Baghdad Science Journal

Layered double hydroxides (LDHs) serve as potential catalysts for biodiesel production through triglyceride (TAG)
transesterication; nevertheless, mass transport limitations hinder their e
ectiveness, especially with larger TAG
molecules. This study addresses this limitation by synthesizing macroporous MgAlLDH and ZnAlLDH using a polystyrene
templating method. Thorough characterization reveals the successful formation of macroporous structures with signi
cantly increased physicochemical properties and their structural design compared to conventional LDHs. X-ray
Di
raction (XRD) conrmed the successful restoration of the LDH structure following calcination and rehydration, with
enhanced crystallinity seen in the macroporous materials. The BET surface area analysis revealed a substantial increase
(10x for MgAlLDH) in the macroporous framework. Scanning Electron Microscopy (SEM) and Transmission Electron
Microscopy (TEM) images revealed a macroporous network with an average pore diameter of 300 nm, consistent
with the dimensions of the polystyrene template. The X-ray Photoelectron Spectroscopy (XPS) analysis revealed slight
changes in surface elemental composition and oxidation states, indicating the impact of the macroporous structure
on surface properties. Catalytic testing with various TAGs (C4–C18) demonstrates a notable enhancement in catalytic
activity, particularly for higher TAGs, due to improved mass transfer. The turnover frequencies of macroporous materials
signicantly exceed those of conventional LDHs, exceeding them by approximately 20 times for the MgAlLDH system
and 15 times for the ZnAlLDH system. The ndings highlight the ecacy of macroporous LDHs as very e
ective catalysts
for biodiesel production, overcoming the inherent mass transfer limitations of conventional LDH catalysts.

This study investigates a green synthesis route for titanium dioxide (TiO₂) nanoparticles (NPs) using Malabar spinach (Basella rubra) leaf extract, with graphene oxide (GO) as a dopant, for application as an electron transport layer in perovskite solar cells (PSCs). The synthesized TiO₂ NPs were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS), and field emission scanning electron microscopy equipped with energy dispersive X-ray spectroscopy (FESEM/EDX). XRD results confirmed the presence of a pure anatase phase with a smaller crystallite size than that of commercial TiO₂, while EDX analysis verified the successful synthesis through elemental analysis. FESEM imaging revealed a mixture of rugged and uniformly distributed nanoparticles. Optical studies showed a reduced bandgap energy (3.0400 eV) compared to commercial TiO₂ (3.2000 eV), indicating improved light absorption potential. The integration of GO suggestively enhanced photovoltaic performance, with the highest power conversion efficiency (0.2467%) observed in the sample synthesized using distilled water, leaf extract, and GO—an order of magnitude higher than commercial TiO₂ (0.0259%). However, performance varied notably with different synthesis media, suggesting that solvent-specific interactions play a critical role in determining device efficiency. These findings demonstrate the viability of eco-friendly synthesis in developing functional nanomaterials and highlight the synergistic benefits of combining plant-based reductants and GO doping. The approach offers a promising pathway toward more sustainable and cost-effective PSC technologies; however, optimizing plant extract composition and nanoparticle morphology remains essential.

Electrospinning is a versatile and widely used technique for producing nanobers with controlled morphology and
high surface area, essential for applications in ltration, biomedical engineering, and sensors. The choice of solvent
plays a crucial role in determining the eciency of the electrospinning process and the properties of the resulting bers.
In this study, poly(methyl methacrylate) (PMMA) nanobers were fabricated via electrospinning using four di
erent
solvents: tetrahydrofuran (THF), dimethylformamide (DMF), chloroform (CHCl3), and acetone at a 20% (w/w) polymer
concentration. Surface tension tests revealed that DMF exhibited the highest surface tension, at 40.11 mN/m, which
inuenced ber formation. Electrospinning parameters were optimized, and the nanobers were characterized using
Field Emission Scanning Electron Microscopy (FESEM) and Atomic Force Microscopy (AFM). FESEM conrmed nanober
diameters ranging from 100 nm to 700 nm, with DMF-based bers showing the smallest average diameter (150 nm).
AFM analysis revealed that DMF-PMMA nanobers exhibited the highest surface roughness, which can be attributed to
the solvent properties. These results demonstrate that DMF is the most e
ective solvent among those tested for producing
ne PMMA nanobers with superior morphology and enhanced surface characteristics, underscoring the critical impact
of solvent selection in electrospinning nanober fabrication

This research aims to improve the porosity structure and adsorption characteristics of rCB through demineralization and physical activation techniques. Hydrochloric acid (HCl) at various concentrations (1, 5, 6, and 10 M) was employed for demineralization, followed by the addition of 6 M potassium hydroxide (KOH). Physical activation was conducted at temperature ranges of 300, 350, 400, 450, and 500 °C for durations of 1, 2, and 3 h. Characterization of rCB was conducted using Fourier Transform Infrared Spectroscopy (FTIR), ash content analysis, Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), Brunauer-Emmett-Teller (BET), and iodine adsorption number (IAN). Demineralization decreased the ash content from 11.36% to 8.05% and removed iron (Fe³+) and magnesium (Mg²+) metals. Conversely, physical activation markedly enhanced the porosity of rCB, attaining a peak BET surface area of 188.52 m2 g-1 and an IAN of 100 mg g-1 at 450 °C for 1–2 h. Increased temperatures led to pore collapse, particularly at 500 °C. The findings indicate that the optimum temperature and duration of physical activation enhance the structure of rCB. This phenomenon facilitates the widespread use of treatment for environmental remediation, including the treatment of wastewater containing dyes.

This work aims to explore the characteristics of TiO₂ NPs green-synthesized via an environmentally friendly method using oil palm (Elaeis guineensis) leaf extract as a green medium and capping agent by utilizing titanium tetra isopropoxide (TTIP) as precursor. The oil palm leaves were extracted with different solvent concentration variations. The natural extract was characterized using liquid chromatography-mass spectroscopy (LC-MS) and infrared spectroscopy for active chemical contents and functional groups. The obtained TiO₂ NPs were also characterized using infrared spectroscopy (FTIR) for the functional groups, ultraviolet spectroscopy (UV-DRS) for the optical characteristics, and X-ray diffraction (XRD) for the phase formation and crystallographic properties. More sophisticated equipment of field emission scanning electron microscopy equipped with energy dispersive X-ray spectroscopy (FESEM/EDX) and Raman spectroscopy were employed to reveal the characteristics of the obtained TiO₂ NPs. The results from X-ray diffraction showed that the obtained TiO₂ NPs are in pure anatase crystal structure. There is also a trend that the bandgap energy of TiO₂ NPs reduces with the use of oil palm leaves extract as a green medium. It is confirmed that the green medium affects the optical characteristics of the synthesized TiO₂ NPs by reducing the bandgap energy from 3.2 eV for commercial and the one synthesized using ethanol only to 3.12 eV by using the green medium of oil palm leaves extract. These findings will provide insight for more novel, environmentally friendly nanomaterials synthesis methods.

This study investigates the effects of dyeing duration and soaking pre-treatment on the coloring performance of Sappan (Caesalpinia sappan) and Tingi (Ceriops candolleana) dyes applied to Gmelina (Gmelina arborea Roxb.) and Lento-lento (Arthrophyllum diversifolium) woods. Test samples were prepared, pre-treated by soaking at 80°C for 24 hours to remove extractives of the wood, and then colored using Sappan and Tingi dyes at 80°C for 6 and 12 hours. The depth of dye penetration was measured using a stereo microscope, and color variations were assessed using the CIELab color space. Gas chromatography-mass spectrometry (GCMS) was employed to analyze the constituents of the wood, dye material, and colored wood. The results indicated that Sappan dye exhibited greater penetration compared to Tingi dye in both wood types, with deeper penetration in Gmelina than Lento-lento wood. Interestingly, both dyes penetrated more effectively during a 6-hour dyeing period than a 12-hour one. Pre-treatment did not significantly influence dye penetration depth. Color analysis revealed that both dyes imparted a strong red color to the wood, with Sappan producing a more intense red and Tingi exhibiting a yellowish tint. GCMS analysis confirmed the presence of flavonoids, polyphenols, and phenolic compounds in the colored wood samples. These findings contribute to the optimization of natural dyeing techniques for sustainable woodcraft applications.

Harvesting energy from humidity is a new approach that has emerged as a potential renewable energy source. However, there are drawbacks to current humidity-based energy-harvesting technologies, such as the absence of a long-term conversion mechanism in hygroscopic materials. In this study, we developed hematite/nickel oxide/graphene (hematite/NiO/Gr) on a cellulose-based substrate as the hygroscopic material for humidity-to-energy applications. We successfully synthesized a novel hematite/NiO/Gr composite using a cost-effective sonicated solution immersion method at different synthesis temperatures. Results indicated that the prepared hygroscopic material is hydrophilic, with a water contact angle of 67.83°. The fabricated humidity-to-energy device utilizing these hygroscopic materials yielded an output voltage of 3.87 ± 0.06 mV and a current density of 0.309 ± 0.05 nA/cm2. This research highlights the potential of cellulose-based hematite/NiO/Gr composites as promising hygroscopic materials that could be utilized in future large-scale green energy technologies.

Mixed-valence perovskite manganites have attracted considerable attention due to their intriguing properties, such as colossal magnetoresistance and charge ordering (CO). In this study, Sm0.5Ca0.5-xBaxMnO3 (x = 0, 0.10, 0.20, 0.30, and 0.40) manganites were synthesized via the conventional solid-state reaction method to investigate the effect of Ba2+ substitution on their structural, magnetic, and electrical properties. X-ray diffraction patterns confirmed that all samples were single-phase and crystallized in an orthorhombic structure with a Pnma space group. Rietveld refinement indicated an increase in unit cell volume with increasing Ba2+ concentration, suggesting the successful substitution of Ba2+ at the Ca2+ site. Four-point probe resistivity measurements performed over the temperature range of 30–300 K under 0 T and 0.8 T revealed that all Ba-substituted samples remained insulating. Nevertheless, the application of an external magnetic field led to noticeable reduction in resistivity, signifying the presence of magnetoresistance. The increasing Ba2+ concentration further reduced resistivity values, indicating the weakening of the CO phase. This effect was attributed to an increase in the average A-site ionic radius, which enhanced the electron bandwidth. Despite the Ba2+ substitution, the A-site mismatch cation had a more dominant impact than the increasing bandwidth, resulting in insulating behavior. Magnetic susceptibility measurements showed that Sm0.5Ca0.5MnO3 exhibited paramagnetic behavior with a charge-ordering temperature, TCO ≈ 270 K. Ba2+ substitution (x = 0.10–0.40) led to a weakening in charge ordered state and a reduction in the Néel temperature, TN.

Ionizing radiation benefits medical treatments and diagnostics. However, it also poses significant health risks, such as from the scattering radiation and unintended exposure. Lead is a common shielding material used for its excellent gamma ray attenuation performance, but it presents ergonomic and environmental challenges. This study assesses the structural and potential radiation absorption properties of the tin-polydimethylsiloxane (PDMS) composites. The composites were fabricated with different metal concentrations and tested for radiation protection efficiency (RPE), structural characteristics, chemical structure, and porosity. Gamma-ray spectroscopy was used to characterize radiation, with Cd-109 as a primary source. Pure tin/PDMS (PT) had the highest RPE compared to copper-tin alloy/PDMS (TA) and copper-tin alloy-pure tin/PDMS (PA), owing to its high atomic number and low porosity. Structural and chemical investigations, including FESEM and FTIR analysis, validated the composites\\\\' homogeneity and chemical bonding. The porosity of the composite is evaluated using ImageJ analysis. The results highlight that each composite\\\\'s porosity increase as the composition of metal filler increases. However, radiation attenuation capabilities more affected by the atomic number of the metal used and metal filler compositions within the composites. Porosity analysis revealed that PT had the lowest porosity among the composites, which may contribute to its superior shielding efficiency. Conversely, TA and PA showed lower atomic number and higher porosity, which disrupted their structural integrity and may reduce photon attenuation. Therefore, methods such as hot pressure, degassing, or vacuum procedures are suggested to reduce the porosity and enhance the radiation shielding within the composites.

The pursuit of advanced materials for radiation protection has highlighted the potential of polymer–metal composites. Porosity is a key factor influencing radiation attenuation, as fewer voids reduce pathways for radiation penetration. Recent studies emphasize the role of filler distribution in enhancing both mechanical properties and shielding performance. This work investigates the structural morphology and porosity of tin–PDMS composites prepared with pure tin (PS) and tin alloy (AS) fillers at 10–60 wt.% loadings, along with a tin–alloy mixture series (TM). Morphological features, particle distribution, oxygen content, and porosity were comprehensively analyzed. Results show that porosity strongly correlates with filler distribution and composition. The AS5 composite (50 wt.% tin alloy) exhibited the most balanced properties, achieving a low porosity of 0.34% with satisfactory density and microstructural stability. Within the TM series, TM6 (60 wt.% tin mixture) demonstrated promising features for high-density shielding applications due to its high tin content with a density of 5.32 g/cm³ and porosity less than 2.0% of the nominal acceptance threshold for the composite. These findings establish the foundational microstructural characteristics of tin–PDMS composites as a step toward developing lead-free radiation shielding materials.

This study has investigated the fabrication of porous Si-doped gallium nitride (GaN) using low-temperature alternating current photoelectrochemical (ACPEC) etching at varying etching durations for MSM photodetector applications. The structural and optical characteristics of the porous GaN samples were examined using a 400 W high-pressure mercury-vapour lamp (Hg-HPVL) and 4% KOH electrolyte with 50 Hz of 100 mA alternating current (AC). Current-voltage (I-V) measurements were conducted to analyse the platinum-deposited GaN for MSM photodetector performance. FESEM micrographs of the etched samples revealed coral-like and nano-tunnel pore formations, while AFM topography showed a significant increase in surface roughness compared to in the as-grown samples. The minimal difference between the average pore depth and RMS surface roughness values indicated successful fabrication of a nano-coral structure with a uniform, evenly distributed depth. Raman spectroscopy of the etched samples showed shifts in the A1 (LO) peak wavelengths and an increase in the E2 (high) peak intensity across different etching times compared to in the as-grown samples. XRD analysis further confirmed the presence of dislocation density along the x- and z-axis across all samples. Additionally, I-V measurements of the MSM photodetector demonstrated a higher current in all porous samples compared to in as-grown samples under various environmental conditions. These findings indicated that low-temperature ACPEC etching could be an effective approach for fabricating porous GaN with higher currents for photodetector applications.

Fusarium oxysporum f. sp. cubense Tropical Race 4, also known as Fusarium odoratissimum (FO), is a fungal pathogen affecting banana cultivars (Musa spp.) by secreting plant cell-wall degrading enzymes (PCWDEs) to penetrate and colonize roots, resulting in wilting symptoms and host death. A mycovirus is a virus that infects fungi. It was discovered that mycoviruses could be biocontrol agents by causing hypovirulence in fungal pathogens, a characteristic symptom of suppressed pathogenicity in an infected host. Hypovirulence was also associated with reduced PCWDE activity in several plant fungal pathogens. To date, no hypovirulent mycovirus has been reported in FO. Hence, this study was conducted to characterize a specific FO strain, P2S isolate, co-infected by FO mycobunyavirales-like virus 1 (FoMYV1) and FO Unclassified RNA virus 1 (FoURV1), on its pathogenicity and production of PCWDEs in comparison to uninfected and virulent POH27 and cured CP2S isolates. The results showed that FoMYV1 and FoURV1 reduced disease severity (45.0 ± 12.6%) in the P2S isolate compared to POH27 (90.0 ± 5.8%) and CP2S (90.0 ± 5.8%) isolates. Furthermore, co-infection with mycovirus and reduced disease severity were likely associated with downregulation of PCWDE production, including exoglucanase, xylanase, and protease. This is the first study to suggest a possible hypovirulence mechanism induced by co-infection with a mycovirus in FO.

A land evaluation study of Muara Batang Toru Sub-district is essential to determine its development prospects for seasonal crops. This study evaluates land suitability for six seasonal plants, including red chili, cucumber, shallot, tomato, eggplant, and bitter melon, in Muara Batang Toru Sub-district, South Tapanuli Regency. This study employs a survey method, integrating field observations and laboratory analysis with predetermined criteria based on a combination of internationally recognized standards and local conditions. Geographic information systems are used to map the actual and potential land suitability classes for vegetable crops. The findings indicate that most of the actual land suitability classes fall under marginally suitable (S3) and currently not suitable (N1) categories, primarily due to limiting factors such as water availability (wa), low nutrient retention (nr), suboptimal rooting media (rc), and high rainfall (tc). Proper management practices, such as making drainage systems and raised beds, applying 15-25 kg of organic fertilizer per hectare annually, and applying lime or dolomite, can improve the land suitability class to moderately suitable (S2) or highly suitable (S1). Effective management practices, including establishing drainage systems and planting beds, applying 15–25 tons/ha of organic fertilizer annually, can improve the land suitability classification to moderately suitable (S2) or even highly suitable (S1). Commodities such as red chili, tomato, and bitter melon have the potential for significant productivity gains with these interventions. Implementing intercropping and crop rotation systems is recommended to optimize yields, maintain environmental sustainability, enhance agricultural output, and promote the socio-economic stability in Muara Batang Toru Sub-district. This research also contributes to achieving the Sustainable Development Goals (SDGs).

Bovine mastitis, particularly subclinical mastitis (SCM), is a major concern in dairy cattle due to its asymptomatic nature and significant economic impact. In Asia, Staphylococcus aureus and coagulase-negative staphylococci (CoNS) are key pathogens. The overuse of antibiotics has led to the emergence of multidrug-resistant (MDR) bacteria, posing risks to animal health and food safety. This study aimed to identify staphylococci associated with SCM and assess their antibiotic resistance (AR) profiles. Milk samples were collected from 20 cows at a dairy farm in Malacca, Malaysia, in August and October 2023. SCM was diagnosed using the California Mastitis Test (CMT), and bacterial isolates were cultured on Mannitol Salt Agar (MSA). Antibiotic susceptibility was assessed using the Kirby-Bauer disk diffusion method. CMT results indicated 56% of samples were SCM-positive. From 153 isolates, 84 were presumptive coagulase-positive staphylococci (CoPS) and 69 were CoNS. AR profiling revealed 52% resistance to penicillin, 12% to cefoxitin, and 5% to tetracycline; all isolates were susceptible to erythromycin. Kruskal-Wallis analysis showed significant differences in antibiotic susceptibility across both CoPS and CoNS (P < 0.001), indicating variable susceptibility patterns. Sequencing analysis of the sodA gene identified that 50% of isolates were Staphylococcus, 33% were Macrococcus, and 17% were Macrococcoides, highlighting their roles in mastitis and AR. This study highlights the importance of SCM and the need for monitoring AR in dairy cattle. These findings emphasize the importance of SCM surveillance and AR monitoring. The emergence of resistance is likely linked to recurrent antibiotic use, underscoring the need for prudent antimicrobial strategies in dairy herd management.

Indonesia is a significant greenhouse gas (GHG)emitter, contributing 12.3% of carbon dioxide (CO2) oftotal emissions.
Carbon dioxide (CO2), a major GHG, is increasing in the Earth’s atmosphere. The CO2 absorption can be increased
through rubber plantations because rubber plants, such as forest plants, can process CO2 as a carbon source for
photosynthesis. This research aims to analyze carbon uptake in relation to tree density, biomass of rubber vegetation,
and soil organic carbon (SOC) content, as well as to map the distribution of carbon potential using remote sensing. This
research was conducted in the Sungai Putih Research Unit, Galang Sub-district, using a survey method by collecting
secondary and primary data. Sampling locations were determined based on Normalized Difference Vegetation Index
(NDVI) classification, while remote sensing image processing utilized Landsat 9 images processed through Google Earth
Engine (GEE). This study found that the potential carbon stock varied across observation plots. Based on calculations
using the SAVI Quadratic model (A13), carbon distribution, derived from field biomass (40–80) and C-Organic (2-3),
indicated a medium carbon distribution. The average potential carbon stock, measured from the standing trunk section,
was 29.43 tons across an area of 3.12 ha. The highest carbon stock and the largest average stem diameter were recorded
in plot 11, with an average diameter of 16.63 cm and a biomass content of 90.64 kg.

Coffea liberica, despite its high antioxidant activity reported in the green bean, has had limited research in the literature on changes in metabolite profiling across different roasting levels. Therefore, the objective of the present study was to evaluate the antioxidant activity and metabolite profiling of the green beans and various roasting levels of Coffea liberica brew using multiple antioxidant assays namely (total phenolic content, total flavonoid content, 1,1-Diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power, 2,2’-azinobis(3-ethylbenzothiazoline-6-sulfonic acid), and hydrogen peroxide scavenging activity) and liquid chromatography mass spectrometry coupled with quadrupole time-of-flight (LC/MS-QTOF), respectively. Notably, light-roasted coffee brew retained the highest antioxidant activity across most antioxidant assays. A total of 95 significant metabolites were identified, encompassing different classes of compounds such as phenolic acids, flavonoids, amino acids, alkaloids, heterocyclics, organic acids, and others. Chlorogenic acid derivatives, flavonoids, and alkaloids were the significant key metabolites (p<0.05) that discriminated Coffea liberica brew across different roasting conditions. These findings demonstrate that light-roasted coffee brew has the potential to enhance the formation and alteration of bioactive compounds that may provide health benefits, thereby highlighting it as a potential source of natural food that is beneficial to health.

Coffee plants (Coffea sp.) are currently a plantation crop in great demand among farmers in Silaen Sub-district. However, the soil conditions in Silaen Sub-district are dominated by sandy soil, which can trigger soil sensitivity to high erosion potential. One of the efforts to mitigate the decline in coffee production can be done through estimating the soil erodibility index in Silaen Sub-district. This research aims to estimate the soil erodibility index and obtain its relationship with agroforestry-based coffee production in Silaen Sub-district, Toba Regency. The method for determining land boundaries using land map units (SPL) in the form of land use, soil type, and slope. The parameters used are soil texture (M), organic matter (a), structure (b), and permeability (c), as well as coffee beans. Statistical analysis of data takes the form of correlation tests and data regression tests. The results of data analysis show that the highest wet weight of coffee cherries is found at SPL 8 with plantation land use, Typic Hapludands soil type, and land slope of 8- 15%. The wet weight value of coffee fruit at SPL 8 is 2.19 t ha-1 with an erodibility value of 0.39. The lowest wet weight of coffee beans is found at SPL 2 with the use of moorland, Andic Eutrudepts soil type, and land slope of 8-15%. The average value of wet weight of coffee beans at SPL 2 is 0.15 t ha-1with an erodibility value of 0.28 while the results of the regression test R2 value of 0.11. So this shows that soil erodibility affects the wet weight of coffee fruit by 11%. Application of conservation techniques, namely making terraces, so that the effectiveness of terracing, organic mulch, and agroforestry in suppressing erodibility for 5-10 years can increase crop yields.

Acrylamide is formed through the Maillard reaction between L-asparagine and reducing sugars during thermal processing. The enzyme L-asparaginase can effectively mitigate acrylamide formation by hydrolyzing asparagine into aspartic acid without affecting product quality. This study investigated the effect of asparaginase application methods, enzyme concentrations, and incubation temperatures on acrylamide reduction and pyrazine preservation in roasted Arabica coffee beans. Two enzyme application methods—soaking and spraying—were compared using 4000 U/g asparaginase. Soaking treatment achieved a greater acrylamide reduction (92.8%) compared to spraying (86.8%), and was therefore selected for optimization. Subsequently, enzyme concentrations (2000, 3000, and 5000 U/g) and incubation temperatures (40, 50, and 60°C) were tested. The optimal condition was found at 3000 U/g enzyme concentration and 50°C incubation for 30 minutes, resulting in 96.5% acrylamide reduction and significant conversion of asparagine to aspartic acid, without altering pyrazine concentrations responsible for coffee aroma. The pH of treated beans remained stable (5.38–5.92), indicating no negative impact on sensory attributes. Overall, asparaginase application effectively reduced acrylamide formation in roasted coffee while maintaining desirable flavor compounds, demonstrating its potential as a practical enzymatic strategy for improving the safety and quality of thermally processed coffee products.

One of the most significant industrial problems is the corrosion of construction metals such as mild steel in pipes and storage tanks of cooling towers. Several preventive measures are used to minimize corrosion. One of them uses various corrosion inhibitors, some of which are organic and others inorganic. Most inorganic corrosion inhibitors are environmentally unfriendly materials. These inhibitors, mixed with cooling tower water, are discharged into rivers or seawater. Many of them are toxic and affect biological life through their ingestion by humans and animals or through their entry into the soil. Basil plant oil is suggested as a new corrosion inhibitor; it is environmentally friendly, sustainable, and low-cost. This study examined its effectiveness compared to the well-known green corrosion inhibitor Eugenol. Various concentration rates were tested to determine the optimal efficiency using Eugenol under the actual temperature, velocity, and pH conditions of Al Daura refinery cooling towers: 40 °C, 300 RPM, and pH = 7. The exact circumstances applied to Basil plant oil to compare efficiencies. The Weight loss method was used. Tests were carried out in accordance with the American Society for Testing and Materials regulations. The corrosion rate recorded 0.022185947 and 0.004108509 mm per year for the Eugenol and Basil plant oils, respectively. The efficiency reached 89.2% using a concentration of 0.5 milliliter/Liter of Eugenol. The efficiency increased to 98% by using the same amount of Basil plant oil. A new equation was derived to compute the increase in the metal age when using a specific corrosion inhibitor

Probability distribution has shown its practicality in almost every area of human effort. This paper introduces a novel family of distributions called the [0,1] Truncated Exponentiated Ailamujia-G family. The [0,1] Truncated Exponentiated Ailamujia Exponential ([0,1] TEAE) distribution, which is a sub-model of the recently created family, is completely constructed. The [0,1] TEAE distribution is created by merging the [0,1] Truncated and Exponentiated Ailamujia distributions. the mathematical features of the [0,1] TEAE distribution, including moments, skewness, kurtosis, incomplete moments, renyi entropy, quantile function, and probability-weighted moments, are extensively examined. The quantiles for the chosen parameter values are clearly defined. The techniques used for parameter estimation include maximum likelihood, least squares, weighted least squares, and Anderson-Darling. This study assesses the efficacy of several estimators using a Monte Carlo simulation. In addition, the estimators were used on three actual datasets, and the Kolomogorov-Simirnov statistics were documented for each of them. The [0,1] Truncated Exponentiated Ailamujia Exponential distribution was used on three real world datasets. Its effectiveness was evaluated by comparing it to other well-known extensions of the Exponential distribution, using criteria such as Hannan-Quinn information criterion, Bayesian information criterion, Akaike information criterion, and consistent AIC and goodness of fit tests such as Anderson-Darling statistic, Cramer-von Mises statistic, and P-value for the KS test.

This research deals with the influencing of rotation and an inclined magnetic field on the analysis of blended convection heat transfer within a viscoelastic fluid that flows peristaltically through an inclined tapered asymmetric channel, that traversing in a porous media. The foundational equations of continuity, momentum, and energy were established in Cartesian coordinates. Furthermore, a range of dimensionless numbers, including Reynolds number, Brandt number, and Froude number, were introduced to regulate the governing equations in their dimensionless forms. The governing equations were further simplified under the presumption of long wave lengths and small Reynolds numbers. The perturbation method was utilized to derive a set of analytically solvable equations for the stream function, velocity, and temperature. However, the research explored several fascinating parameters, as: the inclination angle of the magnetic field, rotation, Brinkman number, Bingham number, Hartmann number, Grashof number. In addition, the influence of these parameters on the stream function, axial velocity, and temperature within the flow system was quantified and discussed, employing graphical representations facilitated by the Mathematical package. The study provides deeper insight into the physics of heat transfer and fluid motion under combined effects.

The internet lately has become an integral part of people’s lifestyles. It impacts various aspects of their daily lives, including education, communication, etc. Hence, the need for efficient and fast cryptography algorithms has increased. In this paper, a new cryptographic algorithm is proposed using the authenticated Three-Pass Protocol (TPP) and the Elliptic Curve Cryptography (ECC). In the proposed algorithm, the ECC is used to encrypt and decrypt data (a text of size 128 bits), and the TPP is used for transmitting this data without sharing the keys. In the standard ECC, the receiver publishes the elliptic curve (EC) equation, the initial point, and the public key, while the only published information in the proposed algorithm is the EC equation. Moreover, in the standard ECC two ciphertexts are sent, while only one ciphertext is sent in the proposed algorithm in each pass, which reduces the transmitting time in the proposed algorithm. Also, the proposed algorithm is proved mathematically by presenting a new proposition with the proof. The NIST tests, such as approximate entropy, block frequency, etc., along with the entropy and histogram tests, are implemented on the cryptographic algorithm (encryption algorithm) to show the effectiveness of the proposed algorithm. The used key in the proposed algorithm is greater than or equal to 128 bit for the algorithm to be efficient against the attacks. The security analysis shows that the proposed algorithm is secure against several common attack algorithms. Thus, the proposed algorithm is secure, fast, and efficient in transmitting data

To achieve the best possible outcomes. The reliability function estimation was also established for the In this study, the scal parameter Maxwell distribution was estimated using both traditional and robust methods, and the results were compared typical estimation methods, which included the Maximum Likelihood method (MLE), the moments method (MOM), and the M- robust method The results were compared using the mean error squared (MSE). The M-robust technique was found to have the lowest mean square error across all sample sizes, as demonstrated by the findings hence, it is regarded as being more effective than the conventional estimate methods. In addition, it was determined that the statistical criterion, also known as MSE, is the most effective way for estimating, and thus, the results of the simulation are based on this. m-strong technique is used to determine the model values (M-strong). According to statistical theory the number of small and medium-sized enterprises (SMEs) decreases as the sample size increases. Because parameter estimates are significantly hindered when the data set has few outliers, m is the method of estimation that is the most reliable. Because of this robust estimation is an essential method for evaluating databases that contain problematic outliers Numerous robust regression estimators as well as M method estimators, are available in a wide variety. The least squares approach is one of the safest methods because of its remarkable efficiency in obtaining the possibilities

The spreading of Sixth Generation (6G) communication within the Internet of Things (IoT) is one of the recent innovations in networking services. As the number of devices becomes increasingly connected through extensive communication network connectivity, energy efficiency has become a significant concern with the widespread use of massive IoT devices. While general lack of energy optimization can lead to several issues, including data loss, link failure, and high-power utilization, directly affect the communication quality of the network. To address these challenges, it is crucial to develop a model that combines effective optimization and resource allocation, based on both Quality of Service (QoS) and energy efficiency. In this study, we propose an Energy with Capacity-Awareness Effective Satellite Communication (ECESC) approach for 6G in an IoT environment. The network environment comprises Base Stations (BS) and User Equipment (UEs), with devices equipped with data transmission models, energy consumption models, and QoS-based service parameters. Our proposed technique is to enhance resource utilization and optimization among devices using the Superior Hidden Markov Model (SHMM) and Improved Neighborhood Field Optimization (INFO) Algorithm, significantly increasing energy and network utility. The ECESC approach was implemented using NS2 and compared with EERS-IoT, SECA-IoT, and DATS-IoT. We evaluated results using varying number of nodes and speeds. The performance metrics include end-to-end delay, throughput, packet delivery ratio, energy efficiency, packet loss, and routing overhead. The experimental results demonstrate that our proposed method achieves significantly improved outcomes in terms of packet delivery, throughput, and energy efficiency compared to earlier studies.

With the fast innovation and transformation that happen in our lives, security systems play an essential role in guaranteeing our safety and have become important part of protecting assets, houses, and campuses. The potential for theft and intrusions is increasing every day, making efficient access control for visitors coming in and out of facilities more critical. This paper focuses on developing an accurate access control system for special campus entrance doors with sensitive missions. The proposed system controls the entry of individuals to the campus by permitting authorized persons to enter while denying unauthenticated persons and notifying security guards via the GSM network.
The system mainly consists of two Arduino microcontrollers connected to sensors that control security. Network facilities (GSM and Wi-Fi) as well as Internet services (website and Telegram bot) are integrated into the system to exchange information and save visitor information within the system. Arduino boards serve as activity centers to receive visitors\' input (fingerprint codes, photos from cameras, position data from ultrasonic sensors), which are then analyzed and processed to accept or reject visitors. Finally, the system sends output to connected servers (Telegram bot, MySQL database, webserver). After testing the proposed system many times, results indicate that better authentication performance can be achieved with average time of only 8 seconds. The multi-channel communication used by this system makes it far more capable than others do while also improving security through two-factor authentication to verify a visitor\'s identity. However, one of the aforementioned channels requires 24/7 Internet services.

Detecting counterfeit Quick Response codes plays a critical role in protecting the integrity of products and documents. This paper presents a novel methodology to increase the reliability of QR code counterfeit detection by integrating neural embedding VGG19 and triplet loss. The approach uses deep neural networks. Specifically, the modification of the VGG19 architecture is used to extract unique features from QR codes. These features are embedded in a multi-dimensional space using neural embedding methods. The use of the triplet loss function aims to increase the discriminative power of the embeddings, ensuring the discriminative power of the embeddings, and ensuring that authentic QR code embeddings are closer to each other and further away from counterfeit embeddings. The effectiveness and robustness of the proposed methodology is substantiated by extensive experimentation on a large dataset. The proposed system effectively identifies counterfeit codes with high confidence, with validation and test scores of up to 99.87% and 99.55% respectively, even when images are distorted, cropped and noisy. Practical implications of the research will be explored, highlighting possible applications for product authentication, anti-counterfeit strategy, and document verification. The combination of neural embedding VGG19 and triplet loss results in improved detection accuracy, enhancing the security and reliability of QR code-based systems. This approach offers a strong answer to the increasingly difficult problems of counterfeit detection in the modern digital age, therefore reflecting a major progress in the area of security and authentication technology. It improves the dependability and integrity of digital systems in addition to solving the technological challenges related to information and counterfeit product identification.

Segmentation of Brain tumors refers to one of the most challenging problems in analyzing a medical image. To establish accurate brain tumor region delineation, brain tumor segmentation is used. Feature extraction refers to one of the basic steps in the processing of an image that aids classification. Various features’ kinds are extracted from MRI images. The traditional classifiers of Machine learning need hand-crafted features that are time-consuming and susceptible to errors made by humans. Against this, Deep learning is too powerful in terms of feature extraction and has already been extensively applied for the aims of classification. The presented technique combines multiple feature extraction strategies (integrating deep and shallow features). Thus, shallow methods extract image features manually and based on prior knowledge from the image using image processing methods. Deep approaches use machine learning and neural networks to automatically extract image information. The proposed method of this paper for image segmentation is based on the combination of the PSO evolutionary algorithm with the K-means clustering algorithm. For image processing, the K-means algorithm works perfectly. In the presented technique, PSO is applied to identify the optimum centers of the cluster. K-means algorithm outcome depends on the basic solution and has good convergence. In Google Colab, the proposed model used the Python programming language to diagnose precision, accuracy, sensitivity, error matrix, and receiver operating characteristic (ROC). Outcomes illustrate that the presented model has a high performance in brain tumor diagnosis. That obtains an accuracy of 87.94% and an average precision of 88.35%.

The Traditional encryption methods, such as AES, can be too slow for large video files, ‎necessitating the development of faster and more efficient solutions for real-time video security. ‎This research aims to enhance video security during transmission by developing a novel video ‎encryption algorithm that is both fast and robust, specifically designed to handle large ‎video datasets efficiently. The proposed video encryption algorithm integrates lightweight streaming ‎algorithms for enhanced speed, modern cryptographic techniques, and chaotic maps for ‎heightened security. The encryption process involves three key steps: Dividing the video into ‎frames and scrambling/encrypting each frame, employing three innovative techniques: vertical ‎image cutting to create two halves, utilizing the Lorenz Chaotic System map in the scrambling and ‎shifting steps, applying a bioinformatics technique (tRNA) for encrypting red and green color ‎channels, and applying an XOR operation between the output of step 2 and a key generated via a ‎chaotic map. Unlike traditional methods, the proposed approach processes all frames without ‎requiring separation procedures. Evaluation based on metrics such as PSNR, MSE, NPCR, SC, ‎similarity, UACI, histogram, EQ, and entropy demonstrates the algorithm\'s robustness. ‎Experimental results reveal that the proposed algorithm achieves faster encryption times and ‎superior encryption effectiveness compared to AES. The system\'s complexity is bolstered by ‎requiring extensive chaos parameters, the Lorenz Chaotic System map, visual cryptography ‎techniques, and advanced processes for generating S-boxes and keys. High-quality ‎security metrics validate the algorithm\'s robustness and effectiveness. ‎The proposed video encryption offers a faster and secure solution for encrypting ‎large video files.

The growing attention towards utilizing salak seeds as a substitute for coffee has increased due to the presence of caffeine and its health-enhancing properties. The present study aimed to determine the optimum roasting conditions for salak seed coffee (SSC) with high total phenolic content (TPC) and desirable lightness (L*) using response surface methodology (RSM). Regular SSC was produced at optimal roasting temperature and time of 189.36 ℃ and 39.54 min, respectively. An instant SSC was produced by hot water extraction at 90 ℃ of regular SSC, followed by freeze drying. Physicochemical and antioxidant properties of regular and instant SSC were compared. The moisture, TPC, pH, and solubility of instant SSC were significantly higher (p &amp;amp;amp;amp;amp;amp;amp;lt; 0.05) than those of regular SSC. Furthermore, instant SSC appeared to be lighter than that of regular SSC. The IC_50 of instant SSC was 0.23 mg/mL, indicating higher antioxidant capacity than that of regular SSC (0.37 mg/mL). There was no significant difference (p &amp;amp;amp;amp;amp;amp;amp;gt; 0.05) in terms of caffeine content between regular and instant SSC. The total number of volatile compounds identified in instant and regular SSC was 140 and 65, respectively. The quantitative descriptive analysis (QDA) results showed that the intensity for all the sensorial attributes (e.g. colour, nutty flavour, bitterness, aftertaste and body) tested was higher in instant SSC. The retention of flavor compounds, enhanced solubility and antioxidant capacity of instant SSC indicated that the commercialization of SSC as a coffee substitute is promising.