Search Results (78)

The global population is rising at an alarming rate and is projected to reach 10 billion by 2050, necessitating a substantial increase in food production. However, the overuse of chemical pesticides, including antibacterial agents and synthetic fertilizers, poses a major threat to sustainable agriculture. This review examines the ecological and health impacts of antibacterial agents (e.g., streptomycin, oxytetracycline, etc.) in horticultural crops, focusing on their effects on non-target organisms such as beneficial microbes involved in plant growth promotion and resistance development. Certain agents (e.g., triclosan, sulfonamides, and fluoroquinolones) leach into water systems, degrading water quality, while others leave toxic residues in crops, leading to human health risks like dysbiosis and antibiotic resistance. To mitigate these hazards, sustainable alternatives such as integrated plant disease management (IPDM) and biotechnological solutions are essential. Advances in genetic engineering including resistance-conferring genes like EFR1/EFR2 (Arabidopsis), Bs2 (pepper), and Pto (tomato) help combat pathogens such as Ralstonia solanacearum and Xanthomonas campestris. Additionally, CRISPR-Cas9 enables precise genome editing to enhance inherent disease resistance in crops. Emerging strategies like biological control, plant-growth-promoting rhizobacteria (PGPRs), and nanotechnology further reduce dependency on chemical antibacterial agents. This review highlights the urgent need for sustainable disease management to safeguard ecosystem and human health while ensuring food security. Full article

Background and Objectives: Growing evidence suggests a strong relationship between hypertension and cancer, which can increase the risk of poor prognosis. However, data regarding mortality related to cancer and hypertension are limited. Our study aims to analyze the mortality trends related to cancer and hypertension in the United States from 1999 to 2023. Materials and Methods: A retrospective observational analysis was conducted using mortality data for the adult U.S. population from 1999 to 2023, retrieved from the CDC WONDER database using death certificates. Age-adjusted mortality rates (AAMRs) were calculated, and annual percentage changes (APCs) were analyzed using JoinPoint Regression. Results: From 1999 to 2023, a total of 1,406,107 deaths related to cancer and hypertension were recorded in the United States. The AAMR increased from 12.59 in 1999 to 35.49 in 2023. Males had a higher mortality rate compared to women throughout the study period (AAMR; 30.3 vs. 20.4). Non-Hispanic (NH) Black Americans, or African Americans had the highest mortality rates, followed by NH white, Hispanic or Latino groups, and other NH groups. The highest AAMR was observed in the South, followed by the Midwest, the Northeast, and the West. Rural areas had higher mortality rates compared to urban areas. Conclusions: Cancer- and hypertension-related mortality rates have consistently increased in the United States from 1999 to 2023, particularly affecting males, NH Black Americans, the southern region, and rural areas. The trends highlight the need for targeted prevention, including early screening, lifestyle changes, and treatment adherence. Full article

With the rising significance of visual marketing, differences in how tourists from various age groups visually engage with tourism promotional materials remain insufficiently studied. This study recruited 48 participants and used a quasi-experimental design combined with eye-tracking technology to examine visual attention, scan path patterns, and their relationship to reading performance among different age groups. Independent t-tests, correlation analyses, and Lag Sequential Analysis were conducted to compare the differences between the two groups. Results indicated that elder participants had significantly higher fixation counts and longer fixation durations in text regions than younger participants, as well as higher perceived novelty scores. A positive correlation emerged between text fixation duration and perceived novelty. Additionally, elder participants showed greater interaction between text and images, while younger participants exhibited a more linear reading pattern. This study offers empirical insights to optimize tourism promotional materials, highlighting the need for age-specific communication strategies. Full article

Background\Objectives: Solving the secrets of the brain is a significant challenge for researchers. This work aims to contribute to this area by presenting a new explainable feature engineering (XFE) architecture designed to obtain explainable results related to stress and mental performance using electroencephalography (EEG) signals. Materials and Methods: Two EEG datasets were collected to detect mental performance and stress. To achieve classification and explainable results, a new XFE model was developed, incorporating a novel feature extraction function called Cubic Pattern (CubicPat), which generates a three-dimensional feature vector by coding channels. Classification results were obtained using the cumulative weighted iterative neighborhood component analysis (CWINCA) feature selector and the t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, explainable results were generated using the CWINCA selector and Directed Lobish (DLob). Results: The CubicPat-based model demonstrated both classification and interpretability. Using 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV, the introduced CubicPat-driven model achieved over 95% and 75% classification accuracies, respectively, for both datasets. Conclusions: The interpretable results were obtained by deploying DLob and statistical analysis. Full article

This study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calculus in the Whale Optimization Algorithm (WOA) to improve the balance between exploration and exploitation during hyperparameter tuning. The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. Experimental results show that FWOA-SVR achieves superior performance with the lowest MSE values (0.036311, 0.03942, and 0.03825), RMSE values (0.19213, 0.19856, and 0.19577), and the highest R² values (0.96392, 0.96104, and 0.96192) for training, validation, and testing, respectively. These results highlight the significant improvements of FWOA-SVR in prediction accuracy and efficiency, surpassing benchmark models in capturing complex patterns within the data. The findings highlight the effectiveness of integrating fractional optimization techniques into machine learning frameworks for advancing solar energy forecasting solutions. Full article

This study determined the interaction between soybean hulls (SHs) and enzymes (β-mannanase) to improve the sustainability and efficacy of feeding programs for laying hens during peak production while ensuring the best health and efficiency. In a completely randomized design (CRD), 200 golden-brown hens were fed for four weeks (33 to 36 weeks) and randomly distributed into four groups, each containing four replicates of ten birds, with one group receiving a control diet (P0) and the others receiving diets that contained four combinations of SHs and enzymes (ENZs). e.g., 3% SHs and 0.02 g/kg ENZs (P1), 3% SHs and 0.03 g/kg ENZs (P2), 9% SHs and 0.02 g/kg ENZs (P3), and 9% SHs and 0.03 g/kg ENZs (P4). Although most egg quality measures remained similar, the P2 group showed enhanced (p = 0.630) egg weight, albumen weight, and height. Moreover, the P2 group improved gut (p < 0.05) shape by increasing villus width, height, crypt depth, and surface area throughout intestinal sections, while the P4 group markedly improved total cholesterol and LDL (p = 0.022) levels. The P1, P2, and P4 groups exhibited a significant enhancement in dry matter (p = 0.022) and crude fiber (p = 0.046) digestibility, while the P2 group demonstrated superior crude protein digestibility (p = 0.032), and the P1 and P2 groups showed increased crude fat digestibility compared to the other groups. In conclusion, adding 3% of SHs and 30 mg/kg of ENZs (β-mannanase) to the feed may help laying hens, enhance gut health and some egg quality indices, and decrease blood cholesterol and LDL levels without compromising nutrient digestibility. Full article

Anaplasmosis is an infectious disease transmitted by ticks and caused by obligate intracellular pathogen of belonging to genus Anaplasma Infections of one-humped camels (Camelus dromedarius) and llamas (Lama glama) have been reported previously. The aim of this study was to investigate the seroprevalence and risk factors of anti-Anaplasma spp. antibodies in Camelus dromedarius of the Punjab, Pakistan. A cross-sectional study was conducted during 2017–2018 to study the seroprevalence of anaplasmosis in Camelus dromedarius of 13 districts in Punjab province of Pakistan and to assess the associated risk factors including age, breed, gender, body condition score, tick infestation, location, season and management type. Serum samples from 728 camels (433 females and 295 males) were examined for anti-Anaplasma antibodies using a commercially available competitive enzyme-linked immunosorbent assay (cELISA) test kit. A univariable analysis was conducted and extended to multivariate logistic regression to find potential risk factors associated with the disease. Overall, the seroprevalence of anti-Anaplasma antibodies was 8.5% (8.5%, CI 6.6–10.8) with 62 positives in 728 camels. The highest seroprevalence was recorded for camels of the Central Punjab districts (16.1%, CI 11.5–21.7) followed by those of the Northwestern (5.4%, 2.8–9.3) and Southern Punjab (5.2%, 2.9–8.4) districts (p < 0.001). Multivariable analysis showed that location (Central Punjab: OR 2.78, p = 0.004), season (summer: OR 7.94, p = 0.009), body condition score (BCS 2: OR 14.81, p = 0.029) and tick infestation (OR 38.59, p < 0.001) are potential risk factors in the corresponding camel populations. The results showed that the camel population in Pakistan is seropositive for Anaplasma spp. The geographical zone, season, body condition and tick infestation were identified as significantly associated risk factors for seroprevalence of anaplasmosis in dromedary camels. To the best of our knowledge, the results of this current study provide the first evidence of exposure of camels to anaplasmosis in Pakistan. Molecular investigations in the future are highly recommended to determine the dynamics of the disease in camels. Full article

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed. This DFE model comprises four primary stages: (i) feature extraction, (ii) iterative feature selection, (iii) classification using shallow classifiers, and (iv) information fusion. The DFE model is self-organizing, producing 14 different outcomes (8 classifier-specific and 6 voted) and selecting the most effective result as the final decision. During feature extraction, heatmaps are identified using gradient-weighted class activation mapping (Grad-CAM) with features derived from these regions via the final global average pooling layer of the pretrained ResNet50*. Four iterative feature selectors are employed in the feature selection stage to obtain distinct feature vectors. The classifiers k-nearest neighbors (kNN) and support vector machine (SVM) are used to produce specific outcomes. Iterative majority voting is employed in the final stage to obtain voted outcomes using the top result determined by the greedy algorithm based on classification accuracy. The presented ResNet50* was trained on an augmented version of the Kvasir dataset, and its performance was tested using Kvasir, Kvasir version 2, and wireless capsule endoscopy (WCE) curated colon disease image datasets. Our proposed ResNet50* model demonstrated a classification accuracy of more than 92% for all three datasets and a remarkable 99.13% accuracy for the WCE dataset. These findings affirm the superior classification ability of the ResNet50* model and confirm the generalizability of the developed architecture, showing consistent performance across all three distinct datasets. Full article

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation. Full article

Background: The primary objective of this research is to propose a new, simple, and effective feature extraction function and to investigate its classification ability using electrocardiogram (ECG) signals. Methods: In this research, we present a new and simple feature extraction function named the minimum and maximum pattern (MinMaxPat). In the proposed MinMaxPat, the signal is divided into overlapping blocks with a length of 16, and the indexes of the minimum and maximum values are identified. Then, using the computed indices, a feature map is calculated in base 16, and the histogram of the generated map is extracted to obtain the feature vector. The length of the generated feature vector is 256. To evaluate the classification ability of this feature extraction function, we present a new feature engineering model with three main phases: (i) feature extraction using MinMaxPat, (ii) cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. Results: To obtain results, we applied the proposed MinMaxPat-based feature engineering model to a publicly available ECG fibromyalgia dataset. Using this dataset, three cases were analyzed, and the proposed MinMaxPat-based model achieved over 80% classification accuracy with both leave-one-record-out (LORO) cross-validation (CV) and 10-fold CV. Conclusions: These results clearly demonstrate that this simple model achieved high classification performance. Therefore, this model is surprisingly effective for ECG signal classification. Full article

Agriculture has consistently improved to meet the needs of a growing global population; however, traditional monoculture farming, while highly productive, is facing challenges such as weed infestation and inefficient resource utilization. Herbicides effectively control weeds. However, their widespread use in weed management has the potential to contaminate soil and water, endangering the ecosystem by damaging non-target plant and animal species. Therefore, the main objective of this study was to evaluate the impact of different maize and soybean cropping systems on weed infestation and resource utilization. The experiment was a randomized complete block design with three replications consisting of three cropping systems: sole maize (SM), sole soybean (SS), and maize–soybean strip intercropping (MSI). In this study, the main difference between SM, SS, and MSI was the planting density, which was 60,000 (SM), 100,000 (SS), and 160,000 (maize–soybean in MSI). We observed that a higher total leaf area index in MSI resulted in increased soil cover, which reduced the solar radiations for weeds and suppressed the weed growth by 17% and 11% as compared to SS and SM, respectively. Whereas the radiation use efficiency for companion crops in MSI was increased by 39% and 42% compared to SS and SM, respectively. Moreover, the increased soil cover by total leaf area index in MSI also increased the efficiency of water use. Furthermore, our results indicated that reduced weed-crop competition increased the resource use in MSI, which resulted in increased crop yield and land equivalent ratio (LER 1.6). Eventually, this resulted in reduced inputs and increased land productivity. Therefore, we suggest that MSI should be adopted in resource-limiting conditions with higher weed infestation as it can simultaneously promote ecological balance and improve agricultural output, thereby reducing the environmental effects of traditional cropping systems. Full article

The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification. Full article

An Fe-based amorphous coating, with the composition Fe₄₈Cr₁₅Mo₁₄C₁₅B₆Y₂, was synthesized by the high-velocity oxygen fuel spray (HVOF) process on a substrate of AISI 1035. The effect of crystallization on the electrochemical and tribological properties of the HVOF-sprayed Fe-based coating was systematically studied. The XRD results validated the fully amorphous nature of the as-sprayed coating by showing a broad peak at 43.44° and crystallization of this coating after heat-treatment at 700 °C by demonstrating sharp peaks of Fe-, Mo-, and Cr-based carbides. After crystallization, an increase in the corrosion current density from 4.95 μAcm⁻² to 11.57 μAcm⁻² and in the corrosion rate from 4.28 mpy to 9.99 mpy, as well as a decrease in the polarization resistance from 120 Ωcm² to 65.12 Ωcm², were observed, indicating the deterioration of the corrosion resistance of the as-sprayed Fe-based coating. This can be attributed to the formation of porous ferrous oxide, providing an easy channel for charge transfer and promoting pit formation. However, a decrease in the coefficient of friction from 0.1 to 0.05 was observed, highlighting the significant improvement in the wear resistance of the Fe-based coating after crystallization. This can be associated with the precipitation of hard carbides (MxCy) at the boundaries of the crystallized regions. Full article

Metasurface absorbers have shown significant potential in stealth applications due to their adaptability and capacity to reduce the backscattering of electromagnetic (EM) waves. Nevertheless, due to the materials used in the terahertz (THz) range, simultaneously achieving excellent stealth performance in ultrawideband remains an important and difficult challenge to overcome. In this study, an ultrawideband absorber is proposed based on indium tin oxide (ITO) and polyethylene-terephthalate (PET), with a structure thickness of only 0.16λ. ITO sheets are utilized to achieve broad-spectrum, optical transparency and flexibility of the metasurface. The results show that absorption higher than 90% can be achieved in the frequency band ranging from 1.75 to 5 THz under normal TE and TM polarizations, which covers a wide THz band. The structure is insensitive to polarization angles and exhibits 97% relative bandwidth above 90% efficiency up to an oblique incident angle of 60°. To further validate the efficiency of the absorption performance, the radar cross-section (RCS) reduction investigation was performed on both planar and conformal configurations. The findings show that under normal incidence EM waves, both flat and curved surfaces can achieve RCS reduction of over 10 dB, covering an extremely wide frequency range of 1.75 to 5 THz. The metasurface presented in this study exhibits significant potential for use in several THz applications, including flexible electronic devices and stealth aircraft windows. Full article

This study investigated the effects of the β-mannanase enzyme and soyhulls on production performance, economics, egg quality, hematology and serum biochemistry, nutrient digestibility, gut morphology, digesta viscosity, and excreta consistency in laying hens during the late peak production phase (37 to 40 weeks of age). Golden brown hens (RIR × Fayoumi; n = 200) were fed a control diet (no soyhulls or enzymes) and diets containing four combinations, i.e., 3% soyhulls with 20 mg/kg β-mannanase (D1), 3% soyhulls with 30 mg/kg β-mannanase (D2), 9% soyhulls with 20 mg/kg β-mannanase (D3), and 9% soyhulls with 30 mg/kg β-mannanase (D4), for four weeks in four replicates of 10 birds each. Overall, a significantly higher (p < 0.05) feed intake, weight gain, feed conversion ratio, and water intake were calculated in the D2 group as compared to the control and remaining combinations of soyhulls and β-mannanase. No mortality was recorded during the entire experiment. Economically, the D1 and D2 groups showed the best results as compared to the D3 and D4 groups. Egg quality parameters like egg weight, shell weight and shell thickness, yolk weight, albumen weight and height, and the Haugh unit remained unchanged (p > 0.05). Similarly, the D2 group showed significantly lower total cholesterol, LDL, and VLDL levels and enhanced gut morphology with greater villus width, height, crypt depth, and surface area across intestinal segments. Crude protein (CP), crude fiber (CF), crude fat, and ash digestibility were higher (p < 0.05) in the D1 and D2 groups compared to the control. Digesta viscosity, excreta consistency, and other egg quality parameters remained unaffected. In conclusion, the dietary inclusion of a combination of 3% soyhulls and 30 mg/kg β-mannanase may have potential benefits for laying hens by improving some production performance and egg quality indicators and economics, lowering blood cholesterol, LDL, and VLDL levels, enhancing nutrient digestibility, and improving gut morphology without affecting egg quality. Full article