Engineering and Applied Science Research

Enhanced real-time paddy moisture content assessment in pneumatic drying using correction factor

2024-12-09T20:07:47+07:00

Accurate and real-time monitoring of paddy moisture content during the drying process is critical to preserving rice quality and ensuring efficient post-harvest management. This research article presents the application of weather sensor (DHT22, AM2302, Aosong Electronics Co.,Ltd) that measures air temperature and relative humidity for evaluating paddy moisture content in drying process of a pneumatic dryer. Khao Dawk Mali 105 paddy with initial moisture content of 26%w.b. was dried by a constant drying air temperature of 70 ^ºC and the drying air flow rate of 0.0631 m³/s with paddy feed rate of 8.45 kg/min throughout the test. Two methods namely DHT-Prediction and DHT-Factor correction methods for evaluating moisture content of paddy were developed by mass balance equation and the result were compared with the reference paddy moisture content analyzed by hot air oven method. The results showed that the DHT-Prediction method gave high moisture measurement error compared to the reference paddy moisture content (R² between 0.274 - 0.359 and RMSE between 5.077 - 7.465). The DHT-Factor correction method gave the lowest discrepancy of paddy moisture content during the first 150 minutes of the drying with R² of 0.987 and RMSE of 0.515. This method also showed low discrepancy results over the entire drying period which indicated that it has high potential for evaluating the real-time paddy moisture content during drying process.

Properties evaluation of natural bitumen-filler mastic mixture

2024-12-26T15:07:24+07:00

Natural bitumen (NB) is a highly precious material and has drawn increasing attention due to its unique properties, especially since it is available in large quantities and has been used in limited fields. In this research, the exploitation of NB from sulfur springs as an alternative energy resource in the production of asphalt pavement is evaluated. It can be concluded from the experimental results that the chemical composition and surface morphology of NB samples are different from those of base asphalt. Besides, the rheological properties of virgin NB are not sufficient for paving work. To overcome this obstacle, NB from five different springs is modified with limestone filler (LSF) to enhance its properties. LSF is a natural material and is available locally at a low price, usually used as filler material in control asphalt mixtures. The study outcomes reveal that LSF is an effective material and plays a fundamental role in improving the properties of NB since it enhances the resistance against temperature susceptibility and improves the ability of NB to disperse in asphalt mixture. Furthermore, treated NB with LSF boosts the mechanical characteristics, increases the stiffness, and strengthens the resistance against water damage for NB mixtures. Particularly, this research clarified that high Marshall stability is achieved with a treated Al-Mamora sulfur spring-NB mixture, which is 30.4% higher than that of the control mixture. At the same time, the treated Al-Askaree sulfur spring-NB mixture has a stiffness index and tensile strength ratio more than the control mixture by 45% and 3.36%, respectively. In conclusion, adding LSF to NB that is extracted from sulfur springs can produce a new type of asphalt binder more suitable for use in road pavement.

Reinforced concrete and embodied carbon in construction: Challenges and pathways to reduction in Thailand

2025-01-05T21:01:24+07:00

The increasing demand for construction in Thailand, driven by urbanization and infrastructure development, has heightened the urgency of addressing embodied carbon emissions in reinforced concrete projects. This study evaluates the life cycle carbon emissions of a six-story outpatient department building, focusing on the manufacturing, transportation, and construction stages. Data were collected through stakeholder interviews and analysis of project-specific documents, such as bills of quantities and construction plans. Results reveal that the manufacturing stage contributes 92.75% of total emissions, with concrete and steel as the primary sources, accounting for 6.506 kt CO₂eq. Transportation and construction contribute 6.41% and 0.84%, respectively. The study identifies practical strategies for reducing embodied carbon, including material substitution with supplementary cementitious materials, optimizing logistics, and improving energy efficiency in construction practices. These strategies not only mitigate environmental impacts but also address economic and quality considerations, ensuring feasibility in the local context. The findings provide actionable insights for policymakers and construction professionals to integrate low-carbon practices, contributing to the national and global goals of sustainable development. This research offers a comprehensive framework for reducing embodied carbon emissions while maintaining economic viability and construction quality, positioning Thailand as a leader in sustainable construction.

An investigation of pyrolysis kinetics in microwave-assisted conversion of oil palm shell and activated carbon for sustainable bio-oil production

2024-11-13T16:44:19+07:00

This study explores microwave (MW) pyrolysis of oil palm shell (OPS) blended with activated carbon (AC) to produce bio-oil, focusing on the pyrolysis kinetics and the effect of different feedstock ratios. Three OPS mixtures (70:30, 75:25, and 80:20) were analyzed at a constant temperature of 400°C. Gas chromatography-mass spectrometry (GC-MS) revealed that acetic acid, phenol, and other oxygenated compounds were dominant in the resulting bio-oils. Pyrolysis kinetics were modeled using a first-order reaction approach, with rate constants and activation energies determined for each blend. The 75:25 ratio yielded the highest bio-oil content and exhibited the most favorable kinetic performance, showing strong alignment between experimental and simulated data (R² = 0.758). The study also examined the impact of pyrolysis temperatures (400°C, 500°C, and 600°C) at the fixed 75:25 ratio, demonstrating that temperature plays a key role in both enhancing bio-oil yield and altering its chemical composition.

Enhancing geopolymer mortars: The role of surgical face masks in modifying mechanical and thermal properties

2024-11-27T16:06:55+07:00

The primary aim of this study is to examine how expanded surgical face masks (SFMs) affect the mechanical properties, thermal conductivity, thermal insulation, and microstructure of geopolymer mortars. In the experimental phase, SFMs were used after removing the inner nose wires and ear loops, then cut into 5 mm × 5 mm pieces. These SFMs were added to various mixtures at different weight percentages (0%, 2%, 4%, and 6%), along with palm oil clinker (POC) and alkali activator. The mechanical properties of the mixtures, such as compressive strength, bulk density, and water absorption, underwent evaluation through testing. Moreover, thermal conductivity and thermal insulation measurements were carried out to gauge the effect of SFMs on this property. Microstructural analysis of the mixtures was conducted using scanning electron microscopy SEM to examine the impact of SFMs on mortar microstructure. The test results indicated that geopolymer mortar containing 2% SFM achieved a compressive strength of 67.3 ksc at 28 days, with an alkaline to fly ash ratio of 0.8 and POC to fly ash ratio of 3. The geopolymer mortars with POC containing SFMs had a bulk density ranging from 1,233 to 1533 kg/m³ at 28 days. Notably, the use of SFMs resulted in a substantial enhancement in thermal conductivity and thermal insulation. Additionally, a strong correlation was found between thermal conductivity and bulk density, suggesting a potential relationship between these properties.

Integrating systematic layout planning and fuzzy analytic hierarchy process in the design of sorting and packing fresh fruit facilities: A case study in Thailand

2024-11-14T14:52:44+07:00

The aim of this study was to contribute to the sustainable development of small and medium-sized Thai fruit exporters who face challenges in business expansion due to limitations in the operational systems of their packing houses, which represent a critical unit in the fruit supply chain. The research had three objectives. The first is to develop a layout that is aligned with the business conditions of the case study entity, with a focus on efficient space utilisation and the incorporation of movement and sorting technologies guided by Muther's systematic layout planning principles. The second involves establishing criteria for selecting operational layouts for agro-businesses, based on the application of supply chain principles that emphasise operational efficiency, food production hygiene, and worker safety. These criteria were developed through a combination of literature review and expert input from agro-industry professionals and engineering academics, within the framework of the SCOR model. The final objective was to evaluate and select the developed layouts using MCDM methods. The results indicated that the requirements for a production system can be divided into export-oriented products, which demand more resource-intensive processes, and products for domestic auction markets. Activities were categorised into two types, processing and storage, and comprised seven activities, which were analysed to develop four distinct layout designs based on differences in input-output flow, production processes, and equipment positioning. Of the criteria considered for layout selection, the highest priorities for organisational management were efficient movement and maintenance (20.03%), effective space utilisation (19.93%) and non-overlapping flow directions (16.1%). Ultimately, the third layout alternative was selected at 30.40%, as it scored the highest on movement efficiency, and featured no overlapping transfer points, optimal space allocation, and minimal waste of space.

Development of a tourist behavior analysis system using public Wi-Fi data to enhance smart tourism management: A case study of Sri Chiang Mai Smart City

2024-11-21T15:43:06+07:00

Sri Chiang Mai Smart City faces challenges in tourism management due to its limited understanding of tourist behavior patterns and inefficient resource management at tourist attractions. The study aims to analyze tourist movement patterns and preferences across key locations, develop a privacy-preserving data collection framework, and provide data-driven recommendations for tourism resource allocation. The system analyzes Wi-Fi data from 11 strategic locations along the Mekong River using spatial and temporal analysis techniques. Tourist data is protected with SHA-256 encryption and tokenization of MAC addresses. Analysis of over 72 million connection records from May 2022 to December 2023 revealed significant patterns in tourist behavior. Tourist numbers showed dramatic increases during cultural events, with a 127.87% increase during the Songkran festival from April 13 to 15 compared to regular days in the same month at Boeng Wiang Courtyard. Four locations consistently showed higher visitor levels: Sri Chiang Mai Community Health Park, Naga Courtyard, In front of Had Pathum Temple, and Boeng Wiang Courtyard. Temporal analysis identified peak hours between 17:00 and 21:00, especially during the Songkran and longboat racing festivals. The findings enabled data-driven decisions in tourism resource allocation, event planning, and infrastructure development. However, the study is limited to tourists who connect to public Wi-Fi, potentially excluding those using mobile data or no digital devices. Despite these limitations, the system demonstrates how smart city infrastructure can effectively support smart tourism management through privacy-preserving data collection and analysis, contributing to the development of sustainable smart tourism initiatives.

Optimal boosted framework for fatigue cracking prediction in polyethylene terephthalate pavement

2024-11-21T15:00:13+07:00

Depleting natural resources for construction purposes and research needs in the field of civil engineering is becoming a dependable process. To minimize this degradation process several techniques are implemented, one such method is using software approaches in research fields. In the consideration of the former investigation works, the focus point of the works is mainly on Crack Prediction on bitumen pavements. Since the bitumen pavement undergoes repeated loading under wheel loads in static conditions during traffic, and is dynamically loaded during the movement of fast moving vehicles. The strength parameters related to the fatigue limit of the bitumen are not analysed. Hence the fatigue limit of the bitumen should also be analysed. To analyze such conditions, deep learning with standardizing features is implemented to estimate the effective Strength parameters and to forecast the essential outcomes. In this study, the Fatigue Cracking Prediction of the Polymer Modified Bitumen (PMB) of grade 40 in Polyethylene Terephthalate (PET) Pavement is carried out. The Suggested Methodology is termed as Hyena based Curriculum Learning (HbCL). It is implemented in the Accuracy Prediction of Fatigue Cracking behaviour of PET pavement at different temperatures in dynamic and static load conditions. The fitness function in the optimization was triggered initially, to determine the ideal strength parameters of the Polymer Modified Bitumen as well as the PET. The tensile strength of the proposed model is 410 KPa in dynamic loading. The compression strength of the proposed model decreases with an increase in temperature. The rutting depth of the proposed model is between 1.7728mm and 1.6870mm. The Marshall Coefficient value gained was 2.7 KN/mm as the maximum for static loading. A value of 1.77mm in maximum is achieved by fatigue crack depth under dynamical load. The mean fatigue crack prediction accuracy estimated was 96.1%.

In-Vessel bioconversion of garden waste into compost with an emphasis on process efficiency and compost quality

2025-01-05T20:04:34+07:00

With the fast-growing urbanization efficient management of garden waste, whether at residential or social levels, has now become a challenge and an opportunity to enhance soil quality. Three different trials T1, T2, and T3 were executed constituting different forms of garden waste like un-shredded, shredded and shredded dry leaves with additives with varying processing conditions carried for 42 days using cylindrical In-vessel composters made up of HDPE. The composting process is scrutinized regularly at fixed intervals for all parameters like temperature, moisture, pH, electrical conductivity, germination index, C/N, micro & macronutrients etc. The temperature reached thermophilic in two trials T2 and T3. The pH reached the basic range of 8-8.5 in the final compost of all trials. The moisture was maintained up to 65% throughout the composting period. The trial 3 was the best combination among all the trials in which the variation of pH was (7.7-8.5), total organic carbon (42.2-35.6), total nitrogen (0.7-1.4), C: N ratio (60.3-25.4), cellulose (43.4-4.2), hemicellulose (10.2-0.9) and lignin (13.1-0.15) at the end of 42 days. The degradation rate of Un-shredded leaves was found to be very slow due large size of dry leaves. The findings highlighted that effective garden waste management is possible by following particular guidelines and it reduces the environmental impact of current disposal techniques and in addition, improves soil health using garden waste compost.

A double blade splitting machine for Cyperus sedge culms (Cyperus corymbosus Rottb.)

2024-09-30T14:54:49+07:00

Sedges are a natural material that can be made into mats and other products to replace plastic. Preparing sedge threads includes harvesting, sorting by size and color, splitting, and finally drying them. Splitting is an important step because using equipment can increase process speed. The objective of this research is to design, build, test, and evaluate the performance of a sedge-splitting machine. The main feature of this machine is its two pairs of blades, 7 mm apart, arranged in a perpendicular manner. This allows splitting the sedges into four threads while simultaneously removing the core. Five sedge clums are simultaneously processed. The machine was tested at three roller speeds, 197, 205, and 214 rpm. Its speed is not constant but varies between the lowest and highest speeds. This process was compared to one employing manual labor. The quantity of sedge threads that the machine split varied with its speed. Therefore, the machine’s overall production was estimated by averaging its output at these three speeds. It could process an average of 649.3 sedge culms/h, which is more than three times faster than the manual process, 195.4 culms/h. This research also studied the effect of culm moisture content on the machine’s operating capacity. It was found that moisture content had no effect on the working rate of either the machine or the manual process. However, it did affect the machine’s efficiency. Processing freshly cut culms enabled the highest efficiency, 90%. However, the manual process showed an average 98% efficiency when the culms had a moisture content between 84-90%.

Towards the decarbonization of residential buildings through roof External Thermal Insulation (ETI) in arid zones: A case study

2024-10-18T11:20:53+07:00

The objective of this article is to present a range of effective thermal insulation techniques for flat roofs, based on natural insulating materials, in order to reduce excessive energy consumption used for heating and air conditioning, improve the thermal comfort of occupants, reduce the high costs associated with energy bills, and minimize the emissions of harmful CO₂ gas from buildings. In this context, the methodology followed involves studying an existing residential apartment located in an arid region. To do this, three combinations of external thermal insulation systems (ETI) have been proposed: a hot roof system, an inverted roof system, and a combined roof system, supported by a numerical simulation carried out with natural-based thermal insulation systems (wood fiber and expanded cork). Subsequently, an economic study was conducted for all the tested systems. The numerical results obtained show that the combined roof thermal insulation system, with double insulation using wood fiber (3 cm) and expanded cork (4 cm), is the optimal solution, yielding savings of 7736.83 DZD per year. Additionally, the reduction in energy consumption (kWh/m². year) and CO₂ carbon emissions (kg/year) were approximately 52.34% and 40.65% respectively.

The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve

2024-10-10T09:31:07+07:00

Water soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.

Evaluation of coal pond ash and quarry dust mix for pavement application

2024-10-30T08:59:34+07:00

The depletion of high-quality natural resources such as soils and aggregates emphasize the necessity of incorporating alternative sustainable materials in road construction. Utilizing these waste materials not only addresses the challenge of their safe disposal but also contributes to sustainable road infrastructure development. The present work examined the potential of coal pond ash (Class F) modified with cementitious material like lime (L) and quarry dust (Q) in the subbase layer of flexible pavements. Through a series of tests including compaction, unconfined compression strength, durability assessment, and repeated load triaxial testing, it was determined that a mixture containing lime modified pond ash (70% P + 10% L) along with quarry dust of 20% exhibited the desired strength and durability characteristics required for subbase material in flexible pavement construction. Also, the proposed mixture demonstrated higher resilient modulus (M_R) than the traditional subbase layer (GSB). Further, the performance of pavement structure with the proposed mix by using KENLAYER analysis showed service life ratio (SLR) values of 1.175 for fatigue and 1.143 for rutting criteria in compared with GSB. From these findings it is suggested that the proposed mix offers a viable and sustainable solution for the road applications.

Mineralogical-based rock abrasivity assessment of siliciclastic and carbonate rocks observed in Mae Moh District, northern Thailand

2024-10-17T09:43:49+07:00

Understanding a target rock's characteristics and tool wear behaviors after cutting that rock significantly leads to appropriate tool selections and reliable tool lifetime prediction for ground excavation or drilling activities in mining and construction industries. Rock abrasivity is defined as the ability of rocks to cause damage to cutting tools. There are several methods for investigating the abrasivity of rocks ranging from micro-scale geotechnical approaches to real-scale in-situ tests. Applying mineralogical analysis to the rock abrasivity assessment methods for the tool wear prediction is lacking in detail, despite its simplicity, effectiveness, and affordability. This study preliminarily tests the abrasivity of four stratigraphic sedimentary units hosted in the Mae Moh Basin, northern Thailand, by investigating a rock abrasivity index (RAI). The method involves microscopic petrographic analysis, equivalent quartz content (EQC) determination, and the uniaxial compressive strength (UCS) tests. The RAI of each representative rock unit is a product of its EQC and UCS. The petrographic results reveal that the rock samples are sandstones and limestones. The sandstones can be divided into two subtypes including sublithic and lithic arenites with the EQC of 90.3% and 43.3%, respectively. The limestones, on the other hand, show the opposite values of below 3%. The UCS results suggest that the strengths of sandstones are higher than limestones. Additionally, the lithic-rich sandstone shows the highest UCS value (92.2 MPa). The calculated RAI of sandstones ranges from 39.9 to 72.5, indicating medium-to-very abrasive materials, whereas the limestones show RAI values of less than 2, indicating non-abrasive rocks. Determination of rock abrasivity using its mechanical and mineralogical properties appears to be a practical method for drilling or excavation strategies.

Effect of steel slag and ceramic residues on the physical and mechanical properties of concrete

2024-10-09T14:28:09+07:00

Cement is a critical component in concrete, but it significantly impacts the environment from extraction to disposal, depleting resources and generating pollution. To address this issue, the use of steel slag (SS) and ceramic waste powder (CWP) was investigated as partial replacements for cement at 8%, 10%, 12%, and 15% by weight. Various mixtures were evaluated, including a control mix with a water-to-cement (w/c) ratio of 0.52, mixtures containing SS, and an optimal blend of SS with different proportions of CWP. Physical and mechanical properties were assessed through slump tests, temperature measurements, air content, and density evaluations. Compressive strength, flexural strength, tensile strength, and modulus of elasticity were also analyzed at 7, 14, and 28 days using standardized cylindrical and prismatic specimens. The results indicated a reduction in slump and temperature of the concrete by up to 45.87% and 9.10%, respectively, with a slight increase in density and air content when incorporating 10% SS and 15% CWP. In terms of mechanical properties, the optimal substitution of 10% SS improved compressive strength by 7.39%, modulus of elasticity by 13.06%, flexural strength by 8.22%, and tensile strength by 14.10% compared to the control mix. The hybrid mix of SS and CWP (10%:10%) showed significant enhancements: compressive strength increased by 17.12%, flexural strength by 19.25%, tensile strength by 26.28%, and modulus of elasticity by 21.21%. It was concluded that substituting 10% SS and 10% CWP by weight of cement enhances the mechanical properties of concrete, promoting efficiency and sustainability in environmentally friendly construction. This hybrid concrete can be effectively used in pavements, sports floors, sidewalks, and curbs.

Influence of rice husk and cabuya fiber on the physical and mechanical properties of adobe

2025-01-21T21:20:22+07:00

Adobe is one of the most widely used materials in housing construction around the world due to its low cost and ease of preparation. However, one of its main disadvantages is its low compressive and flexural strength. This research aims to study the influence of rice husk (RH) and cabuya fibers (CF) on the physical and mechanical properties of adobe. RH, a by-product available in rice-producing countries, offers environmental and economic benefits, while CF are natural fibers obtained from the leaves of plants of the Agave family. Their incorporation in adobe mixes helps prevent cracks and improves the durability of the final product. The methodology used considered proportions of 0.75%, 5%, 10% and 20% of RH and 0.5%, 1%, 1.5% and 2% of CF per weight of dry soil. The results indicated that the optimum content was 0.75% RH combined with 2% CF, where properties such as absorption and unit weight were reduced, while compressive, flexural, stacking and tensile strength increased by 52.31%, 22.49%, 52.64% and 50.48%, respectively, compared to the standard adobe. Concluding that RH and CF are viable as reinforcements in adobe production, supporting the feasibility of using these combinations as an effective strategy to strengthen adobe structures.

An integration of multi-objective goal programming and linear assignment models based on grey relational analysis for the collaborative robot assignment problem in Human-Robot Collaboration (HRC): An application of industry 5.0

2024-11-25T10:35:22+07:00

This research presents a novel two-step assignment method for forming cobot workstations to facilitate collaboration between humans and cobots, in alignment with the Industry 5.0 concept. The method is based on Grey Relational Analysis (GRA) to address limitations in existing cobot task allocation approaches, which typically focus on single-objective optimization. The first step involves a lexicographic goal programming model with dual objectives, optimizing cobot-job and job-cobot assignments. These objectives aim to maximize the mean of the total grey relational grade for both assignments, resulting in optimal cobot-job pairings. In the second step, the integrated GRA assignment model determines the appropriate worker for each cobot set. An illustrative example and comparative analysis demonstrate the advantages of the proposed method over traditional approaches.

Briquettes fuel production from sugarcane bagasse for sustainable community energy solutions

2024-09-02T16:23:41+07:00

The objective of this study is to determine optimum conditions for producing briquettes fuel from biomass, examine the properties of the resulting briquette fuel and transfer the technology to target communities. The production process entails implementing a screw-type briquette press equipped with controlled temperatures through a molded pipe clamp electric heater for shaping the briquettes. This is an innovative approach that allows briquettes to be produced and ready for use in a single step, while other processes require the briquettes to be dried first. The production capacity was estimated to be 12 kg/hr. Bagasse, a byproduct of sugarcane processing, was the biomass used in the current research. For this study, four distinct temperatures were chosen for experimentation, 100, 110, 120, and 130°C. The proportions of bagasse, cassava flour, and water were investigated for briquette production utilizing two formulae: 1 kg bagasse to 0.1 kg cassava flour and 0.5 liters water, and 1 kg bagasse to 0.3 kg cassava flour and 0.5 liters water. The findings indicated that bagasse briquettes could be effectively manufactured at 120°C. The ideal proportion of bagasse particles, cassava starch, and water was 1 kg : 0.3 kg : 0.5 liters, respectively. The moisture content, volatile matter, ash, and fixed carbon were respectively 8.04, 70.20, 13.05, and 16.11 wt.% on a dry basis, based on the physical and chemical property tests of the briquette fuel. The material has a density of approximately 856.70 kg/m³, a compressive strength of 89.34 kg/cm², and a higher heat value (HHV) of 24.05 MJ/kg, with a longer burning time than regular charcoal. The research emphasizes using bagasse for innovative renewable energy production technologies and disseminating this technology in target communities in Thailand.

Integrated neural network-based MPPT and ant colony optimization-tuned PI bidirectional charger-controller for PV-powered motor-pump system

2024-08-02T16:48:36+07:00

This study presents the design and implementation of an efficient off-grid photovoltaic (PV)-powered motor-pump system utilizing a two-stage power converter. The system integrates a neural network-based maximum power point tracking (MPPT-NN) algorithm with a proportional integral (PI) controller and an additional bidirectional PI charger. Controller gains are optimized using ant colony optimization (ACO) to achieve optimal performance. The proposed MPPT-NN-PI/ACO controller enhances control responses and improves energy utilization efficiency by 17% compared to traditional PI controller. Performance comparisons of MPPT techniques demonstrates that the proposed controller outperforms several existing methods, including commercial on-off controllers, the modified Perturb & Observe algorithm, and neural network-based controllers, by approximately 4%–20%. It shows a slightly different performance of about 1%–6% compared to advanced adaptive controllers, including fuzzy logic and neuro-fuzzy controllers. For bidirectional charger performance, the DC bus voltage connecting the boost converter and bidirectional converter remains stable with small ripples and is well-aligned with the reference voltage, ensuring uninterrupted operation under varying weather conditions. The bidirectional charge management effectively maintains battery state-of-charge (SOC), showing a decline during periods of insufficient PV energy and achieving full charging during periods of excess PV energy. System performance is validated through both simulation and laboratory-scale prototyping, ensuring robust operation.

Evaluation of the Highway Capacity Manual (HCM) and Thailand’s Department of Highways’ approaches to estimating the capacity of a multilane highway segment via the empirical method

2024-10-02T14:25:09+07:00

This study aims to evaluate the methods used by the Thailand Department of Highways (DOH) and the Highway Capacity Manual (HCM 2010 and HCM 2016) for estimating the capacity of an urban multilane highway segment in Thailand, by comparing these estimates with the empirical capacity derived from a speed-flow plot. Field data were collected from a six-lane urban highway segment in Thailand, followed by documentation of the roadway conditions and analysis of vehicle composition. Capacity estimates were calculated using the DOH and HCM methods prior to comparing them to the empirically measured capacity. The results showed that the empirical capacity was 1,619 pc/h/ln. The capacities estimated using the HCM 2010 and HCM 2016 methods were 34.8% and 35.3% greater than the empirically measured capacity, respectively, while the capacity estimated using the DOH method was 14.1% lower than the measured capacity. These findings indicate significant discrepancies in the estimates produced by the three models, when applied to the multilane highways segment. Given that the DOH model has not been updated in over two decades, this study concludes that its accuracy in capacity estimation may be compromised by evolving factors, such as driver behavior, traffic flow characteristics, vehicle types, and vehicle performance. Moreover, the DOH method's omission of variables (including access point density, median type, and terrain type) may further affect its accuracy. Similarly, since HCM 2010 and HCM 2016 are tailored to the United States, using both models to estimate the capacity of the multilane highway segment of interest may lead to errors due to differences in driver behaviors between Thai and American drivers, distinct traffic flow characteristics, the model’s omission of the percentage of motorcycles, and the overestimation of speed at capacity on the highway segment.

Effects of spray drying chicken feather keratin with Arabic gum encapsulation for nutrient supplementation

2024-09-02T11:15:23+07:00

Keratin has always been a topic of interest as a novel protein source in human nutrition, consistent with waste-to-wealth initiatives. Hydrolyzed chicken feather keratin is spray-dried to facilitate storage, transportation, and development into dietary supplements. High-heat drying, however, may affect the quality and function of this bioactive compound. The objective of this research is, thus, to determine the suitable spray-drying parameters involving inlet temperature (70 – 190 °C), feed flow rate (3 – 9 ml/min), and concentration of Arabic gum (AG) (0 – 5%w/v) for the encapsulation of hydrolyzed chicken feather keratin as a nutrient supplement. The effects of the spray-drying on powder yield, moisture content, flowability, and total protein content were investigated. Fourier Transform Infrared Spectroscopy (FTIR) determined the presence of functional groups, while Scanning Electron Microscope (SEM) investigated the powder morphology. High Performance Liquid Chromatography (HPLC) was also conducted to obtain amino acid profiling of the powder. Then, capsule quality control tests were done to ensure compliance with industrial standards. Good powder quality was obtained from the parameters of inlet temperature 190 ± 5°C, feed flow rate of 3 ml/min and AG concentration of 2.50% since it showed high yield of 81.88%, moisture content of 3.74% (<5%), flowability with AOR 43.70° (<50°), high (minimal lost) total protein content of 0.7168 g protein/g solid. The keratin functional groups remain distinguishable through FTIR with AG encapsulation. SEM showed spherical and intact structures, which suggests high integrity of encapsulating materials. Amino acid profiling confirmed the presence of essential and non-essential amino acids, which are important as building blocks of proteins in the body. The product met all capsule quality control standards according to National Pharmaceutical Regulatory Agency of Malaysia (NPRA) and United States Pharmacopeia (USP). This result showed that spray-drying hydrolyzed keratin in the presence of AG would retain its function and powder quality.

Security enhancement of decentralized healthcare system by transformer blockchain mechanism

2024-10-25T15:33:56+07:00

Medical data plays an essential role in diagnosing diseases and planning therapeutic. However, securing these data is a very critical function in the healthcare system. Some of the traditional Encryption and decryption mechanisms have resulted in a loss of sensitive medical information. In addition, maintaining the confidential score of the medical information is a much more needed and essential task. Considering these cases, the healthcare application was adopted in this present study. Therefore, to enhance security, a novel Transformer Neural Data Encryption Blockchain (TNDEB) has been proposed in this research. The IoMT database was initially collected and trained to detect and eliminate malicious events. Further, the hashing and encryption process has been carried out to secure the data. Moreover, to check the data similarity, the homomorphism function was performed at the verification module, and the verified data was decrypted using the shared private key. The chief contribution of this study is to keep medical information confidential with the support of the holomorphic concept. Additionally, the cryptanalysis was carried out by launching the brute force attack to compute the performance efficiency of the TNDEB model. Subsequently, the validated performance results are compared with existing models. The decrypt and encrypt time achieved by the TNDEB model is 1.260ms and 1.010ms, respectively. In addition, the gained confidential score is 98.8%. Hence, the proposed model is highly suitable for the IMoT application to secure the information at a high confidential rate.

Optimizing mushroom classification through machine learning and hyperparameter tuning

2024-08-20T10:20:50+07:00

This research explores the application of machine learning in the classification of mushrooms as poisonous or edible, emphasizing the importance of optimal model performance to ensure food safety. This study compares four classification algorithms-Random Forest, Logistic Regression, Decision Tree, and Naive Bayes-before optimizing the two best models through Hyperparameter Tuning using Grid Search. The proposed method involves Exploratory Data Analysis (EDA), Data Preprocessing, Classification Modeling, Performance Evaluation, and Hyperparameter Tuning. The dataset used is Mushroom Classification data, and the results show that the Random Forest algorithm performs better with ROC values close to 100%, high recall, and good F1-Score. Hyperparameter tuning further improved the ROC and recall of the Random Forest model, emphasizing its adaptability to the nature of the dataset. This research emphasizes the importance of robust data processing and model optimization to achieve accurate and reliable predictions in mushroom classification, contributing to food safety endeavors.

Application of strategic environmental assessment for mass transit system: Case study of Uttaradit Province, Thailand

2024-06-10T10:31:44+07:00

This research aims to apply Strategic Environmental Assessment (SEA) at the strategic action. Plan and program of the mass transit system in Uttaradit province, Thailand. Some steps of SEA process were applied to such strategic action by focusing on the development and assessment of the appropriate alternatives together with the selected appropriate indicators. The alternatives for mass transit development plan were proposed as following; Alternative 1: No Development Plan (or No Action Plan), Alternative 2: Conventional Mass Transit System Development Plan (Conventional Plan), Alternative 3: Smart and Environmentally Friendly Mass Transit Development Plan (Smart plan). With the Pairwise Comparison and the Impact Matrix Analysis, the Smart Plan was assessed as the appropriate alternative when compared with the Conventional Plan and No Action Plan as for 49.36%, 32.17%, and 18.47 % respectively. The mass transit system under the Smart Plan were proposed as Road, Railway, and Road with Railway systems. On the same assessment procedure, the impact score of the Road with Railway, Railway and Road system were 36.64 %, 33.90% and 29.46% respectively. The appropriate alternative mass transit system was road and railway system. SEA was practical tool applied to the strategic level of the mass transit development.

Serum glycobiomarker mining suggested the improvement of cholangiocarcinoma detection using combined CA125 and CA242

2024-06-13T13:30:33+07:00

Cholangiocarcinoma (CCA) is a malignant neoplasm originating from biliary epithelial cells. During the early stage, the patients do not show any symptoms, leading to wide and extensive spread of this disease. Nowadays, there has not been a single serum tumor marker which can be used for effective screening of the disease or classification of the patients. This study therefore aims to determine an appropriate serum marker for screening of the patients with early staged CCA by using a technique of data mining. Beginning with the C4.5 Decision tree and Logistic Regression for selection of serum markers for effective screening of the patients with CCA, the selected markers were then used for classification of the patients with CCA from non-CCA patients, and CCA from Benign Biliary Disease (BBD) by C4.5 Decision tree, Logistic Regression, Random Forest, and Artificial Neural Network. In this work, seven serum tumor markers were used, including Carbohydrate Antigen 125 (CA125), Carbohydrate Antigens 19-9 (CA19-9), Carbohydrate Antigen 242 (CA242), Carbohydrate Antigen 50 (CA50), Carbohydrate Antigen 72-4 (CA72-4), Carcinoembryonic Antigen (CEA), Cy-tokeratin-19Fragment (CYFRA 21-1). The model was used to classify the CCA and non-CCA patients and it was discovered that the serum tumor markers which could most efficiently classify the CCA patients from the non-CCA patients were the combination of CA125 and CA242 suggested by the Logistic Regression with C4.5 Decision tree as the classifier, yielding the best performance, with Sensitivity and Specificity being 75.88 % and 86.82%, respectively. In contrast, the classification of CCA patients from BBD patients was best performed by the serum tumor markers CA125 and CA72-4 suggested by C4.5 Decision tree with Logistic Regression or Random Forest as the classifier.

Decolorization of indigo carmine dye using silver nanoparticles synthesized via green method mediated by Leucaena leucocephala: A response surface methodology

2024-08-09T10:37:49+07:00

This study aims to synthesize and characterize silver nanoparticles (AgNPs) from Leucaena leucocephala leaf extract and evaluate their effectiveness in photo-catalytically degrading indigo carmine (IC) dye in an aqueous solution. The synthesized AgNPs were characterized using UV-visible spectrophotometry (UV-Vis), diffuse reflectance spectroscopy (DRS), Fourier transform infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), and X-ray diffraction (XRD) techniques. Response surface methodology (RSM) based on central composite design (CCD) was employed to optimize the conditions for the photocatalytic degradation of IC dye under solar light. The results show that 97.52% of the maximum photodegradation of IC dye was achieved using a dye concentration, catalyst volume, and reaction time of 19.7 mgL^-1, 4.47 mL, and 97 min, respectively. RSM results revealed optimal conditions for IC dye degradation under solar irradiation. Moreover, the kinetics study of IC dye showed a good correlation with the pseudo-first order kinetic model.

IDRCNN and BDC-LSTM: An efficient novel ensemble deep learning-based approach for accurate plant disease categorization

2024-10-09T14:15:22+07:00

Agriculture provides food for everyone, even as the population grows. Early detection of plant illnesses in the agricultural industry is recommended to guarantee enough food for everybody. However, early crop disease detection is impossible. Nevertheless, overfitting is a common problem with these studies, and using test datasets from unfamiliar regions substantially decreases diagnostic performance. This study proposes a new Deep Learning (DL) approach for categorising illnesses affecting plant leaves. The limited number of plant leaf illness datasets can result in inadequate and overfitting generalisation of ML approaches. The use of augmentation contributes to the artificial increase in dataset variation. Remove unnecessary noise, improve the image's contrast, and eliminate the background during pre-processing. Next, the Res2Net technique extracts the appropriate attributes from the picture, including structure, colour, and texture elements. Next, hybrid deep learning (DL) approaches of the Bi-Directional Convolutional Long Short-Term Memory (BDC-LSTM) and Improved Deep Residual Convolutional Neural Network (IDRCNN) categorise plant illness into their categories. Finally, the Enhanced Watershed Segmentation Algorithm (EWSA) will extract the disease portion from the input picture. We utilised a dataset on leaf diseases for model simulation and assessment. Experimental results show that the proposed method is more accurate in classifying and identifying plant leaf disease than other approaches. The uses of the proposed DL method show their efficiency and excellent accuracy.

Correlation between erosion and energy consumption of sandstones

2024-09-17T11:00:06+07:00

This study aims at simulating rock erosion by slake durability testing under wet and dry conditions. Phra Wihan sandstone and conglomeratic and bedded sandstones from Phu Phan formation are used as rock specimens. The test parameters are modified from the standard to accelerate the erosion process, where 2,000 drum revolutions are used instead of 200 revolutions for up to 80 test cycles (80 days). Results indicate that fragment roundness and sphericity increase with test cycles. Bedding planes reduce the roundness of bedded sandstone as the fragments become smaller. Phra Wihan sandstone is physically insensitive to water. The water-sensitive and soft Phu Phan sandstone, however, shows notable increases of porosity and reduction of density under both wet and dry conditions. Scrubbing and colliding processes mainly reduce the fragment sizes, under dry condition. Under submerging condition, even though fragment weight is decreased by its buoyancy force, intergranular bonding of the two Phu Phan sandstones is weakened by water penetration, leading to higher percentage of passing materials and lower energy required to disintegrate the rocks than under dry condition. Water insensitive Phra Wihan sandstone erodes more quickly under dry condition than under wet condition. Even though it requires longer period to erode under water submersion, due to buoyancy force, it consumes less energy than those under dry condition to reach the same fragment sizes. Larger sandstone fragments use energy more efficiently to reduce their size than the smaller ones.

Analyzing the students' moral obligation considering their carpooling intentions in Oman

2024-07-30T11:55:22+07:00

The rise in private vehicle use causes traffic congestion on the road network and increases the social costs of the trips. Travel demand management (TDM) strategies are deployed to mitigate traffic congestion sustainably as these strategies spread travel needs in space and time by influencing individual travel behavior. Carpooling is a TDM measure used to decrease the use of single occupancy vehicles and promote shared mobility among travelers sharing the same origin-destination and following the same route. This study attempts to identify the correlations between the socioeconomic demographics of travelers and their carpooling potential considering the moral obligations. A questionnaire survey was conducted with the students of the University of Nizwa, Oman, and a total of 156 samples were collected. The results revealed a high interest of respondents in carpooling as a passenger as well as a driver. The respondent's moral obligations are high to carpool for the reduction in traffic congestion, air pollution, and energy consumption. The category analysis showed some differences in interests among different groups. The ordered regression analysis revealed that variables of gender, present travel mode, trip distance, car driving, and carpooling as passengers are significant in determining the moral obligations of the travelers. These results implicate that specific carpooling programs based on specific market segments have the potential for consideration and implementation.

Improving anaerobic digestion of various sludge types through alkaline pretreatment

2024-09-16T09:58:55+07:00

This study investigates the impact of alkaline pretreatment on the anaerobic digestion of waste activated sludge (WAS) with varying types and total solids (TS) concentrations. Using NaOH, we optimized pretreatment conditions with a pH of 8.5 and a contact time of 2 hours. Our key findings demonstrate that this pretreatment significantly enhances sludge solubilization, evidenced by a notable increase in soluble chemical oxygen demand (SCOD). Specifically, SCOD increased by 57.5% for domestic wastewater (Plant A) and by 12.1% for industrial wastewater (Plant B) at a 0.5% TS concentration. Higher TS concentrations showed similar trends, with improved solubilization and higher methane production rates. Additionally, pretreatment elevated ammonia nitrogen (NH₃-N) and phosphorus concentrations, with greater releases at higher TS levels. These improvements resulted in enhanced anaerobic digestibility, higher total solids (TS) and volatile solids (VS) destruction, and increased specific methane production. The study underscores the effectiveness of alkaline pretreatment in optimizing anaerobic digestion processes, offering practical implications for wastewater treatment efficiency.