Superior Value Research Articles

Diagnostic performance of Sonazoid-enhanced CEUS in identifying definitive hepatocellular carcinoma in cirrhotic patients according to KLCA-NCC 2022 and APASL 2017 guidelines

ObjectiveThis study aims to assess the diagnostic performance of Sonazoid-contrast-enhanced ultrasound (CEUS) in identifying definitive HCC within hepatic nodules in cirrhotic patients, comparing the KLCA-NCC 2022 and APASL 2017 diagnostic guidelines.Materials and methodsThis retrospective study analyzed cirrhotic patients who underwent Sonazoid-CEUS for liver lesion evaluation between October 2019 and October 2023. HCC diagnosis was based on the KLCA-NCC 2022 and APASL 2017 guidelines. Inter-reader agreement on CEUS imaging features and the diagnostic accuracy of the guidelines were evaluated. Sensitivity and specificity comparisons were made using McNemar’s test.ResultsAmong 400 patients with 432 lesions, CEUS showed excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects. The KLCA-NCC 2022 criteria notably enhanced sensitivity to 96.2%, with specificity and accuracy of 93.8% and 95.8%, respectively. APASL 2017 achieved the highest sensitivity at 97.8%, although specificity dropped to 46.9%, resulting in an accuracy of 90.3%. The KLCA-NCC 2022 guidelines demonstrated significantly higher specificity than APASL 2017 (p < 0.001), while APASL 2017 exhibited the highest sensitivity at 97.8%. Notably, the KLCA-NCC 2022 guidelines also demonstrated an impressive positive predictive value of 98.9%.ConclusionSonazoid-enhanced CEUS, particularly when applied using the KLCA-NCC 2022 guidelines, is an effective diagnostic tool for HCC.Critical relevance statementPerfluorobutane CEUS, particularly in accordance with the KLCA-NCC 2022 guidelines, emerges as a valuable adjunct for diagnosing HCC in cirrhotic patients. It demonstrates superior positive predictive value and specificity compared to APASL 2017, underscoring its potential as an effective diagnostic tool.Key PointsContrast-enhanced (CE)US using Sonazoid with KLCA-NCC 2022 guidelines is highly effective for HCC diagnosis.KLCA-NCC 2022 criteria showed high accuracy, 96.2% sensitivity, and 98.9% PPV.CEUS demonstrated excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects.Graphical

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

High-Precision and Lightweight Model for Rapid Safety Helmet Detection

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications.

Node Importance Estimation for Knowledge Graphs Based on Multi-Perspectives Attention Fusion Mechanism

The advent of the Internet has led to the emergence of multi-source heterogeneous knowledge graphs, which have become a crucial means of storing and disseminating knowledge. Node importance estimation is a technique that is employed extensively in a number of fields, including recommender systems, intelligent search and resource allocation. This study introduces a Multi-perspective attention mechanism Fusion Algorithm for the mapping of multi-perspective features of knowledge graphs to Node Importance Estimation (MFA-NIE). First, structural embedding features, relational predicate features, and attribute features (both textual and quantitative) are established for the nodes. Subsequently, an enhanced attention mechanism is employed to extract, compress, and fuse these features. The fused hidden layer vector is employed in the design of a key-based attention mechanism, which enables the propagation of messages from neighboring nodes to the source node. This process results in the iterative updating of the source node’s hidden features. Finally, TOPSIS centrality, based on the topology of the source node, is employed to dynamically adjust the mapping between the fused features and node importance. Experiments were conducted on real-world, large-scale, multi-source, heterogeneous knowledge graphs, comparing the MFA-NIE algorithm with traditional and advanced baseline algorithms, including PR, PPR, GENI, RGTN, CLINE, MCRL, and others. The results demonstrate that the MFA-NIE algorithm significantly improves effectiveness and accuracy, showcasing its superiority, versatility, and practical application value.

Corporate Boards and Firm Value: Evidence from Nigeria

Aim: The basis for the formation of the corporate board is to, among others, give strategic direction to the firm, monitor the activities of the salaried agents and reduce the likelihood of disincentive to investment. The aim of this study is to examine the efficacy of corporate boards in relation to the value of firms listed in Nigeria. Study Design and Methodology: It is a cross-sectional research design that empirically examined the influence of board mechanisms and Tobin’s Q of firms listed on the Nigerian Stock Exchange. The study used annual data of a panel sample of 85 firms from 2004 through 2023, It applied a Generalized Least Squares (GLS) econometric technique. Place and Duration of Study: This study examined the role of the boards of firms listed on the Nigerian Stock Exchange between 2004 and 2023. Results: The findings argued in favour of smaller boards, increased board independence and higher market capitalisation since they showed significant improvements in value of the firms in the sample. The findings imply that smaller boards that are highly independent tend to have better corporate governance and thus, superior firm value. Conclusion: In conclusion, the results advocate that the regulatory agencies and boards of the studied firms enact policies that will encourage minimum board sizes that are sufficiently independent.

Comparative assessment of extracellular volume metrics in predicting adverse outcomes in patients with transthyretin amyloid cardiomyopathy

Abstract Introduction The indexed total extracellular volume of the myocardium (iECV) has recently been proposed to be superior in predicting adverse outcome in severe aortic stenosis as compared to conventional extracellular volume (ECV). So far, no study has assessed the comparative predictive performance of both measures in transthyretin amyloid cardiomyopathy (ATTR-CM). Aim We aimed to assess and compare the predictive performance of conventional ECV against iECV in determining adverse outcomes in patients with ATTR-CM. Methods From 2012 to 2023, patients with ATTR-CM undergoing contrast cardiac magnetic resonance (CMR) imaging prior to any specific treatment were enrolled. Both, conventional ECV and iECV were quantified on CMR alongside left ventricular volumetrics and late gadolinium enhancement. Adverse outcome was defined as a composite endpoint of hospitalization for heart failure and/or death. Results Analysis of 178 patients with ATTR-CM (82% males, mean age 77±8 years) revealed a mean conventional ECV of 47±13% and a mean iECV of 46±21 mL/m2, with a median follow-up of 2.5 years (IQR: 1.00-4.08 years). Conventional ECV was significantly associated with the composite endpoint (HR=1.03, 95%CI=1.01–1.05, p=0.006), outperforming iECV (HR=1.01, 95%CI=1.00–1.02, p=0.010). On multivariate analysis, adjusted for age, sex and left ventricular function, conventional ECV remained associated with the composite endpoint (p=0.002), while iECV demonstrated a slightly less significant association (p=0.003). Kaplan-Meier analysis, stratified by conventional ECV tertiles, revealed that patients in the highest tertile (ECV ≥53%) demonstrated the highest event rate (p = 0.043 by log rank test, see Figure 1A), in contrast to iECV (p=NS, Figure 1B). Conclusions High conventional ECV, among CMR fibrosis markers, is linked to heart failure hospitalizations and death in ATTR-CM, demonstrating superior prognostic value compared to iECV. Conventional ECV measurements may offer enhanced risk stratification for patients with ATTR-CM.

Cardiac chamber three-dimensional volumes and deformation predict clinical outcomes in chronic mitral regurgitation

Abstract Background In patients with mitral regurgitation (MR), cardiac remodeling assessed by two-dimensional (2D) echocardiographic linear indexes is highly variable and not suitable for individualized risk assessment. Purpose To evaluate whether measurements of myocardial strain and left atrial (LA), left ventricular (LV) and right ventricular (RV) three-dimensional (3D) volumes improve risk assessment in MR. Methods In the prospective 3D Echocardiography and Cardiovascular Prognosis in Mitral Regurgitation study (3D-PRIME), 176 patients with significant (moderate or greater) MR were investigated with 2D/3D echocardiography. Referral criteria for mitral intervention followed current guidelines. LA remodeling was assessed by the maximum and minimum 3D LA volume (3D-LAV), peak reservoir strain (LASr), and stiffness (i.e. the ratio: mitral peak E-wave divided by the annular e’ velocity (E/e´) / LASr). LV and RV were considered dilated in case of increased 3D end-diastolic (LV&amp;gt;=86/74 and RV&amp;gt;=87/74ml/m2 in men/women) and end-systolic volumes indexed for body surface area (LV&amp;gt;=41/33 and RV&amp;gt;=44/36ml/m2 in men/women). Peak global longitudinal strain was measured in the LV (LV-GLS) and the RV free wall (RV-FWS). The primary outcome was a composite of mitral valve surgery, percutaneous mitral edge-to-edge repair and death. Results During a 14 [8-24] months follow-up, the patients (70±13 years, 39% women) experienced 73 events including 14 deaths. In univariate survival analyses, LV and RV dilatation, but not LV-GLS and RV-FWS, were associated with the primary outcome (Figure) and had superior predictive value to 2D volumes and diameters (p&amp;lt;0.05). On contrary, both atrial size and function predicted outcome, with 3D-LAV (HR 1.01, 95% CI 1.00-1.01), LASr (HR 1.03, 95% CI 1.01-1.06) and increased LA stiffness (Figure) all significant predictors of events (p&amp;lt;0.05). In multivariate Cox analysis with adjustment for age, sex, body mass index and MR etiology, dilated LV (HR 2.6, 95% CI 1.5-4.4), dilated RV (HR 2.0, 95% CI 1.1-3.6) and increased LA stiffness ((HR 2.4, 95% CI 1.3-4.6) were independently associated with higher rate of events (p&amp;lt;0.05). Conclusion In patients with moderate or greater MR, increased cardiac chamber 3D volumes and LA stiffness, as well as impaired LA reservoir strain are associated with higher risk of disease progression towards mitral valve intervention and death. Our findings indicate that assessment of both cardiac chamber 3D size and LA deformation can contribute to increased diagnostic and prognostic precision in patients with MR.

Mechanical and Geometric Characterization of a Novel 2-Ply Vacuum-Pressed Biological Scaffold Patch Design for Posterior Mitral Valve Reconstruction.

To assess the mechanical properties of small intestinal submucosal extracellular matrix (SIS-ECM) iterations and choose the optimal version for evaluating functional geometrics after posterior mitral valve reconstruction. Four SIS-ECM versions (2- and 4-ply vacuum-pressed and lyophilized) underwent uniaxial tensile testing. A posterior mitral valve reconstruction patch was developed based on MRI scans (n = 5). Posterior mitral valve reconstruction using 2-ply vacuum-pressed SIS-ECM was performed (n = 7), and geometrics were evaluated using a modified left heart simulator. The vacuum-pressed iterations displayed superior maximum stress values compared to lyophilized (2-ply: median [IQR], 15.8 [15.2-19.0] vs 7.9 [7.3-8.3] MPa, p < 0.001; 4-ply: median (IQR), 15.8 -[14.6-22.0] vs 7.9 [7.6-8.4] MPa). All reconstructed valves were competent with preserved total leaflet area, but individual leaflet segment areas were redistributed. Posterior mitral valve reconstruction with our 2-ply vacuum-pressed SIS-ECM patch design was feasible in vitro. Further in vivo evaluation is warranted.

Beyond 2D echocardiography: A novel multiparametric assessment of right ventricular dysfunction in transcatheter tricuspid valve repair

Abstract Background Patients with severe tricuspid regurgitation (TR) are heterogenous and complex in terms of etiology, comorbidities and state of disease. Right ventricular (RV) function measured by RV to pulmonary artery coupling (RVPAc) is prognostic in transcatheter tricuspid valve repair (T-TEER). However, traditional 2D echocardiographic measures have critical limitations and RVPAc fails to respect the degree of volume overload/dilation of the RV, which is a key clinical tracer for right ventricular dysfunction (RVD). Purpose We aimed to refine RVPAc by incorporating RV dilation, assessed by 3D echocardiography, to improve the assessment of RV dysfunction for an enhanced outcomeprediction in T-TEER patients. Methods We analyzed 262 patients undergoing T-TEER with complete 3D echocardiography. Results Despite similar RV function across groups, RV dilation (HR 1.85; 1.10, 3.12; p=0.020) and impaired RV free wall longitudinal strain (RVFWLS, HR 1.73, 1.02, 2.92, p=0.042) predicted increased 1-year mortality. A novel RVPAc parameter [RVFWLS/(3D-RVEDV*sPAPinvasive)] was developed, associating RVPA-Uncoupling with a tripled risk of death (HR 3.19, 1.7-6.0, p&amp;lt;0.001). NYHA Class IV and the new RVPAc were independent mortality predictors after T-TEER, with the modified RVPAc showing superior predictive value over traditional RVPAc (c-index 0.68 vs. 0.57, p=0.027). Conclusion The novel RVPAc parameter, integrating RV dilation and function, is a powerful predictor of survival post-T-TEER, underscoring the importance of timely intervention in severe TR and RVD. 3D echocardiography plays a critical role in assessing RV dimension and function, with implications for the management and prognosis of TR patients.Central Figure

Comparative analysis of a novel preoperative stratification method versus traditional scores in patients with severe aortic stenosis undergoing aortic valve surgery

Abstract Introduction It is crucial to consider factors that complicate the indication of transcatheter aortic valve replacement (TAVR) in patients with aortic stenosis (AS), such as narrow valve ring, extensive calcification or inadequate vascular access. (1-4) Thus, aortic valve replacement surgery (SAVR) remains a recommended indication, with its pre-surgical stratification being a challenge. (1-3) Despite the widespread use of scores like the STS and EuroSCORE II, they exhibit limitations, being validated primarily for populations outside Latin America and focusing predominantly on individual factors while often neglecting structural parameters like left ventricular hypertrophy, diastolic dysfunction, or left atrium volume. (3-5) Purpose This study aimed to assess whether a novel modified Généreux stratification method offers superior predictive value for postoperative mortality in AS patients compared to traditional scoring systems. Methods We conducted a retrospective, single-center study involving patients with confirmed severe AS who underwent aortic valve replacement surgery between January 2017 and December 2021. The new stratification method categorized patients into three stages based on echocardiographic parameters (see Figure 1). Univariate and multivariate Cox regression analyses were conducted to identify predictors of mortality, with survival analysis performed using Kaplan-Meier curves. A p-value &amp;lt;0.05 was considered statistically significant. Results A total of 508 patients were included in the study. Stage 1 patients had a lower median age (62 years vs. 67 years in stages 2 and 3). The median EuroSCORE and STS were 2.75 and 2.62%, respectively, with stage 3 showing significantly higher scores (p ≤0.001). Over a median follow-up of 81 months, 56 deaths occurred (11%). Kaplan-Meier curve analysis revealed significant differences in all-cause mortality among the three groups (HR 4.073, log-rank p≤0.001), as illustrated in Figure 1. Multivariate analysis identified the three preoperative stages (HR 3.22, [95% CI 1.44-7.20], p=0.004) and mean transaortic gradient (HR 0.96, [95% CI 0.92-0.99], p=0.021) as independent predictors of mortality. The new stages demonstrated a superior area under the ROC curve of 0.758 compared to traditional scores (see Figure 1). Conclusions The novel three-stage preoperative classification and low transaortic gradient serve as significant predictors of mortality in patients undergoing SAVR. These stages exhibit robust predictive capability for mortality compared to EuroSCORE II and STS.Baseline and multivariate analysisKaplan-meier and ROC curve

Xenon plasma-focused ion beam milling for fabrication of high-purity, bright single-photon sources operating in the C-band

Electron beam lithography is a standard method for fabricating photonic micro and nanostructures around semiconductor quantum dots (QDs), which are crucial for efficient single and indistinguishable photon sources in quantum information processing. However, this technique is difficult for direct 3D control of the structure shape, complicating the design and enlarging the 2D footprint to suppress in-plane photon leakage while directing photons into the collecting lens aperture. Here, we present an alternative approach to employ xenon plasma-focused ion beam (Xe-PFIB) technology as a reliable method for the 3D shaping of photonic structures containing low-density self-assembled InAs/InP quantum dots emitting in the C-band range of the 3rd telecommunication window. The method is optimized to minimize the possible ion-beam-induced material degradation, which allows exploration of both non-deterministic and deterministic fabrication approaches, resulting in photonic structures naturally shaped as truncated cones. As a demonstration, we fabricate mesas using a heterogeneously integrated structure with a QD membrane atop an aluminum mirror and silicon substrate. Finite-difference time-domain simulations show that the angled sidewalls significantly increase the emission collection efficiency to approx. 0.9 for NA = 0.65. We demonstrate experimentally a high purity of pulsed single-photon emission (∼99%) and a superior extraction efficiency value reported in the C-band of η = 24 ± 4%.

Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Air quality index prediction with optimisation enabled deep learning model in IoT application

ABSTRACT The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situations, such as emissions, dispersions, and chemical reactions. This paper developed the Internet of Things (IoT)-based Deep Learning (DL) technique for predicting air quality. Initially, the IoT simulation is performed, where the nodes receive input data. The routing technique is used to identify the best route toward the Base station (BS). The proposed Tangent Two-Stage Algorithm (TTSA) is used in the routing mechanism. For AQI prediction, the time series data is transmitted to the BS. The Z-score normalisation is employed to neglect the unessential data. Furthermore, feature indicator extraction is employed to extract the relevant feature indicators. The Deep Feedforward Neural Network (DFNN) is used to predict air quality. Furthermore, the proposed Fractional Tangent Two-Stage Optimisation (FTTSA) is employed for the training process of DFNN. Moreover, metrics such as energy, time, and distance are used to evaluate the routing process, and superior results such as 0.979J, 0.025s and 0.196 m are obtained. Furthermore, the AQI is predicted by metrics like root mean square error (RMSE), R-squared (R2), mean square error (MSE), and mean absolute percentage error (MAPE), whereas the superior values such as 0.602, 0.598, 0.362, and 0.456 are attained.

Comparative Diagnostic Value of 18F-FDG–PET–CT and Intraoperative Examination in Cervical Cancer Staging

Background and Objectives: The primary objective of this study is to assess the effectiveness of 18F-FDG–PET–CT in preoperative staging of cervical cancer, focusing on determining surgical operability and exploring the correlation between its quantitative parameters and clinicopathological characteristics. Materials and Methods: This retrospective study included 62 cervical cancer patients treated at the Department of Gynecology, Clinic for Operative Oncology at the Institute of Oncology Vojvodina between January 2016 and January 2020, where preoperative clinical examinations and 18F-FDG–PET–CT were performed to assess the extent of cancer, followed by intraoperative and pathohistological examinations of surgically removed specimens to provide a comprehensive evaluation. Results: The mean tumor size measured by 18F-FDG–PET–CT was slightly greater than that obtained through clinical examination (26.4 mm vs. 26.0 mm), with a strong linear correlation (r = 0.678, p &lt; 0.001) observed between the two measurement methods. The overall prediction accuracy of 18F-FDG–PET–CT for primary tumors is 88.7% (55/62) [sensitivity 86.8%, specificity 100.0%, PPV 100.0%, NPV 56.2%] and for intraoperative examination is 88.7% (55/62) [sensitivity 98.1%, specificity 33.3%, PPV 89.7%, NPV 75.0%]. The agreement with histopathological examination was good for 18F-FDG–PET–CT and moderate for intraoperative examination for primary tumors. Regarding lymph nodes, the overall prediction accuracy of 18F-FDG–PET–CT is 82.2% (51/62) [sensitivity 53.8%, specificity 89.8%, PPV 58.3%, NPV 88.8%] and for intraoperative examination 66.1% (41/62) [sensitivity 76.9%, specificity 63.3%, PPV 35.7%, NPV 91.2%]. The agreement with histopathological examination was moderate for 18F-FDG–PET–CT and poor for intraoperative examination for lymph node metastasis, highlighting that the overall accuracy of 18F-FDG–PET–CT (82.1%) was significantly higher than that of intraoperative examination (66.1%) (p = 0.002). Conclusions: In conclusion, 18F-FDG–PET–CT provides high accuracy in detecting primary tumors and superior predictive value for lymph node metastases compared to intraoperative examination, highlighting the importance of incorporating this imaging modality into the preoperative evaluation process to enhance diagnostic precision and inform treatment decisions.

Positive Effects of Growth Retardants on Castor (Ricinus communis L.)

The perennial growth habit of castor plants (Ricinus communis L.) limits mechanical harvest because the plant grows very tall, when the environmental conditions are favorable and produces green flush interfering with mechanical harvesting. In order to prevent excessive growth plant growth, retardant are applied to modifying crop architecture. Present investigation was designed to study the effect of growth retardants on growth and yield parameters. The experiment was conducted at the regional agricultural research station, Palem farm of Professor Jayashankar Telangana Agricultural University, Nagarkurnool district, Telangana, taking locally available tall variety PCH-111. The experiment was laid out in Randomized Block Design with three replicates in Kharif, 2019. Treatments comprises of water spray, ethrel@500 ppm, ethrel@1000 ppm, CCC@1.5ml/lit, CCC@2.0 ml/lit and CCC@2.5 ml/lit and sprayings were done at expanding leaf in the 6th node of the primary stem, expanding leaf on the 8th node of the primary stem, appearance of the first inflorescence, one expanding leaf in a secondary stem after the appearance of the first inflorescence, and appearance of the second inflorescence. Results revealed that, plant growth retardants are effective in reducing plant height, whereas no spray recorded significant superior value for plant height (70.73 cm). For leaf number, there is no much deviations though ethrel treatment enhanced senescence in older leaves, newly developed leaves compensated the number but there was reduction in LAI, which were 1.70 and 2.18, respectively, in ethrel spray of 1000 ppm and 500 ppm. The yield reduction is lower in ethrel treatment due to lower capsule formation and lower spike length at higher concentration of ethrel. Hence, it is concluded that, retardant accelerates natural senescence and thus, acting as defoliants overcoming difficulties in mechanical harvesting.

ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches.

Pilot-scale bioremediation of rare earths wastewater by Chlamydomonas sp. YC

The extraction of rare earth elements (REEs) through in-situ leaching with ammonium sulphate [(NH4)2SO4] had resulted in the production of a large volume of ammonium-rich wastewater, causing severe environmental pollution. This study aimed to assess the ability of an indigenous microalga Chlamydomonas sp. YC, isolated from REEs wastewater, to directly treat real REEs wastewater under outdoor conditions in 50 L airlift photobioreactors (AL-PBRs) and 5.0 m3 open race-way photobioreactors (ORWPs). Additionally, the harvested Chlamydomonas sp. YC biomasses from these two pilot photobioreactors were comprehensively analyzed to evaluate the nutritional values. The results showed that Chlamydomonas sp. YC in AL-PBRs exhibited higher biomass production (1.1 g/L), greater removal efficiencies in NH4+-N (24.9%) and total nitrogen (20.4%), as well as higher CO2 fixation rate (125.0 mg/(L·d)), compared to those of ORWPs. Moreover, the Chlamydomonas sp. YC biomasses obtained from the two pilot photobioreactors contained 44.5% and 49.4% protein, 9.1% and 14.3% lipids. Moreover, Chlamydomonas sp. YC in the two pilot photobioreactors displayed essential amino acid indexes (EAAI) of 0.900, which was higher than that of soybean protein (0.657), indicating superior nutritional values. In conclusion, the implementation of the process involving Chlamydomonas sp. YC in AL-PBRs under outdoor conditions holds promise as a coupled microalgal biotechnology for the simultaneous removal of NH4+-N from REEs wastewater, and the capture of CO2 for the production of valuable biomass.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

EFFECT OF UREA AND/OR CRUDE GLYCERIN TREATMENT ON THE NUTRIENT COMPOSITION AND IN VITRO TRUE DIGESTIBILITY OF DIFFERENT VARIETIES OF RICE STRAW

This study aimed to evaluate the nutrient composition and in vitro true digestibility of three rice straw (RS) varieties with varying stem lengths (Vasco (V): short-stemmed, Cammeo (C): medium-stemmed, and Efe (E): long-stemmed) treated with urea and/or crude glycerin. Each RS variety was ensiled in laboratory-scale mini-silos for 30 days using the following treatments: i) Control, ii) 5 % urea (U) treated RS, iii) 5 % crude glycerin (G) treated RS, and iv) 5% urea + 5% crude glycerin (UG) treatment RS. The Vasco variety of RS exhibited superior nutritional value compared to the Cameo and Efe varieties, with a greater (P&lt;0.05) in vitro true digestibility of dry matter (IVTDDM (V:46.11%, C:41.17% and E:43.94%) as well as lower (P≤0.001) NDF (V:66.38%, C:71.68%, and E:68.59%) and ADF (V:43.61%, C:50.64%, and E:49.1%) levels. Silage of all cultivars demonstrated comparable responses to the applied treatments. The crude protein (P&lt;0.001) and IVTDDM (P&lt;0.001) were greater in all treatment groups where urea was used alone or in combination with crude glycerin. All cultivars showed a reduction in NDF (P&lt;0.001) and ADF (P&lt;0.001) levels with the application of crude glycerin treatment alone, but no significant effect was observed on IVTDDM values. In the interaction effect, Efe×UG showed higher (P&lt;0.001) in vitro true digestibility while for in vitro NDF digestibility, Efe×U interaction exhibited higher (P&lt;0.001) digestibility compared to other interaction effects. The findings of the study suggest that adding urea or a combination of urea and glycerin to rice straw improved its nutritional content and digestibility. Treating rice straw with a combination of urea (5%) and glycerin (5%) could be a promising strategy to improve its nutritional value for livestock feed. Keywords: crude glycerin, digestibility, rice straw, silage, treatment, urea