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Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30%, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.

Abstract Background Guangxi is the leading sugarcane-producing area in China. Due to the Panama disease outbreak in banana gardens, sugarcane and banana rotation was recommended. A field experiment with the newly released sugarcane cultivar Zhongzhe 1 (ZZ1) was conducted to understand the role of the sugarcane–banana rotation system in shaping the rhizosphere microbiota. Fields in the region possess characteristics of red laterite soil. Results Using Illumina HiSeq sequencing to analyze soil samples’ 16S rRNA V3-V4 region, the preceding banana rotation field had relatively greater bacterial diversity than the monoculture sugarcane field. Proteobacteria, Chloroflexi, Actinobacteria, and Acidobacteria were the dominant phyla, with distinct taxa enriched in each environment. However, the preceding sugarcane monoculture field enriched functional groups related to nitrogen fixation and cellulolysis. Network analysis highlighted contrasting network structures between sugarcane and banana rhizospheres, suggesting differential stability and susceptibility to environmental influences. Furthermore, correlations between soil properties and bacterial alpha-diversity underscored the influence of preceding crops on rhizosphere microbial communities. Conclusion This research enhances our understanding of crop rotation effects on soil microbial ecology and provides insights into optimizing agricultural practices for enhanced soil health and crop productivity. Future studies should explore the underlying mechanisms driving these interactions and evaluate the long-term impacts of crop rotation on soil microbial dynamics. Graphical abstract

Abstract Objective Septic arthritis is an orthopedic emergency and if not evaluated and treated appropriately, it can lead to poor clinical outcomes. Previously several studies have been performed to identify the etiology of septic arthritis in the pediatric population in developed countries. The main objective of our study was to identify the etiology of septic arthritis in children in Qatar in previously healthy and fully vaccinated children. Methods We performed retrospective chart analysis of children presenting to our emergency department between July 2018 and June 2024, who were diagnosed and treated with septic arthritis. The study was conducted at a level 1 pediatric trauma center and the only children's hospital in the country. We used ICD 9 and ICD 10 codes to identify such cases. After using predefined exclusion criteria, children with positive blood cultures, blood titers for Brucella and/or synovial cultures were included in the analysis. Clinical symptoms and signs, ultrasound findings, and culture results were tabulated using descriptive statistics. Results A total of 45 patients were included. The median age of children was 5 years (interquartile range [IQR] 2–10 years). Majority (60%) were male. The most common clinical findings were limping/limitation of joint movement (100%), fever (80%), and swelling of joints (58%). The median C‐reactive protein and erythrocyte sedimentation rate were 94 mg/L and 47 mm/h. The knee and hip were the most common joints affected. The most common causative organisms were Staphylococcus aureus (56%), Streptococcus pyogenes (13%), and Brucella (11%). Pre‐intervention imaging, such as ultrasound and/or magnetic resonance imaging, was performed in 95% of patients. All patients recovered without any complications. One of the limitations of our study is that cases of Kingella kingae septic arthritis may be underreported as polymerase chain reaction (PCR) analysis of synovial fluid was not performed on all patients. Conclusion Gram‐positive cocci, especially S. aureus, remains the most common cause of septic arthritis in vaccinated children. We also identified Gram‐negative bacilli as causative organisms in our study. We suggest including empiric coverage for both Gram‐ and Gram‐negative bacilli when treating children with suspected septic arthritis till culture results are available.

Intraoperative positioning system (IOPS; Centerline Biomedical, Inc) is a novel technology that allows for real-time intravascular navigation of endovascular devices using an electromagnetic field. In this report, we describe the use of IOPS for effective treatment of bilateral common iliac artery aneurysms with endovascular aortoiliac repair using iliac branch endoprostheses. Our experience suggests that this technology has the potential to reduce radiation and contrast use in endovascular procedures, although its application is currently limited. To the best of our knowledge, this is the first reported case of bilateral internal iliac cannulations for iliac branch endoprosthesis placement using IOPS.

Abstract Background Saccharum spontaneum L. is a closely related species of sugarcane and has become an important genetic component of modern sugarcane cultivars. Stem development is one of the important factors for affecting the yield, while the molecular mechanism of stem development remains poorly understanding in S. spontaneum. Phenylalanine ammonia-lyase (PAL) is a vital component of both primary and secondary metabolism, contributing significantly to plant growth, development and stress defense. However, the current knowledge about PAL genes in S. spontaneum is still limited. Thus, identification and characterization of the PAL genes by transcriptome analysis will provide a theoretical basis for further investigation of the function of PAL gene in sugarcane. Results In this study, 42 of PAL genes were identified, including 26 SsPAL genes from S. spontaneum, 8 ShPAL genes from sugarcane cultivar R570, and 8 SbPAL genes from sorghum. Phylogenetic analysis showed that SsPAL genes were divided into three groups, potentially influenced by long-term natural selection. Notably, 20 SsPAL genes were existed on chromosomes 4 and 5, indicating that they are highly conserved in S. spontaneum. This conservation is likely a result of the prevalence of whole-genome replications within this gene family. The upstream sequence of PAL genes were found to contain conserved cis-acting elements such as G-box and SP1, GT1-motif and CAT-box, which collectively regulate the growth and development of S. spontaneum. Furthermore, quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showed that SsPAL genes of stem had a significantly upregulated than that of leaves, suggesting that they may promote the stem growth and development, particularly in the + 6 stem (The sixth cane stalk from the top to down) during the growth stage. Conclusions The results of this study revealed the molecular characteristics of SsPAL genes and indicated that they may play a vital role in stem growth and development of S. spontaneum. Altogether, our findings will promote the understanding of the molecular mechanism of S. spontaneum stem development, and also contribute to the sugarcane genetic improving.

Abstract In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%.

This study aims to pharmacologically validate Haridra Khanda (HK) and Manjishthadi Kwatham (brihat) (MMK) in allergy management using invivo and invitro studies to rationalize the prescription of these two ayurvedic polyherbal drug formulations, which are currently used in Indian government hospitals. Experimental animals received HK and MMK orally from day 0 to day 14 and histamine (1 mg/kg b.w/i.v) and 1 % evans blue (EB) (0.1 mL) via tail vein on day 14. The compound 48/80 (intracutaneous) challenged mice model followed the same technique. The former mimicked acute anaphylaxis and the latter mast cell degranulation. For both models, EB dye leakage was quantified spectrophotometrically to determine vascular permeability. Plasma histamine was measured in Compound 48/80-induced animals using LC-ESI-MS/MS. The guineapig received HK and MMK p.o. and 0.6 % histamine sprayed in a histamine chamber to simulate allergic rhinitis. Blood eosinophil count and sneeze rate were measured in histamine-challenged guineapigs. Goat R.B.C. membrane stability assay (mammalian cell membrane toxicity) and intracellular histamine-induced cytosolic Ca2+ release assay in Chinese hamster ovary (CHO) cells were performed in vitro. For both histamine and Compound 48/80 challenged animals, HK (22.81 % and 14.58 %) and MMK (19.71 % and 22.40 %) significantly reduced EB dye leakage (p

Case Presentation: We present the case of a six-year-old child with autism who presented with persistent vomiting in the setting of a closed head injury (CHI). Computed tomography of the head was normal, but due to persistent vomiting a radiograph of the abdomen was done, which showed multiple, rare-earth magnets in the abdomen. There was no history of witnessed ingestion. These magnets had caused enteroenteric fistula formation leading to persistent vomiting. Discussion: In the setting of CHI, vomiting can be a sign of concussion or intracranial hemorrhage. In cases of CHI where intracranial pathology is ruled out and vomiting still persists, it is important to explore intra-abdominal causes of vomiting, especially in developmentally challenged children as they have higher incidence of unwitnessed foreign body ingestions.

Recent advancements in image processing and computer vision have brought significant transformations in healthcare technology, leading to significant improvements in diagnosis accuracy, cost-effectiveness, and time efficiency. Magnetic Resonance Imaging (MRI) is employed by the radiologist for its remarkable ability to detect even the most subtle brain abnormalities. This study considers a comprehensive analysis of the two prominent object identification frameworks, YOLOv5 and YOLOv7, leveraging state-of-the-art deep learning architectures to classify and detect brain cancers within MRI. The brain tumor dataset encompasses three distinct classes, including meningiomas, gliomas and pituitary tumors. To ensure precise segmentation of the tumor regions, the preprocessing phase incorporates advanced mask alignment techniques. This preprocessed dataset has been used to evaluate the performance of the deep learning models for brain tumor detection and classification. From the numerical results of YOLOv5, it was noticed that a recall score of 0.905 for box detection and 0.906 for mask segmentation, with a precision score of 0.94 and 0.936, respectively. At an IoU threshold of 0.5, both box detection and mask segmentation achieve a mAP of 0.947, whereas, at an IoU threshold of 0.5 to 0.95, they achieve mAPs of 0.666 and 0.657, respectively. In comparison, YOLOv7 exhibits strong performance with box detection accuracy of 0.936 and a mask segmentation accuracy of 0.935. The recall score are 0.904 for box detection and mask segmentation is 0.903. Notably, the mAP result at the IoU threshold of 0.5 are 0.94 for box detection and mask segmentation is 0.941. Over the broader IoU spectrum of 0.5 to 0.95, the mAP was 0.677 for box detection and 0.659 for mask segmentation. To underscore the novelty of the approach, the performance of the proposed framework is systematically compared with established methods such as RCNN, Faster RCNN, and Mask RCNN.

The current methods, while in use, continue to grapple with accuracy and effectiveness concerns. It is imperative to establish dependable solutions capable of distinguishing and categorizing people according to their assistive devices to tackle these issues. People with disabilities, such as those experiencing paralysis, limb deficiencies, or amputations, may encounter issues related to discrimination and inadequate support. Hence, this research was undertaken to detect and track people with conditions like paralysis, limb deficiency (Amelia), or amputation among the differently-abled population. Earlier investigations have predominantly focused on recognizing people and their mobility aids, utilizing a variety of methods such as Fast R-CNN, Faster R-CNN, RGB or RGB-D cameras, Kalman filters, and hidden Markov models. Modern deep learning models, including YOLO (You Only Look Once) and its variations, have gained substantial acceptance in current applications owing to their distinctive architectural designs and performance attributes. In this study, a substantial dataset comprising 4,300 images and 8,447 labels spanning five distinct categories is employed to assess the efficacy of YOLOv8, YOLOv5, and YOLOv7 models in the identification of people with disabilities. The evaluation findings show that YOLOv8, which achieved an overall precision of 0.907, performs better than both YOLOv5 (precision: 0.885) and YOLOv7 (precision: 0.906). Notably, YOLOv8 has the best wheelchair detection precision (0.998). Furthermore, YOLOv8 outperforms YOLOv5 (recall: 0.887) and YOLOv7 (recall: 0.925) in terms of recall performance (recall: 0.943). YOLOv8 achieves the greatest mean average accuracy (mAP@.5) value of 0.951, followed by YOLOv5 (mAP@.5: 0.942), and YOLOv7 (mAP@.5: 0.954). In a similar vein, of the three models, YOLOv8 has the best performance (mAP@.5:.95: 0.713). The analysis of detection time also shows that YOLOv8 performs best, processing 5,597 frames in just 5.9 milliseconds and achieving a remarkable frame rate of 169.49 frames per second.

Sugarcane smut, caused by the fungus Sporisorium scitamineum (Sydow), significantly affects sugarcane crops worldwide. Infected plants develop whip-like structures known as sori. Significant variations in these whip lengths are commonly observed, but the physiological and molecular differences causing these morphological differences remain poorly documented. To address this, we employed conventional microbe isolation, metagenomic, and metabolomic techniques to investigate smut-infected sugarcane stems and whips of varying lengths. Metagenomics analysis revealed a diverse fungal community in the sugarcane whips, with Sporisorium and Fusarium genera notably present (>1%) in long whips. Isolation techniques confirmed these findings. Ultra-performance liquid chromatography analysis (UHPLC-MS/MS) showed high levels of gibberellin hormones (GA3, GA1, GA4, GA8, and GA7) in long whips, with GA4 and GA7 found exclusively in long whips and stems. Among the prominent genera present within long whips, Fusarium was solely positively correlated with these gibberellin (GA) hormones, with the exception of GA8, which was positively correlated with Sporisorium. KEGG enrichment analysis linked these hormones to pathways like diterpenoid biosynthesis and plant hormone signal transduction. These findings suggest that Fusarium may influence GA production leading to whip elongation. Our study reveals fungal dynamics and gibberellin responses in sugarcane smut whips. Future research will explore the related molecular gibberellin synthesis mechanisms.

ABSTRACTSugarcane is a critical sugar and bioenergy crop in China. However, numerous factors, including root rot disease, hamper its yield. Root rot disease is a severe agricultural issue, reducing yield and threatening sustainable crop production. The current study aimed to explore the fungal community structure, identify and characterize the primary pathogen for sugarcane root rot in Guangzhou, China. Eighty-nine samples of sugarcane root, stalk, rhizosphere soil, and irrigation water were collected from five sites in Guangzhou, China. Subsequently, 276 fungal strains were isolated to identify the primary pathogens. The five most common genera identified were Penicillium, Fusarium, Gongronella, Trichoderma, and Cladosporium. Fusarium was more prevalent in the infected soil samples than in healthy ones. Pathogenic assays of the strains revealed that the strain GX4-46 caused 80% of the disease. The strain was confirmed as Fusarium commune through phylogenetic and genome sequence analysis. Rhizosphere soil samples from different regional crops were collected to better understand the fungal community structure and the primary pathogen. We observed a significant presence of Fusarium in irrigation water, indicating that the root rot disease could originate from the irrigation water and then spread as a soil-borne disease. This research is pioneering and one of the most comprehensive investigations on the occurrence and prevalence of sugarcane root rot disease. This study will serve as a reference for expanding the sugarcane industry and a foundation for further exploration and control of root rot.IMPORTANCESugarcane, a significant economic crop, faces challenges due to root rot pathogens that accumulate each year in plants and soil through ratoon planting. This disrupts soil microbial balance and greatly impedes sugarcane industry growth. Symptoms range from wilting and yellowing leaves to stunted growth and reduced seedling tillers. The rhizosphere microbiota plays an important role in plant development and soil health. Little is known about root rot fungal community structure, especially in sugarcane. Here, we focused on exploring the main causative pathogen of root rot in the area alongside a detailed survey of the rhizosphere soil of different severity sugarcane cultivars and rotation crops of the region. To validate the findings, we also investigated the irrigation water of the area. Our study revealed Fusarium commune as the causative pathogen of root rot in the area, primarily originating from water and later as soil-borne. Using Trichoderma can control the disease effectively.

Sugarcane is a globally significant crop for sugar and energy production, and developing high light-efficiency sugarcane varieties is crucial for enhancing yield and quality. However, limited research is available on the screening of sugarcane germplasm with high photosynthetic efficiency, especially with different leaf positions. The present study, conducted in Guangxi, China, aimed to analyze the photosynthetic characteristics of 258 sugarcane varieties at different leaf positions over three consecutive years in field experiments. The results showed significant differences in photosynthetic characteristics among genotypes, years, and leaf positions. Heritability estimates for various photosynthetic parameters ranged from 0.76 to 0.88. Principal component analysis revealed that the first three principal components accounted for over 99% of the cumulative variance. The first component represented photosynthetic efficiency and light utilization, the second focused on electron transfer and reaction center status, and the third was associated with chlorophyll content. Cluster and discriminant analysis classified sugarcane genotypes into three categories: high photosynthetic efficiency (HPE) with 86 genotypes, medium photosynthetic efficiency (MPE) with 60 genotypes, and low photosynthetic efficiency (LPE) with 112 genotypes. Multi-year trials confirmed that HPE sugarcane genotypes had higher single-stem weight and sucrose content. This study provides valuable insights into the photosynthetic physiological characteristics of different sugarcane varieties, which can contribute to further research regarding high yields and sugar breeding.

The excessive use of natural resources and irresponsible behavior of tourists have posed severe threats to sustainable destination environment. In response, destination managers have been paying more attention to environmental protection and focusing on strategic ways to achieve a sustainable destination environment. Objective of the study is to analyze the influence of destination brand equity (DBE) on tourists' environmentally responsible behavior (ERB). The study considered four dimensions of DBE, including perceived destination awareness, quality, image, and loyalty. Further, the study explored the interrelationship among the dimensions of DBE and considered perceived destination loyalty (PDL) as a mediator. Further the study explained tourists' ERB with the serial mediation of perceived destination trust (PDT) and moderation of destination green equity (DGE). The study employed a self-administrative survey method, and data were gathered from 911 respondents who attended selected nature parks. Results for DBE indicated insignificant effect of perceived destination awareness (PDA) on tourists' ERB, while perceived destination quality (PDQ) and perceived destination image (PDI) have a significant impact on tourists' ERB. Further results indicated that PDL has significantly mediated all three dimensions of DBE. The results also revealed that PDT and PDL serially mediated the effect of PDA, PAQ, and PDI on tourists' ERB. Finally, moderation results indicated that DGE significantly moderated the effect of PDQ, PDI, and PDL on tourists' ERB. The study has enhanced the existing literature on destination branding and tourists' ERB through Stimuli‐Organism‐Response (S‐O‐R) model. The study has proposed significant branding strategies for destination management to influence tourists' ERB. The study has also suggested strategies for policymakers and tourism departments. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) enhances and optimizes operations and product quality by reducing expenses and preserving critical factory components. The IIoT can also be integrated into the processes of small and medium-sized enterprises (SMEs). However, several factors and risks have discouraged SMEs from adopting the IIoT. This study aims to identify the factors influencing IIoT adoption and address the challenges by conducting semi-structured interviews with experienced stakeholders in SME factories. Group quotations and thematic analysis indicate essential themes from these interviews, suggesting two primary categories, human- and machine-related factors, that affect implementation. The main human-related factor is the decision making of high-level management and owners to implement the IIoT in their plants, which requires skilled individuals to achieve IIoT solutions. Machine-related factors present several challenges, including device compatibility-, device management-, and data storage-associated issues. Comprehending and addressing these factors when deploying the IIoT can ensure successful implementation in SMEs, maximizing the potential benefits of this technology.

Abstract The maize (Zea mays L.) is a monocot that is a member of the Poaceae family and a valuable feed for livestock, human food, and raw material for various industries. The halothermal time model determines how plants respond to salt (NaCl) stress under sub-optimal conditions. This model examines the relation between NaClb (g), GR, GP, salinity and temperature stress on germination of seeds dynamics in various crops. Five constant temperatures i.e. 20, 25, 30, 35, and 40 °C and five ψ levels (NaCl concentrations converted to ψ − 0, − 0.2, − 0.4, − 0.6, and − 0.8 MPa) were used in this experiment. In light of the results, the maximum halo-thermal time constant value was recorded at 35 °C temperature, while maximum germination percentage was detected at 30 °C in the controlled condition. Moreover, the lowermost value was recorded at 20 °C at − 0.8 MPa osmotic potential. The highest CAT, APX, and GPX activities were recorded at 15 °C at − 0.8 MPa, while the lowest values were observed for 0 MPa at 30 °C temperature. In conclusion, by employing the halo thermal time model, the germination of maize variety (var.30W52) was accurately predicted for the first time under varying levels of temperature and osmotic potentials.

In this study, we engineered a sub-70 nm nanocomposite of ZnO/Zn2TiO4 using a low-temperature solution-phase method with titanium isopropoxide and zinc acetate as precursors, and isopropyl alcohol and water as solvents. The investigation focused on nanocomposite growth by varying precursor and surfactant concentrations and their efficiency within different pH ranges. All three ZnO/Zn2TiO4 nanocomposites exhibited hexagonal wurtzite ZnO and Zn2TiO4 structures. The crystallite size in these nanocomposites ranged from 39.50 nm to 62.67 nm for ZnO and 21.24 nm to 26.15 nm for Zn2TiO4. Morphological observations using FESEM revealed the formation of dispersed cotton packet-like nanocomposites with sizes ranging from 18 to 350 nm. FTIR analysis showed peaks indicative of Ti–O and Zn–O bond formation, and EDX spectrum confirmed the presence of Ti, O, and Zn. UV spectrums and photocatalytic investigations confirmed the successful formation of ZnO/Zn2TiO4 nanocomposites with notable photocatalytic degradation efficiency for methylene blue dye under various conditions. These findings suggest the potential applicability of the synthesized nanocomposites for environmental pollutant degradation.