Zero-point entropies of spin-jam and spin-glass states in a frustrated magnet

Thermodynamics of glassy states in a quasi-two-dimensional frustrated magnet Ba$_2$Sn$_2$ZnCr$_{7p}$Ga$_{10-7p}$O$_{22}$ where $p$ is the spin density are investigated experimentally. The system features a triangular network of bipyramids of spins with the quantum spin number $s = 3/2$. The DC magnetic susceptibility measurements on a series of samples with $0.44 \le p \le 0.98$ show a freezing transition with the transition temperature $T_\mathrm{f} \le 1.2$ K. $T_\mathrm{f}$ is found to decrease with decreasing $p$. The low-lying excitations in the glassy state of the system are examined via the temperature dependence of the magnetic heat capacity and are shown to consist of two components: the hydrodynamic Halperin-Saslow modes characteristic of a spin jam and the two-level systems of a spin glass. A continuous crossover between the two glassy states is observed via the varying weights of the two components as the spin density is varied. The $p$ dependence of the spin jam's zero-point entropy determined from the exotic perimeter-scaling behavior combined with the observed zero-point entropy of the samples provides the $p$ dependence of the spin glass's zero-point entropy. The obtained result shows that the correlations between orphan spins begin below $p \sim 0.8$, the limit that was also found using a neutron scattering technique in a previous report on the isostructural compound SrCr$_{9p}$Ga$_{12-9p}$O$_{19}$. The domain size of the spin-jam state estimated from the value of the zero-point entropy for the cleanest sample is approximately $4 \times 4$ bipyramids, about 2.5 times the measured spin correlation length

Climate Situation in 5 Top-Rated Tourist Attractions in Thailand Investigated by Using Big Data RSS Feed and Programming

The concern about rising global temperatures is powerful in its effect on the tourism economy sector in the top 5 tourist attractions in Thailand. This study aimed to find techniques for using automatic big data RSS feed that is accessible online in mobile push notification and is freely available on the Internet. The programming technique method was applied for data acquisition, statistical process, and mathematical analysis. The outcomes pointed to a lack of study temperature changes on the local scale that provides insufficient information for decision making about tourism management in the local region. The results in this local level study tended to express decreasing temperature. This is not usually consistent with the IPCC scientific consensus summarization. This result could be involved with geography location and monsoon condition control. The temperatures did not have a significant effect on increase in the number of storms in the West Pacific Ocean. Sea surface temperature results were in agreement with global scale studies.</jats:p

Phenolic content and antioxidant properties of green chilli paste and its ingredients

Green chilli paste and its ingredients (chilli, red onion and garlic) from different stages of processing were analysed for total phenolic content and antioxidant properties, i.e. total antioxidant capacity, DPPH radical scavenging activity, and β-carotene bleaching activity. The effects of processing stage on total phenolic content and antioxidant properties of green chilli paste and its ingredients were discussed, along with the correlation between the total phenolic content and the antioxidant properties

AI-powered in the digital age: Ensemble innovation personalizes the food recommendations

This study proposes and evaluates a novel approach utilizing ensemble machine learning techniques for personalized meal services to address a critical gap in understanding AI-powered decision-making within the food delivery and restaurant industry. We draw inspiration from diverse fields, including non-traditional simulation methodologies and open innovation dynamics, to create a framework that leverages the combined strengths of individual algorithms. Three machine learning algorithms – decision trees, logistic regression, and naïve Bayes – are rigorously evaluated for their efficacy in classifying and assigning algorithms within an ensemble model for a new service. A simulated dataset, informed by expert tagging, is the training ground, ensuring practical relevance. We employ the voting probability metric on a held-out test set to provide a robust measure of accuracy in this critical task. Our findings reveal the significant potential of AI-powered personalized meal services. Ensemble models demonstrate high accuracy, showcasing the collaboration of combining individual algorithms. This originality lies in applying ensemble techniques to a business case with far-reaching implications for management and societal well-being. Beyond technical success, we explore this technology’s broader impact. AI-powered food recommendations can enhance accessibility for individuals with dietary needs, promote healthier lifestyles through nutritious meal suggestions, and generate new job opportunities. Acknowledging limitations and future research avenues, we invite further exploration of diverse machine learning algorithms and broader applications across various domains

Artificial intelligence for target symptoms of Thai herbal medicine by web scraping

Machine learning (ML) is implementing artificial intelligence (AI) research within medicine that has made dramatic progress in recent years. In addition to standard treatments, the role of complementary and alternative medicine should be mentioned. Traditional Thai medicine has received growing acceptance as a complementary approach to modern medicine by using local herbs. A vast amount of Thai herbal knowledge and information is freely available on the Internet. The reader must evaluate each website and decide to use trustworthy and appropriate information. This study aimed to acquire Thai herbal knowledge recorded in the Thai language system on the Internet by scraping websites using programming techniques. The knowledge was extracted with programming, and the types of Thai herbs were classified corresponding to target symptoms by the machine learning algorithm. The ML method organized the process when sufficient achievement was reached in order to give reliable and high accuracy results from the training data set. The validation of extracted knowledge was achieved by using the part-of-speech tag patterns analysis. This study showed that the programming and machine learning system was appropriate for obtaining and classifying Thai herbal medicines knowledge.</jats:p

Studies of Free Falling Object and Simple Pendulum Using Digital Video Analysis

The motion of a free falling object and a simple pendulum were analyzed by digital cameras and computer programs (Sony Vegas, Adobe Photoshop and Microsoft Excel). The positions of the moving objects were evaluated every 33 ms from a series of images and experimental results were compared with fundamental equations in mechanics to verify the technique. For the free falling experiment, the displacement was proportional to the time squared and the velocity can be averaged from the change in position during each 33 ms interval. From the simple pendulum experiment, the angular displacement had a periodic variation with the time. This oscillation exhibited damping amplitudes and a constant time period. The time period squared had a linear relationship with the length of the pendulum. The agreements between the experimental results and the theory led to the acceleration due to the gravity with an acceptable level of accuracy. From this demonstration, a simple setup consisting of a conventional camera and software can not only be applied in the context of simple problems but also shows a potential in the teaching of advanced mechanics