Science & Technology Asia

Edible Macrofungi in Plant Genetic Conservation Area, Chanthaburi Province: Auricularia cornea strain RSPG5 Fruiting Body Formation and Nutritional Value

2024-08-13T10:47:55+07:00

This study aimed to survey the edible macrofungi found in the Plant Genetic Conservation Area of Chanthaburi province, and to study bag cultivation and nutritional value of the Auricularia cornea RSPG5 strain. The molecular identification by internal transcribed spacer ( ITS) region analysis revealed that the strains of RSPG1-11 were identified as Cookeina sulcipes, Cookeina tricoloma, Termitomyces cylindricus, Schizophyllum commune, Auricularia cornea, Amauroderma rugosum, Dacryopinax spathularia, Tremella fuciformis, Pleurotus giganteus, Hohenbuehelia sp., and Phallus lutescens, respectively. Auricularia cornea RSPG5 was selected for the investigation of bag cultivation. The average weight of their mature fruiting body was 69.92±24.79 g/bag, yield data was 87.40 g/kg, and biological efficiency was 8.74. The moisture per 100 g was 87.30 g. The fruiting body contained 0.63 g of protein, 0.04 g of fat, 0.16 g of ash, 11.88 g of carbohydrate, and 11.66 mg of calcium. The total energy was 50.38 kcal /100 g.

Effect of Fish Bone Powder on Dough Rheological and Physicochemical and Microstructural Properties of Dried Instant KGM-Wheat Noodle

2024-08-28T12:44:57+07:00

Convenience foods include dried instant noodles, which are made from wheat flour and lack nutrients, especially calcium. This study made noodles by replacing 3-9% fish bone powder (FBP) for wheat flour. Increasing FBP decreased dough rheological characteristics, while replacing wheat flour with 9% FBP fluctuated considerably. In noodles, substituting FBP led to higher L*, a*, and b* values (p>0.05) and decreased tensile strength (p≤0.05), except in the 9% FBP sample. FBP lowered cooking times compared to wheat flour (p>0.05). All KGM-wheat noodles with FBP exhibited reduced water absorption (p≤0.05) and increased cooking loss (p≤0.05) compared to those without FBP. A sheet-like structure with local rupture and enlarged starch granules was found in the FBP substitution sample. KGM-wheat noodles replaced 3% FBP (calcium 754.42 mg), nearly meeting the 800 mg daily recommendation.

Potential Ectoparasiticide for Dog and Cat Fleas; a Combination of Ficus Minahassae Extract and Latex from Carica Papaya L.

2024-08-28T19:59:26+07:00

The purpose of this study was to examine the chemical composition of mixed extracts of Langusei fruit (Ficus minahassae L.) and papaya latex (Carica papaya L.), as well as to conduct bioassays on fleas of the Ctenocephalides genus. LC-MS/MS was used to determine the combined extract's chemical makeup. The toxicity of the extract combination was tested in vivo on cat and dog flea imago. The phytochemical screening of Langusei extract and papaya latex yielded all of the major phytochemicals. The LCMS/MS results revealed that the combination of Langusei extract and papaya latex contains six chemicals. Four substances were identified: 3-butenyl glucosinolate, erythromycin, aluminum palmitate, and henpentakontilbenzena. Four compounds indicate a novel chemical. The combined extract was most lethal to both Ctenocephalides felis and C. canis in the P4 treatment (10%), with an average death of 100%, matching the control mortality of the synthetic insecticide deltamethrin. C. felis had the best LD50 in the F1 formula (4.003 mg/L), while C. canis had it in the F3 formula (3.733 mg/L). According to the findings, the combination of Langusei fruit extract and papaya latex may include novel chemicals. These chemicals are highly poisonous to Ctenocephalides ticks. As a result, it has the potential to be used as an ectoparasiticide for Ctenocephalides ticks.

Statistical Issues in Malaria Research in Southeast Asia: Systematic Analysis

2024-11-01T09:18:52+07:00

This study systematically analyzed the contribution of statistical misuses in the three main malaria research categories published during 2010-2015 from Southeast Asian countries. Related articles were downloaded from the PubMed and Scopus databases using the predefined keywords. The retrieved articles were checked for compliance with the eligibility criteria and stored in EndNote version X7. Finally, 524 articles were included in the analysis. Most of the research involved was conducted in Thailand (263 articles, 50.19%). Intervention and health care research (n =252, 48.09%) was the primary research category. It was also applied to descriptive and inferential statistics (63.49%). The non-parametric test was the most applied statistical analysis approach (28.63%). Only obvious errors were reported as improper use in this article and analyzed based on the available information. Improper use/misuse of statistics was highest in the intervention and health care research category (47.10%). Results of the systematic analysis revealed a high frequency of inappropriate uses/misuse of statistics. In most articles, there was no rationale for selecting the sample sizes. Sampling issues could be considered the main problem of all the malaria research categories (43.15%). The reliability of the articles and the possibility of incorrect interpretations and conclusions should be noted. To decrease the number of errors stemming from inappropriate uses/misuse of statistical analysis, a clear study protocol and statistical analysis plan should be prepared before a study is conducted, and should include the consultation of statisticians.

Molecular Docking and Molecular Dynamics Simulation Study of Anti-Tuberculosis Drug Candidates from Plant-Derived Natural Products against InhA Protein

2024-11-01T08:37:53+07:00

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis and is responsible for millions of deaths worldwide every year. Tuberculosis has become a serious public health problem that needs to be eradicated. Current treatments, including first line medication, have unwanted side effects and face the serious problem of multi-drug resistance. Therefore, finding new agents to treat tuberculosis is critically necessary. Thus, in the present study, we aimed to evaluate several plant-derived drug candidates for their anti-tuberculosis activity that work by inhibiting the activity of the Enoyl acyl carrier protein reductase (InhA) enzyme as determined through in silico studies. Drug-likeness and toxicity evaluation, molecular docking, and molecular dynamics simulations were performed to assess new anti-tuberculosis candidates. Plant-derived natural products such as sulcanal, stigmasterol, zambesiacolactone B, coronarin B, zeylenol, galanal B, and galanolactone might have anti-tuberculosis activity according to their binding affinity scores compared to control drugs. The results revealed that sulcanal had the greatest antituberculosis activity by inhibiting InhA compared to other compounds with the most favorable binding affinity score and binding interaction properties. Finally, molecular dynamics simulation demonstrated that sulcanal had constant and stable pattern during the initial to terminal stage of the simulation. Finally, we suggest that sulcanal might have the potential for further development as an anti-tuberculosis drug candidate through its InhA inhibition.

In Silico Evaluation of Herbicide Synergism to Identify Effective Mixtures for Weed Management in Indonesia

2024-12-03T10:04:34+07:00

This study presents a comprehensive approach to evaluating herbicide synergism through in silico molecular docking analysis combined with physical observations of herbicide mixtures. The research investigated nine commonly used herbicides in Indonesia, examining their potential synergistic and antagonistic interactions when mixed. Molecular docking analysis was performed using PyRx software to evaluate the interactions between herbicide active compounds and their target proteins. The analysis revealed twelve potentially synergistic combinations, with the clomazone-paraquat mixture emerging as the most promising based on both molecular docking results and compliance with Lipinski's rule of five. Physical observations in simulated tank mix conditions validated the computational predictions, showing consistent results with the in silico analysis. The study demonstrated that synergistic combinations maintained ligand interactions with their respective target proteins while showing favorable physicochemical properties for cellular penetration. The integration of computational methods with experimental validation provided valuable insights into the complex interactions between herbicide active compounds and their target proteins. This research establishes a robust framework for evaluating herbicide combinations, potentially leading to more effective and sustainable weed management strategies in agricultural practices.

Soft Intersection Almost Tri-bi-ideals of Semigroups

2024-10-31T18:13:23+07:00

In this study, we introduce the notion of soft intersection almost tri-bi-ideals of semigroups as a generalization of nonnull soft intersection tri-bi-ideals and investigate its properties in depth. It is aimed to explore the relations of soft intersection almost tri-bi ideals with other certain kinds of soft intersection almost ideals of semigroups. It is shown that an idempotent soft intersection almost tri-bi-ideal coincides with the soft intersection almost bi-ideal of a semigroup. It is also illustrated that every idempotent soft intersection almost tri-bi-ideal is a soft intersection almost subsemigroup. Furthermore, we propose the concepts of soft intersection prime, semiprime and strongly prime almost ideals of a semigroup and explore the relationships regarding minimality, primeness, semiprimeness, and strong primeness between almost tri-bi-ideals and soft intersection almost tri-bi-ideals by deriving a notable result that if a nonempty subset of a semigroup is an almost tri-bi-ideal, then its soft characteristic function is a soft intersection almost tri-bi-ideal, and vice versa. This enables us to construct a bridge between classical semigroup theory and soft set theory.

Coincidence Point Theorems on Odered 𝑏-Metric Spaces via 𝑤𝑡-Distance with Application to Matrix Equations and Numerical Experiments

2024-09-09T18:42:55+07:00

This article aims to create a new type of generalized contraction mapping to modify the concept of an 𝑒𝐾 -simulation function which is defined by Yamaod and Sintunavarat [2019, J. Nonlinear Convex Anal.], we investigate the existence and uniqueness of a point of coincidence in the mapping with respect to a 𝑤𝑡- distance in a partially ordered 𝑏-metric space which extends the results of Roldán López de Hierro et al. [2015, J. Comput. Appl. Math.]. Furthermore, we prove the existence of Hermitian positive definite solutions of nonlinear matrix equations with some examples and numerical experiments.

Maximal and Minimal Congruences on the Semigroup 𝑇𝐸 (𝑋)

2024-11-05T11:03:43+07:00

In semigroup theory, transformations play a crucial role. This paper explores a specific type of transformation semigroup, denoted by . Here, is a non-empty set, and consists of all transformations on X that preserve the equivalence classes established by an equivalence relation on . We delve into the internal structure of by exploring how to partition its elements into the coarsest and finest possible partitions while preserving the validity of the transformation operation within each partition. These partitions correspond to maximal and minimal congruences on , respectively. We then address the existence of a specific type of congruence on where each equivalence class forms a subsemigroup itself.

Assessing Non-Linearity and Stationarity in the Time Series of Albania’s Annual Emissions of CO2 from Land-Use Change

2024-11-04T13:39:47+07:00

The annual emissions of CO₂ from land-use change in Albania are the main focus of this research. The aim is to analyze the presence of non-linearity and stationarity. A mixed-methods strategy is used, which combines descriptive, inferential, and exploratory data analysis in time series data. A data sample was obtained from the Our World in Data website, spanning from 1850 through 2022. After the Isolation Forest technique was employed to identify outliers in the time series, the Long-Short-Term Memory model was used to impute them. Exploratory data analysis was applied to the original and imputed time series to ensure that the basic characteristics of the initial data distribution were preserved. Non-linearity and stationarity were checked in the imputed time series before and after applying the first differences. Non-linearity was assessed using the BDS test and the Teräsvirta Neural Network test. In the presence of non-linearity, stationarity was analyzed using the KPSS test, the Zivot-Andrews Unit Root test, and the Breitung test. The first differencing application transformed the non-stationary series into a stationary one, but it was insufficient to eliminate non-linearity. This highlights the complex nature of CO₂ emissions data and the need for sophisticated modeling techniques.

Application of Data Mining in the Prediction of COVID-19 Outcome

2024-11-04T15:15:21+07:00

In December 2019, the novel coronavirus, COVID-19, emerged in Wuhan, China, and rapidly spread across the globe, leading to a significant increase in morbidity and mortality rates. The virus presented with diverse clinical manifestations, and robust predictive models were needed to anticipate outcomes and implement timely preventive and corrective measures. This study was designed to identify patterns in COVID-19 outcomes and develop prediction models for patient survival using data mining techniques. The study was conducted at a tertiary care hospital, Bharati Vidyapeeth (Deemed to be University) Medical College and Hospital, Sangli, analysing cases from June 2020 to December 2020. Data were retrospectively collected from the Record Department using a structured pro forma form and analysed using Microsoft Office 2016, SPSS-22, and WEKA-3.8.6, with cases completing at least 80% of the information. Various simple and ensemble machine learning algorithms were applied to classify patient survival and COVID-19 test results. Through statistical and data mining approaches, the study identified patterns in parameters for both survivors and nonsurvivors, as well as COVID-positive and negative patients. The finalised model for predicting patient survival or non-survival was functions.SMO, with 71.64% (±0.83%) of instances correctly classified; and for distinguishing COVID-19 positive from negative cases, the best-performing model was trees.RandomForest, achieving an accuracy of 84.41% (±0.35%). These prediction models serve as valuable tools for physicians to diagnose and manage COVID-19, identify critical cases in the early stages, and enhance patient care through timely interventions.

Forecasting the Value of Indonesia’s Exports using Model Hybrid Arimax-NN

2024-10-28T15:15:18+07:00

Time sequence data often exhibits both linear and nonlinear patterns, which can lead to inaccurate forecasts when using traditional methods that are limited to capturing only one type of pattern. To address this limitation, this study employs a hybrid method that combines the strengths of Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) and neural networks (NN). The ARIMAX model effectively captures linear patterns, while the NN excels at modeling nonlinearities. The primary objective of this research is to optimize the ARIMAX-NN hybrid model for forecasting Indonesia’s export values. Through rigorous model selection, the ARIMAX ([1,5,12],1,0)-NN 1 neuron model emerged as the best-performing configuration, achieving the lowest Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Deviation (MAD) values. The forecasts for January December 2024 reveal a pattern of decreasing export values during the month of Eid al-Fitr, a trend consistent with historical patterns and economic insights.

Forecasting Search Trends for “Silverqueen” Chocolate Keywords using the Singular Spectrum Analysis Method and the Hybrid Singular Spectrum Analysis-ARIMA Model

2024-12-03T10:27:45+07:00

This research uses a hybrid model of time series in the process. The Singular Spectrum Analysis (SSA) method will be combined with the Autoregressive Integrated Moving Average (ARIMA) method to model noise from SSA. This research applied the hybrid SSA-ARIMA method to Google Trends data, especially to the chocolate keyword "Silverqueen" search trend. This research aims to assess the accuracy and identify the best forecasting method for search trends for the keyword "Silverqueen" chocolate in Indonesia. Based on the results, the accuracy value obtained for the SSA method was 0.54% (MAPE) and 0.04 (RMSE) for in-sample data and 28.93% (MAPE) and 1.49 (RMSE) for out-sample data. The hybrid SSA-ARIMA (5.1.0) method has two outliers with an accuracy value of 0.35% (MAPE) and 0.02 (RMSE) for insample data and 31.00% (MAPE) and 1.50 (RMSE) for out-sample data. The results of the SSA forecasting method for the next 17 periods show that the trend will increase, with the highest trend occurring in the second week of February 2024, namely 100 points. Then, the forecast results of the hybrid SSA-ARIMA(5,1,0) method with outliers for the next 17 periods, the trend will increase, with the highest trend occurring in the second week of February 2024, namely around 95 points. The best method for forecasting search trends for the chocolate keyword “Silverqueen” is the SSA method.

Modeling Optimum Logistic For Multi-day Climbing In Tropical Mountain By Considering Geometrical Constraint

2024-11-25T11:34:03+07:00

We propose here a set of mathematical model used to predict optimum logistic preparation for multi-day climbing in a tropical mountain. The model is developed based on energy cost paid for the climbing and its corresponding optimum logistic demand where both depend on climbing track profile and necessity to stay overnight. To calculate the energy cost, we need to formulate energy expenditure (EE) based on the track profiles and climber heart rates during the climbing. Moreover, the logistic demand is optimized by using objective functions which are dedicated to fulfill the energy cost with logistic weight and volume of climbing backpack as their main constraints. Additionally, novel geometrical constraint from climbing backpack dimension is proposed and involved to the optimization model. We demonstrate the use of our proposed model to predict optimal number of logistic items for a particular climbing track in the Java Island of Indonesia.

Microfluidics for Greener Flow-Based Colorimetric Analysis of Phosphate and Cinnarizine

2024-10-11T08:41:21+07:00

Microfluidic systems are gaining popularity in analytical chemistry owing to their compact size, reduced reagent consumption, and alignment with the principles of green analytical chemistry. In this study, novel microfluidic systems were successfully employed for the determination of phosphate in surface water samples, and cinnarizine in tablet formulations. The three-dimensional lab on a chip (3D LOC) was designed and microfabricated on polymethyl methacrylate (PMMA) in a rectangular figure similar to the conventional flow-through cell. The outstanding is not only a space for the solution to flow through the cell in the spectrophotometer but also a coil for the reaction of sample and reagent to achieve the compact system and enhancement of chemical analysis performance. The 3D LOC was applied for the determination of phosphate in water samples and cinnarizine in tablet dosage forms incorporating the reverse flow injection analysis (r-FIA) with colorimetric detection. The result obtained linear ranges of phosphate is 0.010-2.0 mg P L^-1 with an R² of 0.9985, and those of cinnarizine were 10-150 mg L^-1 with an R² of 0.9963. These designs demonstrated excellent performance characterized by wide linear ranges, low detection limits, and good precision. The proposed microfluidic system holds significant potential to be a greener analytical chemistry.

Experimental Analysis and Optimization of Power Generation by Waste Heat Thermoelectric Module of Biomass Dryer for Lighting

2024-06-07T11:06:14+07:00

The goal of this study is the application and optimization of electricity generation in lighting systems. There are five steps for examining model optimization as follows: 1) design the thermoelectric module to generate electricity, 2) study the influence of variables through experimental design, 3) analyze variance and mathematical modeling, 4) simulation and surface mapping, and 5) optimize the model to find the optimal condition. A thermoelectric device was used and removed in hybrid circuits. A full factorial design is used for the experimental study of a Light-Emitting Diode (LED) bulb load (5 to 25 W) and a temperature difference (ΔT) between the hot and cold sides from 5 to 30 ˚C. The analysis of variance and mathematical modeling found that the modeling as a function of ΔT and load was non-linear. The model can then be used for surface plots and results can be predicted, and experimental data showed good agreement (R² = 97.13%). The yield power generation increased with increasing ΔT. Finally, a LED bulb load of 13.68 W and a temperature of 30 ˚C was found to be the optimum condition.

Drone Approach for Remote Sensing The Intercrop On Durian Plantations Using YOLOv5 Model

2024-09-20T10:31:23+07:00

This paper proposes a potential solution for monitoring the durian plantations which apply intercropping by using drones equipped with RGB and spectral cameras. Currently, farmers mainly rely on their naked eyes to estimate whether a density of papayas around a durian tree is suitable. This eye estimation is time consuming and often not accurate enough, especially when trees reach the heights above the human head. To help the farmers, the proposed method used drones to create the ortho-mosaic map of the monitoring areas then YOLO V5 model is used to detect and locate durian and papaya trees. These results were used to evaluate the durian growth conditions. The trained model result showed a high accuracy at over 95% in detecting and locating trees which is reliable enough to apply to the practice. Furthermore, in the validating process, durian growth conditions also correctly evaluated and detected regions where density of papaya trees must be adjusted.

Effects of Polysaccharide-Based Viscosity- Modifying Agent on Properties of Self-Compacting Concrete

2024-09-20T10:35:25+07:00

To address the high cost of self-compacting concrete (SCC) in Thailand, this research examines the use of viscosity-modifying agents (VMAs) to enhance sustainability and promote cost effectiveness and carbon dioxide (CO₂) reduction. Encouraging the study of polysaccharide-based viscosity-modifying agents (PVMAs), particularly locally available starch, this research investigated various SCC mixtures. The analysis included properties such as slump flow, T50cm time, V-Funnel time, L-box filling ability, bleeding, setting times, and compressive strength. The concrete mixtures were made with water-to-binder ratios (w/b) of 0.28, 0.32, and 0.37. The cement/fly ash ratio was kept constant at 0.50 for all concrete mixtures. The results revealed that the addition of starch to the concrete decreased slump flow and L-Box filling ability while it increased T50cm and V-funnel times. Moreover, a delay in strength development at early ages was also found, but no effect was seen at later ages. Additionally, the binder content was reduced from 580 kg/m^³ to 500 kg/m^³, resulting in a cost reduction from 4.90% to 5.64% and CO₂ relative emission-reduction of 6.15% to 12.30%, marking a decrease of 5.64% for cost and 12.30% for CO₂ emission, while maintaining the properties of SCC. In conclusion, starch as a locally-sourced polysaccharide-based VMA offers potential benefits for manufacturing SCC with desirable properties, aligning with SCC criteria and showing promise for the construction industry.

Evaluating the Effectiveness of Bond Strength Relative to Plain Reinforcement Bar Diameter in HVFA-SCC Beam Lap Splices

2024-10-04T11:55:22+07:00

The primary issue with Portland cement production, a critical component in concrete, is its considerable CO₂ emissions. An effective strategy to reduce CO₂ emissions from cement manufacturing involves using fly ash as a substantial replacement for cement. Experimental research has concentrated on comparing the bond strength of plain steel reinforcement with concrete constructed using High Volume Fly Ash - Self Compacting Concrete (HVFA- SCC). This comparison involves substituting at least 50% of the cement with fly ash. The experimental program includes six splice beam test specimens as controls. Additionally, this study compares the results with those from a control specimen bond database. The findings indicate that HVFA-SCC beams exhibit superior mean bond strength compared to other types of concrete.

Utilizing Himawari-8/AHI Satellite Data for Accurate Global Solar Radiation Exergy Predictions in Thailand’s Climatic Context

2024-10-29T11:06:38+07:00

This study developed and tested a Global Solar Radiation Exergy model using data from the Himawari-8/AHI Satellite, focusing on four key provinces in Thailand: Chiang Mai, Nakhon Pathom, Ubon Ratchathani, and Songkhla. The model’s accuracy was evaluated based on R-squared, RMSD, and MBD values. For Chiang Mai, the model demonstrated high precision, with an R-squared value of 0.91, RMSD ranging from 0.90 to 0.91, and MBD between 0.21 and 0.30. In Nakhon Pathom, the model achieved R-squared values between 0.87 and 0.89, RMSD from 0.75 to 0.88, and MBD from 0.60 to 0.79, indicating good accuracy with some variability. The results for Ubon Ratchathani showed R-squared values from 0.78 to 0.79, RMSD between 0.51 and 0.55, and MBD from -0.13 to -0.23, suggesting high
accuracy with a tendency to underestimate values. Finally, the model for Songkhla had an Rsquared value of 0.81, RMSD ranging from 0.89 to 1.00, and MBD between -0.31 and -0.59, indicating good accuracy with minor variations in estimations. These findings underscore the model’s effectiveness in predicting solar energy potential and emphasize the necessity for regional adjustments to enhance precision across diverse geographic settings.

Implementation of Particle Swarm Algorithm to Sine-Cosine Optimization: Case Study of Plant-Wide Chemical Processes

2024-09-18T15:10:00+07:00

The sine-cosine algorithm (SCA) is a population-based optimization approach that relies on mathematical models incorporating sine and cosine functions to effectively obtain the global optimum. The SCA necessitates multiple initial solutions to fluctuate outward and toward the best solution. In this study, the particle swarm algorithm was replaced with randomization and selection of the best fit to enhance SCA operations, resulting in faster convergence and improved accuracy, thereby termed the modified SCA (MSCA). The proposed algorithm's performance was investigated and compared using 7 general problems, 19 common benchmarks, and 5 industrial-scale chemical processes. These benchmarks varied in complexity, with and without constraints. For comparative purposes regarding the algorithm's effectiveness, the results were averaged based on a maximum of 1,000 iterations, utilizing 500 populations from 5 replications. Subsequently, 5 common chemical processes were used to demonstrate the implementation using commercial simulation software (Aspen Plus), seamlessly integrated with both optimization algorithms to adjust operational conditions and design parameters. MSCA produced a lower error percentage and CPU time than SCA.

Machine Learning-Driven Efficiency Estimation and Variable Analysis in Combined Cycle Power Plants

2024-09-12T15:28:53+07:00

The combined cycle power plant (CCPP) has seen significant growth as a key player in the energy sector due to its efficient electricity generation and low greenhouse gas emissions. The growing global demand for electricity, fueled by rapid technological advancements, underscores the need for a reliable power supply. However, accurately predicting the efficiency of CCPPs is essential for optimizing performance and cost-effectiveness. The efficiency of power plants is influenced by a variety of environmental and internal factors, but traditional models often fail to capture these complexities. This study addresses these gaps by employing machine learning models to estimate the efficiency of a CCPP in Thailand, using a comprehensive dataset of fourteen input variables. Nine machine learning models, including regression and ensemble methods, were used for evaluation, with Random Forest Regression and Gradient Boosting achieving superior accuracy levels of 99.91% and 99.83%, respectively. Furthermore, the research delves into 14 distinct variables utilized for prediction and aims to determine which variables are of paramount significance in the assessment process.

Application of Scenedesmus Quadricauda Biosorbent for the Biosorption of Heavy Metals in Wastewater

2024-11-12T10:58:23+07:00

Heavy metal contamination in wastewater is a critical environmental concern, affecting ecosystems, water quality, and food safety. Conventional treatment methods, including precipitation and ion exchange, are often resource-intensive and may inadequately remove heavy metals, allowing them to persist in the environment. This study investigated biosorption as a sustainable alternative, specifically evaluating the green microalga Scenedesmus quadricauda for the removal of copper and zinc ions from synthetic wastewater. Batch biosorption experiments were conducted under optimized conditions of pH 6, a contact time of 60 minutes, and a temperature of 28°C. The results revealed high removal efficiencies, with Scenedesmus quadricauda achieving 81% removal of copper and 73% of zinc. Further optimization of pH and biosorbent dosage enhanced performance, with copper removal reaching 91% at pH 6 and zinc removal at 82.85% with a biosorbent dose of 0.5 g/L. These findings demonstrate the efficiency of Scenedesmus quadricauda as an efficient biosorbent for heavy metal remediation, supporting studies on biosorption as a viable and environmentally friendly approach to wastewater treatment. This study thus highlights biosorption's potential as a long-term solution for mitigating heavy metal pollution in wastewater.

Proposing a Location of the New Distribution Center: A Garment Case Study in Thailand

2024-11-21T14:57:57+07:00

This paper applied the principle of the Load Distance technique in order to find a location of a new distribution center. A case study of a garment company in Thailand was conducted. All company policies and constraints were organized and taken into account when considering a location for the new distribution center. A mathematical model was formulated and the location of the new distribution center was determined based on the cost and related company constraints. Sensitivity analysis was then performed to study the robustness of the proposed solution. Due to price and availability of land, the model was rerun to determine a new location for the new distribution center. Finally, the final suggestion for the top management of the case study was made.

The Optimization of Mechanical Properties of HDPE-Pineapple Fiber Composites Using the Taguchi Method

2024-10-09T13:31:00+07:00

This research investigates the potential of High-Density Polyethylene (HDPE) waste as a polymer matrix and reinforcing filler from pineapple leaf fibers (PLFs). This experiment uses the Taguchi orthogonal array L9 design with parameters of fiber volume (5%, 10%, and 15%), fiber direction (0◦, 45◦, and random), and fiber length ratio (continuous, 1:2, and 1:3). Taguchi analysis is used to determine the optimization of parameters on the mechanical properties of the composite. In contrast, analysis of variance (ANOVA) is used to determine significant parameters. Tensile strength test according to ASTM D638-04 and three-point bending according to ASTM D790 02 are conducted. Based on Taguchi, the maximum tensile strength obtained from this composite is 1.48 N/mm2 from 5% fiber volume, while the flexural strength is 1.61 N/mm2 obtained from the 0◦ fiber direction. The S/N ratio shows the composite with 5% fiber, 0◦ orientation, and 1:3 fiber length ratio together provides the greatest tensile strength. For flexural strength, the best choice is a composite with 15%, 0◦ fiber direction, and 1:3 fiber length ratio. The fiber volume parameter has the most significant influence in producing a composite with high mechanical strength. The amount of fiber filler used is influenced by the direction of the load acting on the composite. The composites from this research are suitable for use on materials that receive low to medium loads and provide an alternative composite option.