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Sclerotinia sclerotiorum (Lib.) de Bary is the causative agent of stem white mold disease which severely reduces major crop productivity including soybean and rapeseed worldwide. The current study aimed to explore plant growth-promoting traits and biocontrol of new isolated Bacillus subtilis BS-2301 to suppress S. sclerotiorum through various mechanisms. The results indicated that the BS-2301 exhibited strong biocontrol potential against S. sclerotiorum up to 74% both in dual culture and partition plate experiments. The BS-2301 and its crude extract significantly suppressed S. sclerotiorum growth involving excessive reactive oxygen species (ROS) production in mycelia for rapid death. Furthermore, the treated hyphae produced low oxalic acid (OA), a crucial pathogenicity factor of S. sclerotiorum. The SEM and TEM microscopy of S. sclerotiorum showed severe damage in terms of cell wall, cell membrane breakage, cytoplasm displacement, and organelles disintegration compared to control. The pathogenicity of S. sclerotiorum exposed to BS-2301 had less disease progression potential on soybean leaves in the detached leaf assay experiment. Remarkably, the strain also demonstrated broad-range antagonistic activity with 70%, and 68% inhibition rates against Phytophthora sojae and Fusarium oxysporum, respectively. Furthermore, the strain exhibits multiple plant growth-promoting and disease-prevention traits, including the production of indole-3-acetic acid (IAA), siderophores, amylases, cellulases and proteases as well as harboring calcium phosphate decomposition activity. In comparison to the control, the BS-2301 also showed great potential for enhancing soybean seedlings growth for different parameters, including shoot length 31.23%, root length 29.87%, total fresh weight 33.45%, and total dry weight 27.56%. The antioxidant enzymes like CAT, POD, SOD and APX under BS-2301 treatment were up-regulated in S. sclerotiorum infected plants along with the positive regulation of defense-related genes (PR1-2, PR10, PAL1, AOS, CHS, and PDF1.2). These findings demonstrate that the BS-2301 strain possesses a notable broad-spectrum biocontrol potential against different phytopathogens and provides new insight in suppressing S. sclerotiorum through various mechanisms. Therefore, BS-2301 will be helpful in the development of biofertilizers for sustainable agricultural practices.

Background: The purpose of this study is to explore the trends and causes of established and emerging nations’ stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Spontaneous spinal epidural hematoma (SSEH) is a rare condition, and it usually presents with acute onset neck or back pain, progressive weakness, and other symptoms of spinal cord compression. Catheter-directed thrombolysis is one option for limbs threatened by iliofemoral venous thrombosis; other options, such as venous thrombectomy (either open or percutaneous), are also available. There are few reported cases of SSEH owing to catheter-directed thrombolysis for deep venous thrombosis (DVT). We present a case of a 65-year-old man who presented with left lower limb extensive iliofemoral DVT and received catheter-directed thrombolysis. The patient initially had rapid improvement in his symptoms with restoration of limb perfusion. However, within 6 hours of starting catheter-directed thrombolysis, the patient developed extensive SSEH and underwent emergent spinal decompression surgery with laminectomy of T11 to T12 with complete resolution of the neurological deficit. Clinicians should consider SSEH in differential diagnosis if the patient develops acute onset neck or back pain after catheter-guided thrombolysis for DVT.

Plant parasitic nematodes (PPNs) pose a significant threat to global crop productivity, causing an estimated annual loss of US $157 billion in the agriculture industry. While synthetic chemical nematicides can effectively control PPNs, their overuse has detrimental effects on human health and the environment. Biocontrol agents (BCAs), such as bacteria and fungi in the rhizosphere, are safe and promising alternatives for PPNs control. These BCAs interact with plant roots and produce extracellular enzymes, secondary metabolites, toxins, and volatile organic compounds (VOCs) to suppress nematodes. Plant root exudates also play a crucial role in attracting beneficial microbes toward infested roots. The complex interaction between plants and microbes in the rhizosphere against PPNs is mostly untapped which opens new avenues for discovering novel nematicides through multi-omics techniques. Advanced omics approaches, including metagenomics, transcriptomics, proteomics, and metabolomics, have led to the discovery of nematicidal compounds. This review summarizes the status of bacterial and fungal biocontrol strategies and their mechanisms for PPNs control. The importance of omics-based approaches for the exploration of novel nematicides and future directions in the biocontrol of PPNs are also addressed. The review highlighted the potential significance of multi-omics techniques in biocontrol of PPNs to ensure sustainable agriculture.

Abstract Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time.

The Metaverse envisions a future where immersive online interactions become the norm. However, this vision can only be safely realized if user safety and privacy are prioritized. This review critically evaluates existing user safety and security measures within the Metaverse. We have highlighted the limited ability of existing solutions to protect against harassment, identity theft, and the misuse of personal data collected in this environment. This research comprehensively analyzes user safety and security dimensions within virtual worlds and presents a taxonomy to classify the unique threats and vulnerabilities Metaverse users may encounter. This review reveals critical gaps, including the lack of comprehensive security frameworks and balanced privacy-preserving models in virtual immersive environments. In response, we propose a novel Metaverse Security Architecture designed with Zero-Trust principles. This architecture prioritizes user control over data, identity, and experiences. It also emphasizes proactive security measures to mitigate diverse potential harms in virtual worlds. Our research highlights the critical importance of such robust, user-centric frameworks in enabling a Metaverse that is immersive, safe, and secure for all its participants.

In recent years, large language models (LLMs) have been employed significantly in different domains of computing education. Nevertheless, these models have been focused on essential adherence to their integration as coding assistants in computing education. However, attention has been switched to thoroughly examining and analyzing LLM behavior, particularly in computing education for programming tasks such as code generation, code explanation, and programming error message explanation. Therefore, it becomes imperative to understand their behavior to examine potential pitfalls. This article addresses this gap systematically and details how different LLM-based coding chatbots, such as ChatGPT, Codex, Copilot, and others, react to various coding inputs within computing education. To achieve this objective, we collected and analyzed articles from 2021 to 2024, and 72 studies were thoroughly examined. These objectives include investigating the existing limitations and challenges associated with utilizing these systems for coding tasks, assessing their responses to prompts containing coding syntax, examining the impact of their output on student learning, and evaluating their performance as debugging tools. The findings of this review highlight that it is premature to incorporate these systems into computing education due to their limitations that may limit their effectiveness as comprehensive coding assistants for computer science students. These limitations include issues with handling prompts containing code snippets, potential negative impacts on student learning, limited debugging capabilities, and other ineffectiveness. The finding also reports multiple research directions that can be considered in future research related to LLMs in computing education.

The Know Your Customer (KYC) process is a fundamental prerequisite for any financial institution’s compliance with the regulatory framework. Blockchain technology has emerged as a revolutionary solution to enhance the effectiveness of the KYC procedure. It ensures that the KYC process is transparent, secure, and immutable, thereby offering a robust solution to combat fraudulent activities. The potential of blockchain technology in revolutionizing the KYC process has been acknowledged globally. Blockchain technology provides a decentralized platform for storing customer data, enabling financial institutions to access the information seamlessly. Using ethereum blockchain technology in KYC procedures can enhance the efficiency of financial institutions, significantly reducing the time and cost associated with the process. This work aims to provide a viable and sustainable solution to the challenges that banks experience in implementing KYC procedures and onboarding new customers. The proposed solution involves the central bank maintaining a comprehensive register of all registered banks while closely monitoring their adherence to the existing regulations governing KYC and customer acquisition.

Human Action Recognition (HAR) is a vital area of computer vision with diverse applications in security, healthcare, and human-computer interaction. Addressing the challenges of HAR, particularly in dynamic and complex environments, is essential to advancing this field. The strength of the HADE framework is its carefully curated dataset, which was primarily derived from smartphone camera footage. This dataset encompasses a wide range of human actions captured in various settings, providing a robust foundation for training our novel HAR models, HADE I and HADE II. These models have been specifically designed and optimized for parallel processing on GPUs, showing significant improvements in the efficiency of both training and inference processes. Through a comprehensive evaluation, the HADE framework demonstrated a remarkable improvement in HAR accuracy, achieving 83.57% accuracy on our custom dataset. This marks a considerable enhancement over existing methodologies and underscores the efficacy of the HADE approach in accurately interpreting complex human actions. The framework’s potential applicability in healthcare in the domain of neurological patient care is particularly noteworthy, where it can aid in early detection and facilitate personalized treatment plans. Future research should focus on expanding the range of actions covered by HAR and exploring avenues for real-time processing. The introduction of the HADE framework not only makes a substantial contribution to the field of computer vision but also paves the way for its practical application across various sectors.

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Nowadays, one of the most promising and significant challenges for our society is achieving highly efficient energy utilization. To address the upcoming demands in energy applications, which demonstrate considerable potential for future trends, continuous efforts are necessary to develop improved and higher-performing inorganic multifunctional nanomaterials. Multifunctional inorganic nanomaterials have been extensively researched to meet the requirements of various energy applications or to enhance them further. Specific attention is given to inorganic nanomaterials for advanced energy storage, conservation, transmission, and conversion applications, which strongly rely on the optical, mechanical, thermal, catalytic, and electrical properties of energy materials. At the nanometer-scale range, triboelectric, piezoelectric, thermoelectric, electrochromic, and photovoltaic materials have made significant contributions to numerous energy sector applications. Functional inorganic materials possess unique properties, including excellent thermal and electrical conductivity, chemical stability, and a large surface area, which enhance their competitiveness in energy-related applications. Herein, recent research, development, and advances in inorganic multifunctional nanomaterials to enhance their performance are discussed, highlighting how devices combine the functionalities of nanomaterials. In this manner, we aim to provide insights into future applications and elucidate the ongoing research challenges currently facing this field.

Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

In this article a numerical method for numerical modelling of advection diffusion equation is developed. The proposed method is based on Laplace transform (LT) and Chebyshev spectral collocation method (CSCM). The LT is used for time-discretization and the CSCM is used for discretization of spatial derivatives. The LT is used to transform the time variable and avoid the finite difference time stepping method. In time stepping technique the accuracy is achieved for very small time step which results in a very high computational time. The spatial operators are discretized using CSCM to achieve high accuracy as compared to other methods. The method is composed of three primary stages: firstly the given problem is transformed into a corresponding inhomogeneous elliptic problem by using the LT; secondly the CSCM used to solve the transformed problem in LT domain; finally the solution obtained in LT domain is converted to time domain via numerical inverse LT. The inversion of LT is generally an ill-posed problem and due to this reason various numerical inversion methods have been developed. In this article we have utilized the contour integration method which is one of the most efficient methods. The most important feature of this approach is that it handles the time derivative with the Laplace transform rather than the finite difference time stepping approach, avoiding the untoward impact of time steps on stability and accuracy of the method. Five test problems are used to validate the efficiency and accuracy of the proposed numerical scheme.

Abstract Unmanned aerial vehicles (UAVs) play a vital role in the beyond fifth‐generation (B5G) and sixth‐generation (6G) wireless communication scenarios. This paper proposes a non‐stationary three‐dimensional (3D) geometry‐based multiple‐input multiple‐output channel model for UAV to vehicle (U2V) communications considering UAV heaving motion. The proposed model incorporates the effects of UAV heaving along with the rotation, acceleration, and 3D velocity variation for matching the realistic U2V communication scenarios. The computation methods for time variant channel parameters such as Doppler frequencies, angle of arrival, and angle of departure are given and analysed. The theoretical and statistical properties, including spatial temporal correlation function, TCF, level crossing rate, average fade duration, and Doppler power spectral density are derived and analysed aiming to evaluate the proposed model. Simulation results show that the output statistical characteristics agree with the hypothetical and simulated outcomes, which shows the validity of both the channel model and theoretical derivations. The cover image is based on the Research Article 3D geometry‐based UAV to vehicle multiple‐input multiple‐output channel model incorporating Unmanned aerial vehicle heaving motion by Naeem Ahmed et al., https://doi.org/10.1049/mia2.12393

Abstract Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues against fraudsters. The confirmation of a user legitimation before the money transaction is highlighted by mechanisms and technologies from previous studies that may be helpful in user authentication. This study aims to identify the technologies for user authentication and the opportunity for their transformation to mobile money transaction security despite having all the legally required data for a transaction. This proposed review has identified the role of multifactor authentication techniques for authentication to mitigate the risk of fraudulent transactions—the analysis through 92 articles taken from famous publishers. The most relevant articles address authentication problems, of which 54 percent describe money transaction security, and the rest highlight the supporting technology for user authentication. The study platform described the technology-based approaches with the appreciation of new ideas for secure money transactions. The QR code and multifactor authentication are key terms that increased security by 46%. In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.

Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required Real-Time Price (RTP) and Critical Peak Price (CPP) loads, WOA and EDE capabilities can save costs and ensure the device receives sufficient voltage. The three most recent performance indicators are kWh per charge, energy usage, and the maximum-average ratio. Existing models are evaluated according to their expenses (in rupees), energy consumption, cost per kilowatt-hour, and total cost. All simulation results indicate that HWDE is the optimal solution in every circumstance. In MATLAB simulations, HWDE consistently outperforms its rivals.

Abstract The effects and underlying mechanisms of gastrodin treatment on hypertensive vascular dysfunction and proliferation of vascular smooth muscle cells (VSMCs) were determined in vitro and in vivo. Using a pharmacological target network interaction analysis, 151 common targets and a PPI network were identified containing the top 10 hub genes. Kyoto encyclopedia of genes and genomes (KEGG) analysis identified the PI3K/AKT pathway as a significantly enriched pathway. Both spontaneous hypertensive rats (SHRs) and Wistar Kyoto rats were used to assess the therapeutic effects of gastrodin on hypertension. Gastrodin treatment of the SHRs resulted in a marked attenuation of elevated blood pressure, pulse wave velocity, and pathological changes in the abdominal aorta. Moreover, gastrodin treatment significantly inhibited cell growth and downregulated the expression of PCNA as well as the p-PI3K/PI3K and p-AKT/AKT levels in angiotensin II-stimulated VSMCs. Taken together, gastrodin treatment attenuates blood pressure elevation, vascular dysfunction, and proliferation of VSMCs and inhibits the activation of the PI3K/AKT pathway.

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.

Abstract Driver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which can recognize faces in real-time and offline video reliably and effectively. The feature-fusing technique is utilized to integrate the features extracted from three CNN models, and the fused features are then used to train the suggested ensemble classification model. To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. The custom-developed dataset has been recorded as the best among all under the simulation environment.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural facilities. There aren’t any maps available for most installations, and there are no proven techniques to categorize features within indoor areas to provide location-based services. During a pandemic like COVID-19, the direct connection between the masses is one of the significant preventive steps. Hospitals are the main stakeholders in managing such situations. This study presents a novel method to create an adaptive 3D model of an indoor space to be used for localization and routing purposes. The proposed method infuses LiDAR-based data-driven methodology with a Quantum Geographic Information System (QGIS) model-driven process using game theory. The game theory determines the object localization and optimal path for COVID-19 patients in a real-time scenario using Nash equilibrium. Using the proposed method, comprehensive simulations and model experiments were done using QGIS to identify an optimized route. Dijkstra algorithm is used to determine the path assessment score after obtaining several path plans using dynamic programming. Additionally, Game theory generates path ordering based on the custom scenarios and user preference in the input path. In comparison to other approaches, the suggested way can minimize time and avoid congestion. It is demonstrated that the suggested technique satisfies the actual technical requirements in real-time. As we look forward to the post-COVID era, the tactics and insights gained during the pandemic hold significant value. The techniques used to improve indoor navigation and reduce interpersonal contact within healthcare facilities can be applied to maintain a continued emphasis on safety, hygiene, and effective space management in the long term. The use of three-dimensional (3D) modeling and optimization methodologies in the long-term planning and design of indoor spaces promotes resilience and flexibility, encouraging the adoption of sustainable and safe practices that extend beyond the current pandemic.

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone’s lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model’s performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

During the last few decades, agile methodologies have become the most widely used software development methods. Agile methodologies provide great customer satisfaction and fast product delivery at the low cost. The low cost and customer satisfaction make agile methods highly attractive. Agile methods were designed and used, in its early days, for small-scale projects. There are many challenges in applying agile methods for large-scale projects as determined by some previous studies. The objective of this research is to identify the practices of De-motivators in adopting agile projects at a large scale from management point of view by employing a systematic literature review (SLR).In the review process, a total of 72 practices were discovered. De-motivator factors have been identified and ranked, through the analytical hierarchical process, through SLR in our previous study (Abrar et al., 2020). Analytical hierarchical processing (AHP) is a well-known method used for multi-criteria decisions implemented by Satty (2023). In this study AHP is used to prioritize de-motivators and their categories based on their relative importance. The prioritized de-motivators and their categories provide a strong background to the software project manager and practitioners for adopting Agile Software Development in large-scale projects.

One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ decisions regarding the use of EEG equipment, particularly wireless devices, to implement BCI technology. Moreover, the literature review results offer suggestions for further study and new research areas. We plan to identify the additional characteristics of each EEG equipment and determine which one is most suited for each industry by measuring the user experience based on various devices in future research.

Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used to create the reference data in the format of Comma Separated Values (CSV). This reference data is first analyzed for quality issues and then used for navigation purposes. Further for the localization Sugeno Fuzzy Model is used as a fuzzy inference system for the initial localization and subsequent mapping of the location. The proposed approach is validated using an Android application on seven predefined routes. According to the performed result analysis, the proposed fuzzy logic-based approach is able to provide location identification with 98.9 percent accuracy with a root mean square error value of 3 percent.

Neuropeptides (NPs) are a kind of neuromodulator/ neurotransmitter that works as signaling molecules in the central nervous system, and perform major roles in physiological and hormone regulation activities. Recently, machine learning-based therapeutic agents have gained the attention of researchers due to their high and reliable prediction results. However, the unsatisfactory performance of the existing predictors is due to their high execution cost and minimum predictive results. Therefore, the development of a reliable prediction is highly indispensable for scientists to effectively predict NPs. In this study, we presented an automatic and computationally effective model for identifying of NPs. The evolutionary information is formulated using a bigram position-specific scoring matrix (Bi-PSSM) and K-spaced bigram (KSB). Moreover, for noise reduction, a discrete wavelet transform (DWT) is utilized to form Bi-PSSM_DWT and KSB_DWT based high discriminative vectors. In addition, one-hot encoding is also employed to collect sequential features from peptide samples. Finally, a multi-perspective feature set of sequential and embedded evolutionary information is formed. The optimum features are chosen from the extracted features via Shapley Additive exPlanations (SHAP) by evaluating the contribution of the extracted features. The optimal features are trained via six classification models i.e., XGB, ETC, SVM, ADA, FKNN, and LGBM. The predicted labels of these learners are then provided to a genetic algorithm to form an ensemble classification approach. Hence, our model achieved a higher predictive accuracy of 94.47% and 92.55% using training sequences and independent sequences, respectively. Which is $\sim $ 3% highest predictive accuracy than present methods. It is suggested that our presented tool will be beneficial and may execute a substantial role in drug development and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/Target-ensC_NP.

Amyloid proteins (AMYs) are usually an aggregate of insoluble fibrous that have major pathogenic effects on various tissues. However, its abnormal deposition may lead to several diseases i.e., Parkinson’s, Alzheimer’s, and type 2 diabetes. In addition, AMYs form amyloid aggregates when they are in a misfolded state. Therefore, it is crucial to accurately predict AMYs and their pathogenic characteristics. Various computational predictors have been presented for the accurate prediction of AMYs. Although, the effectiveness of these predictors is unsatisfactory due to their low generalization abilities and high training cost. In this attempt, we proposed an intelligent computational predictor for the accurate prediction of AMYs. The novel embedded evolutionary features are gathered using K-separated bigrams, and the Filter method into the evolutionary descriptors. Moreover, DDE-based enhanced frequency coupling information are gathered from the Amyloid sequences. Additionally, a multi-model vector is obtained by combining the features of the applied formulation techniques. To reduce the computational cost of the proposed model, the eXtreme Gradient Boosting-Recursive Feature Elimination (XGB-RFE) based high-ranked features are selected from the heterogeneous vector. In the next part, the optimal features are evaluated via several learners, i.e., XGBoost (XGB), Light Gradient Boosted Machine (LGBM), Support Vector Machine (SVM), Adaboost (ada), and Extra Trees classifier (ETC),. The proposed model reported an improved predictive prediction accuracy of 93.10% using training sequences and 89.67% using independent sequences, respectively. Which is $\sim 4$ % higher training accuracy than existing predictors. It is anticipated that our predictive approach will be useful for scientists and might play a key role in drug development and academic research.

A tremendous amount of unstructured data, such as comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains challenging. While various DNN architectures have been applied to sentiment analysis with encouraging results, they treat various features equally and suffer from high-dimensional feature space. Moreover, state-of-the-art methods cannot properly utilize semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features. This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weights to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

Abstract Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods.

Background: Paddy during various stages i.e. loading, transportation, storage, handling etc. occurs severe losses, which can be reduced by designing appropriate post-harvest machineries using physico-mechanical properties. Objectives: The study aimed to assess the variation in physico-mechanical properties of thirteen different paddy cultivars. Methodology: The study was carried out at the College of Engineering, Nanjing Agricultural University, China. Thirteen different freshly harvested paddy cultivars were collected from Nanjing. Collected paddy cultivars before experiment were cleaned removing extraneous materials. Paddy cultivars were sealed polythene bags, and were then kept in refrigerator at a temperature of 5 ºC for 7 days for uniform distribution of moisture. The required amount of samples from each cultivar was accordingly taken for evaluating their physical and mechanical properties. Results: The results when assessed were statistically different (p?0.05) from each other. The average values of different paddy cultivars for length, width, thickness, geometric mean diameter, arithmetic mean diameter, square mean diameter, equivalent diameter, surface area, volume, sphericity, aspect ratio, thousand kernel weight, bulk density, true density, porosity, hardness and toughness (for both horizontal and vertical orientations) were calculated to be 7.94mm, 3.32mm, 2.26mm, 3.84mm, 4.51mm, 2.56mm, 3.64mm, 46.53mm2, 29.74mm3, 0.49%, 0.42, 28.64 gm, 568.31 kg m-3, 1225.20 kg m-3, 54%, 46.09 N, 14.75 N, 16.06 mJ and 6.90 mJ respectively. Conclusion: A wide variation was observed for each parameter while comparing different paddy cultivars. This wide variation in result will negatively affect the performance of the equipment and machinery being used for post-harvest practices. The study concludes that it is essential for industries involved in equipment design and machinery production, as well as end-users responsible for their implementation, to take into account these substantial differences among cultivars when making their decisions.

Abstract Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

Gestational diabetes mellitus (GDM), which is considered the most prevalent endocrine condition in pregnancy, has been linked to insulin resistance. Several traditional risk factors for GDM have been confirmed, including lifestyle and environmental factors, while recent studies have focused on the genetic factor, such as mitochondrial DNA (mtDNA) mutation and its association with GDM. Previous research has found a relationship between mutations in the tRNALys and tRNALeu(UUR) genes and diverse types of diabetes. However, the analysis of mutations in the mitochondrial tRNALeu(UUR) gene and its relationship with GDM patients in Pakistani women is not well investigated. The goal of this study was to investigate whether there is a relationship between the A3243G tRNALeu(UUR) gene mutation and GDM in Pakistani women. We selected 20 GDM pregnant women for this investigation, and DNA was extracted from their saliva. The mitochondrial tRNALeu(UUR) gene was amplified using PCR with specified primers, and 10 samples from different families were sequenced. The present study did not find the A3243G tRNALeu(UUR) mutation in Pakistani GDM women. Further studies are needed for confirmation.

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.

In this research, we focused on the production of amylose–lipid nanocomposite material (ALN) through a green synthesis technique utilizing high-speed homogenization. Our aim was to investigate this novel material’s distinctive physicochemical features and its potential applications as a low-glycemic gelling and functional food ingredient. The study begins with the formulation of the amylose–lipid nanomaterial from starch and fatty acid complexes, including stearic, palmitic, and lauric acids. Structural analysis reveals the presence of ester carbonyl functionalities, solid matrix structures, partial crystallinities, and remarkable thermal stability within the ALN. Notably, the ALN exhibits a significantly low glycemic index (GI, 40%) and elevated resistance starch (RS) values. The research extends to the formulation of ALN into nanocomposite hydrogels, enabling the evaluation of its anthocyanin absorption capacity. This analysis provides valuable insights into the rheological properties and viscoelastic behavior of the resulting hydrogels. Furthermore, the study investigates anthocyanin encapsulation and retention by ALN-based hydrogels, with a particular focus on the influence of pH and physical cross-link networks on the uptake capacity presenting stearic-acid (SA) hydrogel with the best absorption capacity. In conclusion, the green-synthesized (ALN) shows remarkable functional and structural properties. The produced ALN-based hydrogels are promising materials for a variety of applications, such as medicine administration, food packaging, and other industrial purposes.

The most important factor impacting wheat production is water stress that occurs during the reproductive growth stage. Therefore, the plant responses and water productivity as affected by drought priming were investigated during Rabi seasons 2021 and 2022. The field trials were conducted in the research field of the Department of Irrigation and Drainage, Faculty of Agricultural Engineering, Sindh Agriculture University, Tandojam. The Hamal-BNS wheat variety was subjected to differing irrigation water regime levels (40%, 50% and 60% of soil water holding capacity, SWHC) after being subjected to drought priming, irrigation water recovery (water closure period) and drought priming. There were six treatments: (1) DPP-40 (drought priming plants at 40% of SWHC), (2) DPP-50, (3) DPP-60, (4) CTP-40 (controlled treated plants at 40% of SWHC), (5) CTP-50 and (6) CTP-60. During the experiment period, soil moisture content was significantly affected by the different treatments at various growth stages of wheat. The results indicated that winter wheat pre-exposed to drought priming attained a stress imprint that improved the subsequent deficit water levels which occurred during the later plant growth stage as demonstrated by the progress of test weight, grain yield, plant level water use efficiency and irrigation water use efficiency as well as relative yield compared to CTP-50 (control treatment). Under the irrigation water regime levels during the post-anthesis period, primed wheat plants sustained grain yield and higher relative yield than wheat plants without priming due to the better irrigation water regime for drought-primed wheat plants. Similarly, primed wheat plants consumed 18.3% less irrigation water as compared to non-primed plants, which significantly increased plant level WUE and irrigation WUE and decreased dry biomass and root development of drought-primed wheat plants. Therefore, to conserve fresh water for other field crops and increase water productivity in the Sindh province, it is recommended that drought priming is used during the early growth period of wheat plants as a successful irrigation method.

This work presents a highly accurate method for the numerical solution of the advection–diffusion equation of fractional order. In our proposed method, we apply the Laplace transform to handle the time-fractional derivative and utilize the Chebyshev spectral collocation method for spatial discretization. The primary motivation for using the Laplace transform is its ability to avoid the classical time-stepping scheme and overcome the adverse effects of time steps on numerical accuracy and stability. Our method comprises three primary steps: (i) reducing the time-dependent equation to a time-independent equation via the Laplace transform, (ii) employing the Chebyshev spectral collocation method to approximate the solution of the transformed equation, and (iii) numerically inverting the Laplace transform. We discuss the convergence and stability of the method and assess its accuracy and efficiency by solving various problems in two dimensions.

Nanocrystalline copper ferrite shows distinct photocatalytic properties, but it suffers from a high recombination rate of photogenerated electrons (e−) and holes (h+) due to its narrow bandgap. Herein, Al3+ doping is shown to reduce the (e−/h+) recombination rate and improve the charge carriers’ availability in doped CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles produced by a solid-state, mechanochemical process. CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles exhibit the growth of a nanocrystalline cubic spinel lattice when annealed at 1000 °C. The lattice parameter is reduced by Al3+ doping due to the smaller ionic radius of Al3+ ions substituting bigger Fe3+ ions. However, a higher degree of sintering and greater crystallite size are observed for Al3+ doped samples. The surface morphology and topography also reveal an increase in the particle size, but significantly narrow size distribution and greater homogeneity. The effect of Al3+ doping on the optical properties of CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles is demonstrated by a decrease in the photoluminescence signal that is attributed to the lower rate of (e−/h+) recombination. Thus, Al3+ doping increases transition time and improves the availability of charge carriers for potential photocatalytic applications. Resumen: La ferrita de cobre nanocristalino muestra propiedades fotocatalíticas distintas, pero sufre de una alta tasa de recombinación de electrones fotogenerados (e−) y agujeros (h+) debido a su estrecha banda prohibida. Aquí, se muestra que el dopaje Al3+ reduce la tasa de recombinación (e-/h+) y mejora la disponibilidad de los portadores de carga en nanopartículas de CuFe2-xAlxO4 (0 ≤ x ≤ 1) dopadas producidas por un proceso mecanoquímico de estado sólido. Las nanopartículas de CuFe2-xAlxO4 (0 ≤ x ≤ 1) exhiben el crecimiento de una red de espinela cúbica nanocristalina, cuando se recocen a 1000 °C. El parámetro de red se reduce por el dopaje Al3+ debido al radio iónico más pequeño de los iones Al3+ que sustituyen los iones Fe3+ más grandes. Sin embargo, se observa un mayor grado de sinterización y un mayor tamaño de cristalito para las muestras dopadas con Al3+. La morfología y la topografía de la superficie también revelan un aumento en el tamaño de partícula, pero una distribución del tamaño significativamente más estrecha y una mayor homogeneidad. El efecto del dopaje Al3+ sobre las propiedades ópticas de las nanopartículas de CuFe2-xAlxO4 (0 ≤ x ≤ 1) se demuestra por una disminución en la señal de fotoluminiscencia que se atribuye a la tasa más baja de recombinación (e-/h+). Por lo tanto, el dopaje Al3+ aumenta el tiempo de transición y mejora la disponibilidad de portadores de carga para posibles aplicaciones fotocatalíticas.

Abstract Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins. The features from primary protein sequences are extracted by group dipeptide composition, reduced amino acid alphabet, and novel encoder pseudo amino acid composition segmentation. To select the best feature set, eXtreme Gradient Boosting-recursive feature elimination is implemented. The best feature set is provided to eXtreme Gradient Boosting (XGB), Random Forest, and Extremely Randomized Tree classifiers for model training and prediction. The performance of these classifiers is evaluated by tenfold cross-validation. The empirical results show that XGB-based predictor achieves the best results compared with other classifiers and existing methods in the literature.

Sustainable maize production under changing climatic conditions, especially heat and water stress conditions is one of the key challenges that need to be addressed immediately. The current field study was designed to evaluate the impact of water stress on morpho-physiological, biochemical, reactive oxygen species, antioxidant activity and kernel quality traits at different plant growth stages in maize hybrids. Four indigenous i.e., YH-5427, YH-5482, YH-5395, JPL-1908, and one multinational maize hybrid i.e., NK-8441 (Syngenta Seeds) were used for the study. Four stress treatments (i) Control (ii) 3-week water stress at pre-flowering stage (iii) 3-week water stress at anthesis stage (iv) 3-week water stress at grain filling/post-anthesis stage. The presence of significant oxidative stress was revealed by the overproduction of reactive oxygen species (ROXs) i.e., H2O2 (1.9 to 5.8 µmole g−1 FW) and malondialdehyde (120.5 to 169.0 nmole g−1 FW) leading to severe negative impacts on kernel yield. Moreover, a severe reduction in photosynthetic ability (50.6%, from 34.0 to 16.8 µmole m−2 s−1), lower transpirational rate (31.3%, from 3.2 to 2.2 mmol m−2 s−1), alterations in plant anatomy, reduced pigments stability, and deterioration of kernel quality was attributed to water stress. Water stress affected all the three studied growth stages, the pre-flowering stage being the most vulnerable while the post-anthesis stage was the least affected stage to drought stress. Antioxidant activity was observed to increase under all stress conditions in all maize hybrids, however, the highest antioxidant activity was recorded at the anthesis stage and in maize hybrids YH-5427 i.e., T-SOD activity was increased by 61.3% from 37.5 U mg−1 pro to 60.5 U mg−1 pro while CAT activity was maximum under water stress conditions 8.3 U mg−1 pro as compared to 10.3 U mg−1 pro under control (19.3%). The overall performance of maize hybrid YH-5427 was much more promising than other hybrids, attributed to its higher photosynthetic activity, and better antioxidant defense mechanism. Therefore, this hybrid could be recommended for cultivation in drought-prone areas.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Regular monitoring of the number of various fish species in a variety of habitats is essential for marine conservation efforts and marine biology research. To address the shortcomings of existing manual underwater video fish sampling methods, a plethora of computer-based techniques are proposed. However, there is no perfect approach for the automated identification and categorizing of fish species. This is primarily due to the difficulties inherent in capturing underwater videos, such as ambient changes in luminance, fish camouflage, dynamic environments, watercolor, poor resolution, shape variation of moving fish, and tiny differences between certain fish species. This study has proposed a novel Fish Detection Network (FD_Net) for the detection of nine different types of fish species using a camera-captured image that is based on the improved YOLOv7 algorithm by exchanging Darknet53 for MobileNetv3 and depthwise separable convolution for 3 x 3 filter size in the augmented feature extraction network bottleneck attention module (BNAM). The mean average precision (mAP) is 14.29% higher than it was in the initial version of YOLOv7. The network that is utilized in the method for the extraction of features is an improved version of DenseNet-169, and the loss function is an Arcface Loss. Widening the receptive field and improving the capability of feature extraction are achieved by incorporating dilated convolution into the dense block, removing the max-pooling layer from the trunk, and incorporating the BNAM into the dense block of the DenseNet-169 neural network. The results of several experiments comparisons and ablation experiments demonstrate that our proposed FD_Net has a higher detection mAP than YOLOv3, YOLOv3-TL, YOLOv3-BL, YOLOv4, YOLOv5, Faster-RCNN, and the most recent YOLOv7 model, and is more accurate for target fish species detection tasks in complex environments.

In this paper, the integrated free-space optics (FSO) and fiber optic model is evaluated using new radio (NR) sub-THz link to sustain next generation 5G capacity. The proposed integrated model effectively applies over 25 km single mode fiber, 0.5 m RF wireless, and 500 m optical wireless. In addition, four different sub-THz frequencies (125, 150, 175, and 200 GHz) are estimated on NR-based 5G FSO network, including 22 Gbps 64quadrature amplitude modulation-orthogonal frequency division multiplexing (64QAM-OFDM) signal speed. The proposed FSO enabled fiber optic system is also measured mathematically to satisfy the data transmission accuracy. For confirmation, the theoretical approach of the presented FSO and fiber optic network is realized with an aggregate 342 Gbps speed 16×22. The performance metrics comprising forward error limit (FEL), bit error rate (BER), and error vector magnitude (EVM) are used for weighing simulation results. The outlets of an integrated fiber-FSO network show that by applying NR 5G sub-THz, a high data rate with multiple inputs and multiple outputs (MIMO) transmission capacity can be adjusted victoriously.