Latin American Applied Research - An international journal
Latin American Applied Research is dedicated to the rapid dissemination of high quality research communications in the scientific branches of Chemical Engineering, Applied Chemistry, Heat & Mass Transfer, Applied Mechanics and Control & Information Processing. There are no processing charges for authors. The Journal is currently indexed in the following ISI products and services: <strong class="ng-star-inserted" style="box-sizing: border-box; color: rgba(0, 0, 0, 0.87); font-family: &quot; source sans pro&quot;,sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px; padding: 0px; margin: 0px;">Web of Science Core Collection: <span style="display: inline !important; float: none; background-color: #ffffff; color: rgba(0, 0, 0, 0.87); font-family: 'Source Sans Pro',sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Science Citation Index Expanded. <strong class="ng-star-inserted" style="box-sizing: border-box; color: rgba(0, 0, 0, 0.87); font-family: &quot; source sans pro&quot;,sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px; padding: 0px; margin: 0px;">Additional Web of Science Indexes: <span style="display: inline !important; float: none; background-color: #ffffff; color: rgba(0, 0, 0, 0.87); font-family: 'Source Sans Pro',sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Current Contents Engineering, Computing & Technology, <span style="display: inline !important; float: none; background-color: #ffffff; color: rgba(0, 0, 0, 0.87); font-family: 'Source Sans Pro',sans-serif; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Essential Science Indicators (<span style="list-style: none; text-align: left; color: #4b4b4b; text-transform: none; text-indent: 0px; letter-spacing: normal; font-family: 'Source Sans Pro',Arial,Helvetica,sans-serif; font-size: 13px; font-style: normal; font-variant: normal; text-decoration: none; word-spacing: 0px; display: inline !important; white-space: normal; orphans: 2; float: none; -webkit-text-stroke-width: 0px; background-color: #f8f8f8;">ESCI, CPCI-SSH, SCI-EXPANDED, DCI-S, SSCI, BKCI-SSH, A&HCI, DCI-SSH, CPCI-S, BKCI-S). It is also indexed by Scopus, Scimago, Chemical Abstracts Service and LATINDEX. Published quarterly (January, April, July and October) with the financial support of Universidad Nacional del Sur (UNS). Printed at EdiUNS, Santiago del Estero 639, Bahia Blanca, Argentina. Founding Editor: Jorge J. Ronco. Statistics, reviewers, regularity and affiliation of the contributors
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Analysis of mixed convective Casson fluid flow through vertical porous surface in presence of radiation
The investigation explains the mixed convective Casson fluid flow behavior passed an exponentially stretching porous surface in the presence of radiation and chemical reaction under the influence of mixed convection. Perpendicular to the flow a uniform magnetic field Bo is applied. The controlling equations are mathematically turned into ordinary differential equations by implementing appropriate similarity transformations. The MATLAB built-in bvp4c approach is then used to solve the equations numerically. The results are graphically examined for a range of flow parameter values. Special attention is paid to how the mixed convection parameters (Gr/(Re^2) and Gc/(Re^2)) affect entropy production, as well as how the magnetic parameter (M) affects the temperature profile of the system. We have demonstrated that the mixed convection parameters and magnetic parameter have a positive impact on the entropy and temperature of the system, respectively. The increase of 200 % (approximately) in (Gr/(Re^2) and Gc/(Re^2)) there is an increment in the entropy by 113 % (approximately) and 84 % (approximately), respectively. Also, with the increase of 400 % (approximately) in magnetic parameter (M) there is an increase of 34 % (approximately) in the temperature profile.
Copyright (c) 2025 Latin American Applied Research - An international journal https://creativecommons.org/licenses/by-nc-nd/4.0
Effects of cold-pressed oils on physicochemical, textural, biochemical, and sensorial properties of muffin cakes: SAW method
The present study aims to use cold-pressed oils, which have a significant role in terms of nutritional value, in muffin cake production, determine the product properties, and assess consumer preferences. For this purpose, muffin cakes were produced by using cold-pressed oils instead of sunflower oil. The addition of cold-pressed almond, apricot kernel, and safflower and pomegranate seed oils into the muffin cake formulation caused an increase in the volume index and hardness values of the cake samples. Antioxidant activity values of cake samples were found to range between 12.2 and 29.4 µM Trolox/kg. On the other hand, the color values of the cake samples were observed to change after the addition of cold-pressed oils. At the end of the analysis performed using the SAW method, it was determined that the most preferred sample in terms of sensorial properties was the muffin cake added with cold-pressed safflower oil (sample AS).
Group approach techniques are used to solve laminar MHD free convection flow from a vertical cone with non-uniform surface heat flux
This paper develops a Group transformation to formulate the laminar natural convection phenomenon, which involves a magnetic field perpendicular to the surface, incompressible fluid, viscosity, as well as a vertically oriented cone on a surface in which the variations in thermal flux are governed by a power function of the length from the cone tip (x=0). The ODEs with corresponding appropriate conditions are reduced from non-dimensional governing PDEs with boundary conditions. The R-K approach based on the shooting technique was used to solve the non-linear ODEs. We visually inspected the temperature and velocity fields for different Prandtl numbers (Pr), the exponent m value, and the magnetic parameter (M) values.
Copyright (c) 2026 Latin American Applied Research - An international journal https://creativecommons.org/licenses/by-nc-nd/4.0
Mineral oil hydrocarbons in marinated anchovy products
Mineral oil hydrocarbons (MOH), including MOSH and MOAH, pose a contamination risk in food due to their presence in packaging, processing, and environmental sources. While regulatory limits in the EU are still evolving, this study analyzed MOSH and MOAH levels in marinated anchovy samples, a widely consumed fish in Turkey and Europe. Samples were assessed under different marination and packaging conditions. Results showed MOSH contamination primarily in the C25–35 range, varying with the processing method. Notably, the oil-free, drained sample had no detectable MOSH or MOAH, indicating that sunflower oil and packaging significantly contribute to contamination. These findings highlight the need for stricter monitoring and regulatory measures to minimize hydrocarbon exposure in marinated fish products and ensure consumer safety.
Mineral oil saturated hydrocarbons (MOSH)
Mineral oil aromatic hydrocarbons (MOAH)
Photocatalytic degradation of metsulfuron methyl in an herbicidal formulation by TiO2 supported on magnetite particles coated with SiO2-TiO2 oxides
Aqueous pesticides may be degraded through heterogeneous photocatalysis with TiO2. Supporting it on different materials, for example, magnetic particles, its further removal is facilitated. In this work, we report the synthesis of TiO2 supported on TiO2-SiO2-coated magnetite particles by a sol-gel process. These supporting materials were synthesized by varying the Si/Ti ratio, thus composites with different structural characteristics were obtained. With a theoretical Si/Ti molar ratio equal to 10, a supporting material with the highest specific surface area was synthesized (FTS-10). By supporting TiO2 over FTS-10, CM-2 catalyst was obtained. This catalyst degraded 70 % of the herbicide Metsulfuron methyl present in a commercial formulation at persistent conditions, with similar activity to that of the bulk TiO2. CM-2 can be separated by magnetic attraction, and it can be reused for at least five cycles. We propose this catalyst as a good alternative for the remediation of contaminated effluents with this herbicide.
Effects of Soret, Dufour and melting on micropolar fluid flow near stagnation point over a stretching sheet embedded in porous medium
The current work examines the mixed action of Soret and Dufour effects, melting and chemical reaction on stagnation point flow of micropolar fluid on a stretching sheet in a porous medium. Suitable non-dimensional transformations are adopted to transform the governing equations of flow, heat and mass transfer to dimensionless form. Bvp4c numerical scheme is applied to acquire the result numerically in graphical form for velocity, microrotation, temperature and concentration distributions. The accuracy of the solution has been verified by making comparison with already published work. Numerical values are computed for coefficient of skin-friction, couple stress, Nusselt number and Sherwood number for important parameters to get a deeper insight of the impact of dimensionless shear stress at the surface, heat and mass transmission process. The study discloses the fact that the combined effect of Soret and Dufour hike the fluid temperature but reduces the concentration.
Effect of surface modification treatments on the properties of electrodeposited Cu-CNT coatings for self-lubricating surfaces.
An eco-friendly alkaline glutamate-based electrolyte was used to produce copper-carbon nanotubes composite coatings with improved tribological properties. Experiments were performed at different current densities to deposit pure Cu, Cu-CNT and Cu-activated CNT coatings. The electrolyte was agitated by low frequency ultrasound to disperse the particles and consequently, help their incorporation in the Cu matrix. The coatings were characterized by SEM and XRD. The wear resistance and coefficient of friction were evaluated with tribological tests. Cu-CNT and Cu-activated CNT composites were successfully produced with improved tribological properties respect to those of pure Cu. It was found that the codeposition modified the morphology of the coatings as well as their crystallographic orientation. These modifications were more noticeable when the CNT were activated by acid-treatment.
Composition and antibacterial activity of bio-oil of sugarcane bagasse agricultural waste through pyrolysis assisted by active natural zeolite
Sugarcane bagasse waste is an alternative resource to be a pyrolyzed liquid smoke and has potential antibacterial activity. This study presents liquid smoke produced from sugarcane bagasse, determine the effect of active natural zeolite on the production of liquid smoke, and determine the antibacterial activity of liquid smoke. Liquid smoke was synthesized by pyrolysis of the sugarcane bagasse catalyzed by active natural zeolite (0%, 1%, 2% and 3% w/w). Liquid smoke was identified for its physicochemical properties including density, refractive index, viscosity, and acid number and characterized by FTIR and GC-MS. The results showed that active natural zeolite contained Heulandite and clinoptilolite phases. The addition of active natural zeolite catalyst increased the yield. The optimum catalyst usage was 2% w/w catalyst with 47.05% yield. The main compound component contained in the liquid smoke was ethanoic acid. By antibacterial activity, the zone of inhibition produced in the antibacterial test of gram-positive S. aureus and gram-negative E. coli on liquid smoke pyrolysis without catalyst and catalyzed by active natural zeolite were very strong.
Efficient production of honey-enhanced banana chips: A multi-objective optimization approach
Food industries aim to optimize processes for quality products in shorter times. For fruits, specific storage and transport conditions are essential to maintain quality, extend shelf life, and preserve sensory and nutritional properties. This study aims to maximize honey incorporation into banana slices during osmotic dehydration, assisted by ultrasound, while minimizing processing time. The process includes three stages: 1) 20 min ultrasound-assisted immersion in distilled water, 2) osmotic dehydration in hypertonic honey solution, and 3) hot air drying. The second and third stages were mathematically modeled using a multi-objective MINLP optimization in GAMS with the Baron solver. Optimal conditions achieved an 8.75% gain in soluble solids (SG) in 11.48 h, reducing processing time by 64% with a 70% honey solution. This optimization allows a compromise solution between two contradictory objectives for planning the production process: maintaining product quality and reducing production time. The process leads to lower total costs.
Development of a new automatic rebar tying robot and its adaptive force control
The automation of repetitive and labor-intensive tasks in construction has become increasingly important for enhancing safety, efficiency, and cost-effectiveness. Rebar tying, a fundamental step in reinforced concrete construction, is traditionally carried out manually, resulting in high labor demands, ergonomic risks, and limited precision. Existing automated solutions often fall short in addressing dynamic disturbances such as the impulsive backlash generated during the tying process. This paper presents a novel mobile manipulator designed for fully automatic rebar tying, featuring a reconfigurable locomotion mechanism and an RRR-type robotic arm integrated with a robust adaptive force control scheme. The proposed control algorithm actively compensates the unpredictable backlash forces during the tying operation, ensuring positional stability and consistent performance. Simulations conducted in MATLAB-Simulink demonstrate accurate trajectory tracking, even in the presence of external disturbances. These results highlight the potential of the proposed system to improve operational speed, reduce physical strain on workers, and advance automation in construction environments.
Gate to gate life cycle inventory analysis and life cycle assessment of marble processing industry: Indian prospective
This study provides a comprehensive Life Cycle Inventory (LCI) and Life Cycle Assessment (LCA) analysis of the marble processing industry in Rajasthan, India. The research aims to fill a critical gap in the literature by analyzing large-scale LCI data and LCA specific to marble industries, particularly in India. Through direct engagement with workers and plant managers, the study gathered data from key marble processing regions, Kishangarh and Jalore. The data was meticulously analyzed using GaBi software to assess energy use, material flows, and waste generation, based on Abiotic Depletion, Acidification Potential, Eutrophication Potential, FreshWater Aquatic Eco-toxicity, Eco-Toxicity Potential, Human Toxicity Potential, Photochemical Ozone Creation Potential and Global Warming Potential. The study's gate-to-gate approach captures real-time environmental impacts directly from the marble processing plants, offering a level of detail previously unavailable due to limited data transparency in the sector. The findings are beneficial for policymakers and industry leaders seeking to minimize the ecological footprint of the marble industry while enhancing productivity. The research contributes to the broader discourse on industrial sustainability, providing a valuable resource for achieving eco-friendly operations within the marble processing industry and aligning with global sustainability goals.
Enhanced cooling techniques for Inconel 718 drilling using nano-ionic liquid coolants
The manufacturing industry has thoroughly Examined the use of cutting fluids improved by nanotechnology that have higher heat conductivity to increase the efficiency of drilling operations. Due to their diverse physical & chemical characteristics and environmental compatibility, ionic liquids show significant promise for use as cutting fluids. This study examines how well different combinations of ionanofluids with nanoparticles perform as cutting fluids in drilling, by analyzing heat transfer through computational fluid dynamics and Ansys Fluent software. The thermal properties of a mixture containing TiO2, Al2O3, and Multi-walled carbon nanotube (MWCNT) nanoparticles are studied by mixing them with the ionic fluid 1-ethyl-3-methylnidazolium tetra-fluoroborate at various Reynolds numbers and particle volume fractions. Inconel 718 titanium alloy is used to make the workpiece, and tungsten carbide-cobalt is used to make the drill bit. The drilling temperature of the pure ionic liquid decreases by 23.18% with the addition of MWCNT and ionic coolant. While the particle volume (%) remains higher. For pure ionic coolant, the Al2O3, TiO2, and MWCNT ionanofluids improve the mean heat transfer coefficient by 33.44%, 44.32%, and 59.05%, respectively. When it comes to thermal efficiency and rate of heat dissipation, MWCNT nanocoolants outperformed Al2O3 and TiO2 ion nanocoolants.
Determining the moisture diffusion coefficient during soaking and cooking of chickpeas
This study aimed to apply a numerical/experimental approach to determine the moisture diffusion coefficient in chickpeas during the soaking and cooking processes, considering the seed swelling. The mathematical model combines Fick's second law to describe moisture transfer with the Peleg model to describe the expansion of the seed. It was solved numerically using finite differences method and fitted to experimental data of hydration kinetics at different temperature conditions (26, 40, 50, 63, 74, and 86 ºC). The numerical results showed that the D-values estimated considering deformable seeds were around 25-30 % greater than those estimated considering non-deformable seeds. This modeling approach could be useful in analyzing legumes' soaking and cooking process, but further studies are necessary to confirm its efficacy in different seed types and experimental conditions.
Development of a superhydrophobic concrete coating: Using paper sludge ash and lignosulfonate adhesive
In the contemporary construction, concrete stands out as a prominent material, recognized for its widespread utilization across the globe. Despite its remarkable performance in structures over the past three decades, concrete remains susceptible to degradation, particularly through reinforcing corrosion induced by water absorption. This paper introduces a novel sustainable solution, the nanomaterials-based superhydrophobic for waterproofing concrete coating. The paper sludge ash solution was sprayed on a layer of lignosulfonate adhesive coated on grade 25 concrete, which exhibited a remarkable water contact angle of 156.296°. The cumulative water uptake was significantly curtailed and the void content in the coated concrete was minimized. This innovative approach not only addresses the inherent water absorbency of concrete but also showcases the potential for repurposing waste materials towards enhancing construction elements.
Hydrophobicity measurement and water repellence test
Lung sound analysis for pulmonary diseases classification using machine learning algorithms
Lung sound analysis has emerged as a promising non-invasive method for the early detection and diagnosis of pulmonary diseases. However, the presence of noise, such as ambient sounds, heartbeats, and motion artifacts, often distorts the lung sounds, making accurate diagnosis challenging. This study aims to address these challenges by proposing a novel approach for pulmonary disease classification through the analysis of lung sounds using machine learning algorithms. In this research, the lung sound signals are denoised using an Adaptive Variational Mode Decomposition (AVMD) technique. Additionally, a novel multifractal detrended fluctuation analysis (MFDFA)-based feature extraction method is proposed to enhance the analysis of lung sounds. Machine learning algorithms, specifically K-Nearest Neighbors (KNN) and Random Forest classifiers, are then employed to detect lung diseases. The study utilizes the publicly available ICBHI 2017 challenge database for analysis. Results indicate that the Random Forest classifier outperforms other models, achieving an accuracy of 99.10 %, precision of 96.15 %, and specificity of 98.75 %. These findings suggest that the combination of AVMD-based denoising, MFDFA-based feature extraction, and machine learning classification significantly enhances the performance of pulmonary disease diagnosis, offering a reliable and efficient tool for clinical applications.
Multi-way machine learning estimation of hardness of high-carbon martensitic stainless steels depending on the influences of austenitizing temperature, quenching media, and annealing temperature
In recent years, parallel to the new industrial trends, the applicability of novel artificial neural network systems and machine learning techniques in the manufacturing and metallurgy sectors has risen due to the increasing competitiveness among steel, pipeline, and construction firms. This work focuses on estimating the hardness of high-carbon martensitic stainless steels depending on the heat treatment media, austenitizing temperature (AT), and secondary annealing temperature using different machine-learning methodologies for the first time in the literature. The attained outcomes indicated that the highest average hardness level was found in the medium-level tempering temperature and low AT in brine. As the tempering temperatures rose to the upper limits, measured hardness results diminished in both quenching media. Besides, according to all fold types and error metrics, the random forest (RF) machine-learning model was the strongest approach to estimate the final hardness values of the heat-treated samples whereas the Bayesian ridge (BR) was the poorest way.
Study on mechanical and abrasive wear properties of pistachio shell flakes filled polyester composites
This study reports on the development of polyester composites reinforced with pistachio shell powder (PSP), an eco-friendly filler sourced from agricultural wastes. The PSP was processed through grinding and sieving before being added to the polyester resin in different weight fractions, ranging from 0 % to 5 %, using the hand lay-up route. These composites were then tested to evaluate their mechanical strength and tribological performance, particularly for applications requiring high wear resistance and notable frictional stability. A pin-on-disc apparatus was employed to assess the abrasive wear response under various applied loads and sliding speeds. Results indicated a notable improvement in the mechanical and tribological properties of the polyester composites due to PSP inclusion. Surface morphology analysis of the worn regions showed the presence of cracks and grooves at lower filler contents, while higher filler content led to increased formation of wear debris from the material.
Optimizing beta-carotene bioaccessibility predictions with advanced machine learning algorithms and feature selection strategies
Since the artificial intelligence is of great attention in food science and technology, in the current study it was aimed to utilize machine learning methods including TreeBoost (TB), Multilayer Neural Network (MLP) and Support Vector Machine (SVM) to predict the bioaccessibility of beta-carotene. The emulsion type (micro- or nano-), oil/water phase ratio, the oil type, the type of the emulsifier (protein or carbohydrate), and beta-carotene concentration were selected as variables for the predicted models. Results demonstrated that TB-based model provide the best prediction for the bioaccessibility of beta-carotene with the values of R2 = 0.4325, RMSE = 17.2484, NMSE = 0.5675, and MAE = 13.3809. Besides, according to TB-based Model 7, the emulsion type (micro- or nano-), oil/water phase ratio, the oil type, the type of the protein, and beta-carotene concentration were found to be efffective on the bioaccessibility of beta-carotene. Based on the comparison of method performance, The RMSE value of TB-based Model 7, showed an improvement of 9.27 % compared to the SVM method and 13.4 % compared to the MLP method. To conclude, the outcomes of this study will shed light to future experiments by simplifying the variables of any process using different machine learning methods.
A novel prediction model of heat transfer characteristics of helically coiled tube heat exchanger using Dual Optimized LSTM (DOL) regression
The investigation and examination of heat transfer and fluid flow behavior in micro device configurations is a highly pertinent subject in contemporary research. This is due to the increasing applications of micro devices, which require small size and high efficiency. Surface effects play a major role in micro scale applications, especially in heat transfer. The superior heat transfer characteristics of helical coiled tubes have led to their widespread adoption in industry. Moreover, altering fluid thermal conductivity offers a viable passive technique for augmenting heat transfer rates. Studies have revealed that the addition of nanoparticles to traditional heat transfer fluids significantly boosts their thermal conductivity, resulting in improved heat transfer performance. This work introduces a Dual Optimized LSTM (DOL) regression algorithm to predict heat transfer properties and nusselt numbers based on particle mass flow rate and Dean number. The efficiency of the proposed DOL prediction method is evaluated in a simulation environment using water and Al2O3 nanofluid flowing through a circular tube with uniform heat flux. The simulation results are analyzed for temperature-dependent characteristics, including nusselt number, friction factor, pressure drop, convective heat transfer coefficient, and inner nusselt number. The performance of the DOL regression model is evaluated under both laminar and turbulent flow conditions of nanofluids, with key metrics derived for each regime. The model's accuracy is demonstrated by a low RMSE of 0.00221 between experimental and predicted data, achieving a prediction accuracy of 99.5 %.
Beef sausages containing mushroom (Agaricus bisporus) powder: Physicochemical, lipid oxidation, color and sensory properties during storage
The aim of this study was to investigate the quality of beef sausages containing mushroom (Agaricus bisporus) powder (MP) with varying rates (0, 0.5, 1.0, and 1.5 %) under cold storage for 60 days. In particular, the study focused on changes to the jelly and fat separation (JFS) of sausage batter, as well as chemical, lipid oxidation, color, and sensory characteristics in the final product. The incorporation of MP resulted in a decline in the JFS, with the most notable reduction occurring at 1.5 % MP, from 10.92 to 7.67 % within the batter. The protein content of the sausage increased from 13.46 % to 15.76 % with the addition of 1.5 % MP. The pH level, which was elevated due to the existence of basic amino acids in MP, decreased during the storage period and reached a pH value comparable to that of the control. The considerable rise in thiobarbituric acid reactive substances (TBARS) observed in the control sausages showed a marked decrease throughout the storage period in the sausages to which MP had been incorporated. Additionally, these sausages exhibited a lower free fatty acid content than the control.
Application of the brine method to extend the shelf life of zucchini (Cucurbita pepo L.) flowers: Changes in antioxidant activity, total phenolic contents and phytochemical profile
Edible flowers, demanded by consumers for their distinct sensory properties and health benefits, have a short shelf life. This study aimed to apply the brine method to extend the shelf life of zucchini (Cucurbita pepo L.) flowers, one of the edible flowers. The phytochemical profile of the samples was determined using LC-MS/MS, a method that allows the amount of 56 different phytochemicals to be determined. In this context, zucchini flowers were stored in brines containing 5% and 10% NaCl for the 7th, 14th, 21st and 28th days. The highest antioxidant activities were found in DPPH (19.23±0.74 mgTE/g extract) and CUPRAC (181.43±1.08 mmol Trolox Eq/g) on the 7th day of brining at 10% salt concentration. The highest TPC (32.10±0.33 mg GAE/g) was determined on 7th day of brining and at 10% salt concentration. The dominant phytochemicals were determined as quinic acid, fumaric acid, protocatechuic acid, 4-OH-benzoic acid, routine, hesperidin, isoquercitrin, nicotifluorine, quercetin and kaempferol at 10% salt concentration on the 7th and 14th days. This study demonstrates that the brine method can be used as an appropriate processing technology to extend the shelf life of zucchini flowers and also to broaden their application. Additionally, the findings of this study contributed to optimizing the storage time and salt concentration of brined zucchini flowers. It is thought that brined flowers can gain a place in the market as a commercial food product.
Evaluation of wheat bran from two different cultivars from grain to bread
Wheat bran (WB) has gained significant importance since containing some valuable nutritional compounds, predominantly dietary fibers (DFs). The aim of this study was to compare the brans of two different bread wheat cultivars from grain to bread in order to evaluate the effects of their chemical, functional and bioactive properties on bread making quality. Therefore, two different wheat cultivars obtained from Thrace region of Turkiye, named Tekirdag and Rumeli. These wheat grains were similar in terms of thousand grain weight, hectoliter weight, protein, and gluten content while gluten index and Zeleny sedimentation value of Rumeli wheat cultivar were higher (p < 0.05). After milling wheat grains, the composition of WBs were determined and significant differences were observed except for moisture and protein content. Rumeli WB contained higher insoluble and total DF, leading to have higher total water retention capacity (T-WRC). Lower T-WRC of Tekirdag WB provided to have a better bread making quality than Rumeli WB since showing higher specific volume (p < 0.05). The breads produced from these WBs were similar in terms total phenolic content (TPC) and antioxidant properties although there were significant differences between WBs in terms of TPC and antioxidant activity (p < 0.05). The results of this study are suggestive in assessing the properties of WB from different cultivars for bread quality. It was indicated that some WB characteristics such as total DF content and T-WRC showed their effects on bread making while bioactive properties of WBs were not able to maintain in corresponding breads.
Unsteady chemically reactive MHD Casson fluid over a plate under convective slip boundary conditions and activation energy
Here, a chemically reactive unsteady magnetohydrodynamic Casson fluid flow in a plate with convective slip boundary conditions and activation energy is considered. Graphs were produced to examine the implications of the model's various parameters on the temperature, concentration, and velocity profiles. The model's foremost equations are solved using MATLAB - bvp4c. The Sherwood and Nusselt numbers and the skin friction are three relevant physical parameters of engineering. To get an understanding of these factors, numerical values have been estimated for a variety of scenarios. Velocity and concentration drop as the activation energy parameter improves. The tabulated statistics demonstrate that skin friction rises when the thermal and solutal Grashof numbers grow, but boosting the Biot number has the opposite effect. The current findings are compared to those found in the literature, and they are consistent.
Graph neural networks: A paradigm shift in static signature classification via Delaunay triangulation
Handwritten static signatures are a fundamental biometric characteristic extensively utilized across various sectors such as finance, law, and business. Graph neural networks (GNNs) have emerged as a promising avenue, offering inherent advantages in processing graph-based data. Inspired by the success of neural networks, the present investigation centers on harnessing the potential of GNNs for graph classification, particularly in the domain of static signature verification. The present study introduces a novel end-to-end methodology named Static Signature Verification using Delaunay Triangulation-based Graph Neural Network (DTGNN). This innovative approach involves converting static signature images into graph-structured data while integrating position embeddings to effectively preserve spatial information. By leveraging the capabilities of GNNs, especially in aggregating graph information, the approach aims to enhance the performance of static signature verification systems. It introduced novel Delaunay Triangulation-based Features for efficient model training, facilitating enhanced accuracy and reliability. The experimental assessments carried out on the GPDSsynthetic_100 and MCYT_75 datasets reveal notable improvements in performance, characterized by increased accuracy (GPDSsynthetic_100 - 99.91%; MCYT_75 - 100 %) and minimal loss (GPDSsynthetic_100 & MCYT_75 - 0.0012) utilizing our proposed approach. These results underscore the potential of DTGNN in addressing real-world verification challenges, highlighting its efficacy in writer-independent and writer-dependent static signature verification scenarios.
Graph classification, Graph Neural Network, Delaunay triangulation graph, Static signature
Presumptive identity of metabolites with antifungal activity produced by lactic acid bacteria isolated from tejuino and tepache, two fermented beverages
Some lactic acid bacteria (LAB) are used in the food industry due to have antifungal activity against different phytopathogenic fungi. The objective of this work was to contribute to the partially elucidation of the presumptive nature of metabolites with antifungal activity produced by 10 lactic acid bacteria from fermented beverages (tejuino and tepache) that exhibit different degrees of antagonism against Colletotrichum gloeosporioides Penz and other phytopathogens. Cell-free supernatants (CFS) of each strain were treated with proteolytic enzymes, pH neutralization, high temperatures, and catalase enzyme, and subsequently challenged against fungal spores (microtiter plate test) and against developing mycelium (diffusion on agar). Of 10 strains studied, eight owe their antifungal activity mainly to organic acids. The antifungal activity of two strains is also explained by the action of H2O2. Additionally, the TEJ4 (Lactiplantibacillus pentosus) and TEP6 (Lacticaseibacillus paracasei) strains showed antifungal activity mainly due to protein compounds.
Experimental study of impact localization in aluminum plates using Taguchi method
Several internal defect types can have an impact on structural performance and shorten its lifetime. Structural Health Monitoring (SHM) proposes a viable alternative by integrating a set of sensors for continuous structure monitoring. This enables early detection of the initiation and propagation of structural damage. Sensors permanent integration requires first determining their best placement, to ensure that a large area of the structure is monitored. However, using many sensors to cover a large area can have a negative impact on the structure's weight and thus, its performance. Hence, the main objective of this paper is to design an optimal sensor grid for acoustic source localization in plates using a network of four sensors along with triangulation algorithm. This work aims to validate experimentally the technique and to suggest a new procedure for implementing sensor networks for impact localization. The procedure is based on robust design methodology and sensor positions are determined based on the optimization of an objective function using the Taguchi SN ratio. A 400x400x2 mm aluminum plate is used herein, and the impact is generated by dropping a small steel ball at its center. Impact signals are acquired by piezoelectric sensors bonded to the plate's surface and captured by a four-channel oscilloscope. The efficiency of the proposed approach has been proved and the optimized sensor network located the impact with an error of 0.46 %.
Determining dynamic load carrying capacity for bipedal robot using torque of actuators, stability and jerk limits constraints
The paper refers to deriving dynamic equations of motion and applications to obtain dynamic load-carrying capacity (DLCC) for bipedal robots walking. The governing equations for determining the DLCC of a given end-effector trajectory are presented at the beginning. Then via an algorithm for finding the greatest load value, the DLCC for a specified trajectory is obtained for a biped robot for the curve path.10 degrees of freedom (DOF) biped is used, and a kinematic and dynamic model for this robot was obtained. Under these conditions of research, the main limiting factor to calculating the maximum allowable load for a prescribed dynamic trajectory is the constraint of the torque actuator. At the same time, the biped robot walking dictates the need for additional constraints, that is, the stability constraints of the robot. The actuator torque constraint of the joints was formulated in this study depending on typical (torque-speed) characteristics of DC motors. The study also considers the jerk limits, an important constraint in a robotic system's dynamic motion and trajectory planning. A strategy to calculate DLCC subjected to these limitations above-mentioned is formulated. Given an arbitrary path, a general computational technique for a 10 DOF biped robot case is laid out in detail. The results showed that the value of the maximum allowable load when the end-effector moved in a curve path was 0.702 kg. The path describes the strategy of dynamic motion by controlling dynamic forces on the robot's joints where the lower values of dynamic forces lead to decreasing energy consumption, which means an increased ability to lift larger loads. Lastly, the simulation results were validated by implementing an experimental biped robot.
Actuator constraint, biped robot walking, dynamic load carrying capacity, jerk limits, stability constraint
Automated brain tumor segmentation with deep learning
Accurate tumor delineation is crucial for effective diagnosis, treatment planning and risk factor identification. This study presents an advanced Deep Learning (DL) framework designed for the precise segmentation of brain tumors and reliable survival prediction for tumor patients. In this work, a cutting-edge approach that leverages an ensemble strategy, combining two distinct 3D UNet architectures (3D U-Net and Attention 3D U-Net) is incorporated for segmentation purpose. This ensemble approach employs a majority rule mechanism and ensures a more reliable and comprehensive delineation of tumor regions. The proposed system's effectiveness and performance are evaluated using BraTS 2018 dataset. The proposed deep learning network trained and validated for brain tumor segmentation achieved promising results on the online final dataset, as evidenced by the Dice coefficient and Hausdorff metric scores. Specifically, the performance metrics for different tumor regions were as follows: Enhancing Tumor (ET), 0.805 Dice Score and Hausdorff Distance about 2.779. For Tumor Core (TC), its about 0.851 and 6.378 and for Necoratic core 0.904 and 6.323.
3D U-Net, Attention U-Net, Brats 2018, Accuracy.
Thermal and mass diffusive studies of transient free convective radiative hybrid nanofluid along an infinite vertical plate
Objectives: This research study scrutinizes the combined effect of heat and mass transfer on the unsteady free convective flow of a hybrid nanofluid past an infinite vertical plate, considering, mass diffusion and thermal radiation. Methods: The present study examines the influence of four nanoparticles, namely Al2O3, Cu, TiO2 and Ag - on flow behavior, heat, and mass transfer. Using the Laplace transform technique, the governing equation for concentration, temperature, and velocity profiles are solved and validated against existing literature. The research includes water, nanofluids, and hybrid nanofluids with nanoparticle volume fractions ranging from 0 to 0.04, and features a graphical representation of the Sherwood number, skin friction, and Nusselt number. Findings: Results show that the velocity profiles improve with increased radiation parameters and nanoparticle concentration, while temperature profiles vary among nanofluids, with TiO2-Al2O3/water achieving optimal velocity performance and TiO2-Ag/water provides the best temperature regulation. Novelty: This research is innovative in exploring the combined effects of TiO2-Al2O3, TiO2-Ag and TiO2-Cu nanoparticles in hybrid nanofluids for transient natural convection, which has not been previously investigated. This combined effect of nanoparticles enhances the efficiency of heat exchangers, cooling systems, renewable energy technologies, material processing, and nuclear reactor cooling systems.
The role of thermal conductivity variation in MHD Carreau fluid slip flow: An analysis of heat transport in porous material
This analysis is novel as it examines the behaviour of a Carreau hydromagnetic fluidnovelty of this analysis is to examine the behaviour slip flow in Carreau fluid u hydromagnetic fluid over a porous medium with heat transport and viscous dissipation, while accounting for the impact of thermal radiation across an elongating surface with varying thermal conductivity and thickness. It is assumed that the fluid's thermal conductivity changes linearly with temperature. The non-linear governing equations have been transformed into ODEs by employing an appropriate similarity transformation. The impact of several parameters such as porosity parameter (D), magnetic parameter (M), wall thickness parameter (alpha), velocity parameter (lambda), Heat source (Q), power index parameter (m), Thermophoresis Diffusion (Nt), Brownian motion (Nb), Schmidt number (Sc) and chemical reaction parameter (Cr) are analyzed and studied graphically. It is observed that skin friction and Nusselt number having a reverse behaviour for velocity power index (m), both growing for m<1 but has reverse effect for m>1. Lower porosity in a porous medium reduces the amount of space available for fluid to flow through, which leads to an increase in fluid velocity to maintain the flow rate. Also, the concentration profile has a reverse effect on exothermic and endothermic reactions. These techniques can help future researchers develop advance technologies. The real-world applications of this research are that it can contribute to optimizing recovery strategies, and improving reservoir simulation models for cooling electronic devices or enhancing heat exchangers, and reducing environmental impact by understanding groundwater flow through porous layers.
Variable Thermal conductivity, Thermophoresis dif-fusion, Slip velocity, Material porosity, Brownian motion.