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.
In electrochemical biosensors, all efforts are focused on its immobilization between the biocomponent and the analyte. This preserves biological activity and keeps the biocomponent close to the electrode. The aim of this study is to test the efficacy of the designed alginate-gelatin based polymers for use in biosensors. SEM and FTIR analyzes of alginate and alginate-gelatin polymer were investigated. EDC: NHS was used as crosslinking agent. The Glucose Oxidase-Peroxidase enzyme system was used. In SEM analysis, changes occurred in the supramolecular structure. It is thought that the polymer has a porous foam-like appearance and the cavities it contains will act as a basis for enzyme activity. In the FTIR spectra, we report those strong intermolecular interactions occur between alginate-gelatin and EDC: NHS. Redox peaks were found in the range of 0-1.2 V in cyclic voltammograms. Our results show that alginate-gelatin-EDC: NHS polymer structure can be used effectively in active layer design.
The main energy conservation opportunities in a dairy plant are in refrigeration, and steam generation. This paper aims to identify potential energy and water savings to improve the thermal efficiency of a fluid milk processing plant. Methodologies for energy analysis and Pinch Analysis with the use of HENSAD and Aspen Energy Analyzer are applied. The main specific energy consumptions are defined as indicators of the progress of improved energy efficiency. The determination of energy performance indicators and energy targets of the heat exchanger network, as well as its design, allowed identifying opportunities for improvement to reduce fuel and water consumption through heat recovery in the milk pasteurisation process. Current hot and cold utilities duties are satisfied, for a minimum allowable temperature difference of 20 degrees C. Total annual savings of fuel oil and water allow assessing the feasibility of an investment project for improved heat recovery.
In this study, earlier isolated fungal strains from soil were retrieved from the fungal data bank of KUST. Molecular characterization of the strains according to the sequencing of 18S rDNA and phylogenetic tree analysis identified the strains as ZN1 and ZN2. Notably, the fungal strains demonstrated the ability to degrade commonly used pesticides, including )lambda-cyhalothrin, atrazine, bifenthrin, and imidacloprid. Strain ZN2 degraded )lambda-cyhalothrin by 82 % at day 15, while ZN1 showed no clear effect. By day 15, ZN1 degraded atrazine (65 %) and bifenthrin (68 %), whereas ZN2 degraded only bifenthrin (23 %). At day 30, bifenthrin degradation increased to 80 %. In soil experiment, the strain ZN1, degraded atrazine (76 %), )lambda-cyhalothrin (35 %), and imidacloprid (35 %). In contrast, ZN2 degraded )lambda-cyhalothrin (72 %), atrazine (68 %), bifenthrin (65 %), and imidacloprid (25 %), showing time-dependent variation between strains. This unique ability of fungal strains emphasizes their significance as effective and sustainable bioremediation agents that can potentially help reduce environmental contamination caused by harmful agrochemicals.
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.
This paper develops a group transformation to formulate the laminar natural convection phenomenon influenced by a magnetic field perpendicular to the surface and an incompressible viscous fluid. The study considers a vertically oriented cone where the surface heat flux varies as a power function from the cone tip at (x = 0). The ordinary differential equations (ODEs) are obtained by transforming the non-dimensional partial differential equations (PDEs) along with their boundary conditions. The Runge-Kutta approach based on the shooting technique was used to solve the non-linear ODEs. We numerically analyzed the temperature and velocity fields for different Prandtl numbers (Pr), the exponent m value, and the magnetic parameter (M) values.
The gas radon is one of the most significant elements released when natural uranium and radium decay. Hence, the gas concentration may be higher in enclosed locations, especially in underground spaces, and it is vital to measure the amount of radon gas radiation in dense underground stations. Here, in this experiment, a domestically manufactured environmental radon gas measuring device-electret ionization chamber detector-was utilized to degree the concentration in various underground of Tehran-Karaj plain. During the spring season, all 42 underground stations of Tehran and Karaj cities were measured at continuous time intervals. The trace of radon gas was observed in all undergrounds, but unfortunately, high radon concentrations were recorded in some locations, such as Ghaem and Tajrish undergrounds in the northern plain by virtue of fault type, water resources, and discrete geological constructions. Based on the US Environmental Protection Agency (EPA) standard, the maximum allowable concentration of radon gas in the air is 148 Bq/m3, while the average quantity at Tajrish station was 156 Bq/m3. This amount is above the permissible limit and therefore can be considered a health hazard. Accordingly, measures to improve ventilation and facilitate air movement are required in such undergrounds.
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 ( Gr ) of flow parameter values. Special attention is paid to how the mixed convection parameters Re2 and Gc Re2 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 ( Gr ) temperature of the system, respectively. The increase of 200 % (approximately) in Re2 and Gc Re2 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.
This study aimed at the effect of the fermentation process on bioactive compounds, physicochemical, microbial, and sensory properties changes in vegetables such as white cabbage (Brassica oleracea var. capitata f. alba), cucumber (Cucumis sativus L.), green beans (Phaseolus vulgaris L.). In this study, brine with 6 % salt content, sugar, and vinegar was added to vegetables in different formulations. While the antioxidant capacity of fresh cucumber, bean, and white cabbage samples was found as 33, 31, and 33 mu mol Trolox equivalent (TE)/g dry weight (DW), these values for fermented (pickled)vegetables were found as 57-100, 48-92, and 94-181 mu mol TE/g DW, respectively. Lactic acid bacteria (LAB) count in cucumber, bean, and white cabbage during lactic fermentation was found as 5.02-6.71, 4.17-6.66, and 5.12-7.28 log CFU/g, respectively. This study showed that the bioactive of the samples was increased during the fermentation process. The LAB changed the amounts of bioactive in the samples during fermentation. Therefore, a high correlation was found as a result of the correlation analysis between the LAB count and total phenolics, flavonoids, and antioxidant capacity.
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.
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.
Sugarcane bagasse waste is an alternative resource to be a pyrolyzed liquid smoke (bio-oil) and has potential an antibacterial activity. This study presents a liquid smoke produced from sugarcane bagasse, determines the effect of active natural zeolite on the production of the liquid smoke, and determines the antibacterial activity of the liquid smoke. The liquid smoke was synthesized through pyrolysis of the sugarcane bagasse waste catalyzed by active natural zeolite (0, 1, 2 and 3 % w/w of the waste). The 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 that resulted 47.05 % yield. The main compound 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 without catalyst and with active natural zeolite were very strong.
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 sensor system for continuous structure monitoring. which 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 %.
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 mu M 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).
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.
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 %.
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.
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.
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.
This paper introduces a new dual-mode power-split hybrid electric transmission. The system has two modes of operation which provide high-efficient performance of the hybrid electric vehicle in different driving conditions. Dynamic programming method is used as the approach of energy management strategy. This method provides best results for the performance of any hybrid transmission system and its results are used as the benchmark for other optimal control methods. According to the simulation results, the introduced system improves the fuel consumption comparing the well-known hybrid electric vehicle Toyota Prius.
