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Spinor bosons realization of the SU(3) Haldane phase with adjoint representation
No full textThe Haldane phase with local SU(3) adjoint representation constitutes the simplest non-trivial symmetry-protected topological phases in the SU Heisenberg spin chains. In this paper, we propose to realize this phase by a two-species spinor Bose gas, with each species labeling the quark or antiquark states of SU(3) symmetry. In the strong-coupling limit, we determine the ground-state phase diagram, and identify a quantum phase transition from the Haldane phase to a dimer phase. We show how to characterize the Haldane phase through its edge excitations. We also explain the physics at the dimer phase, by constructing an explicit ground-state ansatz at the dimer point
Developing A Prototype: Biofeedback Integrated Thoracic Expansion Measuring Device
No full textBackground: Thoracic expansion is defined as the difference in chest circumference between Maximal. Inhalation and Maximal Exhalation. As per WHO guidelines the ability to accurately evaluate Thoracic Expansion is very important in disorders like COPD, Asthma, Bronchiectasis etc. Till the date chest expansion is measured clinically by measuring tape, so the chances of human error persist, also Constant & Continuous data is not possible until it is performed manually and it is also difficult for the subject to interpret it. So it was needed to develop a biofeedback integrated thoracic expansion device, in order to accurately measure and improve chest expansion among people having reduced lung volume and Capacity. Aim: To Develop Biofeedback Integrated Thoracic Expansion Measuring Device. Materials and Methods: It was a Multiphasic study. Phase 1 was for the Development of the Prototype. Phase 2 was for Checking Reliability and Validity of the Device. Phase 3 was for checking the effectiveness of device. Results and Discussion: Result showed that the Biofeedback Integrated thoracic expansion-measuring device demonstrated excellent reliability when assessed by Test- Retest Method using ICC test and showed Very Strong Agreement with Gold standard Method of Measuring tape when Concurrent parallel Validity was applied to it by Spearman Correlation test. Hence, the findings suggested that the Biofeedback Integrated thoracic expansion-measuring device could confidently be used as a valid alternative for assessing Thoracic Expansion digitally. From Group A to Group C, Statistical significance difference (<0.05) was found between Pre intervention and post intervention when Wilcoxon Signed Rank test was applied to it. Conclusions: In context to multiphasic study, the present prototype is highly reliable and valid tool for assessing thoracic expansion. Not only for Diagnosis but for its feature of providing visual biofeedback makes it a useful tool for treatment purpose also in pulmonary rehabilitation. Its ability to provide accurate and consistent measurements makes it a promising tool for usage in the clinical practice
Scale Up and Process Optimization of 20 kg Palm Sucrose Ester Production
No full textIndonesia is the world's largest producer of CPO, with a production volume of 48 million tons. The abundance of CPO production allows for the diversification of palm oil derivative products, including the development of sucrose ester surfactants. Sucrose ester is an ionic oleochemical surfactant with a high affinity for water. This study aims to determine the efficiency of scaling up sucrose ester production using methyl palmitate and stearate as raw materials, with different turbine and anchor mixers, in a batch stirred tank reactor made of stainless steel 304. Sucrose ester production employs a molar ratio of sucrose to methyl ester of 1:3, with a potassium carbonate catalyst concentration of 6% w/v. The optimization temperature was set at 60 °C and maintained for 30 minutes, then gradually increased to 100 °C over 90 minutes. The turbine stirrer produced better mixing homogeneity than the anchor stirrer, with palmitate sucrose ester yielding the best results, achieving an HLB value of 15–16, a yield of 84%, and surface tension and interfacial tension values of 22.62 dyne/cm and 2.16 dyne/cm, respectively. The sucrose ester produced aligns with the sucrose esters available on the market and can be commercialized as a surfactant in cosmetic products
Effect of Salinity, Sugar Level, and Fermentation Time for Bioethanol Production from Nipa Palm Sugar
No full textThis study aims to determine the effect of salinity, sugar level, and fermentation time on the bioethanol production from nipa (
Nypa fruticans Wurmb.) palm sugar. The research was conducted in two stages using batch fermentation. The first stage aims to determine the optimal salinity of nipa palm sugar from Nusadadi, Cikembulan, and Pedasong Village, Central Java, for bioethanol production. The results showed that salinity significantly affected bioethanol productivity. Nipa palm sugar from Nusadadi, with the lowest salinity, produced the highest bioethanol content after distillation (39.31% v/v). The second stage was conducted to evaluate the effects of fermentation time (24, 48, and 72 hours) and sugar-to-water ratio (1:4, 1:5, 1:6) on bioethanol production. Nipa palm sugar from Nusadadi was chosen as feedstock due to the highest ethanol yield. The combination of fermentation time and sugar solution ratio had a significant effect on the total sugar and reducing sugar content of the fermentation broth, as well as density and bioethanol content. The treatment with a 48-hour fermentation time and a sugar solution ratio of 1:4 produced the highest ethanol content after distillation (42.97% v/v)
Artificial Neural Network Approach to Predict Biodiesel Production using Algae-Based Heterogeneous Catalyst
No full textThe rising dependence on fossil fuels has intensified environmental issues such as greenhouse gas emissions and resource depletion. Biodiesel offers a renewable alternative with lower emissions. However, conventional biodiesel production are sensitive to free fatty acids and water, causing soap. Heterogeneous catalysts derived from biomass provide a cleaner and reusable alternative. In this study, Ulva lactuca, a green macroalga with rapid growth and no need for arable land or fertilizers, was used as a sustainable source for catalyst preparation. This research integrates an U. lactuca-based heterogeneous catalyst with an Artificial Neural Network (ANN) to predict biodiesel yield under different process conditions. The objective was to develop a robust predictive model for biodiesel production from waste cooking oil. Transesterification was performed at 50–70 °C, catalyst loadings of 2–5 wt%, and reaction times of 60–180 min, with a fixed methanol-to-oil ratio of 6:1. The ANN, trained using the Levenberg–Marquardt algorithm in MATLAB R2022a, achieved an optimal architecture of 4–18–1. The model showed excellent predictive accuracy, with R values of 0.9989, 0.9969, 0.9980, and 0.9987 for training, validation, testing, and overall datasets, and minimum MSE values of 2.81 × 10⁻⁴. The highest experimental biodiesel yield of 0.96 mol mol⁻¹ closely matched the ANN-predicted yield of 0.97 mol mol⁻¹ at 60 °C, 90 min, and 4 wt% catalyst loading. These results confirm the ANN’s strong predictive capability and demonstrate its potential for optimizing biodiesel production using sustainable algae-based catalysts
Intelligent defect detection in electroluminescence images of photovoltaic modules using MobileNetV2
No full textWith the rapid growth of the photovoltaic (PV) industry, fast and accurate defect-detection techniques are becoming increasingly important. Manual inspection of PV modules using electroluminescence (EL) imaging is time-consuming and prone to errors. This study proposes a clever method for detecting defects using a lightweight deep learning model based on the MobileNetV2 architecture. The model learns from a dataset of EL images showing two common types of defects: cracks and dark areas. It also contains defect-free cells. To improve robustness to typical EL acquisition variability, an EL-tailored data augmentation pipeline is applied, including geometric transformations and photometric adjustments (brightness and contrast). During testing, it takes only 0.913 seconds to predict an image. This demonstrates a good compromise between speed and accuracy. This approach offers a promising solution for low-cost, near-real-time quality inspection of photovoltaic modules using artificial intelligence
Evaluation of the thermal performance of buildings using NePCM under various climatic conditions in three cities of Morocco
No full textClimatic conditions are extreme in some parts of Morocco, with extreme outdoor temperatures. Researchers in the construction sector are focusing more and more on combination applications of NePCMs in buildings. Indeed, as these materials enable passive temperature adjustment, they will contribute to reducing the energy consumption of heating, ventilation, and air conditioning (HVAC) systems. To this end, the effect of using nanoparticles CuO (3%) with PCM RT25 for a melting temperature of 25°C on the hottest day under different climatic conditions in Morocco will be assessed. This research aims to maintain the temperature within the thermal comfort range for occupants by reducing temperature variations and energy consumption, while comparing the effect of climatic conditions for the two seasons of the year (winter and summer) in each zone to determine the effect of NePCM on the buildin
PrepMind AI: A RAG Based Intelligent Learning Assistant for Competitive Exam Preparation
No full textCompetitive Examination such as UPSC and MPSC involves rapid and correct responses, yet majority of trainees rely on conventional learning or coaching schools which are more expensive. Learners with access to the current digital tools will risk the failure when answering handwritten questions, text recognition and proper adjusting to their individual learning requirements making the difference between accessibility and quality answers a significant one. In order to address these weaknesses, this paper presents PrepMind AI, and AI powered learning system that is aimed at offering an immediate and accurate response using an integrated pipeline of Optical Character Recognition (OCR), Retrieval Augmented Generation (RAG), and state-of-the-art Large Language Models (LLMs). This research aims at producing a platform that is able to respond to text and image queries, finding the correct answers to such queries in the verified sites, and generating the correct answers to the particular students. In essence, it works by OCR to extract text in the form of pictures and a tool named RAG to extract the correct information and images in PDFs. The AI then responds to you in the same way a real human being would respond. The entire idea behind PrepMind AI is that it will be much easier to study for exams, and accessible to all students, regardless of their origin
Climate change impact on the marine environment of northern Morocco
No full textClimate change represents a growing pressure on biological diversity. In particular, with Mediterranean ecosystems, which host rich and fragile biodiversity, being particularly vulnerable. The Mediterranean Sea is characterized by a distinct climate, featuring four contrasting seasons and considerable interannual variability. This study investigates the impact of climate change on the marine environment and explores potential adaptation strategies. Our research emphasizes the pivotal role of geographic information system (GIS) techniques in evaluating the impacts of climate change on marine biodiversity. By focusing exclusively on GIS results, we aim to provide a focused framework for understanding spatial variations and supporting conservation strategies in the regio
AI-Powered Ancient Inscription Analysis and Translation (Halmidi)
No full textAncient inscriptions are vital primary sources for understanding linguistic evolution, socio-political structures, and cultural history. The Halegannada script, used in early Kannada inscriptions, presents significant challenges for computational analysis due to its complex orthography, weathered stone surfaces, and limited annotated datasets. Conventional epigraphic analysis relies heavily on manual interpretation, making it time-consuming and difficult to scale. This paper proposes an end-to-end AI-driven framework for the automated recognition and translation of Halegannada inscriptions. The methodology incorporates image preprocessing techniques for noise reduction and contrast enhancement, followed by a hybrid Optical Character Recognition (OCR) approach that combines Convolutional Neural Networks (CNNs) for character feature extraction and Recurrent Neural Networks (RNNs) for sequence modeling, supported by Tesseract as a baseline OCR engine. The recognized text is then mapped to Modern Kannada and translated into English using Natural Language Processing (NLP) techniques. Preliminary experiments conducted on digitized inscription datasets from Mysuru University show encouraging recognition accuracy and translation consistency. The proposed framework supports digital preservation efforts, improves accessibility to historical inscriptions, and contributes to the field of computational epigraphy. Future work includes expanding training datasets and integrating transformer-based language models