Abstract Glioma brain tumors have similar textural patterns to other tumors, making their detection and segmentation a challenging process. The approach of the Modified Tumor Detection System (MTDS) is presented in this study to identify and categorize brain images of gliomas from images of healthy brains. The Spatial Gabor Transform (SGT), feature calculations, and deep learning structure comprise the training work flow of the suggested MTDS technique. The features are computed from the glioma brain image dataset images and the normal brain image dataset images and these features are fed into the classification architecture. In this paper, the proposed IVGG architecture is derived from the existing Visual Geometry Group (VGG) architecture to improve the detection rate of the proposed system and to decrease the computational time complexity. The testing work flow of the proposed system is also consist of SGT, feature computation and the IVGG architecture to produce the classification result of the source brain images into either normal or glioma. Furthermore, the Morphological Segmentation technique has been used to find the tumor locations in this glioma image. Two separate brain imaging datasets have been used in this study to evaluate and validate the suggested MTDS's performance efficiency. BRATS Imaging 2020 (BI20) and Kaggle Brain Imaging (KBI) are the datasets. Analysis of the performance efficiency has been done in relation to the Jaccard index, recall, precision, and detection rate.

Abstract This work reports a real-time PCR assay to specifically detect the presence of gluten in complex food matrices and to carry out an in-silico prospection of primers used in scientific research. The primers used were “tritprglut” and “Planta 18S” (reference gene), which had mean quantification cycle values (Cq) of 34.30 and 16.98, respectively. The real-time PCR protocol was validated in different meats (beef, chicken, pork, horse and lamb) with an average Cq of 25.69. Tests to verify fraud in industrialized foods were carried out with the following products: cereal bars, chocolate, crackers and two types of snacks. All foods complied with the information contained on the label, except for the cereal bar that was identified as “may contain gluten” and had a “high content” concentration (1,925 mg/kg). The LD value was 36 cycles and the LQ was 60 mg/kg, being within the “low content” classification range. The in-silico tests were performed using two software, MFE and NETprimer, and the content parameters GC, Tm (°C), ∆G (kcal/mol), dimer formation and hairpins. The “Wheat-w-Gliadin” primer showed the best average parameters: size= 24 bp; GC= 44%; Tm = 62.5 °C; ∆G= -32.25 kcal/mol; no dimer or hairpin formation; and a maximum primer rating (100). There were differences in results between the software used. The results highlight the potential of the real-time PCR technique in detecting gluten and/or allergens in foods with a complex matrix, such as chocolate and cereal bars tested in this study, proving to be sensitive and robust to detect the presence of potentially high gluten concentrations. harmful for celiac consumers.

Abstract To ensure the quality of the energy in distribution networks, configurations of voltage regulators (VRs) and Shunt Capacitor Banks (BCshunts) are traditionally used along the feeders. Series Capacitors Bank (BCseries) along the feeders can also help to improve the power quality. In this paper, a strategy is proposed based on a mathematical formulation for an optimization problem where the aim is to allocate and size BCshunts and VRs (as traditionally implemented in the electricity sector) together with BCseries in distribution networks. These devices are allocated using Genetic Algorithm together with Multiperiod Optimal Power Flow. Using the proposed strategy, it is analyzed the network performance by studying: (i) the allocation of VRs and BCshunts simultaneously, and (ii) the allocation of VRs, BCshunts and BCseries simultaneously. The objective function is based on the minimization of active power loss costs, penalties for voltage violations, and equipment costs. Results are presented for a 90-bus radial distribution system which show that the use of BCseries is suitable for feeders with abrupt load variations, and that this approach competes positively with the simultaneous allocation of VR and BCshunt devices, with shorter investment amortization times and improved quality indices.

Abstract This study aimed to develop a bromelain-based gel and evaluate its effects in a ligature-induced periodontitis model in rats. The bromelain-based gel was formulated in two concentrations (1% and 10%). Ten animals were used per group: Control, Periodontitis, Periodontitis + Neutral Gel, Periodontitis + 1% Bromelain Gel, Periodontitis + 10% Bromelain Gel and Periodontitis + 2% Chlorhexidine Gel. The clinical parameters of tooth mobility (TM), gingival bleeding index (GBI) and probing pocket depth (PPD) were evaluated and, after euthanasia, gingival and liver tissue were collected by biopsy for biochemical evaluation of myeloperoxidase (MPO), malonaldehyde (MDA), glutathione (GSH) and from the jaws for morphometric analysis of alveolar bone height. The clinical parameters of MT, GBI and PPD of the groups treated with bromelain gel showed a significant reduction (MT = 67.04%, GBI = 67.72% and PPD = 41.22%) compared to the group with periodontitis. The results for MPO dosage in the gingival tissue showed statistically significant differences between the groups when compared to the group with periodontitis, MDA and GSH showed promising results in the groups treated with gels in the dosages of gingival and liver tissue. The results for ABL showed significant differences when compared to the groups that received gel treatment. Therefore, the bromelain orabase gel evaluated in periodontitis showed a positive response for the following parameters TM, GBI and PPD, MPO, MDA and ABL of the oral tissues, preventing alveolar bone resorption caused by the disease, highlighting the potential as an adjuvant treatment of periodontitis, in addition to reducing the systemic effects on the liver tissue.

Abstract Heat treatment used in milk powder processing cannot always inactivate spore-forming microorganisms. Toxin deterioration and production by some species are significant concerns for the dairy industry, making the identification of these microorganisms essential for solving problems with the quality of the products. Given this scenario, this study sought to quantify and identify the predominant species in milk powder, aiming to know the isolated genetic varieties. Fourteen samples were analyzed for aerobic/anaerobic, mesophilic, and thermophilic bacterial spore presence, totaling 52 analyses. Spore contamination was verified in all samples analyzed, and the thermophilic spores had the highest contamination levels. 16S rRNA sequencing revealed 47 isolates: Weizmannia coagulans (21%), Anoxybacillus flavithermus (5.3%), Clostridium butyricum (5.3%), Bacillus amyloliquefaciens (2.6%), Geobacillus stearothermophilus (2.6%), Clostridium pabulibutyricum (2.6%), Clostridium sporosphaeroides (2.6%), Bacillus licheniformis (2.6%), Bacillus sp. (34%), Geobacillus sp. (15.8%), and Clostridium sp. (5.3%). The results indicate the need to establish control measures to improve the hygiene and processing conditions of powdered milk, thereby ensuring product quality and consumer safety.

Abstract In the present study, a cost-effective and eco-friendly approach is being explored to remove the harmful dye Congo red from synthetic wastewater, using an equatorial strain of Rhodococcus biphenylivorans. The immobilized cells’ Congo red-decolorizing activity in calcium alginate showed an increase of 217% compared to the free cells. By improving the alginate concentration to 4% (w/v), the bead diameter to 3 mm and using 50 beads, a 67% decrease in the time required for complete decolorization was accomplished. Both free and immobilized cells followed first-order kinetics for the elimination of Congo red, with immobilized cells displaying a decolorization rate 3.2 times greater than that of free cells, resulting in an 84% reduction in half-life. The immobilized cells displayed the capability to remove 0.10 g/L Congo red without any significant decline in their efficiency for up to twelve cycles, offering an economic advantage due to their reusability. Examining the decolorized products of Congo red showed no hindrance in the germination of Triticum aestivum and Vigna radiata, indicating the effective removal of all toxic components from the original dye via the biological method. Therefore, this strain has significant potential as a biological agent to eliminate wastewater contaminated with Congo red efficiently.

Abstract Z. kleinii is an endemic species in Brazil. No reports of popular use, regarding its chemical composition or biological activities were reported to date. The study aimed to investigated the chemical composition of the essential oil of Zanthoxylum kleinii leaves and evaluate its antibacterial activity against Escherichia coli, Pseudomonas aeruginosa, Enterococcus faecalis and methicillin-sensitive Staphylococcus aureus. The essential oil was extracted from fresh leaves by hydrodistillation method, using a Clevenger apparatus, having a yield of 0.12% (v/m) volume of essential oil in relation to the amount of plant material. Quantitative analysis was performed by GC-MS and the results showed that the essential oil contains 30 compounds. The major compounds are monoterpene hydrocarbons, including β-phellandrene (37.80%), α-pinene (13.32%) and myrcene (10.05%). The essential oil showed no antibacterial activity against the strains tested (E. coli, P. aeruginosa, E. faecalis and S. aureus).

Abstract In the production of kale, nitrogen is one of the nutrients that the plant needs in greater quantity. Its application must be divided throughout the cycle, due to the loss by leaching. The objective of the research was to evaluate the effect of splitting organic fertilizers in topdressing on the production of kale. The experimental design was randomized blocks, with four replications, with seven treatments being evaluated in a 2 x 3 + 1 factorial scheme, the first factor was the fertilizers (castor bean cake and bokashi) and the second the application intervals (7; 14 and 21 days), in addition to the control without topdressing fertilization. The amount of each fertilizer was calculated to provide the same dose of N (40 kg ha-1) every 21 days. The following characteristics were evaluated: plant height (PH), stem diameter (SD), number of leaves per plant (NLP), leaf fresh matter mass (LFMM), leaf length at the beginning (LLB), middle (LLMe) and end (LLE) of the cycle and “SPAD” index. There was no interaction between the factors, for any characteristic evaluated. For all traits, the values observed in control was lower than treatments with topdressing. The increases of treatments of factorial in relation to the control were 37.8% (plant height); 31.8% (SD); 157.1% (NLP); 213.5% (MLFMM); 7.6% (LLB); 80.2% (LLMe) and 126.6% for the “SPAD” index. It is recommended to use castor bean cake and split it every 21 days as it results in less labor for application.

Abstract The precision agriculture discipline swiftly adopted blockchain as a key technology in numerous applications. From just smart farms to an internet of smart farms in precision farming, the Internet of Things (IoT) and blockchain is going to boost crop yield in precision agriculture. This proposed methodology presents a comprehensive approach for secure data sharing in precision agriculture. It integrates advanced techniques through multiple layers, including Data Collection, Data Preprocessing, Intelligent Analysis, Security, and Blockchain. IoT sensors collect data on soil moisture, temperature, humidity, crop health, and weather conditions. Preprocessing involves removing outliers, normalizing values, and extracting relevant features, then the required features are selected using the hybrid Sand Cat with Fire Hawk Algorithm (HSCFHA) which is the combination of standard Fire Hawk Optimization (FHO) and Sand Cat Swarm Optimization (SCSO). Deep learning models like Capsule Neural Network (CapsNet), Recurrent Neural Network (RNN), and Bi-directional Long Short-Term Memory (Bi-LSTM) networks provide valuable insights for prediction, classification, and anomaly detection. The blockchain layer establishes a decentralized and tamper-resistant ledger for transparent and immutable data transactions. Smart contracts automate and enforce data sharing rules. By incorporating optimized clustering, deep learning models, hybrid encryption, and blockchain technology, this framework empowers precision agriculture while ensuring secure and efficient data sharing with 98.31% of accuracy.

Abstract The big problems of rubber (Hevea brasiliensis) horticulture are the present of pathogenic fungus, Rigidoporus microporus, which causes white root disease. Endophytic bacteria have potential to inhibit the pathogenic fungus. This study aims to isolate, identify and evaluate the diversity of endophytic bacteria that can suppress the growth of R. microporus on rubber. Identification of endophytic bacteria based on morphology characters and molecular marker using 16S rRNA primer. In vitro assessments of biological agents were carried out using fermentation medium. In vivo tests were carried out in the nursery using rubber. The isolation results obtained 55 isolates of endophytic bacteria. Isolate B49 from bark showed the highest percentage of inhibition against the growth of R. microporus (82.47%), then followed by isolates D8 (81.44%) and D20 (72.16%) from leaves, and A27 (61.86%) from roots. The 16S rRNA gene sequence of the D8 isolate exhibited 98% similarity to Bacillus paramycoides, D20 isolate was shown 95% similarity to Acinetobacter nosocomialis, A27 isolates identified as Enterobacter cloacae (99%), and B49 identified as Bacillus cereus (99%). In addition to inhibiting the growth of R. microporus, several endophytic bacterial isolates used in this study were shown to be able to solubilize phosphate thus suppress white root disease. Bacillus D8, Acinetobacter D20, and Bacillus B49 can be used to control white root disease in rubber plantations. Selected bacteria isolated from rubber have the potential to be used as a biocontrol and as a biofungicide against white root disease caused by R. microporus.

Abstract Auxiliary bioartificial liver (BAL) is a promising direction for supporting liver functions in acute liver failure. However, clinical trials of BAL have not shown convincing results so far. We present a prototype of a hybrid biocomposite plasma filter containing living immortalized hepatocytes. This filter is designed to integrate into the biological circuit of the extracorporeal blood purification system with the aim of mimicking some functions of the liver. Due to the presence of hepatocytes in the filter, adequate metabolism of plasma substrates is ensured when blood contacts with special perforated membranes. We used HepG2 cell line as working cells. For evaluating the performance of the filter, we applied plasma from patients with liver failure who had abnormalities in biochemical indicators of blood. We analyzed concentrations of albumin, total and direct bilirubin and urea as criteria for effectiveness. Our results indicate normalization of these parameters after passing plasma through 7 filters and improvement after passing through 1 filter. This confirms the effectiveness of our prototype and creates a basis for developing a new extracorporeal BAL.

Abstract High-definition images can provide valuable reference for research in fields such as medical image analysis and machine vision. Therefore, image feature enhancement is performed to obtain high-quality images. Conventional methods for image feature enhancement extract features only from a single scale, resulting in low image information entropy. To address this issue, a fuzzy image feature enhancement method using mathematical morphology and deep learning (FIFEMD) is proposed. First, a near-infrared transmission light source is used as the main light source to construct a capture platform, and the grayscale values are normalized and filtered. Second, basic mathematical morphology operations are employed to remove noise points in the original image, and a separable residual dense block is used to extract multi-scale features from the image. Finally, the generated image features are fused and reconstructed using the Hessian matrix method to achieve image feature enhancement. Experimental results demonstrate that the images processed using the FIFEMD method have higher information entropy values, with entropy values above 6.75. The processing time is approximately 2.5 s, indicating high efficiency. Therefore, the FIFEMD method can achieve better image feature enhancement.

Abstract Genetic diversity plays a critical role in crop improvement. Radish is one of the important vegetable crops of Rabi season, however, there is a lack of information about radish germplasm resources available in Pakistan. The present research was carried out to characterize radish gene pool on the basis of qualitative and quantitative traits. Twelve genotypes of radish comprised of local and exotic sources were evaluated for genetic variability through morphological, quality, and quantitative traits. Analysis of variance depicted maximum variation among genotypes for all studied straits. High genotypic co-efficient of variance (GCV) value (>20%) in all traits indicates that these traits can be improved through selection. The estimate of genotypic co-efficient of variance (GCV) in present research was closer to phenotypic co-efficient of variance (PCV), suggesting the key role of genotype as compared to the environment. However, high heritability along with genetic advance (as % of mean) among studied genotypes suggests that the selection of studied traits simply on phenotypic basis can also be effective for the breeding program. Cluster analysis based on morphological traits, grouped genotypes into three clusters and cluster I, contained the maximum number of genotypes. In correlation analysis, significant positive correlation among plant fresh weight, root length, root weight, number of leaves and yield can be directly used a selection criteria. Studying of variability in qualitative traits through frequency distribution exhibited maximum variation for root shape and root base. This study showed considerable variability exists in studied genotypes that can further be used for the improvement of radish breeding.

Abstract The Trust-Aware Aggregation Authentication Protocol for SIoT Networks is a security process intended for SIoT platforms. It concentrates on ensuring the reliability of communication and data aggregation between interrelated IoT devices. This protocol deploys authentication systems for verifying the identities of devices and integrates trust-aware mechanisms to estimate the trustworthiness of data exchanged from the social environment of SIoT. By establishing a trustworthy and secure communication structure, this protocol improves the entire integrity and security of SIoT networks, addressing potential vulnerabilities connected with social communications between IoT devices. Therefore, this study develops an enhanced Trust Management using Locally Weighted Salp Swarm Algorithm with Deep learning (ETM-LWSSADL) technique for SIoT Networks. The ETM-LWSSADL technique computes direct and indirect trust values and is assessed depending upon different weighing factors for maximizing the application performance and creating a secure data transmission process. During authentication process, when the SIoT device with total trust value (TTV)is not greater than the threshold value (THV) or authentication token is invalid, the gateways then disregard the node. Besides, bidirectional gated recurrent unit (BiGRU) model is applied to generate a THV on collected traffic data. Moreover, the ETM-LWSSADL technique exploits the LWSSA technique for optimum hyper parameter selection of the BiGRU algorithm. To highlight the enhanced performance of the ETM-LWSSADL methodology, an extensive range of simulations can be involved.

Abstract Green beans are a legume used in human diets throughout the world. One of the most important factors limiting the yield of bean crops in the world is high lime. Two registered varieties (Beyzade and Lida) resistant to high lime and three commercial varieties (Ribera, Albeni, and Garrafol) which are superior in terms of some agricultural characteristics were used as materials. In this study, 20 F1 hybrid combinations were obtained from green bean varieties Beyzade, Lida, Riberia, Albeni, and Garrafol using full diallel crosses. The effects of GCA and SCA on the traits examined in the study were found to be important and the most suitable parent and cross combinations for green bean breeding were identified. The study found that additive genes can effectively inherit grain yield and plant height, while non-additive genes can effectively inherit other traits studied. Garrafol had the highest GCA for grain yield. The “Lida x Garrafol” cross exhibited high SCA effects for seed yield per plant. These results indicate that sufficient levels of genetic variation exist in the study population for the agricultural traits observed in this study.