Deep Research Research Articles

Near‐Storm Environmental Relationships With Tropical Oceanic Convective Structure Observed During NASA CPEX and CPEX‐AW

AbstractDeep tropical oceanic convection (TOC) is a prevailing component of the tropical atmosphere and plays a significant role in modulating global weather and climate. Despite its importance, prediction challenges remain, partly attributed to a lack of understanding of how TOC relates to its near‐storm environments. Prior studies suggest location‐dependent relationships between TOC structure and associated environments, necessitating targeted regional studies. The NASA 2017 Convective Processes Experiment (CPEX) and 2021 CPEX—Aerosols & Winds (CPEX‐AW) field campaigns collected high‐resolution measurements of convective storms and their environments in the Gulf of Mexico, Caribbean, and western Atlantic basins, providing a rare opportunity to investigate near‐storm environmental relationships with 3‐D TOC structure where in situ non‐tropical cyclone‐related deep TOC research is comparatively lacking. Collocated CPEX and CPEX‐AW airborne observations from the multi‐wavelength Airborne Precipitation Radar, Doppler Aerosol Wind Lidar, and dropsondes revealed large near‐storm environmental variability across TOC of similar convective type (i.e., isolated, organized) and within individual convective systems. However, trends still emerged amongst the large environmental variability. Horizontal TOC structure was most consistently linked to planetary boundary layer and mid‐tropospheric near‐storm environments, with organized TOC being associated with generally greater relative humidity (RH) and vertical speed shear than isolated TOC. TOC intensity was linked to upper tropospheric (i.e., above melting level) near‐storm environments, with isolated TOC intensity most consistently associated with upper tropospheric CAPE and organized TOC intensity associated with upper tropospheric RH. Mesoscale low‐level convergence was also linked to greater organized TOC intensity, motivating further research using these unique data sets.

Deep Learning Hyperspectral Pansharpening on Large-Scale PRISMA Dataset

Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth’s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification

Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification. We analysed the testing error with increasing the training samples and model size for four different ConvNet architectures. The focus of our experiments is width scaling, and we have increased the number of parameters to up to 1.8 million. Our evaluation was based on two publicly available datasets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus, which together contain 10,707 training samples. The results show that the testing error follows a saturating power-law with both model and dataset size. This pattern is consistent across different datasets and ConvNet architectures. Furthermore, empirically observed accuracies saturate at 85%–87%, which may be due to an imperfect inter-rater agreement on the clinical labels. The empirical scaling behaviour of the test performance with dataset and model size has significant implications for deep EEG pathology classification research and practice. Our findings highlight the potential of deep ConvNets for high-performance EEG pathology classification, and the identified scaling relationships provide valuable recommendations for the advancement of automated EEG diagnostics.

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson

Background and objectives:Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson’s Disease (PD), leading to falls and significantly impacting patients’ quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients’ conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser–Meyer–Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention. Method:The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient’s shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG. Results:Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained. Conclusion:This study validates CNN’s effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

The challenges and benefits of public health in smart cities from a 4 M perspective.

With the acceleration of urbanization, public health issues have become increasingly prominent in smart city construction, especially in the face of sudden public health crises. A deep research method for public health management based on a 4M perspective (human, machine, materials, methods) is proposed to effectively address these challenges. Methods: The method involves studying the impact of human factors such as population age, gender, and occupation on public health from a human perspective. It incorporates a machine perspective by constructing a public health prediction model using deep neural networks. Additionally, it analyzes resource allocation and process optimization in public health management from the materials and methods perspectives. The experiments demonstrate that the public health prediction model based on deep neural networks achieved a prediction accuracy of 98.6% and a recall rate of 97.5% on the test dataset. In terms of resource allocation and process optimization, reasonable adjustments and optimizations increased the coverage of public health services by 20% and decreased the response time to public health events by 30%. This research method has significant benefits for addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, helping smart cities respond more quickly and accurately to potential large-scale public health events in the future. This approach holds important theoretical and practical significance.

U-Pb detrital zircon age determination and provenance of the lower Karoo succession from the Karoo Research Initiative (KARIN) borehole KWV-1, Eastern Cape Province, South Africa

Abstract In recent years, the maximum age of sedimentation and possible sources of detritus to the sedimentary successions of the Karoo Supergroup has received a lot of interest, with various authors presenting age data from across the main Karoo Basin (MKB). Our paper aims to contribute to this ever-growing geochronological database as we report weighted mean ages for the youngest zircons as well as detrital zircon age distributions for the lower Karoo Supergroup succession sampled from the Centre of Excellence for Integrated Mineral and Energy Resource Analysis (CIMERA)-Karoo Research Initiative (KARIN) drill core KWV-1. A total of 880 near concordant zircon ages were determined using Laser Ablation Quadrupole Inductively Coupled Plasma Mass Spectrometry (LA-Q-ICP-MS) from fifteen samples representing the Dwyka Group, the Collingham-, Ripon-, Fort Brown- and Waterford formations of the Ecca Group and the Koonap Formation of the Beaufort Group as intersected by this ~2 352 m deep research borehole drilled in the southeastern sector of the basin. The weighted mean ages for the youngest zircons calculated for the formations of the Ecca- and Beaufort groups compares with findings reported by other authors and with the ages reported for tuffaceous layers associated with these units. The weighted mean ages for the youngest zircons of the Dwyka Group reflect the age of the youngest source area and do not aid in constraining the maximum age of deposition. A significant change in detrital zircon age distribution was noted between the Dwyka Group and the overlying formations of the Ecca and Beaufort groups, which we interpret as a change in provenance. The major Meso-to Neoproterozoic detrital zircon age fractions present in the Dwyka Group are attributed to source areas located towards the north and northeast of the MKB, in line with regional ice flow directions. A significant late Carboniferous to Permian detrital zircon age fraction is however observed throughout the formations of the Ecca- and Beaufort groups, with a lesser fraction of older zircon ages. The prevailing youngest age fraction reflects ages associated with magmatic source areas in the Gondwanide Orogeny located towards the south of the MKB at the time of deposition, while the recycling of deformed sedimentary strata of the Cape Supergroup contained in the Cape Fold Belt is considered the source of older detritus. The source of detritus to the MKB remained unchanged during the deposition of the respective formations of the Ecca- and Beaufort groups, despite changes in the depositional environment brough on by regional tectonics.

Ornamental Variety of Tombs of Sigam Kings in 1855-1900 in the Wetland Area of Sigam Sea Island, Kotabaru Regency

History and culture have begun to be fulfilled by the younger generation, it takes effort to map history and concrete explanatory evidence of each heritage site from the past. Kotabaru Regency is one of the centers of study of this research because many historical relics were found in the form of tombs from the Sigam Kingdom that received less attention from related agencies. Therefore, deeper research is needed so that it can be an evaluation material for future policies and strategies. This study aims to find out how the shape of the tomb (typology) and the variety of ornamental tombs of the kings of Sigam in 1855-1900. The research method uses historical methods: (1) heuristics, (2) source criticism, (3) interpretation, (4) historiography. The results of this study are as follows: This cemetery is a complex of tombs of kings, Kingdoms, Sea Island and their relatives. The King of Pulau Laut who is buried in this complex there are three people, namely Prince Jaya Sumitra bin Pangeran Muhammad Nafis, Prince Abdul Kadir bin Pangeran Muhammad Nafis, and Pangeran Brangta Kesuma bin Pangeran Abdul Kadir. The tombstones of the kings are currently covered in yellow cloth by the surrounding community.

Emphasis on awareness of early-onset colorectal cancer

The incidence and mortality rates of early-onset colorectal cancer (EOCRC) among people under 50 years old are showing an upward trend. Although traditional epidemiological studies have conducted relatively deep research and screened out environmental factors related to EOCRC, our understanding of the causes, mechanisms, and treatment of this disease is still far from sufficient. In this review, we clarify the current progress of EOCRC, with a particular focus on epidemiology, screening status, clinical symptoms, and prognosis. This provides new evidence for secondary prevention, including precision screening, and offers new ideas for improving the diagnosis and treatment of EOCRC.

Deepfake Detection using Deep Learning

The detection of deep fakes, which are synthetic media generated using deep learning techniques, has become increasingly important due to their potential to deceive and manipulate individuals, organizations, and society at large. This abstract explores recent advancements in deep fake detection methodologies, including machine learning algorithms, neural network architectures, and forensic techniques. Key challenges such as the rapid evolution of deep fake generation methods and the emergence of highly realistic forgeries are discussed. Additionally, the abstract examines the ethical implications of deep fake technology and the need for robust detection methods to mitigate its harmful effects. Finally, future directions in deep fake detection research are outlined, emphasizing the importance of interdisciplinary collaboration and the development of innovative approaches to combat this growing threat to information integrity and trust

Queering Neverland

During the spring 2023 semester, I assistant directed a double-cast, queer production of Kimberly Belflower’s Lost Girl at American University. In the process, I developed a “dramaturgy of care”—an approach to integrating dramaturgical principles into a variety of theatrical disciplines. Using this approach, theatre makers of all stripes should make the time for deep text analysis, research (on the text and how it’s situated culturally, historically, and politically), discussions, and purposeful cross-role collaboration—from the prep and pre-rehearsal phase through a show’s opening. By putting this method into practice with Lost Girl, our creative team fostered a more thoughtful, caring process, as could anyone who keeps these principles in mind.

The largest European forest carbon stocks are in the Dinaric Alps old-growth forests: comparison of direct measurements and standardised approaches

BackgroundCarbon (C) sink and stock are among the most important ecosystem services provided by forests in climate change mitigation policies. In this context, old-growth forests constitute an essential reference point for the development of close-to-nature silviculture, including C management techniques. Despite their small extent in Europe, temperate old-growth forests are assumed to be among the most prominent in terms of biomass and C stored. However, monitoring and reporting of C stocks is still poorly understood. To better understand the C stock amount and distribution in temperate old-growth forests, we estimated the C stock of two old-growth stands in the Dinaric Alps applying different assessment methods, including direct and indirect approaches (e.g., field measurements and allometric equations vs. IPCC standard methods). This paper presents the quantification and the distribution of C across the five main forest C pools (i.e., aboveground, belowground, deadwood, litter and soil) in the study areas and the differences between the applied methods.ResultsWe report a very prominent C stock in both study areas (507 Mg C ha− 1), concentrated in a few large trees (36% of C in 5% of trees). Moreover, we found significant differences in C stock estimation between direct and indirect methods. Indeed, the latter tended to underestimate or overestimate depending on the pool considered.ConclusionsComparison of our results with previous studies and data collected in European forests highlights the prominence of temperate forests, among which the Dinaric Alps old-growth forests are the largest. These findings provide an important benchmark for the development of future approaches to the management of the European temperate forests. However, further and deeper research on C stock and fluxes in old-growth stands is of prime importance to understand the potential and limits of the climate mitigation role of forests.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Advancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis

Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.

The Myth of Male Dominated Patriarchal Tyranny in Society from “The Storm by Mcknight Malmar” Short Story

The world is too complex to explain and deconstruct without a thorough objective, fair, and deep research. Simplification of meaning becomes the alternative to thorough research. Something would be easier to understand if people could make the meaning simple. It is easier to understand if a case that involve a man as a perpetrator and especially the woman as a victim is a case of patriarchy. But there are many factors to be considered in order to judge a problematical case. This is the case for the short story Storm by Mcknight Malmar, the story suggest that it was liberating for female to given up and left to seek a better love. It is a good solution if the lover or in this case the husband is an evil person with evil personality. This study uses a descriptive qualitative method analysis to deconstruct the myth of male dominated patriarchal tyranny in the story. Using the reader-response theory can be usefully integrated with narrative psychology by situating personal narratives at the top of an individual’s knowledge hierarchy. Keywords: Patriarchy, Male, Female, Tyranny, Simplification

Centering the Scholarly Practitioner Within the EdD

The Educational Doctorate (EdD) is designed to meet the needs of current practitioners who aim to expand their professional expertise by leveraging deep knowledge and research methods to address specific and contextualized problems of practice. This approach centers on developing scholarly practitioners that are equipped with the skills necessary to bridge the knowledge-to-doing gap and contribute meaningfully to school improvement (CPED, 2020; Donovan, 2013; Hochbein & Perry, 2013; Jackson & Sun, 2022; Lewis et al., 2020; Perry, Zambo, & Crow, 2020). To achieve this goal, program faculty and their respective thinking must be concerned with and oriented to the scholarly practitioner and their unique positionality. This paper explores two considerations related to centering scholarly practitioners and their impact to illustrate the possibilities inherent within EdD programs.

Revealing the hidden RBP-RNA interactions with RNA modification enzyme-based strategies.

RNA-binding proteins (RBPs) are powerful and versatile regulators in living creatures, playing fundamental roles in organismal development, metabolism, and various diseases by the regulation of gene expression at multiple levels. The requirements of deep research on RBP function have promoted the rapid development of RBP-RNA interplay detection methods. Recently, the detection method of fusing RNA modification enzymes (RME) with RBP of interest has become a hot topic. Here, we reviewed RNA modification enzymes in adenosine deaminases that act onRNA (ADAR), terminal nucleotidyl transferase (TENT), and activation-induced cytosine deaminase/ApoB mRNA editing enzyme catalytic polypeptide-like (AID/APOBEC) protein family, regarding the biological function, biochemical activity, and substrate specificity originated from enzyme selves, their domains and partner proteins. In addition, we discussed the RME activity screening system, and the RME mutations with engineered enzyme activity. Furthermore, we provided a systematic overview of the basic principles, advantages, disadvantages, and applications of the RME-based and cross-linking and immunopurification (CLIP)-based RBP target profiling strategies, including targets of RNA-binding proteins identified by editing (TRIBE), RNA tagging, surveying targets by APOBEC-mediated profiling (STAMP), CLIP-seq, and their derivative technology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Processing > RNA Editing and Modification.

Progress in Rice Breeding Based on Genomic Research.

The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands for various improvements. Here, we review the significant contributions of rice genomics research to breeding progress over the last 25 years, discussing the profound impact of genomics on rice genome sequencing, functional gene exploration, and novel breeding methods, and we provide valuable insights for future research and breeding practices.

Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

COMPARATIVE ANALYSIS ON DEEP LEARNING METHODS FOR BOT DETECTION

Social bots are automated accounts on social media that are managed by software and overseen by people behind the scenes. While some bots are created for positive reasons, like sharing news updates or offering assistance in crises, others have been misused to spread false information, rumors, or influence political events. There are tools in place to identify and eliminate harmful bots automatically, but creators are continually evolving their techniques to evade detection. Hence, there is a pressing need for improved ways to differentiate between genuine and automated bot accounts. In recent years, several research studies have delved into the realm of social media bot detection, offering an extensive overview of different detection strategies, including advanced approaches such as machine learning (ML) and deep learning (DL) techniques. To the best of our knowledge, this study stands out as the first to solely focus on DL techniques, evaluating their efficacy and rationale in comparison to each other and to conventional ML methods. In this research, we offer a comprehensive classification of the characteristics utilized in deep learning research and provide information on the necessary preprocessing methods for creating appropriate training data for a deep learning model. We highlight the gaps identified in review papers discussing deep learning and machine learning studies, offering insights into future developments in this area. In general, deep learning methods have proven to be efficient in terms of computation and time for detecting social bots, demonstrating superior or comparable performance compared to traditional machine learning techniques. Key Words: Bot Detection, Social Media, Deep Learning, Computation, Automation

Present and paleo-hydrogeological reconstruction of a complex deep groundwater system in a tectonically active region (Golan Heights, Middle East)

The geochemical and isotopic composition of deep groundwater in sedimentary aquitards reveals a complex paleo-hydrological system affected by intensive tectonic activity. Water samples collected from deep research boreholes in the Golan Heights (Middle East) exhibit a unique combination of high salinity (>2,000 mg/L Cl) with low Na/Cl (<0.7) and Mg/Ca (<0.3) equivalent ratios, calcium chloride water type [Ca > (HCO3 + SO4)], relatively low δ18OVSMOW and δ2HVSMOW values (–7 and –42‰, respectively), and enriched 87Sr/86Sr ratios compared to the host rocks. The salinity source is related to ancient lagoonary hypersaline brines (10–5 Ma) that existed along the Dead Sea Rift (DSR). These brines intruded into the rocks surrounding the DSR and, based on the current study, also extended away from the rift. Following their subsurface intrusion, the brines have been gradually diluted by 18O- and 2H-depleted freshwater recharged at high elevations, nowadays leaving only traces of the brines that originally intruded. It is also shown that variable hydraulic conductivities in different formations control the dilution rates and subsequently the preservation of the entrapped brines. A paleo-hydrological reconstruction is provided to demonstrate intrusion and backflow dynamics and also the relationship to freshwater dilution, which was triggered by a tectonically active basin of the nearby continental DSR. Brines that initially migrated from the rift have since been gradually flushed back to the rift through the current natural outlets. As the system discharges, it mixes and converges with a separate hydrogeological system, while still preserving some of the geochemical signals of the ancient brines.