This work proposes a metaheuristic-based approach for hyperparameter selection in a multilayer perceptron to classify electromyographic signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and gray wolf optimizer significantly improves the performance of a multilayer perceptron for classifying EMG motion signals. The final model achieved an average classification rate of 93% for the validation phases. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost) and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong county, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (AUC 0.974 and accuracy=93.3%), while the LR model performed the worst (AUC 0.974 and accuracy=93.3%) and CatBoost model performed better (AUC 0.974 and accuracy=93.3%). Besides, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, did a more detailed analysis of conditioning factors but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.

Over the last several decades, large wildfires are increasingly common across the United States causing disproportionate impact on forest health and function, human well-being, and economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011-2020) using a wide array of meteorological, vegetational, and topographical features in the Deep Neural Network model. A total of 4,538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43 % of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the climatological forcings, land cover, location, and elevation of the ecosystem. Overall, results may serve useful guide in managing landscapes under changing climate and disturbance regimes.

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fiber, and wireless power amplifiers can be handled by deep learning equalization algorithms. Besides, the PS technique is considered an effective measure to further increase the capacity of the modulation-constraint channel. However, since the proba-bilistic distribution of m-QAM varies with the amplitude, there have been difficulties in learning valuable information from the minority class. This limits the gain of nonlinear equalization. To overcome the imbalanced machine learning problem, we propose a novel two-lane DNN (TLD) equalizer with the random over-sampling (ROS) technique in this paper. The combination of PS at the transmitter and ROS at the receiver improves the overall performance of the W-band wireless transmission system, and our 4.6-km ROF delivery experiment verifies its effectiveness for the W-band mm-wave PS-16QAM system. Based on our proposed equalization scheme, we realize single-channel 10-Gbaud W-band PS-16QAM wireless transmission over 100 m optical fiber link and 4.6-km wireless air-free distance. The results show that compared with the typical TLD without ROS, the TLD-ROS can improve the receiver sensitivity by 1dB. Furthermore, a reduction of 45.6% in complexity is realized, and training samples can be reduced by 15.5%. Considering the actual wireless physical layer with its requirements, there is much to be gained from the joint use of deep learning and balanced data pre-processing techniques.

Indoor localization is an active area of research dominated by traditional machine-learning techniques. Deep learning-based systems have shown unprecedented improvements and have accomplished exceptional results over the past decade, especially the Transformer network within natural language processing (NLP) and computer vision domains. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer with multiple classification heads (one per class) and learnable embeddings, to investigate the effectiveness of Transformer-based models for received signal strength (RSS) based WiFi fingerprinting. HyTra leverages learnable embeddings and the self-attention mechanism to determine the relative position of the wireless access points (WAPs) within the high-dimensional embedding space, improving the prediction of user location. From an NLP perspective, we consider a fixed order sequence of all observed WAPs as a sentence and the captured RSS value(s) for every given WAP at a given reference point from a given user as words. We test our proposed network on public and private datasets of different sizes, proving that the quality of the learned embeddings and overall accuracy improves with increments in samples.

Raman-based Distributed Temperature Sensing (DTS) is a valuable tool for field testing and validating heat transfer models in Borehole Heat Exchanger (BHE) and Ground Source Heat Pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method is proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods are implemented for a Distributed Thermal Response Test case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty of ± 0.58, 0.74, 1.1 and 1.7 K found at depths of 200, 300, 500 and 800 m, respectively. The temperature uncertainty increases non-linearly with depth and is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation. Distributed temperature measurements and their uncertainties are useful tools in determining the strengths and drawbacks of ground heat transfer models.

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including Data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen Cyber-Physical Security (CPS) with minimal human intervention. It is a Human Activity Recognition (HAR)-based approach where a GoogleNet-BiLSTM network hybridization has been used to recognize suspicious activities in cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates Machine Vision at the IoT Edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and GoogleNet-BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security with $4.29 per month only.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in early-stage of cancer, from whom all survived cancer. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually that leads to increase the risk of wrong decisions for cancer detection. Thus, utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and availability of datasets obtained from various imaging modalities have opened an opportunity to surpass limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Fresh potatoes were deep-fried in olive oil (OO) & extra virgin olive oil (EVOO) and their blends with 5%, 10% and 20% v/v sesame oil (SO). This is the first report on the use of sesame oil as natural source of antioxidant for olive oil deep-frying. Oil was evaluated for Peroxide Value (PV), Free Fatty Acids (FFA), K₂₃₂, K₂₇₀, Trolox Equivalent Antioxidant Capacity (TEAC) and Total Phenols (TP) until Total Polar Compounds (TPCs) reached 25%. Sesame lignan transformations were monitored through Reverse-phase HPLC. While TPCs in olive oils increased at a steady rate, the addition of 5%, 10% and 20% v/v SO created a time window lasting 1, 2 and 3 hours, respectively, where TPCs were constant. SO addition to OO increased the total frying time. Furthermore, the addition of SO reduced the peroxides formation rate for both OO and EVOO. EVOO was more resistant to oxidation than OO as measured by TPCs and TEAC, while frying time raised from 21.5 to 25.25 h when EVOO replaced OO. The increase in frying time for olive oil but not for extra virgin olive oil, after SO addition, is pointing out a niche market for extra virgin olive oil in deep-frying.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can provide predictions, reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman Filter to enhance the perception performance. In addition, a back-end UAV path planning for surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.

Neural decoding from spiking activity is an essential tool for understanding the information encoded in population neurons, especially in applications like brain-computer interface (BCI). Various quantitative methods have been proposed and have shown superiorities under different scenarios respectively. From the machine learning perspective, the decoding task is to map the high-dimensional spatial & temporal neuronal activity to the low-dimensional physical quantities (e.g., velocity, position). Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. Recently, the Transformer-based models are widely used in processing natural languages and images due to its superior performances in handling long-range and global dependencies. Hence, in this work we examine the potential applications of Transformers in neural decoding and introduce two Transformer-based models. Besides adapting the Transformer to neuronal data, we also propose a data augmentation method for overcoming the data shortage issue. We test our models on three experimental datasets and their performances are comparable to the previous state-of-the-art (SOTA) RNN-based methods. In addition, Transformer-based models show increased decoding performances when the input sequences are longer, while LSTM-based models deteriorate quickly. Our research suggests that Transformer-based models are important additions to the existing neural decoding solutions, especially for large datasets with long temporal dependencies.

The Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in-situ power transfer to recharge UAV batteries hold potential in extending their mission time. In this paper, we study the use of far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer. The use of MIMO antennas further boost the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs, and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. Simulation results demonstrate that with the use of PPO, the system achieves a tenfold flight time extension compared to no wireless recharging. Further, PPO outperforms the benchmark movement strategies of ’Traveling Salesman Problem’ and ’Low Battery First’ when used by the tUAVs.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. This disease is known to the globe in early 2019. Poor status of mental health is often caused by unemployment, ongoing socio-economic condition. Poor mental health may even accelerate the process of panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper utilizes multiple related factors those have impact on causing panic attack. Recurrent Neural Network (RNN) based framework is utilized in this paper that assembles multiple RNN layers along with other parameters into a single platform. This method is implemented by capturing interfering factors and predicts panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances.

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

Owing to the reduction of rupture instability and the avoidance of wrinkle defect, hydrodynamic deep drawing (HDD) process is gradually becoming attractive for fabricating lightweight and complicated products. Meanwhile, since metallic material presents anisotropic deformation behavior, it is necessary to select an appropriate constitutive model for the prediction of plastic deformation behavior of applied material with high precision. In the present research, several anisotropic yield criteria namely, Hill’48, Yld2000-2d and BBC2005 are implemented to investigate the effect of yield functions on the prediction accuracy of the critical process window and deformation behavior for HDD process of 2024 and 5754 aluminum alloys. Material constants in the yield criteria are determined by applying uniaxial and equi-biaxial tension tests and optimizing an error-function by using the Levenberg-Marquardt algorithm. Furthermore, the process window diagram is computed utilizing the stress analytical model combined material properties with workpiece geometrical features. Numerical simulation results of predicted material anisotropic parameters, process window and HDD deformation for aluminum alloys are compared with the experimental data. Through the comparison of diverse yield criteria based on materials anisotropic coefficients, critical process window prediction, earing profile, and thickness distribution, it is revealed that the Yld2000-2d and the BBC2005 yield criteria can offer more precise models of material behavior in planar anisotropy properties for HDD process of 2024 and 5754 aluminum alloys.

In this study, by using Least Square Method, the dataset for the Gross Domestic Product per capita is modeled as a function satisfying the fractional differential equation. The function itself is assumed to be the finite summation of its previous values and the derivatives with unknown coefficients. Then, the prediction for the upcoming years is done by having an approach dividing the dataset into 4 regions corresponding to four different tasks. The mathematical model of the Gross Domestic Product (GDP) per capita of the countries (and union) which are Brazil, China, European Union (EU), India, Italy, Japan, UK, the USA, Spain, and Turkey is constructed with a new methodology called as the deep assessment method which comes from the expressing an arbitrary function modeling the dataset as the finite summation of its previous values and the derivatives with unknown coefficient. The method uses the fractional calculus properties combining with Least Square Method and is compared to Long short-term memory (LSTM) algorithm which is a special type of neural network used for time sequences in general.

Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.

Background: Post-traumatic stress disorder (PTSD) is a common debilitating psychiatric condition for which pharmacological therapy is not always solvable. Various treatments have been suggested for these patients. Deep brain stimulation (DBS) is currently under investigation for patients affected by PTSD. 2) Methods: We review the neurocircuitry and up to date clinical concepts that may be of relevance for the implementation of DBS in posttraumatic stress disorder (PTSD). 3) Results: The role of DBS in treatment-refractory PTSD patients has been investigated relying on both preclinical and clinical studies. 4) Conclusions: DBS for PTSD is in its preliminary phases and likely to provide hope to patients with medical refractory PTSD following the results of randomized controlled studies.

This paper presents an approach to the two-dimensional analysis of elastic isotropic deep beams using the finite difference method (FDM). Deep beams are subjected to in-plane loading and present a shear span to height ratio of less than 2.50; consequently, Euler-Bernoulli beam theory and Timoshenko beam theory do not apply. Deep beams analysis is generally conducted using numerical methods such as the finite element method and to a lesser extent the FDM; the strut-and-tie model and the stress field method are also widely utilized. Analytical approaches usually make use of the Airy stress function, where stresses are formulated in terms of the stress function; however, the exact solution of this function satisfying all of the boundary conditions can hardly be found, even for simple cases. In this paper, deep beams were analyzed using the FDM. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected nodes of the structure. Therefore, the deep beam was discretized with a two-dimensional grid, and additional nodes were introduced at the boundaries and at positions of discontinuity (openings, brutal change of material properties, non-uniform grid spacing), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at boundary nodes and satisfy the boundary and continuity conditions. An Airy stress function approach and a displacement potential function approach were considered in this study whereby strong formulations of equations (equilibrium, kinematic, and constitutive) were set. Stress and stability analyses were carried out with this model; furthermore, deep beams of varying stiffness, layered beams, and beams having openings were analyzed. For slender beams, the results obtained with the Airy stress function approach showed good agreement with those of the Euler-Bernoulli beam theory, and for deep beams, the shapes of stress distributions were in good agreement with a proper understanding of the behavior of structures. On the other hand, the displacement potential function approach delivered unsatisfactory results, probably due to the use of an inefficient equation solver; a more powerful tool will be needed in future research for this purpose.

The agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. Grad-CAM, or gradient-weighted class activation mapping, creates heatmaps that highlight significant regions for prediction, which enhances the interpretability of the model.To increase robustness and accuracy, techniques such as spectral-shifted adversarial perturbation for data augmentation and spectrally-weighted global average pooling for feature aggregation are used. This technique provides 99.75% accuracy with minimal processing needs, making it acceptable for real-time applications in agricultural situations. This considerably improves apple disease management.

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

Attention mechanisms have become a fundamental component of deep learning, including the field of computer vision. The key idea behind attention in computer vision is to help the model focus on the relevant spatial regions of the input image, rather than treating all regions equally. The traditional approaches to attention mechanisms in computer vision often suffer from distribution inconsistencies in the attention maps, resulting in sharp transitions that negatively affect model’s focus and lead to poor generalization on complex shapes. The problem of spatial incoherence is particularly pronounced in the task of semantic segmentation, where accurate pixel-level predictions require a detailed understanding of the spatial relationships within the image. In this paper, we propose an attention mechanism called Smooth Attention designed for convolutional neural networks to address the problem of spatial inconsistency in attention maps through multidimensional spatial smoothing. We conduct a series of experiments to evaluate the effectiveness of the proposed mechanism and demonstrate its superior performance compared to traditional methods.

Results: This case series study included 20 PD participants with STN-DBS, with a mean age of 61.2±10.1 years. There were no differences in equilibrium scores of SOT conditions between 60, 130, and 180 Hz frequencies (p> 0.05), except the SOT6 score (p= 0.003), where 60 Hz showed better equilibrium performance in SOT6, indicating an advantage in postural control when visual and somatosensory cues are disturbed, and vestibular cues are not.

In the recent years, with the advent of Artificial Intelligence (AI) traditional methods have seen a significant transformation in the agriculture sector, especially in soil management. Soil management involves practices that maintains and improves the physical, chemical as well as the biological properties of the soil. Soil health management is essential for both the environmental conservation and sustainable agriculture production, ensuring the soil productivity and functional aspects associated with the ecosystem. Soil health management is one of the most important aspects of agriculture and food production, hence preserving and enhancing the soil health is an essential factor for supporting agriculture. Integration of Artificial Intelligence (AI) technologies with soil health management offers the potential to enhance agricultural sustainability, productivity, adapt to the climatic changes and resource constraints. The study of AI tools that can help improve soil health management by providing more accurate and efficient monitoring, analysis and decision-making capabilities. This paper studies the potential AI technologies including machine learning, robotics, and remote sensing in enhancing soil health, raising crop yields, and lowering environmental concerns by examining previous research and case studies.

Recently, deep neural networks (DNNs) have been widely used in various fields such as autonomous vehicles and smart homes. Since these DNNs can be directly implemented on edge devices, they offer advantages such as real-time processing in low-power and low-bandwidth environments. However, deployment of DNNs in embedded systems, including edge devices, exposes them to threats such as fault injection attacks. In this paper, we introduce two methods to induce misclassifications by using clock and voltage glitch-based fault attacks in devices where DNN models are executed. As a result of experiments on a microcontroller implemented with a DNN for image classification, we show that glitch injection attacks can lead with high probability to serious misclassifications. Furthermore, we propose a countermeasure to glitch attacks on the softmax function, and confirm that this method is effective in preventing misclassifications.

Fire detection is crucial for ensuring human safety and minimizing property damage. By utilizing advanced technologies, we can identify fires early, before they escalate. Autonomous fire detection systems are particularly vital in high-risk areas with minimal human presence. To address this need, we propose an automated fire-detection model based on YOLOv8. We modified the architecture of YOLOv8 and achieved impressive performances. Through comprehensive analysis of different YOLO models and their predecessors, we identified and addressed substantial gaps in this field. Also, by augmenting the framework with additional capabilities, we surpassed previous models, enabling real-time fire detection. Our proposed model achieves impressive performances, with 98% accuracy in fire detection and 97.8% in smoke detection. Moreover, recall rates for fire and smoke are 97.1% and 97.4%, respectively, with a mean average precision (mAP) accuracy of 99.1% for both cases. Finally, we applied an explainable artificial intelligence technique to interpret our proposed model and its results. This study lays the foundation for future research aimed at enhancing fire detection efficiency. The reliability of our modified model presents promising opportunities for future advancements, ensuring increased effectiveness and precision in efficient fire detection.

This study introduces an advanced method for climate downscaling using a Super-Resolution Generative Adversarial Network (SRGAN), referred to as Elevation-integrated TEmperature and Precipitation SRGAN downscaling (E-TEPS), to enhance the spatial resolution of climate data, specifically 2m-temperature above the surface and total precipitation, from the high-resolution Euro-Mediterranean Center on Climate Change (CMCC) dataset over Italy. Traditional Numerical weather prediction (NWP) as well as global climate models (GCMs) generally lack the resolution required for local applications, and this research addresses that gap by refining GCM outputs to provide detailed local climate insights. E-TEPS incorporates elevation maps as auxiliary inputs, significantly improving the accuracy of downscaled temperature and precipitation data. Utilizing Google Cloud's computational resources, the model processes large climate datasets and runs complex training operations efficiently. The system is exceptionally fast, delivering all results in under 10 seconds. The model demonstrates superior performance over two interpolation methods (bicubic and linear), used as traditional downscaling methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), higher Pearson Correlation for temperature, and higher agreement values for the precipitation. Visual comparisons confirm that E-TEPS generated high-resolution outputs exhibit fewer artifacts and better detail preservation, particularly in regions with complex terrain and variable climatic conditions. These findings underscore the potential of our model in delivering precise, high-resolution climate predictions, thereby enhancing the performance of early-warning systems and supporting more effective climate-related decision-making. This paper is the first part of a two-part series; the second part will demonstrate the integration of this downscaling method with the FourCastNet global forecasting model to enhance climate predictions at a regional level, particularly focusing on high-resolution forecasts for Italy.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), often resulting in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models, analyze the key geometric information in the G-code generated during CNC machining, and uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bezier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. Taking the optimization of a gear model’s process file as an example, the effectiveness of the proposed method is verified. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths, reduce machining defects, and has substantial application value.

Deep inhalation breath hold (DIBH) has become a valuable technique in left breast cancer radiotherapy, offering the possibility to reduce radiation exposure to organs at risk (OARs) and minimize the risk of cardiac complications. The treatment method involves patients stopping breathing during irradiation to temporarily distance the heart from the radiation field, reducing potential cardiac and other complications. Research shows that DIBH reduces the radiation dose to the heart and lungs. However, the effectiveness of DIBH is determined by a variety of factors, including the patient's training, cooperation, anatomical features, age and choice of radiotherapy technique. Additionally, cardiovascular risk factors: diabetes, smoking, hypertension can affect the effectiveness and potential complications of DIBH. Moreover, lack of consent, fear, anxiety, depression and comorbid psychiatric disorders disqualify some patients from the DIBH treatment method. Careful patient selection, comprehensive training and optimization of treatment parameters are essential to maximize the benefits of DIBH while minimizing potential side effects. DIBH enhancement techniques, such as IMRT or VMAT, also have an important role to play. The purpose of this review article is to summarize the factors affecting the efficacy and side effects of DIBH in radiation therapy for left breast cancer, with the aim of optimizing its clinical application while minimizing side effects.

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

In this work, we introduce the first pipeline that combines a refraction-aware Structure-from-Motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach the real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements on accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at 2 locations in the southern Mediterranean Sea, with varying seafloor types which demonstrate the benefits of refraction correction for the deep learning framework. We open-source our refraction-aware SfM, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.

Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.

This literature review delves into the realm of fault detection techniques for transmission lines, aiming to provide a comprehensive overview of recent advancements and key trends in the field. Employing a structured approach, the review synthesizes a plethora of research spanning from 2019 to 2024, sourced from diverse databases including IEEE Xplore, ScienceDirect, ResearchGate, Scopus, Litmaps, and Google Scholar. The methodology encompasses a systematic literature search protocol, stringent inclusion and exclusion criteria, meticulous data extraction, and a multi-dimensional analysis framework. The literature review uncovers a spectrum of fault detection methodologies, ranging from traditional signal processing techniques like Discrete Wavelet Transform and phase angle-based methods to cutting-edge deep learning algorithms such as Capsule Networks and Convolutional Neural Networks. Insights gleaned from the review underscore the critical importance of fault detection in maintaining the reliability, safety, and efficiency of power grids, highlighting its role as a frontline defense against widespread outages and equipment damage. Key findings from the review shed light on the efficacy of different fault detection approaches, showcasing their strengths and limitations across diverse system conditions. Furthermore, the review identifies common trends and challenges, including the need for real-world validation, scalability, adaptability to various network configurations, and cybersecurity considerations. This literature provides valuable insights and recommendations for future research endeavors in fault detection for transmission lines. By embracing advancements in both traditional and emerging techniques, researchers can continue to enhance the resilience and dependability of power transmission systems, ensuring their ability to withstand evolving challenges and safeguard critical infrastructure.

Recommendation systems are crucial in navigating the vast digital market. However, user data’s dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the Non-stationary Transformer, designed to capture and leverage the temporal dynamics within data effectively. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for recommendation systems. Our experimental analysis, encompassing deep learning and reinforcement learning paradigms, demonstrates the framework’s superiority over benchmark models. The empirical results confirm the efficacy of our proposed framework, which not only provides significant performance enhancements, approximately 8% in Logloss reduction and up to 2% increase in F1 score but also underscores its potential applicability across accumulative reward scenarios. These findings advocate adopting Non-stationary Transformer models to tackle the complexities of today’s recommendation tasks.

Crop production in Afghanistan suffers from limited phosphorus (P) availability, which severely hinders national agriculture sustainability. This present study hypothesized that deep fertilizer placement could significantly enhance uptake of immobile P and thus tissue P accumulation and crop yield. A two-year pot experiment growing two maize (Zea mays) hybrid cultivars (Xida-789 and Xida-211) was therefore conducted to test these hypotheses under three contrasting fertilizer placement methods (broadcast, side band, and deep band). In doing so, P concentrations in both maize tissues and soils were compared at 45, 60, and 115 days after sowing (DAS) under nine combinations of nitrogen (N) and P fertilizer rates (kg ha-1: N112P45, N112P60, N112P75, N150P45, N150P60, N150P75, N187P45, N187P60, N187P75). Results have shown that deep band placement significantly increased P uptake efficiency, leading to greater P concentration and accumulation in maize tissues compared to other two fertilization methods. This improved P uptake was attributed to several factors associated with deep placement, including reduced P fixation, enhanced root access to P, and moisture availability for P uptake. Additionally, deep band placement combined with higher N application rates (N187 and N150) further enhanced plant P uptake by promoting P availability and utilization mechanisms. Deep band placement also resulted in significantly higher total soil P, Olsen-P, and P use efficiency than broadcast and side band methods, indicating a more efficient P fertilization strategy for maize that can improve growth and yield. This present study also found positive correlations between P concentration in plant organs and soil Olsen-P, highlighting the importance of adequate soil P levels for optimal plant growth. Overall, our results have showed that deep band fertilizer placement emerged as a superior strategy for enhancing P uptake efficiency, utilization, and maize productivity compared to broadcast and side band placement. This approach can potentially optimize P fertilizer utilization, improve maize growth and yield, while minimizing environmental P losses associated with broadcast applications.

The experience of ego dissolution and self-transcendence induced by psychedelic substances offers a profound challenge to our conventional understanding of the self and our relationship with the natural world. This paper delves into the philosophical underpinnings and implications of this phenomenon, exploring how the temporary breakdown of ego boundaries during a psychedelic experience may foster a sense of profound interconnectedness with nature. Drawing from Western and Eastern philosophical traditions, as well as recent insights from neuroscience and consciousness studies, we examine the potential of psychedelic-induced self-transcendence to catalyze a shift in our perspectives on the human-nature relationship. The paper investigates the philosophical implications of this experience, including its resonance with deep ecological philosophies and its potential to inspire more ecocentric ethical frameworks. Furthermore, we explore the possible practical implications of fostering a sense of ecological connectedness through psychedelics, such as promoting environmentally sustainable behaviors and addressing the pressing challenges of climate change and ecological degradation. Ultimately, this exploration highlights the value of engaging with altered states of consciousness as a means to reconsider our place within the intricate web of life on Earth.

Facial acne is a prevalent dermatological condition regularly observed in the general population. This study introduces a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrates improvement in performance, offering a novel direction for future acne analysis.

Despite their relatively low incidence globally, CNS tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes..

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the Energy transition. This study focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.

Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.

In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

Deep eutectic solvents are a mixture of two or more components, that at a particular composition become liquids at room temperature. When the compounds that constitute the DES are primary metabolites, namely, amino acids, organic acids, sugars, or choline derivatives, the DES are so called natural deep eutectic solvents (NADES). NADES fully represent green chemistry principles. These solvents are highly welcome, as they are obtained from renewable resources, and gratifyingly biodegradable and biocompatible. They are an alternative to room temperature ionic liquids (RTILs). From the pharmaceutical industry point of view, they are highly desirable, but unfortunately have been rarely used despite their enormous potential. In this review, we will look at their impact on the asymmetric catalytic synthesis of key target molecules, via metal-based catalysis, biocatalysis and organocatalysis. In many cases the NADES that have been used are chiral and can even promote enantioselective reactions, this crucial and very exciting aspect will also be discussed and analysed.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

In areas where conventional fundus cameras are logistically unaffordable, the smartphone-based approach is considered as a promising future of glaucoma screening due to their affordability and ease-of-use. Optic disc localization is a critical stage during glaucoma screenings. For fundus images captured with standard fundus cameras, the majority of the models available out there can locate the optic disc with satisfactory performance. However, for images that are captured with smartphone-based fundus cameras, the inherent noise and lower quality of the fundus image makes it difficult for models to detect the optic disc region. In this study, we have proposed to utilize YOLOv8n for optic disc localization due to the model’s cutting-edge performance in diverse tasks and its lightweight nature. We have used a public dataset which has 2000 low quality fundus images that are captured using a smartphone-based fundoscopy device. From these, 60% of the data was used for fine-tuning the model, and 25% for testing. By using a confidence of 50% set as threshold, the model was able to detect the optic disc successfully on over 97% of test images with intersection over union of above 0.85. These results highlight the potential of the lightweight YOLOv8n model for deployment on resource-constrained environments, offering a promising performance on accurately localizing the OD and enhancing the feasibility of affordable glaucoma screening on smartphones.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

The peculiar physicochemical features of deep eutectic solvents (DESs), in particular their tunability, make them ideal media for various applications. Despite their ability to solubilize metal oxides, their use as rust removers from valuable substrates has not yet been thoroughly investigated. In this study we chosen three known DESs, consisting of choline chloride and acetic, oxalic or citric acid for evaluating their ability to remove corrosion products from a cellulose-based material as linen fabric and two different lithotypes, as travertine and granite. The artificial staining was achieved by placing a rusty iron grid on their surfaces. The DESs were applied by means of cellulose poultice on the linen fabrics, while on the rusted stone surfaces with a cotton swab. Macro- and microscopic observations, colorimetry and SEM/EDS analysis were employed to ascertain the cleaning effectiveness and the absence of side effects on the samples after treatment. Oxalic acid-based DES was capable of removing rust stains from both stone and cellulose-based samples, while choline chloride/citric acid DES was effective only on stone specimens. The results suggest a new practical application of DESs for the elimination of rust from lithic and cellulosic substrates of precious and artistic value.

Membrane filtration is a popular choice in the treatment of wastewater for reuse. However, when used to produce clean water, these devices are subject to concentration polarization and fouling, which inhibit their ability to operate efficiently and provide high-quality permeate streams. Patterned membrane surfaces offer a hydrodynamic approach to mitigating concentration polarization and the surface fouling that follows. However, when subject to a steady feed flow rate, surface patterns generally produce stagnant zones in cavity-like spaces where particulate from the bulk flow recirculates in stationary vortices. To prevent aggregation in these recirculation zones, and therefore the onset of surface fouling, we study a rapidly pulsed feed flow. When combined with cavity-like geometries, such as the valleys present in membrane surface patterns, a rapidly pulsed flow generates mixing mechanisms (i.e., the deep sweep and the vortex ejection) that disrupt otherwise stagnant regions. First, we confirm the existence of these mechanisms, which are typically induced in impermeable cavities at relatively low Reynolds numbers, for near transitional feed Reynolds numbers in permeable cavity spaces via numerical simulation of the flow field. Following this confirmation, we demonstrate the ability of these mechanisms to remove particles trapped in recirculation zones via massless particle tracking studies. The results of this work suggest that when combined with a rapidly pulsed inlet flow, patterned membrane surfaces can not only alleviate concentration polarization and the surface fouling that follows but also reduce the need for traditional cleaning methods, which require operational downtime and often involve the use of abrasive chemical agents.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target tracking algorithms. Additionally, traditional target-tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target-tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and Transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into sub-sequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target-tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.

The artificial intelligence (AI) is a machine or computing platform that is capable of making intelligent decision similarly to the human mind. The AI could improve diagnosis, treatment prognosis, and clinical workflow, particularly in the field of radiology. Concerning acute respiratory distress syndrome (ARDS) is a rather heterogeneous syndrome characterized by an inflammatory lung edema leading to an increased lung weight, decreased lung aeration with the presence of alveolar collapse and interstitial opacities mainly in the dependent area. Thus, lung imaging is an essential tool to assess not only the morphology but also the mechanical characteristics of ARDS patients. Chest computed tomography (CT) and ultrasound have a higher sensitivity and specificity than conventional chest radiography. This narrative review summarizes the state of art of AI in the field of lung imaging, focusing on CT and ultrasound technique in ARDS patients. A total number of 18 articles were retrieved. The application of AI in lung imaging was mainly devoted to assess the prediction of ARDS, the quantification of alveolar recruitment, the possible alternative diagnosis and the outcome. Although the presence of a physician is still essential to ensure a high quality of examinations the AI could assist the clinical team to provide the best possible care.

Natural deep eutectic solvents (NADES) have been shown as selective and environmentally friendly solvents for the extraction of bioactive compounds. However, studies on the solubility of low molecular weight carbohydrates (LMWC) in NADES are scarce. In this work, new solubility data of LMWC in NADES are provided and a new approach based on the use of these solvents for the efficient fractionation of bioactive carbohydrates was explored for the first time. Several mono- and disaccharides and three NADES based on choline chloride (ChCl) and different donors [2-ethylene glycol (EtG), glycerol (Gly) and ethanedioic acid dihydrate (Eth)] were considered. While degradation of carbohydrates, mainly ketoses, was detected with ChCl:Eth due to its acidic nature, ChCl:EtG and ChCl:Gly were found to be useful alternatives for selectively separating bioactive ketoses and their corresponding aldoses (e.g. lactulose/lactose and tagatose/galactose) present in equimolar binary mixtures. In addition, the usefulness of ChCl:EtG for the selective enrichment of lactulose to be used as food ingredient or nutraceutical was proven (from a 25% in the reaction mixture to a 56% in the purified sample). NADES could be used for the selective fractionation of value-added carbohydrates from interfering sugars for several applications including food science, engineering or pharmaceuticals.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.

Balanced nitrogen (N) and phosphorus (P) rates coupling with rational fertilization methodology could promote crop N accumulation, N use efficiency and yield production, particularly in semi-arid and arid regions. To test these characteristics, a two-year (2018 and 2019) pot experiment was therefore performed by growing summer maize in a rain-proof glass greenhouse, under 9 combined N (112, 150 and 187 kg ha-1, urea) and P (45, 60, 75 kg ha-1, calcium superphosphate) rates and three contrasting fertilizer placements. The fertilizers were placed by broadcast on soil surface (Broadcast), side band on 4 cm strip soil surface within 7 cm from sowing line (Side band) and deep band on 4 cm strip below 7 cm soil depth within 7 cm from sowing line (Deep band). Results from three maize growth stages (eight-leaf, 45 days after sowing, DAS; tasseling, 60 DAS; and harvest, 115 DAS) showed that leaf, stem, root N accumulation and total soil N were significantly increased under Deep band than under both Side band and Broadcast at N150P60, N187P60, N150P75 and N187P75, but not at N112P45, N150P45, N187P45, N112P60 and N112P75. Significantly greater leaf, stem, and root N accumulations were also displayed at N150 and N187 than at N112 for the same P60 or P75 under Deep band at 60 DAS and 115 DAS, while were for leaf and stem N accumulations at P75 and P60 than at P45 for the same N150 under Deep band at 45 DAS, 60 DAS and 115 DAS. Significantly greater agronomy N use efficiency, partial factor productivity and N use efficiency exhibited under Deep band than under Side band and Broadcast at N150P75 and N187P75, but at N150P60 and N187P60 for NUE only. In addition, leaf, stem, seed, root N concentrations positively correlated with their own N accumulations or soil N concentration at tasseling and harvest stages. Our results demonstrate that a synchronized N150P60, N187P60, N150P75 or N187P75 fertilization rate with Deep band placement can improve soil N availability and root N uptake, and hereby greater aboveground N accumulation, N use efficiency and yield production of maize particularly practical for small-holder farmers globally.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

The application of deep learning in the detection of Synthetic Aperture Radar (SAR) targets has been primarily limited to large objects such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5,392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478x1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate 9 benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex background. Mix MSTAR is available at: https://github.com/TheGreatTreatsby/Mix-MSTAR-mmrotate.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Social media platforms are increasingly enabling the propagation of content from groups related to drug use, thus posing risks for the wider population and, in particular, individuals who are amenable to drug use and drug addiction. The detection of drug use content on social media platforms is a priority for governments, technology companies, and drug law enforcement organizations. To counter this issue, various techniques have been developed to identify and promptly remove drug use content, while also blocking its creators from network access. In this paper, we introduce a manually annotated Twitter dataset, comprising 156,521 tweets published between 2008 and 2022, specifically compiled for the purpose of drug use detection. The dataset underwent annotation by several group of expert annotators who classified the tweets as either drug use or non-drug use. Exploratory data analysis was conducted to comprehend the dataset's characteristics. Various classification algorithms, including SVM, XGBoost, RF, NB, LSTM, and BERT were employed using the dataset. Among the traditional machine learning models, SVM utilizing term frequency-inverse document frequency features achieved the highest F1-Score (0.9017). However, BERT with textual features concatenated with numerical and categorical features in ensemble method surpassed the performance of traditional models, attaining F1-Score of 0.9112. To facilitate future research and enhance English online drug use classification accuracy, the dataset will be made publicly available.

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Keywords: Deep mining; Drill bit structural parameters; Fidelity coring; Abaqus simulation and analysis.

: Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learn-ing-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) tech-niques. A U-Net based model, Wetland-SAV Network, was trained to identify SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha) with a significant loss of SAV habitats observed post-Hurricane Barry (~2,300 ha) in 2019. The habitat areas and habitat-specific carbon fluxes were then used to quantify net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a Carbon Balance Model. The total net GHG flux was in the range of -0.13 ± 0.06 to -0.86 ± 0.37 ×105 tonne CO2e yr-1 and increased up to 40% (-0.23 ± 0.10 to -0.90 ± 0.39 ×105 tonne CO2e yr-1 ) when SAV was accounted for within the calculation. At the hectare scale, inclusion of SAV resulted in an increase of ~60% for net GHG sink in shallow areas adjacent to emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring of SAV in turbid sub-tropical coastal deltas as a component of increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

UAV-captured multispectral imagery was used to characterize and associate Moriche’s palm 1 canopy features with the maturity stage of the corresponding fruits. Deep learning models based on 2 convolutional neural networks (CNN) were trained in order to determine correlations between the 3 photosynthetic radiation of the palms with the fruit. Here, we compare several approaches for feature 4 extraction based on vegetation indices and graph-based models. Also, a comprehensive dataset has 5 been collected and labeled, containing plant data for an entire phenological cycle of the Moriche 6 palms. Experimental results reported an average estimation accuracy of 72%, by using the proposed 7 method in dense forests of the Amazonian region.

Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.

In this paper, we introduce a model for the automatic generation of literary sentences in French. It is based on algorithms that we have previously used to generate sentences in Spanish and Portuguese, and on a new corpus consisting of literary texts in French that we have constructed, called [FR]. Our automatic text generation algorithm combines language models, shallow parsing and deep learning, artificial neural networks. We have also proposed and implemented a manual evaluation protocol to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfill four simple criteria. We have obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.

The Median American Sign Language Interpretation Software (ASL) Interpretation Software is a web application that is capable of interpreting American Sign Language in real-time, utilizing an internet connection and a primary web camera, complete with basic phrases and letters. Extensive use of Deep Learning and Neural Networks, specifically Convoluted Neural Networks, enables Median to interpret video inputs and generate accurate results directly displayed to the user in text format. The ultimate goal for Median is to have it act as a bridge between hearing people and members of the deaf community, allowing deaf people to communicate with non-signing people using American Sign Language. Furthermore, Median has been designed to benefit people who lack access to a human ASL Translator, as its format as a website allows it to be accessible anywhere at any time, giving increased availability over human interpreters. Median is designed to be a very versatile program with great potential for growth and expansion.

In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification. However, the proposed network models have a higher model complexity and do not provide high classification accuracy if few-shot learning is used. This paper pre-sents an HSI classification method that combines random patches network (RPNet) and re-cursive filtering (RF) to obtain informative deep features. The proposed method first convolves image bands with random patches to extract multi-level deep RPNet features. Thereafter, the RPNet feature set is subjected to dimension reduction through principal component analysis (PCA) and the extracted components are filtered using the RF procedure. Finally, HSI spectral features and the obtained RPNet-RF features are combined to classify the HSI using a support vector machine (SVM) classifier. In order to test the performance of the proposed RPNet-RF method, some experiments were performed on three widely known datasets using a few training samples for each class and classification results were compared with those obtained by other advanced HSI classification methods adopted for small training samples. The comparison showed that the RPNet-RF classification is characterized by higher values of such evaluation metrics as overall accuracy and Kappa coefficient (https://github.com/UchaevD/RPNet-RF).

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Motor imagery(MI)-based Brain-Computer Interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been made in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them are using offline data and not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed Deep Learning methods do not outperform the traditional machine learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, Functional Electrical Stimulation (FES) yielded the best performance compared to non-FES systems.

Embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommen- dation and question-answer systems. Semantic relationships are words with a relationship between them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model involving semantic relationships will provide better performance for tasks that use them. This paper presents three embedding models based on semantic relations extracted fromWikipedia to classify texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered in this work since previous experiments have shown that they are the ones that provide the most semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it. Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a combination of the first two. A convolutional neural network was trained for text classification to evaluate the performance of each model. The results obtained were evaluated with the metrics of precision, accuracy, recall, and F1-measure. The best values obtained with the second model were accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.

Tuberculosis (TB) disease still remain a major global threat due to the growing number of drug-resistant species and global warming. Despite the fact that there are new molecular diagnostic approaches, however, majority of developing countries and remote clinics depends on conventional approaches such as Tuberculin test, microscopic examinations and radiographic imaging (Chest X-ray). These techniques are hindered by several challenges which can lead to miss-diagnosis especially when interpreting large number of sample cases. Thus, in order to reduce workload and prevent miss-diagnosis, scientists incorporated computer-aided technology for detection of medical images known as Computer aided Detection (CADe) or Diagnosis (CADx). The use of AI-powered techniques has shown to improve accuracy, sensitivity, specificity. In this review, we discussed about the epidemiology, pathology, diagnosis and treatment of tuberculosis. The review also provides background information on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Transfer Learning (TL) and their applications in detection of tuberculosis from both microscopic slide images and X-ray images. The review also proposed an IoT/AI powered system which allows transfer of results obtained from DL models with end users through internet networks. The concept of futuristic diagnosis, limitations of current techniques and open research issues are also discussed.

Fog, haze, or smoke are usual atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of them very relevant for decision-making for the security or autonomous vehicle industries. Several dehazing methods have been proposed, however, to the best of our knowledge, all proposed metrics compare the defogged image to its ground truth for evaluation of the defogging algorithms, or need to estimate parameters through physical models. This fact hinders progress in the field as obtaining proper ground truth images is costly and time-consuming, and physical parameters greatly depend on the scene conditions. This paper aims to tackle this issue by proposing a contour-based metric for image defogging evaluation that does not need a ground truth image. The proposed metric only requires the original hazy RGB image and the RGB image after the defogging procedure. A comparison of the proposed metric with metrics currently used in the NTIRE 2018 defogging challenge is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics.

The establishment of thresholds is integral to environmental management. This paper introduces the use of thresholds in the context of deep-seabed mining, a nascent industry for which an exploitation regime of regulations, standards and guidelines is still in the process of being developed, and for which the roles and values of thresholds have yet to be finalised. There are several options for integrating thresholds into the International Seabed Authority’s regulatory regime, from being stipulated in regulations to being part of a mining contract, each option having its own advantages and disadvantages. Here we explore the range of ways that thresholds can be derived, set out the challenges in translating ecological and management data into thresholds, highlight factors for acceptance and operationalisation of thresholds in deep-seabed mining, and explain the necessity of refining thresholds as knowledge on impacts to features improves. Some comparable marine industries already use thresholds and these could potentially be used as starting points for the development of thresholds for deep-seabed mining. In order to be acceptable to the wide range of deep-seabed mining stakeholders, thresholds need to strike a balance among levels of harm acceptable by society, levels of environmental precaution justifiable by governments, scientific robustness, and operational practicality.

Deep neural networks (DNNs), founded on the brain's neuronal organization, can extract higher-level features from raw input. However, complex intellect via autonomous decision-making is way beyond current AI design. Here we propose an autonomous AI inspired by the thermodynamic cycle of sensory perception, operating between two information density reservoirs. Stimulus unbalances the high entropy resting state and triggers a thermodynamic cycle. By recovering the initial conditions, self-regulation generates a response while accumulating an orthogonal, holographic potential. The resulting high-density manifold is a stable memory and experience field, which increases future freedom of action via intelligent decision-making.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.

The study purpose was to train and validate a deep-learning approach to detect micro-scale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep-learning models for eight micro-scale features guided by the Microscale Audit of Pedestrian Streetscapes-Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train--correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N=512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between micro-scale features and GIS-measured- and participant reported-macro-scale walkability. Classifier precision, recall, and overall accuracy were all >84%. Total micro-scale was associated with overall macro-scale walkability (r=0.300,p<.001). Positive associations were found between model-detected and self-reported sidewalks (r=0.41,p<.001) and sidewalk buffers (r=0.26,p<.001). Computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

(1) Background: Deep hypothermic circulatory arrest (DHCA) with selective antegrade cerebral perfusion (ACP) is an established cerebral protection technique for the conduction of complex surgical procedures involving the aortic arch. It is controversial if the duration of DHCA is associated with adverse outcome in patients with acute type A aortic dissection (AAAD). Our goal was to investigate whether DHCA time was associated with surgical outcome in patients undergoing a surgical treatment of AAAD. (2) Methods: 410 Patients were divided into two groups concerning the DHCA time less than 60 minutes and equal or longer than 60 minutes. (3) Results: Patients with longer DHCA time were significantly younger (p=0.001). Intraoperatively, complex procedures with aortic arch surgery were more common in patients with longer DHCA time (p&lt;0.001). Accordingly, cardiopulmonary bypass (p&lt;0.001), cross-clamping (p&lt;0.001) and DHCA times (p&lt;0.001) were significantly longer in this group. Postoperatively, only the duration of mechanical ventilation (p&lt;0.001) and the rate of tracheotomy were significantly higher in these patients. 30-day mortality was satisfactory for both groups (p=0.746). (4) Conclusions: Our results showed that improvements in perioperative management including ACP allow a successful performance of surgical treatment of AAAD under DHCA with duration of even longer than 60 minutes.

Drought decreases grain yield of sorghum [Sorghum bicolor (L.) Moench], and understanding the mechanism(s) related to drought tolerance is critical for sustaining sorghum production. Variation in root and shoot traits associated with drought tolerance were analyzed to provide an integrated view of factors that underlie the drought tolerance of sorghum. The plants were grown in the root column up to the five-leaf stage, then exposed to either 0.9 fraction of transpirable soil water (FTSW) or 0.4 FTSW for five days. In another experiment, at the five-leaf stage, stress was imposed for 14 days. Various root and shoot traits associated with drought tolerance were recorded. The seminal root angle of IS13540 was lower (24.4) than IS23143 (42.6). Drought stress increased the maximum root length (40%) and total root length (58%) of IS13540 than its irrigated control. In contrast, the maximum root length and total root length were decreased in IS23143. Similarly, across the lines, drought stress decreased stomatal conductance (37%), transpiration rate (42%), photosynthetic rate (40%), photosystem II quantum yield (20%), photochemical quenching (44%), and total dry matter production (34%) than irrigated control. An increased transpiration rate was observed in IS23143 than IS13540 under irrigated and drought stress. In IS23143, the reduction in photosynthetic rate under drought may be a combination of stomatal and non-stomatal factors. However, in IS13540, the reduction is especially by the stomatal factors. It is evident that IS13540 is a drought-tolerant line, and tolerance is related to a deep prolific root system and reduced tran-spiration rate.