Science & Technology Asia

Optimal Ultrasound–Assisted Extraction of Concentrated Protein from Cricket Powder

Mon, 24 Mar 2025 00:00:00 +0700

Edible insects are a valuable protein source, and extracting these proteins is essential for creating customized products with precise compositions. Ultrasound technology offers an environmentally friendly method to improve the extraction process. This study investigated the optimal conditions for protein extraction from cricket powder (Acheta domesticus) using ultrasound-assisted extraction. The key parameters analyzed included the solid-to-liquid ratio (S/L), temperature, and extraction time. The compositional analysis revealed that the protein and lipid contents in the cricket powder were 59.37%w/w and 16.78%w/w, respectively. Utilizing the response surface methodology (RSM) with a Box-Behnken design, the optimal conditions for protein extraction were identified: an S/L ratio of 1:6, a temperature of 40 °C, and an extraction time of 30 min at a constant pH of 11. Under these conditions, the protein content obtained was 22.32%w/w. Subsequent freeze-drying of the extracted cricket protein resulted in a powder with a protein content of 69.57%w/w, a yield of 59.55%, and a protein extraction efficiency of 69.78%. Cytotoxicity assessments using the MTT assay on RAW 264.7 macrophages and to mononuclear cells demonstrated that both the raw and freeze-dried cricket powder exhibited no cytotoxic effect. Therefore, the resultant cricket protein powder shows potential for incorporation into functional food products.

Evaluation of Genetic Diversity in Fourth- Generation Selective Breeding Lines of Pacific White Shrimp Using Microsatellites

Mon, 24 Mar 2025 00:00:00 +0700

The Pacific white shrimp (Litopenaeus vannamei) is a vital aquaculture species in Thailand with substantial economic importance. Ensuring genetic diversity is critical for sustaining a robust gene pool in farmed populations, ensuring resilience to diseases, environmental changes, and enhancing overall productivity. In this study, genetic diversity and structure of 35 families of fourth generation selective breeding lines of L. vannamei were monitored using five microsatellite DNA markers. The polymorphism information content (PIC) of all microsatellite loci exhibited high levels of polymorphism, with PICs greater than 0.70, indicating that the DNA markers were highly informative and effective in detecting allele variations in this population. The number of alleles per locus and the number of effective alleles (N_e) ranged from 3 to 6 and 2.809 to 4.598, with averages of 4.4 and 3.734, respectively. These values indicate that the population possessed a moderate level of genetic diversity. The overall mean observed heterozygosity (H_o) ranged from 0.209 to 0.492, which was lower than the expected heterozygosity (H_e) (0.714-0.784). The mean inbreeding (F_is) coefficient across all loci was 0.336 indicating a moderate level of inbreeding within this population. The population structure analysis showed that the 35 families were divided into two subpopulations (K=2), related to the sources of the base population collected from two private hatcheries. This study provides valuable insights into population genetics and will help guide breeding strategies of Pacific white shrimp.

Identification of Gut Microbiota in Blue Swimming Crabs Collected from the Eastern Coast of the Gulf of Thailand Containing Gill Net Debris

Mon, 24 Mar 2025 00:00:00 +0700

The aim of this study was to investigate the gut microbiota of blue swimming crabs (Portunus pelagicus) in which a piece of gill net debris was found. Next-generation sequencing was performed to analyse the V1–V3 sequences of the 16S rRNA gene for bacteria and the internal transcribed spacer sequences for fungi. Samples of stomach crabs were collected from the coastal wetlands of eastern Thailand. Four fragments of gill nets were found (a single piece per gut sample), with lengths of 5.2–12.5 mm. Stomachs without gill net fragments from a total of four samples comprised Group A, whereas stomachs containing gill net fragments in the four samples comprised Group B. Groups A and B shared 131 OTUs (Operational Taxonomic Units), while they contained 51 and 26 OTUs, respectively. Photobacterium was the predominant Vibrionaceae present in both groups, but Marinobacter of Alteromonadaceae was present at high levels in Group A. Interestingly, a single sample in Group B was dominated by Vibrio. For fungi, 276 and 195 OTUs were included in Groups A and B, respectively, whereas 224 OTUs were shared by Groups A and B. Malassezia was predominant in both groups. Moesziomyces, Ustilago, Erythrobasidium and Schizophyllum were more common in Group B than in Group A. In contrast, Cladosporium, Ramicandelaber, Claroideoglomus and Stachybotrys were more common in Group B than in Group A. These results provide the first evidence of the microbiota in blue swimming crabs that have gill nets in their stomachs.

An Investigation of Extended Proinov Contraction under Non-Triangular Metric Structure and the Corresponding Applications

Ram Jeevan, Jayesh Savaliya, Dhananjay Gopal — Mon, 24 Mar 2025 00:00:00 +0700

This article demonstrates several fixed point theorems of extended Proinov-type, on the existence and uniqueness of fixed points within the context of non-triangular metric spaces. Additionally, we provide two applications of our primary result, in solving existence and uniqueness of a solution for a non-homogeneous linear parabolic partial differential equation and a stochastic integral equation.

A Study of Right Truncated Lomax Rayleigh Distribution and Application for Group Acceptance Sampling Plan

Kanittha Yimnak, Wimonmas Bamrungsetthapong — Mon, 24 Mar 2025 00:00:00 +0700

In this paper, the right-truncated Lomax-Rayleigh distribution (R-TLRD) is presented with some important statistical properties, such as the survival function, hazard function, moment, parameter estimation, and application for group acceptance sampling plans. The right-truncated Lomax-Rayleigh distribution provides the model corresponding to the three real datasets, while the Lomax-Rayleigh distribution fits only the two real datasets. Then, the group acceptance sampling plan when the product lifetime is determined as a right-truncated Lomax-Rayleigh distribution provides the optimum of the number of groups (g), the number of items for each group (r), the acceptance number (c), and the operating characteristic function (OCF) value. The group acceptance sampling plan under right truncated Lomax-Rayleigh distribution gives a smaller sample size for testing than the group acceptance sampling plan under Weibull distribution.

A New Existence and Stability Results of a Backward Impulsive FDEs

Nabila Benahmed, Maamar Benbachir, Farid Chabane, Mohammad Esmael Samei — Mon, 24 Mar 2025 00:00:00 +0700

This article focuses on studying a type of non-local and local backward problem involving impulsive fractional differential equations. The equations include a special type of derivative called the 𝜒−Caputo fractional derivative. The use of Krasnoselskii’s fixed-point theorem and Banach’s contraction principle helps us prove that there is only one solution and it definitely exists. In addition, we found some results about the stability of Hyers-Ulam and generalized Hyers-Ulam equations. Finally, some examples are given to demonstrate that the results are correct.

Closed Form of Nearest Point of Linear Pentagonal Fuzzy Number and its Application to Course Assignment Problem with Fuzziness in Preference Level

Phantipa Thipwiwatpotjana, Artur Gorka, Worrawate Leela-apiradee — Mon, 24 Mar 2025 00:00:00 +0700

This study focuses on optimizing course assignments for instructors based on their preferences, which are inherently uncertain due to personal conflicts affecting decision making. To address this, we model each preference level using two triangular fuzzy numbers, generated based on distinct instructor personality types. When instructors experience uncertainty in selecting a precise preference level, they can choose from these predefined triangular fuzzy numbers. The MIN aggregation of these triangular fuzzy numbers results in a linear pentagonal fuzzy number, whose nearest point in closed form is derived and utilized to represent fuzzy preference levels in our course assignment model. The assignment results obtained using this approach are comparable to those derived from optimal fuzzy weight assignments for specific preference levels. Our method is validated with data from the Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, demonstrating its effectiveness in handling uncertainty in course assignment decisions.

Hydroxyapatite/MgO Composites from Biowaste Pokea Clam Shells (Batissa violacea): Structure and Mechanical Properties for Medical Application

Armid, Fahmiati, Halimahhtussaddiyah Ritonga, Muh. Nurhidayat, Laode A. Kadir — Mon, 24 Mar 2025 00:00:00 +0700

In this research, hydroxyapatite was prepared from Pokea clam shells as home industrial waste by a hydrothermal method. HAp-MgO composites were successfully synthesized through an in-situ hydrothermal technique. The impact of MgO incorporation on the mechanical characteristics of HAp and its composites with 5 wt.% MgO was investigated. An X-ray diffraction (XRD) was used for samples characterization and to assess their phase stabilities. The existence of MgO in the composite material was indicated by its specific peaks at 37.6° and 26.7°. Several indices, including both hardness and compressive strength, were used to measure the mechanical characteristics of genuine HAp and HAp-MgO composite. We found that the inclusion of MgO considerably improved HAp's mechanical characteristics and particle growth. The best mechanical properties were observed in the HAp-MgO composite sintered at 900^oC with 5 wt.% MgO. This composite exhibited a hardness of 30.72 VHN and a compressive strength of 3.12 MPa. The present study recommends that composites with mechanical properties are suitable for biomedical purposes.

Circular Bootstrap on Residuals for Interval Forecasting in K-NN Regression: A Case Study on Durian Exports

Parattakorn Kamlangdee, Patchanok Srisuradetchai — Mon, 24 Mar 2025 00:00:00 +0700

Traditional k-nearest neighbor (K-NN) methods for time series forecasting often fail to capture prediction uncertainty. This study addresses this limitation by integrating circular bootstrap on residuals with K-NN regression to forecast Thailand’s monthly durian export volumes, which exhibit strong non-linearity and seasonality. The proposed methodology includes data scaling and explicit seasonality handling. Circular bootstrap generates multiple residual samples, constructing forecast intervals that quantify uncertainty while preserving temporal dependencies. Comparative analysis demonstrates that the proposed method outperforms the seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing state space (ETS) models by producing forecast intervals that are, on average, 20% narrower. However, coverage is slightly lower, with actual values falling within the intervals in 11 out of 12 months, compared to full coverage by SARIMA and ETS. The results highlight the potential of combining statistical resampling with machine learning to enhance K-NN forecasting, offering a practical solution for improving time series forecast reliability, as demonstrated in the case study of Thailand’s durian exports.

Effect of Distance and Radionuclide Activity on Radiation Dose Exposure around the Chernobyl Nuclear Power Plant (ChNPP): Analysis with Poisson Regression Models and Correlation Tests

Nadrah Nadrah, Ruben Cornelius Siagian — Mon, 24 Mar 2025 00:00:00 +0700

Researchers studied radiation exposure around the Chernobyl Nuclear Power Plant (ChNPP) after the accident, focusing on factors like distance from radiation sources and radionuclide activity. The analysis aimed to improve radiation exposure prediction models for mitigation and public health. Pearson correlation examined the relationship between exposure dose and distance, while Poisson regression identified significant factors, such as Zr-95 and Cs-134 activity. The model’s validity was confirmed through overdispersion and variance inflation factor (VIF) tests, and residuals analysis assessed bias. Results showed radiation exposure decreased with distance, with a moderate negative correlation. Radionuclide activity significantly affected radiation levels, and a perfect correlation was found between exposure dose and absorbed dose. Poisson regression highlighted distance and radionuclide activity as key factors. This study fills a gap in understanding the complex relationship between distance, radionuclide activity, and radiation exposure around ChNPP. The novelty lies in applying Poisson regression with specific isotopes, providing a more accurate model to predict radiation exposure around nuclear facilities.

Interval-Valued Picture Fuzzy Ideals of Semigroups

Anusorn Simuen, Ronnason Chinram, Winita Yonthanthum — Mon, 24 Mar 2025 00:00:00 +0700

In this article, we define an interval-valued picture fuzzy subsemigroup and an interval-valued picture fuzzy left ideal[right ideal, ideal, bi-ideal, interior ideal, quasi-ideal] of a semigroup, as well as investigate some properties of an interval-valued picture fuzzy subsemigroup and various types of an interval-valued picture fuzzy ideal of a semigroup. Furthermore, we will study the relationship between each ideal of a semigroup and its intervalvalued picture fuzzification.

Enhancing Olive Mill Wastewater Treatment: A Study of pH Influence in Multi-Step Column

Mohamed Elbagermi, Khaled Elsherif, Amina Raba, Azdeen Ben Hameda — Mon, 24 Mar 2025 00:00:00 +0700

Olive mill wastewater (OMW), a byproduct of olive oil production, presents significant environmental challenges due to its high organic load and non-biodegradable polyphenols. In Libya, untreated OMW is frequently discharged into the environment, impacting soil and water quality. This study evaluates the pollution potential of OMW and examines a multi-step treatment process utilizing natural materials. The system comprised three columns: the first filled with iron slag and sand, and the second and third with activated carbon prepared from dried olive leaves. Parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, biochemical oxygen demand (BOD₅), and total phenols (TP) were monitored. Results demonstrated significant improvements in water quality under basic pH conditions, with reductions in EC (61.9%), TDS (62.9%), and turbidity (99.8%) and complete removal of BOD₅. However, the reduction in TP was lower, showing a decrease of 43.2%. In contrast, the treatment without pH control achieved lower removal efficiencies for EC (50.5%), TDS (51.5%), and turbidity (97.1%), but a higher reduction in TP (80.9%). The lower TP removal at basic pH is due to phenol deprotonation, weakening adsorption. Acidic conditions improved turbidity removal but were less effective for EC, TDS, and BOD₅. pH influences adsorption and microbial activity, impacting treatment efficiency. This multi-step approach offers a promising solution for OMW treatment, reducing its environmental impact and enabling reuse in irrigation.

On Designing of Modified Exponentially Weighted Moving Average Control Chart based on Sign Rank for Zero-Inflated Data

Suganya Phantu, Yupaporn Areepong, Saowanit Sukparungsee — Mon, 24 Mar 2025 00:00:00 +0700

The performance of parametric and nonparametric control charts that are carefully created for zero-inflated count data—which are commonly modeled using zero-inflated Poisson (ZIP) and zero-inflated binomial (ZIB) distributions—is thoroughly assessed in this study. The evaluation of the three primary control chart performance metrics—average run length (ARL), median run length (MRL), and standard deviation run length (SRL)—forms the basis of the comparative analysis. The performance evaluation’s new results clearly show that nonparametric control charts are more effective at identifying shifts in the probability of small-scale inflation. On the other hand, changes in large-scale inflation are better detected by parametric control charts. These empirical observations are further substantiated through the applications of real-world data, which serves as a robust case study for assessing the effectiveness of both types of control charts in practical settings.

Cybersecurity Insurance Modeling Using Archimedean Copulas

Nasya Kanya Puteri, Felivia Kusnadi, Farah Kristiani — Mon, 24 Mar 2025 00:00:00 +0700

While technology has brought many benefits, it has also created challenges, including a rise in cyber-crime. One major issue is the spread of malware, such as viruses, which can threaten personal, business, and national security. This can be countered by purchasing cybersecurity insurance. To accommodate the increasing demand, it is necessary to provide the adequate method to measure the risks and estimate the premium charge. In this paper, the modeling of the infection and recovery process of an electrical device that is represented as a node in a network system is discussed. The model that is discussed is a non-Markov model with dependence between cybersecurity risks, which is modeled by the Archimedean copula function, namely Clayton, Frank, and Gumbel. Furthermore, the premium charge is estimated using standard deviation and the exponential utility principle. Based on the simulation, it can be concluded that a node connected with several nodes is more likely to be infected than a node connected with fewer number of nodes. Modeling infection times between nodes using the Gumbel copula function generates higher premiums than other copula functions, therefore it is better to use the Gumbel copula function for modeling cybersecurity insurance in the first contract period because insured companies tend to be more interested in extending the contract if the premium charge of the subsequent year is less than or equal to the rate of the previous year. In addition, changes in parameter of times-to-infections from neighbors does not cause a significant difference in expected number of infections, expected losses, and premium charge.

Investigation and Design of T-Type Inverter for Power Distribution Network

Jogeswara Sabat, Mrutyunjaya Mangaraj, Ajit Kumar Barisal — Mon, 24 Mar 2025 00:00:00 +0700

Green energy and clean power are the recent trends of modern power distribution networks (PDN). In recent years, great attention has been focused on T type inverters due to their advantages over conventional voltage source inverters (VSI), such as fault-tolerant, overload capability, less total harmonic distortion (THD), better output waveform and high efficiency. An inductor coupled T type (IC-T type) inverter-based distribution static compensator (DSTATCOM) is built for active power filtering of 3-phase 3-wire PDN connected nonlinear load in this paper. The proposed topology is composed of three inductors connected between the VSI and common source. The proposed PDN is obstructed by the DSTATCOM using icos𝜙 control algorithm for the inverter DC link voltage reduction, filter inductor rating minimization, decreasing the switching stress, increasing the life span of an inverter, reliable operation, stress balancing, loss reduction and increase in efficiency. Apart from these, other improvements such as power factor (PF) correction, better voltage regulation, harmonics reduction and load balancing are obtained. The efficacy of the IC-T type inverter in different loading scenarios is justified using MATLAB/Simulink software captivating in reflection of the IEEE-514-2017 and IEC- 61000-1-3 benchmark.

Effective Price Prediction of Cryptocurrencies using CNN-Based Dual Directional Model

Preeti Pandey, Geeta Sharma — Mon, 24 Mar 2025 00:00:00 +0700

In recent days, predicting cryptocurrency trends has become essential for most individuals, including stockholders and traders, as it aids them in making better-informed selections regarding the digital asset market’s future. Predicting the price of the cryptocurrency leads to profitability in trading strategies. Therefore, the main objective of the study is to create an effective deep learning architecture using forecasting models such as recurrent neural networks (recurrent NN), Convolutional neural networks (convolutional NN) and Long Short-Term Memory (LSTM) for predicting Bitcoin and Ethereum prices. The model utilizes CNN to extract features from historical price data. Where, CNN has the ability to detect complex patterns in price movements and incorporates bidirectional LSTM (Bi-LSTM) layers to capture both past and future price trends. Moreover, Bi-LSTM effectively manages the temporal dynamics and makes it suitable for financial time series, which demonstrate non-linear behavior. Experimental results on a dataset of major cryptocurrencies demonstrate the efficacy of the proposed model in forecasting cryptocurrency prices with high accuracy. The dual-directional model outperforms traditional time series forecasting methods and singledirectional models, showcasing its potential for improving price prediction in the cryptocurrency stock market.

Analysis of Laser Absorption Value Using LLLT-50 Device on Biological Samples with 650 nm Wavelengths

Mon, 24 Mar 2025 00:00:00 +0700

The study aims to determine the absorbance coefficient value, with the light intensity observed before and after using Low-Level Laser Therapy-50 (LLLT-50) device on chicken and cow-meat tissues. The device has 20 mW, 25 mW and 50 mW output power and energy variations of 0.2J, 0.4J, 0.5J, 1.0J, 1.5J, and 2.0J. Samples of chicken meat with thicknesses of 0.65 mm, 0.85 mm, and 1.6 mm, as well as cow meat with thicknesses of 0.30mm, 0.60mm, and 0.85mm are measured using a spindle range. Each measurement is carried out 10 times to ensure the accuracy. The results show that chicken meat has a lower absorption coefficient than cow meat, due to lower myoglobin content and a simpler tissue structure. For chicken tissues with output power of 20 mW, 25 mW and 50 mW respectively, the LLLT-50 devices have absorption coefficients (μ) of -2.2, -2.17; and -2.9. However, the output capacity of the LLLT-50 devices with the same value on the cow meat tissue have μ of 1.2; 1.94; and 2.0. The findings support the development of non-invasive laser therapies, such as acupuncture lasers, by optimizing therapeutic parameters for better efficiency and safety. The research also provides insights into the interaction of laser light with biological tissues that are important for medical and scientific applications.

RFAB: The Hybrid Model for the Heart Disease Prediction

Priti Shinde, Mahesh Sanghavi — Mon, 24 Mar 2025 00:00:00 +0700

The need for fast and accurate diagnostic systems is crucial as heart disease currently ranks as the leading cause of death worldwide. This study proposes a hybrid ensemble model called RFAB, which combines the predictions of Random Forest and Adaboost classifiers for reliable heart disease prediction. The proposed model introduces a significant improvement by utilizing an expanded dataset, increasing from 303 to 27,597 records, and applying advanced feature extraction and dimensionality reduction techniques. The testing accuracy was 95% which indicates the higher performance of the RFAB compared with SVM-81.71% and Extra Tree classifier-84%. These findings offer a non-invasive, low-cost method for early diagnosis, allowing for prompt clinical interventions and enhancing patient outcomes.

Assessment of Suitability and Potential of Rural Settlement Land in Tanah Datar Regency, Indonesia: A Method Based on Comprehensive Criteria for Physical Factors

Eggy Arya Giofandi, Dhanu Sekarjati — Mon, 24 Mar 2025 00:00:00 +0700

Anthropogenic activities interact with the structure of rural dwellings and metabolic processes, affecting the livability of villages. This study aims to assess the suitability and potential of rural settlement land using a method of comprehensive criteria to support sustainable rural development. The research method employed in this study involves a Multi-Criteria Decision-Making (MCDM) procedure, which is chosen for its capability to integrate multiple factors in decision-making. This approach enables the evaluation of land suitability based on topographic and soil characteristics, ensuring an in-depth and objective assessment. The Analytical Hierarchy Process (AHP) was used to assign weights to criteria. The findings classify land suitability for settlements into four zones: highly suitable, suitable, marginally suitable, and unsuitable. The potential land suitability for rural settlements is approximately 1,234 hectares, while existing settlements span 1,260 hectares (highly suitable), 1,700 hectares (suitable), 344.64 hectares (marginally suitable), and 208.54 hectares (unsuitable). This method ensures accurate land suitability assessment, aiding settlement optimization. It supports government spatial planning, disaster risk reduction, and community resilience. The findings serve as a guide for sustainable rural settlement management.

Enhancing Geopolymer Mortar with Coir and Pineapple Leaf Fibers through Various Curing Methods

Mon, 24 Mar 2025 00:00:00 +0700

This study investigates the properties of geopolymer mortar blended with natural biofibers (GMBf), focusing on density, water absorption, and compressive strength. Coir and pineapple leaf fibers were utilized to enhance the mechanical properties and sustainability of geopolymer composites. The results indicate that coir fibers, with higher lignin content, significantly improve compressive strength compared to pineapple leaf fibers. Microwave curing, employed to enhance GMBf properties, proved effective in increasing density and compressive strength while reducing water absorption by quickly removing moisture and promoting better matrix-fiber bonding. The findings align with the Thai Industrial Standard (TIS) No. 878-2566 for high-density cement-bonded particleboards, demonstrating that GMBf can be a viable, sustainable alternative in construction materials.