Search Results (121)

Background: The current study aimed to evaluate the effects of the supplementation of protease sources on growth and carcass response, gut health, nutrient digestibility, and cecal microbiota profiles in broilers fed poultry-by-product-meal (PBM)-containing diets. Methods: In total, 800 one-day-old mixed-sex broilers (Arbor Acres) were weighed and allocated to one of the four dietary treatments in a completely randomized design, with eight replicates and 25 birds each per replicate. The treatments were as follows: (1) T₀, control diet (without protease supplementation and 3% PBM); (2) T₁, control diet supplemented with acidic protease at 100 g/ton (50,000 U/g); (3) T₂, control diet supplemented with alkaline protease at 200 g/ton (25,000 U/g); (4) T₃, control diet supplemented with neutral protease at 200 g/ton (25,000 U/g). Results: Protease supplementation enhanced (p < 0.05) body weight gain and the feed conversion ratio, predominantly in broilers fed PBM-based diets containing alkaline protease. Alkaline protease supplementation increased (p < 0.05) the apparent ileal digestibility of proteins (AIDP) by 4.3% and the apparent ileal digestibility of amino acids (AIDAA) by up to 5.8%, except for ornithine. Increments (p < 0.05) in carcass, breast, and leg quarter yields due to protease supplementation were evident, particularly in broilers fed diets containing alkaline protease. Alkaline protease improved (p < 0.05) the duodenal villus height (VH), reduced the crypt depth (CD), and increased the villus height to crypt depth ratio (VCR). Alkaline protease supplementation reduced (p < 0.05) cecal counts of Salmonella, Escherichia coli, and Clostridium in the broilers, whereas it increased (p < 0.05) the Lactobacillus counts. Conclusions: the supplemented alkaline protease resulted in improved growth performance and carcass traits, better gut health, as well as improved ileal digestibility of nutrients, including crude protein (CP) and acid insoluble ash (AIA), with a more balanced cecal microbial composition in broilers. Full article

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice. Full article

The effective management of the triple constraints, time, cost, and quality, is imminently essential for the success of construction projects, and it is considered a hot research topic nowadays. For this purpose, we carried out this study to systematically analyze the influence of these constraints on project success, specifically emphasizing how weather conditions intensify the difficulties associated with these constraints. A survey questionnaire was administered to 242 industry experts, and the collected data were evaluated utilizing the software named Statistical Package for Social Sciences (SPSS), Version 30.0. Further, we also analyzed the obtained data by employing Cronbach’s alpha, correlation, and regression analyses, which obviously confirmed the effects of these constraints on project success. In addition, the results clearly indicated that weather-related delays increased the durations of projects by 25.7% and caused an average cost increase of 23.8%. Focused attention was required for effective management of these constraints. This study further highlights the need for strategic planning and effective risk management to mitigate weather-related risks. As a result, proficient management of these elements is imminently crucial for ensuring project success in the construction sector. Thus, we concluded that this study will allow construction projects in future endeavors to be carried out with high proficiency and effectiveness. Full article

Sustainable building construction encounters challenges stemming from escalating expenses and time delays associated with geotechnical assessments. Developing and optimizing geotechnical soil maps (SMs) using existing data across heterogeneous geotechnical formations offer strategic and dynamic solutions. This strategic approach facilitates economical and prompt site evaluations, and offers preliminary ground models, enhancing efficient and sustainable building foundation design. In this framework, this paper aimed to develop SMs for the first time in the rapidly growing district of Gujrat using the optimal interpolation technique (OIT). The subsurface conditions were evaluated using the standard penetration test (SPT) N-values and soil classification including seismic wave velocity to account for seismic effects. Among the different geostatistical and geospatial models, the inverse distance weighting (IDW) model based on an optimized spatial analyst approach yielded the minimum error and a higher association with the field data for the understudy region. Overall, the optimized IDW technique yielded root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) ranges between 0.57 and 0.98. Furthermore, analytical depth-dependent models were developed using SPT-N values to assess the bearing capacity, demonstrating the association of R² > 0.95. Moreover, the study area was divided into three geotechnical zones based on the average SPT-N values. Comprehensive validation of different strata evaluation based on the optimal IDW for the SPT-N and soil type-based SMs revealed that the RMSE and MAE ranged between 0.36–1.65 and 0.30–0.59, while the CC ranged between 0.93 and 0.98 at multiple depths. The allowable bearing capacity (ABC) for spread footings was determined by evaluating the shear, settlement, and seismic factors. The study offers insights into regional variations in geotechnical formations along with shallow foundation design guidelines for practitioners and researchers working with similar soil conditions. Full article

Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5). Full article

This study investigates novel optical solitons within the intriguing (4+1)-dimensional Korteweg–de Vries–Calogero–Bogoyavlenskii–Schiff (KdV-CBS) equation, which integrates features from both the Korteweg–de Vries and the Calogero–Bogoyavlenskii–Schiff equations. Firstly, all possible symmetry generators are found by applying Lie symmetry analysis. By using these generators, the given model is converted into an ordinary differential equation. An adaptive approach, the generalized exp(-$\mathfrak{S}$($\chi$)) expansion technique has been utilized to uncover closed-form solitary wave solutions. The findings reveal a range of soliton types, including exponential, rational, hyperbolic, and trigonometric functions, represented as bright, singular, rational, periodic, and new solitary waves. These results are illustrated numerically and accompanied by insightful physical interpretations, enriching the comprehension of the complex dynamics modeled by these equations. Our approach’s novelty lies in applying a new methodology to this problem, yielding a variety of novel optical soliton solutions. Additionally, we employ bifurcation and chaos techniques for a qualitative analysis of the model, extracting a planar system from the original equation and mapping all possible phase portraits. A thorough sensitivity analysis of the governing equation is also presented. These results highlight the effectiveness of our methodology in tackling nonlinear problems in both mathematics and engineering, surpassing previous research efforts. Full article

Background: Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are well-recognized conditions resulting from high blood pressure, rising blood glucose, and elevated eye pressure. Later-stage symptoms usually include patches of cotton wool, restricted veins in the optic nerve, and buildup of blood in the optic nerve. Severe consequences include damage of the visual nerve, and retinal artery obstruction, and possible blindness may result from these conditions. An early illness diagnosis is made easier by the use of deep learning models and artificial intelligence (AI). Objectives: This study introduces a novel methodology called CAD-EYE for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues. Methods: The proposed system combines the features extracted using two deep learning (DL) models (MobileNet and EfficientNet) using feature fusion to increase the diagnostic system efficiency. The system uses fluorescence imaging for increasing accuracy as an image processing algorithm. The algorithm is added to increase the interpretability and explainability of the CAD-EYE system. This algorithm was not used in such an application in the previous literature to the best of the authors’ knowledge. The study utilizes datasets sourced from reputable internet platforms to train the proposed system. Results: The system was trained on 65,871 fundus images from the collected datasets, achieving a 98% classification accuracy. A comparative analysis demonstrates that CAD-EYE surpasses cutting-edge models such as ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception in terms of classification accuracy. A state-of-the-art comparison shows the superior performance of the model against previous work in the literature. Conclusions: These findings support the usefulness of CAD-EYE as a diagnosis tool that can help medical professionals diagnose an eye disease. However, this tool will not be replacing optometrists. Full article

The aim of the present research was the efficient degradation of industrial textile wastewater dyes using a very active cloned laccase enzyme. For this purpose, potent laccase-producing bacteria were isolated from soil samples collected from wastewater-replenished textile sites in Punjab, Pakistan. The laccase gene from locally isolated strain LI-81, identified as Bacillus megaterium, was cloned into vector pET21a, which was further transformed into E. coli BL21 codon plus. The optimized conditions for the increased production of laccase include fermentation in a 2% glucose, 5% yeast extract and 250 mg/L CuSO₄ medium with pH 7.5; inoculation with 5% inoculum; induction with 0.1 mM IPTG at 0.5 O.D.; and incubation for 36 h at 37 °C. The crude enzyme produced was employed for the removal of commercially used textile dyes. The dyes were quickly precipitated under optimized reaction conditions. Rose bengal, brilliant green, brilliant blue G, Coomassie brilliant blue R and methylene blue were precipitated at rates of 10.69, 54.47, 84.04, 78.99 and 7.40%, respectively. The FTIR and UV–Vis spectroscopic analyses of dyes before and after confirmed the chemical changes brought about by the cloned laccase that led to the dye removal. Full article

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement. Full article

A major challenge for massive multiple-input multiple-output (MIMO) technology is designing an efficient signal detector. The conventional linear minimum mean square error (MMSE) detector is capable of achieving good performance in large antenna systems but requires computing the matrix inverse, which has very high complexity. To address this problem, several iterative signal detection methods have recently been introduced. Existing iterative detectors perform poorly, especially as the system dimensions increase. This paper proposes two detection schemes aimed at reducing computational complexity in massive MIMO systems. The first method leverages the symmetric accelerated over-relaxation (SAOR) technique, which enhances convergence speed by judiciously selecting the relaxation and acceleration parameters. The SAOR technique offers a significant advantage over conventional accelerated over-relaxation methods due to its symmetric iteration. This symmetry enables the use of the conjugate gradient (CG) acceleration approach. Based on this foundation, we propose a novel accelerated SAOR method named CGA-SAOR, where CG acceleration is applied to further enhance the convergence rate. This combined approach significantly enhances performance compared to the SAOR method. In addition, a detailed analysis of the complexity and numerical results is provided to demonstrate the effectiveness of the proposed algorithms. The results illustrate that our algorithms achieve near-MMSE detection performance while reducing computations by an order of magnitude and significantly outperform recently introduced iterative detectors. Full article

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from $O\left(K^3\right)$ to $O\left(K^2\right)$ making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate. Full article

Increasing soil and water salinity threatens global agriculture, particularly affecting rice. This study investigated the residual effects of microbial biochar and nitrogen fertilizer in mitigating salt stress in paddy soil and regulating the biochemical characteristics of rice plants. Two rice varieties, Shuang Liang You 138 (SLY138), a salt-tolerant, and Jing Liang You 534 (JLY534), a salt-sensitive, were grown under 0.4 ds/m EC (S0) and 6.84 ds/m EC (S1) in a glass house under controlled conditions. Three types of biochar—rice straw biochar (BC), fungal biochar (BF), and bacterial biochar (BB)—were applied alongside two nitrogen (N) fertilizer rates (60 kg ha⁻¹ and 120 kg ha⁻¹) in a previous study. The required salinity levels were maintained in respective pots through the application of saline irrigation water. Results showed that residual effects of microbial biochars (BF and BB) had higher salt mitigation efficiency than sole BC. The combination of BB and N fertilizer (BB + N120) significantly decreased soil pH by 23.45% and Na⁺ levels by 46.85%, creating a more conducive environment for rice growth by enhancing beneficial microbial abundance and decreasing pathogenic fungi in saline soil. Microbial biochars (BF and BB) positively improved soil properties (physicochemical) and biochemical and physiological properties of plants, ultimately rice growth. SLY138 significantly had a less severe response to salt stress compared to JLY534. The mitigation effects of BB + N120 kg ha⁻¹ were particularly favorable for SLY138. In summary, the combined residual effect of BF and BB with N120 kg ha⁻¹, especially bacterial biochar (BB), played a positive role in alleviating salt stress on rice growth, suggesting its potential utility for enhancing rice yield in paddy fields. Full article

In this study, we investigate the symmetry analysis and explicit solutions for the Estevez–Mansfield–Clarkson (EMC) equation. Our main objectives are to identify the Lie point symmetries of the EMC equation, construct an optimal system of one-dimensional subalgebras, and reduce the EMC equation to a set of ordinary differential equations (ODEs). We employ the Riccati–Bernoulli sub-ODE method (RBSODE) to solve these reduced ODEs and obtain explicit solutions for the EMC model. The obtained solutions are validated using numerical analyses, and corresponding figures are presented to illustrate the physical implications of the derived solutions. Full article

This paper aims to appraise the influence of strategic control tactics on financial performance. The goal is to examine the mediating effect of the resource allocation process on the relationship between financial performance and five strategic control tactics. A quantitative hypothetico-deductive methodology was used in this study. A basic random sample of the Ministry of Finance–North Lebanon’s workforce was used to conduct an electronic questionnaire. A total of 232 valid responses were collected. Two statistical analysis methods, an exploratory and a confirmatory factor analysis, were implemented. The sample adequacy was confirmed by a KMO value higher than 0.7 before instigating the principal component analysis (PCA). The latter kept more than 60% of the initial data while structuring the data. The findings of the KMO and Barlett tests supported the adoption of PCA. The correlation matrix confirmed a statistically significant relationship between resource allocation, financial success, and strategic control techniques. The structural equation model (SEM) validated the linear correlations and statistical significance between the variables. The hypotheses were examined. Results confirmed that the model satisfactorily fits the data. The RMSEA is below the 0.05 threshold. The incremental indices are higher than 0.9. Results confirmed that the resource allocation process mediates the relationship between preventive control, operational control, special alert control, implementation control, and financial performance. Full article

Objectives: To examine the distribution, clinical characteristics, and management of priapism in a Saudi Arabian tertiary care setting to provide a regional perspective. Subjects and Methods: This retrospective chart review included 29 male patients presenting with priapism at a tertiary care hospital in Riyadh, Saudi Arabia, from January 2011 to June 2023. Data were collected on patient demographics, clinical presentation, treatment modalities, and outcomes. Results: The study found recurrent episodes of priapism in many patients, with a significant number associated with hematological diseases, notably sickle cell disease. Most treatments involved non-surgical methods. A notable finding was the correlation between the duration of priapism episodes and the likelihood of hospital admissions, suggesting that prolonged episodes often required more extensive medical attention. Conclusions: Priapism often presents as a chronic and recurrent condition requiring personalized management strategies. This study emphasizes the importance of recognizing regional occurrence patterns to enhance the management of priapism and suggests a need for further research in regions where this condition is less common. Full article