Background Environment Research Articles

An Investigation on Joint Data Assimilation of a Radar Network and Ground-Based Profiling Platforms for Forecasting Convective Storms

Abstract A summer convective precipitation case, occurring in eastern China on 16–17 July 2020, is selected to investigate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include the Doppler wind lidar (DWL), a wind profiler (WP), and a microwave radiometer (MWR). Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20-km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates “the ramp-down issue” during the forecast stage. Assimilating profiling observations with an update interval less than 30 min does not have as pronounced an effect on convective forecasts as horizontal spacing. Furthermore, assimilating profiling observations at a 20-km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation. This work emphasizes the need to consider implementing a joint mesoscale detection system that incorporates weather radars and profiling observations for leveraging convective storm forecasting.

An Analysis of Plant Diseases Identification Based on Deep Learning Methods.

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

Impact of Subtropical ISO Propagation Routes on Summertime Submonthly Wave Patterns and Tropical Cyclone Activity in the Western North Pacific

Abstract This study examined the impact of northward- and westward-propagating summertime intraseasonal oscillations (ISOs) on submonthly wave patterns and tropical cyclones (TCs) in the subtropical western North Pacific. In the ISO westerly phase, submonthly wave patterns associated with the northward-propagating ISO appeared to be more energetic and most of the corresponding TCs maintained their wind speed for a relatively long period. Perturbation kinetic energy exhibited a stronger maximum in the ISO northward mode than in the westward mode. The analysis of barotropic conversion in the ISO northward mode revealed that an increase in barotropic conversion can be attributed to a strong association between the perturbation zonal wind component and the background flow. Therefore, submonthly wave patterns moving in a direction similar to that of the northward-propagating ISO continuously extracted energy from the background flow to the south of the submonthly base region. However, in the westward mode, the ISO propagating in a direction almost perpendicular to the submonthly wave pattern tracks not only altered the direction of the wave pattern but also created a background environment that was detached from submonthly perturbations. Thus, the background flow transferred less energy to submonthly wave patterns, resulting in shorter TC durations in the ISO westward mode than in the northward mode. Significance Statement In this study, we focused on the northward and westward ISO propagation routes in the subtropical western North Pacific to investigate their impact on the submonthly wave pattern and TCs. This is important because the ISO propagating behavior can change the background flow for the submonthly wave pattern. The results showed that the northward ISO tended to enhance the wave pattern through strengthening the background component of the barotropic conversion. TCs associated with submonthly wave patterns tended to maintain their intensity longer in the ISO northward mode. The wave pattern associated with the westward-propagating ISO remained weaker.

HIGH PERFORMANCE THROUGH WALL HUMAN ACTIVITY RECOGNITION USING WIFI

ABSTRACT: Passive human activity recognition without requiring a device is crucial in various fields, including smart homes, health care, and identification. However, current systems for human activity recognition require a dedicated device, or they need to be more suitable for scenarios where signals are transmitted through walls. To address this challenge, we propose a system for device-free, passive recognition of human activity that utilizes CSI-based Wi-Fi signals and does not require any dedicated devices. The proposed approach uses two techniques to distinguish different human activities. First, we introduce an opposite robust method to eliminate the influence of the background environment on correlation extraction and to obtain the correlation between human activity and its resulting changes in channel state information values. Second, we propose a normalized variance sliding windows algorithm to segment the time of human action from the waveforms, which can differentiate human actions' start and end times. We also implemented a model CSI based using Nexmon with an LSTM algorithm with commodity Wi-Fi devices and evaluated it in several environments. Our experimental results demonstrate that we achieve an average accuracy of 95% when signals pass through concrete walls.

DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases

The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well as changes in the lighting environment, have a massive impact on the accuracy of the detection results. We improved the C2F module based on DenseBlock and proposed DCF to extract low-level features such as the edge texture of pests and diseases. Through the sensitivity of low-level features to the diversity of pests and diseases, the DCF module can better cope with complex detection tasks and improve the accuracy and robustness of the detection. The complex background environment of pests and diseases and different lighting conditions make the IP102 data set have strong nonlinear characteristics. The Mish activation function is selected to replace the CBS module with the CBM, which can better learn the nonlinear characteristics of the data and effectively solve the problems of gradient disappearance in the algorithm training process. Experiments show that the advanced Yolov8 algorithm has improved. Comparing with Yolov8, our algorithm improves the MAP50 index, Precision index, and Recall index by 2%, 1.3%, and 3.7%. The model in this paper has higher accuracy and versatility.

Fusing Self-Attention and CoordConv to Improve the YOLOv5s Algorithm for Infrared Weak Target Detection.

Convolutional neural networks have achieved good results in target detection in many application scenarios, but convolutional neural networks still face great challenges when facing scenarios with small target sizes and complex background environments. To solve the problem of low accuracy of infrared weak target detection in complex scenes, and considering the real-time requirements of the detection task, we choose the YOLOv5s target detection algorithm for improvement. We add the Bottleneck Transformer structure and CoordConv to the network to optimize the model parameters and improve the performance of the detection network. Meanwhile, a two-dimensional Gaussian distribution is used to describe the importance of pixel points in the target frame, and the normalized Guassian Wasserstein distance (NWD) is used to measure the similarity between the prediction frame and the true frame to characterize the loss function of weak targets, which will help highlight the targets with flat positional deviation transformation and improve the detection accuracy. Finally, through experimental verification, compared with other mainstream detection algorithms, the improved algorithm in this paper significantly improves the target detection accuracy, with the mAP reaching 96.7 percent, which is 2.2 percentage points higher compared with Yolov5s.

Retracted: Optimization of Artistic Image Segmentation Algorithm Based on Feed Forward Neural Network under Complex Background Environment.

[This retracts the article DOI: 10.1155/2022/9454344.].

Analysis of the Differences in Internal Solitary Wave Characteristics Retrieved from Synthetic Aperture Radar Images under Different Background Environments in the Northern South China Sea

Two internal solitary waves (ISWs) with very long fronts observed by synthetic aperture radar (SAR) images in the northern South China Sea (NSCS) are comparatively analyzed based on oceanic reanalysis data and the Korteweg–de Vries (KdV) theory. The differences in the environmental parameters, wave half-width, and amplitude of the two ISW fronts in the two distinct oceanic environments are studied. In the presence of a weak westward surface current of approximately 0.05 m/s, the values of the linear wave speed increase by up to 0.056 m/s, and the retrieved ISW amplitudes decrease by up to 14 m. On the contrary, for another background oceanic environment considering a relatively strong eastward surface current of approximately 0.2 m/s, there are decreases of up to 0.17 m/s in the linear wave speed and increases of up to 32 m in the retrieved amplitudes. However, the results retrieved from both the SAR observations commonly show that the ISW amplitudes along the fronts reach their maximums at roughly 21°N and decrease toward the southern and northern sides, in spite of their distinct background environments.

Retracted: Emotional Expression and Playing Psychology in Accordion Playing under Complex Audio Background Environment.

[This retracts the article DOI: 10.1155/2022/2482899.].

A New Target Detection Method of Ferrography Wear Particle Images Based on ECAM-YOLOv5-BiFPN Network.

For mechanical equipment, the wear particle in the lubrication system during equipment operation can reflect the lubrication condition, wear mechanism, and severity of wear between equipment friction pairs. To solve the problems of false detection and missed detection of small, dense, and overlapping wear particles in the current ferrography wear particle detection model in a complex oil background environment, a new ferrography wear particle detection network, EYBNet, is proposed. Firstly, the MSRCR algorithm is used to enhance the contrast of wear particle images and reduce the interference of complex lubricant backgrounds. Secondly, under the framework of YOLOv5s, the accuracy of network detection is improved by introducing DWConv and the accuracy of the entire network is improved by optimizing the loss function of the detection network. Then, by adding an ECAM to the backbone network of YOLOv5s, the saliency of wear particles in the images is enhanced, and the feature expression ability of wear particles in the detection network is enhanced. Finally, the path aggregation network structure in YOLOv5s is replaced with a weighted BiFPN structure to achieve efficient bidirectional cross-scale connections and weighted feature fusion. The experimental results show that the average accuracy is increased by 4.46%, up to 91.3%, compared with YOLOv5s, and the detection speed is 50.5FPS.

Technology Affordance in an Information and Communication Technology Delivered Group Psychotherapy and Exercise Program for Older People With Depressive Symptoms: A Multiple Triangulation Qualitative Study.

Health and mental health interventions, such as psychotherapy and exercise programs, delivered via information and communication technology (ICT) may improve service access. However, adjustment among older people and in synchronous group interventions is more challenging. Technology affordance concerns the possibilities engendered by technology for various users and purposes and can help understand challenges in ICT-delivered groups and identify possible solutions. Adopting a multiple triangulation approach, we observed ICT-delivered groups of acceptance and commitment therapy and exercise for older people with depressive symptoms, conducted focus groups with older people who had received group psychotherapy with or without an exercise component, and obtained clinical notes from interventionists. We conducted a thematic analysis of the observation notes, focus group transcriptions, and clinical notes. Four focus groups were conducted with 22 participants (mean age = 72.6 years, standard deviation = 7.2, 86% female). We identified 3 challenges: (1) seeing-be seen dilemma, (2) speaking-hearing dilemma, and (3) blurred therapy-home boundary, and 2 solutions: (1) maneuvering layouts and collaborative tools, and (2) cross-platform mediated strategies. Participants struggled to observe the interventionist while simultaneously demonstrating their posture in front of a camera. Remaining silent and moderated turn-taking allowed for clearer hearing but limited interactions. Interruptions from the background environment and intersections of family living spaces disrupted audio-visual communication and jeopardized the sense of security. As a solution, interventionists maneuvered layouts and collaborative tools on teleconferencing applications to achieve intervention goals and provided support through different media. The identified challenges and potential solutions can be understood from interactivity, portability, temporality, persistence, and multimediality. Technology affordance can guide ICT-delivered group design by matching the affordance of various technologies and communication media with the characteristics of the intervention and users to enhance efficacy and avoid an unnecessary digital divide.

Ready for LHC Run III: the ATLAS New Small Wheel and the MicroMegas chambers performances

The two New Small Wheels (NSW) for the upgrade of the ATLAS Muon Spectrometer are now installed in the experiment and ready to collect data in LHC Run III, started in July 2022. The NSW is the largest Phase-1 upgrade project of ATLAS. Its challenging completion and readiness for data taking is a remarkable achievement of the Collaboration. The two wheels (10 meters in diameter) replace the first muon stations in the high-rapidity regions of ATLAS and are equipped with multiple layers of two new detector technologies: the small strips Thin Gap Chambers (sTGC) and the MicroMegas (MM). The latter, belong to the family of Micro Pattern Gaseous Detectors (MPGD), for the first time used in such a large scale in HEP experiments. Each detector technology will cover more than 1200 m2 of active area. The new system is designed to assure high tracking efficiency, reduction of fake trigger rates and precision measurement of muon tracks, also in view of the higher background environment foreseen for Hi-Lumi LHC. In this presentation, the motivation of the NSW upgrade and the steps from the commissioning to the data taking together with the first results using the Run III data will be presented, focusing on the MicroMegasperformances.

Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images

Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.

An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.

Efektivitas Intervensi Pendidikan untuk Meningkatkan Literasi Keuangan Islam pada Mahasiswa: Studi Kasus di Universitas PTIQ Jakarta

This research aims to investigate the effectiveness of educational interventions in enhancing Islamic financial literacy among students at PTIQ Jakarta University. A qualitative approach was employed through a case study within the university environment. Data was gathered through in-depth interviews and observations of students participating in the educational intervention program. The research findings indicate that educational interventions play a crucial role in improving students' understanding of Islamic financial principles, such as the prohibition of riba (interest), zakat (obligatory charity), and sharia-compliant investments. Students who participated in the educational intervention program showed improvements in their financial attitudes and behaviors aligned with Sharia principles. They were more inclined to utilize Sharia-based financial products and services in their daily lives. Additionally, the students' educational background, religious understanding, and social environment also influenced the effectiveness of the educational interventions. This study offers recommendations for educational institutions and policymakers to develop more creative and innovative educational intervention programs to enhance Islamic financial literacy among students. Furthermore, the research highlights the significant role of universities in equipping students with knowledge and understanding of Sharia finance to become agents of change, contributing to sustainable economic development in line with Islamic values.

BIOL-14. FROM NON-MALIGNANT TO MALIGNANT: GENERATING AND TARGETING DIFFUSE MIDLINE GLIOMA IN A NOVEL HUMAN PONTINE ORGANOID MODEL

Abstract Diffuse Midline Glioma (DMG) is a rare and aggressive pediatric cancer with no chance of survival, demonstrating a critical need for therapy development. Predictive preclinical models that take into account the unique developmental background and anatomical environment of DMG, are so far lacking. We developed a new regionally patterned brain organoid protocol based on a timely regulated sequence of morphogens. The patterned organoids give rise to pontine and medulla oblongata identity, as confirmed by transcriptomic profiling and immunofluorescence imaging. As the tissue matures, the complexity inherently takes shape and gives rise to pons-specific neurons and glial cells, offering the first human in vitro model for this region of the brain. Introducing H3K27M, including its partner mutations P53 and PDGFRA-D842V via electroporation forms de novo H3K27M diffuse midline glioma with a similar genetic make-up as found in patients. Indeed, contrary to common cell lines or PDX models, single cell sequencing and 3D imaging reveal that Organoid-DMG resembles WHO-defined primary patient cancer, including its diverse cell population heterogeneity and invasive nature. Utilizing barcode-based lineage tracing allows us to unravel and track the cancerous developmental evolution. Thus, we can now model early emerging populations and identify their transcriptomic signatures, which is conceptually impossible to conduct with patient-derived tumor tissue. In summary, we generated a bona fide DMG in vitro model which will give insights into tumorigenesis of DMG and ultimately contribute to pre-clinical drug- and immunotherapy development.

Visual and thermal camouflage on different terrestrial environments based on electrochromism

Abstract Hiding terrestrial objects from aerial monitoring has long been an important objective in national security and public safety. However, the diversity of terrestrial environments raises great challenges to traditional camouflages optimized for a single spectral band or single type of background environment, rendering them vulnerable in other bands or backgrounds. Herein, we experimentally demonstrate simultaneous visual and thermal camouflage that can adapt to two different environments based on a thermally emissive electrochromic layer. We first explore diverse possible theoretical solutions for dual-band dual-environmental camouflage by solving analytic constraints for camouflage and steady-state thermal conditions and select the most viable approach. Based on the theoretical analysis, we design active camouflage thin-film material systems that can approximate two different target visible and infrared signatures of backgrounds under proper bias voltage. Our potentially flexible camouflage surfaces can also conceal heat sources such as human body as well with tailored designs. These results provide new directions in multi-band stealth designs.

The backward problem in plasma-assisted combustion: Experiments of nanosecond pulsed discharges driven by flames

Plasma technologies are a promising way of addressing a number of challenges in combustion, ranging from stabilization and flame-holding for hypersonic vehicles, to enabling low-emissions propulsion and power. Whereas most works in the literature have centered on the forward problem: the impact of the plasma on different combustion metrics, including static and dynamic flame stabilization; fewer studies have been devoted to the backward problem: or the significance of having a strongly inhomogeneous environment, often unsteady in time, on the discharge characteristics. In this perspectives article, the backward problem is addressed in a hierarchical manner. First, the implications of the discharge proceeding in an inhomogeneous background gas, with gradients in density and composition, and nonuniform flows, are discussed. Next, these foundation experiments are used as elementary building blocks to explain experiments of nanosecond pulsed discharges in dynamic combustion environments, including a transient laminar flame and a combustor undergoing dynamics. Finally, the strong coupling between the forward and backward problem is illustrated with an imaging study, using transparent electrodes, of a mesoscale premixed flame and a nanosecond pulsed dielectric barrier discharge. The experiments demonstrate the importance of considering the pulse-to-pulse evolution due to changes in the background environment when using plasma assistance in unsteady combustion processes, including active control of combustion dynamics.

Sustainability of Local Communities in a New Oil and Gas Region: The Case of Eastern Siberia

The need of the global economy for natural resources encourages the movement of extractive industries to new areas, significantly affecting local communities. The study of community sustainability under the influence of extractive industries is multidimensional, as it depends on the geographical characteristics of the area, the historical background, the sociocultural and institutional environments, current government policies, and so on. Therefore, it is important for these local-level studies to comprehensively consider heterogeneous qualitative and quantitative data, paying special attention to the views of local people. This paper examines the impact of industrial natural resource development on community sustainability through a comprehensive approach that considers qualitative and quantitative socioeconomic data, including residents’ perceptions of current and future settlement development. The study is based on the example of communities located in areas of new oil and gas development in the north of the Irkutsk region (Eastern Siberia, Russia). Social science methods (semistructured interviews, surveys, questionnaires, and participant observations), comparative geographical, and statistical methods are used. A comprehensive approach allows us to identify various issues associated with maintaining the sustainability of local communities manifested under the influence of oil and gas extraction in different types of settlements. The findings contribute to the study of community sustainability during the industrial development of natural resources and have practical implications for decision-makers in terms of the socioeconomic management of the studied area and other territories with similar conditions.

Mechanisms Governing the Formation and Long-Term Sustainment of a Northeastward Moving Southwest Vortex

From 10 July to 12 July 2021, a long-lived (~66 h) southwest vortex (SWV), moved from Southwest China to Northeast China and caused a series of heavy rainfall events. This SWV case was rarely seen, as its lifespan was much longer than the SWVs’ mean lifespan, and the vast majority of SWVs showed a quasi-stationary behavior. It was found that the SWV formed and sustained in favorable background environments, which were characterized by a strong upper-level divergence (related to the South Asia High), a notable middle-tropospheric warm advection (related to a shortwave trough), and a vigorous low-level jet. The SWV showed remarkable interactions with a middle-tropospheric mesoscale vortex. The strong southwesterly wind in the eastern section of a deep shortwave trough east of the Tibetan Plateau acted as the steering flow for the northeastward movement of both vortices. Vorticity budget showed that the convergence-related vertical stretching dominated the SWV’s formation and development; the convection-related upward transport of cyclonic vorticity was the most favorable factor for the SWV’s sustainment, whereas, during the decaying stage, the SWV dissipated mainly due to the tilting effects and the net export transport of cyclonic vorticity. Backward trajectory analyses showed that most of the air particles that formed the SWV (at its formation time) were sourced from the lower troposphere. These air particles mainly ascended and experienced a rapid increase in cyclonic vorticity during the SWV’s formation stage. The topography of the Yunnan–Guizhou Plateau was crucial for the SWV’s formation, as around a half of the air particles (that formed the SWV) came from this region. Most of these air particles enhanced in their cyclonic vorticity and convergence when they descended along the topography of the plateau.