distinctions between human and AI texts by analyzing perspectives from human points of view. It aims to present the outcome of those five questions that we examined about human perceptions of text composition and contrasting them with computer-generated text. By exploring this, it aids in upholding academic integrity and contributes to advancing our comprehension of Natural Language Processing. In essence, this research strives to maintain academic credibility in a landscape transformed by AI and nurtures the growth of more equitable AI technologies
Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade
Get PDFThis study presents a comprehensive overview of the approaches employed in recommendation systems over the last decade. The review primarily draws from two categories of filtering techniques: content-based filtering and collaborative filtering methods. We have reviewed and tabulated approximately forty articles that have been published. Major findings include: (1) collaborative filtering is more often used than content-based filtering, 70% to 23%, the rest is hybrid methods of these two; (2) more than half of the machine learning approaches adopted are supervised learning; however, (3) algorithm-wise, K-means the unsupervised learning algorithm emerged as the most frequently adopted approach in recommendation systems. Also notably, cosine similarity stands out as the prevalent measurement technique
Additively Manufactured Waveguide Hybrid Septum Coupler Optimized Using Machine Learning
Get PDFThis paper describes a waveguide septum coupler design having a smooth profile well suited for additive manufacturing. The large aperture of this hybrid coupler is shaped with even-degree Legendre polynomials. Machine learning-assisted global optimization is employed to extend the operating bandwidth of the component. A design in K-band is detailed and a prototype is manufactured and tested. The experimental results confirm an improvement of 19% in operating bandwidth compared to the previously reported design in the same band while keeping all other key properties mostly unchanged, specifically the physical dimensions. The use of additive manufacturing leads to a mechanically simple and lightweight component of interest for the design of integrated microwave devices, such as beamforming networks and compact feed systems
A Reflecting/Absorbing Dual-Mode Textile Metasurface Design
No full textA textile-based reflecting/absorbing dual-mode metasurface is proposed in this letter. For the reflecting mode of the design, a conventional square patch electromagnetic bandgap (EBG) structure is adopted, and the zero-degree reflection phase center is tuned to 2.4 GHz. For the absorbing mode, a carbon-coated resistive net is applied on top of the EBG patches to redirect the current flow at resonance and, hence, achieve energy dissipation with the resistance. The underlying reconfigurable logic is analyzed with a dispersion diagram, surface current distribution, and equivalent circuit/impedance matching analysis. By applying a state-of-the-art AI-driven antenna design technique, self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA) method, the geometry parameters can be accurately determined meanwhile maintaining absorption and reflection band of the design centered at the same frequency. The fabricated prototype of the design can achieve a maximal absorption of 99.8% (−29.2 dB) and maintain an absorption over 90% in the frequency range of 2.39 GHz to 2.42 GHz. To verify the reflection properties, a textile monopole antenna was fabricated and tested along with the reflection metasurface. A 5 dB realized gain enhancement can be achieved at 2.4 GHz with the applied metasurface. Both simulations and measurements verify the effectiveness of the proposed dual-mode metasurface design
An Artificial Intelligence-Assisted Optimization of Imperceptible Multi-Mode Rectenna
No full textIn this letter, we propose and experimentally demonstrate compact, low-profile, and optically-transparent antennas for multi-band and multi-range wireless power transfer (WPT) applications. Specifically, we put forward new types of transparent multi-band antennas that can perform the near-field reactive WPT (13.56 MHz), as well as the far-field radiative WPT (980 MHz and 2.45 GHz) within a single device. Further, such an antenna is integrated with compact, frequency-scalable rectifying circuits to form an unseeable multi-mode WPT device. We show that a hybrid inductive (13.56 MHz) and radiative (980 MHz and 2.4 GHz) WPT device can be realized with a modified inverted-F antenna (IFA) structure connected to spiral-coil virtual ground. To meet the stringent design requirements of this unobtrusive multi-band antenna, a state-of-the-art machine learning-assisted global optimization method (parallel surrogate model-assisted hybrid differential evolution for antenna optimization or PSADEA) is exploited for global optimization. We envision that the proposed transparent and flexible WPT and energy harvesting devices can be beneficial for many applications, including ubiquitous wireless charging based on smart windows and glasses, solar-radio frequency (RF) integrated power supply, wearable or textile electronics, and internet-of-things (IoTs)
Impact of Large Language Models on Scholarly Publication Titles and Abstracts: A Comparative Analysis
No full textArtificial Intelligence (AI) tools become essential across industries, distinguishing AI-generated from human-authored text is increasingly challenging. This study assesses the coherence of AI-generated titles and corresponding abstracts in anticipation of rising AI-assisted document production. Our main goal is to examine the correlation between original and AI-generated titles, emphasizing semantic depth and similarity measures, particularly in the context of Large Language Models (LLMs). We argue that LLMs have transformed research focus, dissemination, and citation patterns across five selected knowledge areas: Business Administration and Management (BAM), Computer Science and Information Technology (CS), Engineering and Material Science (EMS), Medicine and Healthcare (MH), and Psychology and Behavioral Sciences (PBS). We collected 15 000 titles and abstracts, narrowing the selection to 2000 through a rigorous multi-stage screening process adhering to our study's criteria. Result shows that there is insufficient evidence to suggest that LLM outperforms human authors in article title generation or articles from the LLM era demonstrates a marked difference in semantic richness and readability compared to those from the pre-LLM. Instead, it asserts that LLM is a valuable tool and can assist researchers in generating titles. With LLM's assistance, the researcher ensures that the content is reflective of the finalized abstract and core research themes, potentially increasing the impact and accessibility and readability of the academic work
Application of Machine Learning-Assisted Global Optimization for Improvement in Design and Performance of Open Resonant Cavity Antenna
No full textOpen resonant cavity antenna (ORCA) and its recent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits. This work explores machine learning (ML)-assisted antenna design techniques aiming to improve and optimize its major radiation parameters over the maximum achievable operating bandwidth. A state-of-the-art method, e.g., parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA) has been exercised upon a reference ORCA geometry revealing a fascinating outcome. This modifies the shape of the cavity which was not predicted by EM-based analysis as well as promising significant improvement in its radiation properties. The PSADEA-generated design has been experimentally verified indicating 3dB-11dB improvement in sidelobe level along with high broadside gain maintained above 17 dBi over the 18.5% impedance bandwidth of the ORCA. The new design has been theoretically interpreted by the theory of geometrical optics (GO). This investigation demonstrates the potential and possibilities of employing artificial intelligence (AI)-based techniques in antenna design where multiple parameters need to be adjusted simultaneously for the best possible performances
Liquid-liquid biopolymers aqueous solution segregative phase separation in food: From fundamentals to applications - A review
Get PDFAs a result of the spontaneous movement of molecules, liquid-liquid biopolymer segregative phase separation takes place in an aqueous solution. The efficacy of this type of separation can be optimized under conditions where variables such as pH, temperature, and molecular concentrations have minimal impact on its dynamics. Recently, interest in the applications of biopolymers and their segregative phase separation-associated molecular stratification has increased, particularly in the food industry, where these methods permit the purification of specific particles and the embedding of microcapsules. The present review offers a comprehensive examination of the theoretical mechanisms that regulate the liquid−liquid biopolymers aqueous solution segregative phase separation, the factors that may exert an impact on this procedure, and the importance of this particular separation method in the context of food science. These discussion points also address existing difficulties and future possibilities related to the use of segregative phase separation in food applications. This highlights the potential for the design of novel functional foods and the enhancement of food properties
Preparation and characterization of hydrophobically-modified sodium alginate derivatives as carriers for fucoxanthin
No full textMicelles are nano-architectured structures capable of encapsulating hydrophobic substances in aqueous solutions, thereby improving their stability, solubility, and bioavailability to control the release of bioactive compounds. In this study, the sodium alginate backbone was modified via a hydrophobic modification scheme where octyl chains were covalently attached to the alginate chains via esterification reactions. Fourier-transformed infrared spectrometry (FTIR), 1H nuclear magnetic resonance (1H NMR), X-ray diffraction (XRD), and thermal gravimetric analysis (TGA) were used to characterize the structure of the sodium alginate derivatives of varying molar mass (ALG-C8). Fluorescence spectroscopy, surface tension measurements, and dynamic light scattering (DLS) were employed to assess the self-assembly performance of ALG-C8. The three methods produced consistent results, indicating that self-assembly decreased with higher molar mass. The self-assembled ALG-C8 micelles were utilized to encapsulate fucoxanthin. The loading capacity (LC) and encapsulation efficiency (EE) were determined using UV–Vis spectrophotometry. The molar mass of ALG-C8 has a key influence on fucoxanthin loading and release behavior. The ALG-C8 molecules with the lowest molar mass produced micelles with the smallest hydration diameter, resulting in the highest LC and EE. Furthermore, the release of fucoxanthin is significantly influenced by the pH and ionic strength of the medium. ALG-C8 micellar-like aggregates exhibit resistance to low pH and high ionic strength environments, releasing encapsulated material at the target site under alkaline conditions. Therefore, synthesized ALG-C8 is a potential candidate for preparing pH-responsive self-assembled micellar-like aggregates, enabling targeted delivery and gradual release of hydrophobic functional food components
A Brief Overview of the Diagnosis and Treatment of Cobalamin (B12) Deficiency
No full textAbstract
Background: An increasing number of adult individuals are at risk of vitamin B12 deficiency, either from reduced nutritional
intake or impaired gastrointestinal B12 absorption.
Objective: This study aims to review the current best practices for the diagnosis and treatment of individuals with vitamin B12
deficiency.
Methods: A narrative literature review of the diagnosis and treatment of vitamin B12 deficiency.
Results: Prevention and early treatment of B12 deficiency is essential to avoid irreversible neurological consequences. Diagnosis
is often difficult due to diverse symptoms, marked differences in diagnostic assays’ performance and the unreliability of second-line
biomarkers, including holo-transcobalamin, methylmalonic acid and total homocysteine. Reduced dietary intake of B12 requires
oral supplementation. In B12 malabsorption, oral supplementation is likely insufficient, and parenteral (i.e. intramuscular) supplementation
is preferred. There is no consensus on the optimal long-term management of B12 deficiency with intramuscular
therapy. According to the British National Formulary guidelines, many individuals with B12 deficiency due to malabsorption can be
managed with 1000 mg intramuscular hydroxocobalamin once every two months after the initial loading. Long-term B12 supplementation
is effective and safe, but responses to treatment may vary considerably. Clinical and patient experience strongly
suggests that up to 50% of individuals require individualized injection regimens with more frequent administration, ranging from
daily or twice weekly to every 2-4 weeks, to remain symptom-free and maintain a normal quality of life. ‘Titration’ of injection
frequency based on measuring biomarkers such as serum B12 or MMA should not be practiced. There is currently no evidence to support that oral/sublingual supplementation can safely and effectively replace injections.
Conclusions: This study highlights the interindividual differences in symptomatology and treatment of people with B12 deficiency.
Treatment follows an individualized approach, based on the cause of the deficiency, and tailored to help someone to
become and remain symptom-free