Level Of Pyramid Research Articles

Advancing Sparse Classical Scene Flow into the Maritime Domain

For Unmanned Surface Vehicles (USVs) to navigate safely without collisions, target tracking of other boats is essential. By utilizing stereo cameras, instead of for example time-of-flight sensors like lidar and radar, the frame-to-frame data association can be solved based on image patch similarity, and the frame rate is higher. In this paper we develop a method for sparse scene flow using a dual baseline stereo camera setup, propagating the short baseline disparity to see farther. By utilizing a dense disparity image and successively refining the depth using pyramidal Lucas-Kanade optical flow (pLK), the disparity of points across a wide baseline has been successfully estimated. An analysis of what parameters to use in the pLK, both for sparse stereo matching and in optical flow in the maritime domain, shows that too large window size makes pLK track the background. The parameters that are analyzed are the window size and the pyramid levels in pLK. We furthermore demonstrate the use of scene flow for tracking in the maritime domain. The methods have been tested on real data gathered during the summer of 2023 in Trondheim, Norway.

MLGPnet: Multi-granularity neural network for 3D shape recognition using pyramid data

This paper presents a Multi-Granularity 3D shape recognition network comprising point-granularity, line-granularity, and Pyramid-granularity networks, as well as multi-granularity convolutional layers (MLGPnet). The network takes pyramid data with high-level features generated from mesh data as input. The point-granularity, line-granularity, and pyramid-granularity networks respectively generate features at the point, line, and pyramid levels. Finally, two multi-granularity convolutional layers merge the features from these different levels to generate more efficient 3D shape global features. Compared to some classical 3D shape recognition network models, the proposed network achieves superior results on three publicly general-purpose datasets. Notably, among all mesh-based recognition networks, the proposed network demonstrates the best recognition accuracy and retrieval rate. Furthermore, the proposed network model performs better in terms of training time and model complexity, with faster training time and fewer model parameters, resulting in faster recognition speed.

Multi-Modal Image Fusion via Deep Laplacian Pyramid Hybrid Network

Fusion of images acquired using different sensors generates a single output with enhanced information for high-level visual perception applications. The transformer architecture has demonstrated its powerful ability to obtain important global contextual dependencies for multi-modal image fusion tasks. However, transformer-based image fusion methods face many critical issues, such as incurring huge computational burdens, limited ability to learn local features, and the difficulty of handling images of arbitrary sizes. To address the above limits, we proposed a novel Laplacian Pyramid Hybrid (LapH) network to combine the advantages of CNN and transformer architectures for multi-modal image fusion tasks. With the divide-and-conquer philosophy, we first build a light-weight CNN-based branch, performing effective extraction and fusion of texture/edge features via central difference convolutions, to process the high-resolution components with abundant details encoded in the lower pyramid levels of the Laplacian pyramid. Then, we design a transformer-based branch to process the low-resolution base components, learning long-range dependencies of global-contextual features without incurring extensive computational loads. Here, we design a multi-scale recurrent modulation mechanism to integrate the edge/texture features from the CNN branch as guidance to progressively refine the feature extraction and fusion on low-frequency components. Finally, we propose a new multi-scale spatial consistency loss term based on the neighbor contrast in source images, generating fused images with more natural and realistic appearances. Extensive experiments on two different multi-modal image fusion tasks verify the superiority of our method. The source codes are made publicly available at https://github.com/rgttadv/LapH.

Using a Virtual Simulation Workshop to Teach Interns Evidence-Based Feedback Techniques.

Background TheAccreditation Council for Graduate Medical Education requires residents to demonstrate competence in integrating feedback into their daily practice. With the shift to virtual medical education during the pandemic, the need for new skills in delivering effective feedback through virtual media has emerged. Methodology This study aimed to assess the feasibility of a virtual bootcamp for interns, utilizing virtual simulation workshops to teach effective feedback skills. The curriculum employed a situated learning-guided participation framework. Virtual standardized students participated, and interns engaged in activities such as providing virtual feedback, completing self-assessments, and receiving instruction on feedback principles, including the one-minute preceptor's five micro-skills. Interns repeated the feedback process, with virtual students providing assessments. Data were collected from 105 incoming interns at Arrowhead Regional Medical Center in June 2021 and June 2022, using Zoom® as the online platform. Results Competency assessments revealed a significant post-training increase in proficiency/expert milestones (88% versus 47%, p = 0.007). Interns' self-assessments also significantly improved (18.02 versus 16.74, p = 0.001), particularly for previously trained interns (18.27 versus 16.7). Non-primary care interns outperformed primary care interns in milestone scores. The majority of interns (80%) reported valuable learning experiences during the workshop, with 70% expressing confidence in using the one-minute preceptor technique during residency. The one-minute preceptor step "reinforce what was right" was deemed the easiest, while "obtain commitment" and "explore emotional reaction" presented significant challenges. Conclusions This study demonstrates the potential of virtual workshops to enhance intern competency in delivering effective feedback through formal processes and the one-minute preceptor. These virtual approaches offer innovative alternatives to in-person teaching, enabling evaluation at higher levels of Miller's pyramid of clinical competence.

Motion estimation from noisy data with unknown distributions using multi-frame phase-preserving denoising

Phase-based motion estimation method is promising due to its high resolution and wide measurement range. Generally, noise exists in digital images and ruins the constant phase assumption of the same target among different frames. However, traditional image-denoising techniques do not consider the phase and result in inaccurate motion estimation. A novel phase-preserving denoising method considering multiple frames is proposed. In this approach, multi-scale and multi-orientation quadrature filters transform noisy images into complex pyramids to preserve the phase of the content. An instability indicator is designed to evaluate the relative noise intensity at each level of the complex pyramid among multiple frames. The threshold is then estimated to retain the phase of interest and reconstruct a denoised image with unknown noise distributions. Numerical and experimental examples demonstrate that the proposed method effectively preserves the phase of interest and decreases the error of the estimated motion, outperforming other typical denoising methods under various types of noise.

HIV viral load measurement: knowledge, attitudes and practices of health workers, Burkina Faso

Approximately a quarter of people living with HIV (PLHIV) had their plasma viral load (PVL) measured in 2020 in Burkina Faso. The purpose of this study was to assess the knowledge, attitudes, and practices (KAP) of health workers regarding HIV PVL measurement. A cross-sectional study was conducted among health workers involved in the care of PLHIV in the 13 regions of Burkina Faso in 2021. Scores were constructed to assess their KAP on PVL measurement. Factors associated with knowledge and practices were identified by logistic regression. A total of 255 health workers were surveyed. The majority had good knowledge (73%) and favorable attitudes (93%). However, 40% had inadequate practices. Taking into account the availability of a laboratory to carry out PVL tests within the health center, having a coordinating role increased the likelihood of having good knowledge, while not having a medical qualification reduced this likelihood. Good practices were more common among health workers working at the second level of the health pyramid. Interventions to increase the demand for a measurement of PVL are essential to improve the care of PLHIV. For example, future investigations could explore the role of mediators in increasing the demand for PVL among PLHIV and their caregivers.

GAMNet: Global attention via multi‐scale context for depth estimation algorithm and application

AbstractDeep neural networks significantly enhance the accuracy of the stereo‐based disparity estimation. Some current methods suffer from inefficient use of the global context information, which will lead to the loss of structural details in ill‐posed areas. To this end, a novel stereo network GAMNet is designed, composed of three core components (GDA, MPF, DCA) for estimating the depth prediction in challenging real‐world environments. First, a lightweight attention module is presented, integrating the global semantic cues for every feature position across the channel and spatial dimensions. Next, the MPF module is constructed to fuse the diverse semantic and contextual information from different levels of the feature pyramid. Finally, cost volume is aggregated with a stacked encoder‐decoder composed of the DCA module and 3D convolutions, filtering the transmission of matching clues and capturing the rich global contexts. Substantial experiments conducted on KITTI 2012, KITTI 2015, SceneFlow, and Middlebury‐v3 datasets manifest that GAMNet surpasses preceding methods with contour‐preserving disparity predictions. In addition, the first 3D scene reconstruction linear evaluation strategy on spatial grasping points for the end‐to‐end stereo networks in an unsupervised mode is proposed, and it is deployed on the designed robot vision‐guided system. In application experiments, the method can produce densely high‐precision 3D reconstructions to implement the grasping task in complex real‐world scenes, and achieve excellent robust performance with competitive inference efficiency.

Organisation des soins palliatifs intégrés en Belgique

In the Declaration of Astana (2018), the World Health Organization underlines the emergency of strengthening our health systems and the relevance of the primary health care (PHC) model to achieve this, including regarding palliative care. In the light of the principles of PHC, this article presents and questions the current organization of palliative care in Belgium: do they meet their main criteria, which are comprehensiveness, accessibility, acceptability, permanence and continuity of care? As palliative support is provided at all levels of the health care pyramid, the base – the first line – supports a large part of it. This organization encourages practices where local health professionals – the general practitioner and the family nurse – are the main actors in the support. Specialists in the palliative sector only intervene to help front-line professionals in difficulty or to enable them to accompany particularly complex situations. The decompartmentalization of health specialties and the meeting of palliative care with PHC brings here a gain in quality and meaning. However, this perspective on our care model also reveals certain shortcomings – mostly financial – and certain inadequate practices that jeopardize an organization that is nevertheless sustainable, up to the challenges of today and tomorrow.

Pyramidal 3D feature fusion on polar grids for fast and robust traversability analysis on CPU

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based traversability analysis method that combines geometric features with a pyramid-polar space representation based on SVM classifiers. In particular, we show that by fusing geometric features with information stemming from coarser pyramid levels that account for a broader space portion, as well as integrating important implementation details, allows for a noticeable boost in performance and reliability. The main goal of this work is to demonstrate that traversability analysis is possible with effective results and in real-time even on cheaper hardware than expensive GPUs, e.g. CPU-only PCs. The proposed approach has been compared with state-of-the-art deep learning approaches on publicly available datasets of outdoor driving scenarios, running such algorithms both on GPU and CPU to compare runtimes. Our method can be fully executed on CPU and achieves results close to the best-in-class methods, runs faster, and requires fewer and less expensive hardware resources, consuming less than 30% electrical power with respect to deep learning models on embedded processing units. We release with this paper the open-source implementation of our method.

Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github.io/pyramid_texture_filtering/.

Learning radiologists’ annotation styles with multi-annotator labeling for improved neural network performance

BACKGROUND: One of the common problems in labeling medical images is inter-observer variability. The same image can be labeled differently by doctors. The main reasons are the human factor, differences in experience and qualifications, different radiology schools, poor image quality, and unclear instructions. The influence of some factors can be reduced by proper organization of the annotation; however, the opinion of doctors frequently differs.
 AIM: The study aimed to test whether a neural network with an additional module can learn the style and labeling features of different radiologists and whether such modeling can improve the final metrics of object detection on radiological images.
 METHODS: For training artificial intelligence systems in radiology, cross-labeling, i.e., annotation of the same image by several doctors, is frequently used. The easiest way is to use labeling from each doctor as an independent example when training the model. Some methods use different rules or algorithms to combine annotation before training. Finally, Guan et al. use separate classification heads to model the labeling style of different doctors. Unfortunately, this method is not suitable for more complex tasks, such as detecting objects on an image. For this analysis, a machine learning model designed to detect objects of different classes on mammographic scans was used. This model is a neural network based on Deformable DETR architecture. A dataset consisting of 7,756 mammographic breast scans and 12,543 unique annotations from 19 doctors was used to train the neural network. For validation and testing, a dataset consisting of 700 and 300 Bi-Rads-labeled scans, respectively, was taken. In all data sets, the proportion of images with pathology was in the 15%20% range. A unique index was assigned to each of the 19 doctors, and a special module at each iteration of the neural network training found a vector corresponding to this index. The vector was expanded to the size of the feature map of each level of the feature pyramid, and then attached by separate channels to the maps. Thus, the encoder and the decoder of the detector had access to the information about which doctor labeled the scan. The vectors were updated using the back-propagation method. Three methods were chosen for comparison:
 
 Basic model: Combining labels by different doctors using the voting method.
 New stylistic module: For predictions on the test dataset, a single doctors index was taken, which showed the best metrics on the validation dataset.
 New stylistic module: The indexes of the five doctors with the best metrics on the validation dataset were used for predictions on the test dataset. Weighted Boxes Fusion was chosen to combine the predictions.
 
 The area under the receiver operating characteristic curve (ROC-AUC) was used as the primary metric on the test dataset (Bi-Rads 3, 4, and 5 categories were referred to pathology). The sum of maximum probabilities of detected malignant objects (malignant masses and calcinates) by cranio-caudal and medio-lateral oblique projections was assumed as the probability of malignancy for each method.
 RESULTS: The following ROC-AUC metrics were obtained for the three methods: 0.82, 0.87, and 0.89.
 CONCLUSIONS: The information about the labeling doctor allows the neural network to learn and model the labeling style of different doctors more effectively. In addition, this method may obtain an estimate of the uncertainty of the networks prediction. The use of embedding from different doctors, leading to different predictions, may mean that this data is difficult for an artificial intelligence system to process.

Digital Twin to Control and Monitor an Industrial Cyber-Physical Environment Supported by Augmented Reality

Increasing industrial development and digital transformations have given rise to a technology called Digital Twin (DT) that has the potential to break the barrier between physical and cyberspace. DT is a virtual and dynamic model enabled through a bidirectional data flow that creates high-reliability models with interconnection and fusion between the physical and digital systems for full integration. In smart manufacturing, this technology is increasingly used in research and industry. However, the studies conducted do not provide a definition or a single integrally connected model. To develop the Digital Twin shown in this research, the literature was reviewed to learn about the enabling technologies and architectures used at the industrial level. Then, a methodology was used to obtain the physical process information, create the digital environment, communicate the physical environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real-time to obtain the digital twin supported with augmented reality. The system was implemented in the MPS-500 modular production station that has industrial sensors and actuators. The virtual environment was designed with Blender and Vuforia to create the augmented reality environment. In the proposed methodology, robust devices (field and control level) and low-cost embedded systems were used for the creation and communication of the virtual environment (monitoring and control); for the joint work of these technologies, they were carried out through the use of the following protocols: Open Platform Communications United Architecture (OPC UA), Ethernet, and machine to machine (M2M), with which a communication was achieved between the different levels of the automation pyramid. The results show that the proposed methodology for the implementation of the DT allows bidirectional communication between the physical and virtual environment and can also be visualized with the support of AR, thus providing its characteristics to the proposed DT. Digital Twin is an essential factor in creating virtual environments and improving applications between the real and digital world, establishing a bidirectional communication through the Ethernet protocol, with a communication time of approximately 100 ms. This technology interacts with the virtual environment and performs mappings, thus achieving timely and dynamic adjustment. This improves data management and production and incorporates process simulation and physical control in real-time, allowing to execute and trigger actions in the physical equipment simultaneously.

Peran Perempuan dalam Proses Bina Damai: Studi Kasus Aktivis Perempuan di Sudan Selatan (2005-2018)

Amidst the oppressive patriarchal culture, the lingering conflict in South Sudan between 2005-2018 has caused women to experience discrimination and violence. The dissatisfaction of women activists with the social system and the protracted conflict eventually encouraged them to participate in the peacebuilding process. This article examines the role of women in the peacebuilding activities by describing the work of women activists in South Sudan. The study is conducted qualitatively by applying the case study method and utilizing the Feminist methodology to comprehensively examine individuals and women's activist organizations activities in peacebuilding through the women perspective. This research finds that women activists play a role in the peacebuilding process at every level of the actor pyramid, from grassroots, middle, to top level. Positive and transformative changes take place as the result of their roles. At the top level, the peacebuilding efforts have encouraged the South Sudanese government to involve women activists in the peace negotiation process withSudan and ensure gender equality in the formulated constitution. At the middle and lower levels, peacebuilding efforts succeeded in convincing South Sudanese to support the implementation of gender equality as part of conflict resolution. Keywords: Women; Peacebuilding; Gender Equality; Discrimination; South Suda

Choosing the Best Design in Surgical Research.

Choosing the Best Design in Surgical Research.

Behavioral economics and monetary wisdom: A cross‐level analysis of monetary aspiration, pay (dis)satisfaction, risk perception, and corruption in 32 nations

AbstractCorruption involves greed, money, and risky decision‐making. We explore the love of money, pay satisfaction, probability of risk, and dishonesty across cultures. Avaricious monetary aspiration breeds unethicality. Prospect theory frames decisions in the gains‐losses domain and high‐low probability. Pay dissatisfaction (in the losses domain) incites dishonesty in the name of justice at the individual level. The Corruption Perceptions Index, CPI, signals a high‐low probability of getting caught for dishonesty at the country level. We theorize that decision‐makers adopt avaricious love‐of‐money aspiration as a lens and frame dishonesty in the gains‐losses domain (pay satisfaction‐dissatisfaction, Level 1) and high‐low probability (CPI, Level 2) to maximize expected utility and ultimate serenity. We challenge the myth: Pay satisfaction mitigates dishonesty across nations consistently. Based on 6500 managers in 32 countries, our cross‐level three‐dimensional visualization offers the following discoveries. Under high aspiration conditions, pay dissatisfaction excites the highest‐ (third‐highest) avaricious justice‐seeking dishonesty in high (medium) CPI nations, supporting the certainty effect. However, pay satisfaction provokes the second‐highest avaricious opportunity‐seizing dishonesty in low CPI entities, sustaining the possibility effect—maximizing expected utility. Under low aspiration conditions, high pay satisfaction consistently leads to low dishonesty, demonstrating risk aversion—achieving ultimate serenity. We expand prospect theory from a micro and individual‐level theory to a cross‐level theory of monetary wisdom across 32 nations. We enhance the S‐shaped Curve to three 3‐D corruption surfaces across three levels of the global economic pyramid, providing novel insights into behavioral economics, business ethics, the environment, and responsibility.

Vehicle Detection in High-Resolution Aerial Images with Parallel RPN and Density-Assigner

Vehicle detection in aerial images plays a significant role in many remote sensing applications such as city planning, road construction, and traffic control. However, detecting vehicles in aerial images remains challenging due to the existence of tiny objects, the scale variance within the same type of vehicle objects, and dense arrangement in some scenarios, such as parking lots. At present, many state-of-the-art object detectors cannot generate satisfactory results on vehicle detection in aerial images. The receptive field of the current detector is not fine enough to handle the slight scale variance. Moreover, the densely arranged vehicles will introduce ambiguous positive samples in label assignment and false predictions that cannot be deleted by NMS. To this end, we propose a two-stage framework for vehicle detection that better leverages the prior attribution knowledge of vehicles in aerial images. First of all, we design a Parallel RPN that exploits convolutional layers of different receptive fields to alleviate the scale variation problem. To tackle the densely arranged vehicles, we introduce a density-based sample assigner in the vehicle-intensive areas to reduce low-quality and occluded positive samples in the training process. In addition, a scale-based NMS is proposed to filter out redundant proposals hierarchically from different levels of the feature pyramid. Moreover, we construct two challenging vehicle detection datasets based on the AI-TOD and xView datasets which contain many tiny objects. Extensive experiments on these two datasets demonstrate the effectiveness of our proposed method.

ENTREPRENEURIAL SUPPLY CHAIN STRATEGIES AT THE BOTTOM OF THE PYRAMID: A SUPPLY CHAIN RESOURCE ORCHESTRATION PERSPECTIVE

Bottom-of-the-pyramid (BOP) markets include an estimated 689 million people living in extreme poverty and, with some controversy, are frequently lauded as the “next frontier” for economic prosperity. In this context, supply chain resource orchestration (SCRO) could be a game changer; however, not enough is known about SCRO at the BOP. We examine SCRO theory and practice and identify strategies to enable more BOP players to achieve sustainable social and economic benefits. Our methodology involves: (1) a critical review of extant supply chain BOP literature to develop a SCRO model, (2) a content analysis with interview data triangulation that examines 21 case studies of business ventures at the BOP, (3) abstraction of a typology of common SCRO strategies, (4) development of a classification pyramid and SCRO process model that illustrate SCRO tactics across pyramid levels and (5) creation of a classification matrix, which encapsulates our observed connections among motivations, strategies, tactics and outcomes across the case studies. The research sheds light on opportunities for future scholars to examine supply chains and SCRO at the BOP level. It also provides practical guidance on how to orchestrate a successful BOP supply chain and suggests the SCRO strategies propagating from the grass roots level tend to benefit the most people.

The Virginia Venture: American Colonization and English Society, 1580–1660 by Misha Ewen

Reviewed by: The Virginia Venture: American Colonization and English Society, 1580–1660 by Misha Ewen L.H. Roper The Virginia Venture: American Colonization and English Society, 1580–1660. By Misha Ewen. Philadelphia: University of Pennsylvania Press, 2022. What can we learn from another book on Jamestown? Actually, quite a bit, as Misha Ewen’s study of the first permanent English settlement in North America demonstrates. In the first instance, Ewen has significantly augmented the record of this venerable topic thanks to her visits to a range of British archives customarily ignored by historians of “early America.” The range of evidence she has unearthed provides a fine platform for tracking the connections that English people from all levels of the social pyramid had with early Virginia. These “micro-histories” (3) enable “us to get at the discreet, harder-to-reach corners of early modern society. In myriad unexpected ways, empire building seeped into the everyday life, and the accompanying documentary record, of early modern English society.” (152) Secondly, Ewen irrefutably corrects the still-prevalent view of the Virginia Company, which founded the enterprise in 1606, as an inept entity whose administration lurched from crisis to crisis until the Crown inevitably assumed direct control of Virginia in 1624. Rather, as she relates, the company leadership secured further governmental support for their initiative ranging from approval of lotteries to fund their operations to judicial approval of the transportation of criminals to labor on plantations to sermons promoting American prospects to prospective migrants. The establishment of Virginia helped set the pattern for English colonization by which “private” networks of merchants, aristocrats, and colonials took the lead, drawing upon their own connections, including governmental ones, to legitimize their ventures, raise money and recruit colonists. Ewen demonstrates then how the Virginia Company entrenched the concept of empire in metropolitan minds, just as it entrenched the English presence in America. Her work thus merits careful attention from historians of empire, colonization and seventeenth-century England. More particularly, it examines female investors, such as Lady Cecily West, whose jointure was used to fund the contribution of her husband, Thomas, baron De la Warr and Jamestown governor, but who invested in the colony in her own right, as well as the less elevated Mabel Collymore (35–55). It also chronicles the removal of the indigent to Virginia to solve crime, vagrancy and other social problems; transportation of felons and the indigent provided a ready alternative to hanging and an attempt to alleviate suffering but also manifested an insistence that the poor improve both themselves and a seemingly overpopulated realm (87–116). These accounts illustrate how colonization seamlessly grafted onto social practice. Ironically, though, they indicate the secondary, even epiphenomenal, effect of America on that consciousness. The successful introduction of tobacco cultivation and the end to the long, nasty war with the Powhatans. These crucial developments were underscored by the triumphal 1617 visit to the metropolis by the man largely responsible for them, John Rolfe, and his new wife, the Powhatan “princess,” Metoaka (a.k.a., Pocahontas). The couple’s grand tour (oddly unmentioned in The Virginia Venture despite a chapter devoted to “Creating Capital,” 57–86) included attendance at a court masque and a reunion between Metoaka and the tireless colonizing cheerleader, Captain John Smith, generated fevered interest in Virginia and a recapitalization of the Virginia Company. Its revamped leadership, implemented indentured servitude to facilitate migration and created a colonial House of Burgesses to provide self-government for planters. The devastating Powhatan attack of Easter 1622, though, doused these hopes and expectations. This “massacre” also generated such furious finger-pointing over the management and direction of the colony between clients of the Earls of Southampton and Warwick that the Crown was obliged to intervene. Meanwhile, tobacco proved alltoo-successful: planters in new colonies, most notably in Barbados, joined their Virginia counterparts in cultivating the weed thereby generating a price collapse as buyers of lottery tickets and readers of travel literature turned their attentions elsewhere. Who picked up the pieces? The Virginia Venture leaves open the wider question of who drove English overseas interests, which limits this otherwise fresh analysis; its fifth chapter lapses into the familiar, but misleading, comprehension...

Retracted: An edge-computing based Industrial Gateway for Industry 4.0 using ARM TrustZone technology

Secure and efficient communication to establish a seamless nexus between the five levels of a typical automation pyramid is paramount to Industry 4.0. Specifically, vertical and horizontal integration of these levels is an overarching requirement to accelerate productivity and improve operational activities. Vertical integration can improve visibility, flexibility, and productivity by connecting systems and applications. Horizontal integration can provide better collaboration and adaptability by connecting internal production facilities, multi-site operations, and third-party partners in a supply chain. In this paper, we propose an Edge-computing based Industrial Gateway for interfacing information technology and operational technology that can enable Industry 4.0 vertical and horizontal integration. Subsequently, we design and develop a working prototype to demonstrate a remote production-line maintenance use case with a strong focus on security aspects and the edge paradigm to bring computational resources and data storage closer to data sources.

The natural food base of the ponds in Noginsky district of Moscow region

The purpose of this work was to study the natural food base of the ponds in the Noginsk district of the Moscow region in October-November 2022. The selection of hydrobiological materials was carried out in October-November 2022. The pond №1 is a main pond of the EPB of RRIFF, a branch of the FSBSI FRC RIAH named after L.K. Ernst, with an area of 4,48 ha, fertilized with goose and duck droppings. Pond № 2 is ownerless, with peat soil, with an area of about 21,27 hectares. From a fishery point of view, the state of phytoplankton turned out to be more favorable in the ownerless pond. The biomass of phytoplankton in this pond is higher, it is formed mainly by green algae, consumed by both juvenile fish and planktonic organisms, through which the primary production is transferred to the higher levels of trophic pyramid. In the pond № 1, the biomass is much lower and in many respects formed by low-edible blue-green algae. The species diversity of the zooplankton in the EPB RRIFF pond, fertilized with waterfowl droppings, is significantly higher than in the big ownerless pond. The zooplankton biomass here is more than 2 times higher. The number of zooplankton is 1,34 times higher. The percentage of oligochaetes in the benthic invertebrate communities samples from the pond № 1 (68%) indicates moderate water pollution in October-November 2022 with organic matter. Thus, the head pond of the EPB RRIFF, fertilized with waterfowl droppings (the integrated technologies), demonstrated for the benthic invertebrate communities the best indicators of the natural food base state, both in general and in terms of indicators of the individual taxons. The components of the natural food base, which are crucial for the fish nutrition: phytoplankton and benthic invertebrate communities, turned out to be higher in October-November 2022 in the EPB RRIFF pond, fertilized with waterfowl droppings. However, the indicators of phytoplankton were better in the ownerless pond of the Noginsk district of the Moscow region. One of the reasons for the less favorable state of phytoplankton in the EPB RRIFF pond may be the eating of green algae by zooplankton.