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Açai (E. oleracea) is a fruit known for its health benefits. Its industrial processing generates a large volume of seeds, a waste rich in phenolic compounds. In this work, three techniques of extraction were applied to this residue: dynamic maceration (DM), microwave-assisted extraction (MAE), and energized dispersive guided extraction (EDGE); this last one is a novel sample preparation technique. It evaluated the recovery of phenolic compounds in the extractions. The extraction processes were investigated using a central composite design to obtain an extract rich in phenolic compounds, being the EDGE's extract exhibited the highest phenolic content. With this result, the antioxidant capacity, phenolic profile (HPLC–DAD), and in vitro digestion model were investigated in this extract. The antioxidant capacities found were 6150 µmol Trolox/g (ORAC), 1.39 mmol Trolox/g (DPPH), and 4.45 mmol Eq Fe2 + /g (FRAP). Procyanidin B1 (4.60 mg/g) and B2 (1.05 mg/g) and catechin (1.62 mg/g) were the prominent phenolics. Bioaccessibility ranged from 6.20 to 179.86%; being caftaric acid, quercetin 3-glucoside, and procyanidin B1 bioaccessible in dialyzed fraction. These remarkable results show the EDGE efficiency for recovering bioactive compounds that remain available for intestinal absorption. [ABSTRACT FROM AUTHOR]

Edge computing has emerged as a transformative data processing method by decentralizing computations and bringing them toward the data source, significantly reducing latency and enhancing response times. However, this shift introduces unique security challenges, especially within the detection and prevention of cyberattacks. This paper gives a comprehensive evaluation of the edge security landscape in peripheral computing, with specialized expertise in identifying and mitigating various types of attacks. We explore the challenges associated with detecting and preventing attacks in edge computing environments, acknowledging the limitations of existing approaches. One of the very interesting novelties that we include in this survey article is, that we designed a Web application that runs on an edge network and simulates SQL injection attacks-a common threat in edge computing. Through this simulation, we examined every one of the cleanup strategies used to discover and prevent such attacks using input sanitization techniques, ensuring that the malicious SQL code turned neutralized. Our studies contribute to deeper know-how of the security landscape in edge computing by providing meaningful insights into the effectiveness of multiple prevention strategies. [ABSTRACT FROM AUTHOR]

Performing a pancreaticoduodenectomy (PD) in patients having undergone a Roux-en-Y gastric bypass (RNYGB) poses a significant surgical challenge. We present a patient with a history of RNYGB and endoscopic ultrasound-directed transgastric ERCP (EDGE) procedure who underwent a successful PD. This 77-year-old female with history of open RNYBG presented with resectable pancreatic adenocarcinoma. A preoperative EDGE procedure was required for biliary decompression. A PD was performed by removing the entire biliopancreatic limb for oncologic resection. The reconstructive technique here involved utilizing the old common channel for the hepaticojejunostomy, pancreaticojejunostomy, and remnant gastrojejunostomy. The case also included Axios stent placement during a preoperative EDGE procedure. This case describes the first reported successful PD in a patient with prior RNYGB and EDGE procedure. Although the optimal technique for this clinical scenario remains unestablished, this unique case contributes to the literature by demonstrating an effective approach for practicing surgeons. [ABSTRACT FROM AUTHOR]

We propose a new commercial laser triangulation sensor modification to enable the measurement of slots or bores side distance. The study showed the possibility of extending the sensor depth range for a slot or bore side distance measurement using a bypass of the illumination laser beam compared to a simple single mirror attachment to the sensor probe. We derived relations allowing for evaluation of the modified sensor side measurement range in desired depth based on the sensor parameters and the reflective mirror size and position. We demonstrated the functionality of the proposed measurement arrangement with an attachment to the commercial laser triangulation sensor and assessed the side-wall distance measurement. The results show the correct measurement depth and range prediction and the ability to perform side surface distance measurements at depths of more than 3.5 times the slot size. [ABSTRACT FROM AUTHOR]

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor's stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter's data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter's data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) refers to a network of physical devices that are embedded with sensors, software, and network connectivity, allowing them to collect and share data. The devices are the core of any IoT ecosystem. Browsing the extant literature, it emerges that the meaning of the term device depends on the reference context. It follows that, it is an important topic to investigate the reasons behind such a degree of indeterminacy. This paper elaborates on the evolution of the concept of device moving from IoT to artificial intelligence of things (AIoT). The finding that comes from this study is that this evolution is a direct consequence of the evolution of the IoT computing paradigms. [ABSTRACT FROM AUTHOR]

The growth of two-dimensional van der Waals magnetic materials presents attractive opportunities for exploring new physical phenomena and valuable applications. Among these materials, Fe3GeTe2 (FGT) exhibits a variety of remarkable properties and has garnered significant attention. Herein, we have for the first time created a nanomesh structure—a honeycomb-like array of hexagonal nanopores—with the zigzag pore-edge atomic structure on thin FGT flakes with and without oxidation of the pore edges. It is revealed that the magnitude of ferromagnetism (FM) significantly increases in both samples compared with bulk flakes without nanomeshes. Critical temperature annealing results in the formation of zigzag pore edges and interpore zigzag-edge nanoribbons. We unveil that the non-oxide (O) termination of the Fe dangling bonds on these zigzag edges enhances FM behavior, while O-termination suppresses this FM by introducing antiferromagnetic behavior through edge O–Fe coupling. FGT nanomeshes hold promise for the creation of strong FM and their effective application in magnetic and spintronic systems. [ABSTRACT FROM AUTHOR]

This paper presents a new method for extracting the image features and lineaments related to local extrema of an image or a digital elevation model (DEM) such as ridges and valleys based on the continuous wavelet transform (CWT) of a set of variously illuminated hillshades. The method originates from the principle that a hillshade can exactly reflect the lineaments nearly perpendicular to the illumination direction of the hillshade, but not other ones. The method consists of four steps: (1) preparation of a set of differently illuminated hillshades of the input data, (2) detection of directional edges nearly perpendicular to the illumination direction from each hillshade based on the CWT, (3) a combination of multidirectional edges into an omnidirectional feature image, and (4) identification of lineaments through linkage and linearization of image feature lines. CWT coefficients of each hillshade are used to calculate the gradient and its direction of the hillshade. For each hillshade, directional edge pixels where the gradient direction is parallel to the illumination direction are selectively detected to form accurate and solitary image feature lines related to local extrema of the input data. Directional edges of each hillshade are easily classified into positive and negative edges using the hillshade gradient. As they have similar directions, they are easily linked to form longer line raster objects, which are converted into vector objects, that is, directional lineaments. The multidirectional edges and lineaments given from all the hillshades are combined to form an omnidirectional feature image and a group of omnidirectional lineaments. Its application to real DEMs shows the demonstrated advantages of the proposed method over other methods and the similarity between detected lineaments and fault lines in the study area. [ABSTRACT FROM AUTHOR]

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid's throughput, contributing to significant cloud resource optimization. [ABSTRACT FROM AUTHOR]

In recent years, containerization is becoming more and more popular for deploying applications and services and it has significantly contributed to the expansion of edge computing. The demand for effective and scalable container image management, however, increases as the number of containers deployed grows. One solution is to use a localized Docker registry at the edge, where the images are stored closer to the deployment site. This approach can considerably reduce the latency and bandwidth required to download images from a central registry. In addition, it acts as a proactive caching mechanism by optimizing the download delays and the network traffic. In this paper, we introduce an edge-enabled storage framework that incorporates a localized Docker registry. This framework aims to streamline the storage and distribution of container images, providing improved control, scalability, and optimized capabilities for edge deployment. Four demanding XR applications are employed as use cases to experiment with the proposed solution. [ABSTRACT FROM AUTHOR]

‎In this paper, we use the double counting method to find some upper bounds for the independence number of a simple graph in terms of its order, size and maximum degree. Moreover, we determine extremal graphs attaining equality in upper bounds. In addition, some lower bounds for the energy of graphs in terms of their size and maximum degree and the number of odd cycle, are determined.

Kubernetes, as a powerful open-source container orchestration platform, enables the automated deployment and scaling of containerized applications. It has fundamentally transformed the deployment of large-scale applications, providing robust support and infrastructure for the transition from monolithic to microservices architecture. traditional container scheduling algorithms primarily focus on resource allocation. However, In the landscape of Internet of Things (IoT) edge computing, nodes often exhibit diverse network performance due to factors like geographical location and network topology. Meanwhile, modern applications relying on microservices architectures impose higher demands on server network performance. Consequently, scheduling methods aimed at improving infrastructure resource utilization efficiency are gradually becoming less applicable to such complex edge environments. The article proposes a Network-Aware Container Scheduling (NACS) algorithm, based on the TOPSIS method, which dynamically obtains real-time network and resource information of nodes in a cluster. This algorithm aims to provide applications with a high-stability layout characterized by low latency and low packet loss rates, thereby enhancing overall system performance. The experimental results indicate that compared to Kubernetes' default scheduler, NACS significantly improves the network performance of the system. Furthermore, NACS can increase the throughput of web applications by 26% and the throughput of Redis databases by 29.6%. [ABSTRACT FROM AUTHOR]

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Abstract Texture segregation studies indicate that some types of textures are processed by edge-based and others by region-based mechanisms. However, studies employing nominally edge-based textures have found evidence for region-based processing mechanisms when the task was to detect rather than segregate the textures. Here we investigate directly whether the nature of the task determines if region-based or edge-based mechanisms are involved in texture perception. Stimuli consisted of randomly positioned Gabor micropattern texture arrays with five types of modulation: orientation modulation, orientation variance modulation, luminance modulation, contrast modulation and contrast variance modulation (CVM). There were four modulation frequencies: 0.1, 0.2, 0.4 and 0.8 cpd. Each modulation type was defined by three types of waveforms: sinewave (SN) with its smooth variations, square-wave (SQ) and cusp-wave (CS) with its sharp texture edges. The CS waveform was constructed by removing a sinewave from an equal amplitude square-wave. Participants performed two tasks: detection in which participants selected which of two stimuli contained the modulation and discrimination in which participants indicated which of two textures had a different modulation orientation. Our results indicate that threshold amplitudes in the detection task followed the ordering SQ

Abstract Patient care, research, and decision-making are all aided by real-time medical data analysis in today’s rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study’s findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare.

We present sequence of time-aligned edge plots (STEP) : a sequence- and edge-scalable visualization of dynamic networks and, more broadly, graph ensembles. We construct the graph sequence by ordering the individual graphs based on specific criteria, such as time for dynamic networks. To achieve scalability with respect to long sequences, we partition the sequence into equal-sized subsequences. Each subsequence is represented by a horizontal axis placed between two vertical axes. The horizontal axis depicts the order within the subsequence, while the two vertical axes depict the source and destination vertices. Edges within each subsequence are depicted as segmented lines extending from the source vertices on the left to the destination vertices on the right throughout the entire subsequence, and only the segments corresponding to the sequence members where the edges occur are drawn. By partitioning the sequence, STEP provides an overview of the graphs' structural changes and avoids aspect ratio distortion. We showcase the utility of STEP for two realistic datasets. Additionally, we evaluate our approach by qualitatively comparing it against three state-of-the-art techniques using synthetic graphs with varying complexities. Furthermore, we evaluate the generalizability of STEP by applying it to a graph ensemble dataset from the architecture domain. [ABSTRACT FROM AUTHOR]

Several filtering methods have been introduced to estimate the edges of potential field sources. The selection of the appropriate technique depends on the type of data and the target. Among filtering techniques, phase-based filters are the most widely used methods due to the flexibility of their design, but they do not provide information on the source depth. In this study, some novel filtering approaches are proposed, highlighting the edge of adjacent sources with different intensities by initially removing the regional anomalies. These approaches generate low amplitude anomalies over the deep sources, and higher amplitude anomalies over the shallow sources, providing information on relative depths of the sources. To evaluate the designed approaches, synthetic and real data from the Finnmark area of North Norway were used. The results were compared with those obtained from other approaches. These results showed that the proposed approaches considerably simplify the interpretation of the anomaly maps with higher efficiency and broader interpretation scope than the classical techniques. [ABSTRACT FROM AUTHOR]

Tropical forests are incredibly diverse in structure and function. Despite, or perhaps because of, this diversity, tropical biologists often conduct research exclusively in one or perhaps a few forest types. Rarely do we study the ecotone—the interstitial region between forest types. Ecotones are hyper-diverse, dynamic systems that control the flow of energy and organisms between adjacent ecosystems, with their locations determined by species' physiological limits. In this review, we describe how studying ecotones can provide key indicators for monitoring the state of Neotropical forests from organisms to ecosystems. We first describe how ecotones have been studied in the past and summarize our current understanding of tropical ecotones. Next, we provide three example lines of research focusing on the ecological and evolutionary dynamics of the ecotone between tropical dry forests and desert; between tropical dry and rainforests; and between Cerrado and Atlantic rainforests, with the latter being a particularly well-studied ecotone. Lastly, we outline methods and tools for studying ecotones that combine remote sensing, new statistical techniques, and field-based forest dynamics plot data, among others, for understanding these important systems. [ABSTRACT FROM AUTHOR]

The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems' deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management. [ABSTRACT FROM AUTHOR]

In its first phase of exploitation, JT-60SA will be equipped with an inertially cooled divertor, which can sustain heat loads of 10 MW/m[sup 2] on the targets for a few seconds, which is much shorter than the intended discharge duration. Therefore, in order to maximize the duration of discharges, it is crucial to develop operational scenarios with a high radiated fraction in the plasma edge region without unacceptably compromising the scenario performance. In this study, the core and edge conditions of unseeded and neon-seeded deuterium H-mode scenarios in JT-60SA were investigated using METIS and SOLEDGE3X-EIRENE codes. The aim was to determine whether, and under which operational conditions, it would be possible to achieve heat loads at the targets significantly lower than 10 MW/m[sup 2] and potentially establish a divertordetached regime while keeping favorable plasma core conditions. In first analysis, an investigation of the edge parameter space of unseeded scenarios was carried out. Simulations at an intermediate edge power of 15 MW indicate that, without seeded impurities, the heat loads at the targets are higher than 10 MW/m[sup 2] in attached cases, and achieving detachment is challenging, requiring upstream electron densities at least above 4 χ 10[sup 19] m[sup -3]. This points toward the need for impurity injection during the first period of exploitation of the machine. Therefore, neon seeding simulations were carried out, performing a seeding rate scan and an injected power scan while keeping the upstream electron density at the separatrix at 3 χ 10[sup 19] m[sup -3]. They show that at 15 MW of power injected into the edge plasma, the inner target is easily detached and presents low heat loads when neon is injected. However, at the outer target, the heat fluxes are not lowered below 10 MW/m[sup 2], even when the power losses in the edge plasma are equal to 50% of the power crossing the separatrix. Therefore, the tokamak will probably need to be operated in a deep detached regime in its first phase of exploitation for discharges longer than a few seconds. In the framework of core-edge integrated modeling, using METIS, the power radiated in the core was computed for the most interesting cases. [ABSTRACT FROM AUTHOR]

Experimental results from several tokamaks suggest a strong impact of divertor density regimes on turbulent transport in the edge plasma. Reciprocally, the change in transverse transport and SOL width affects the access to density regimes, making it a fundamental topic for heat exhaust issue. Such phenomenology is highly nonlinear and can only be approached quantitatively using numerical simulations treating turbulence and neutrals recycling physics self‐consistently. In this study, the SOLEDGE3X edge multi‐fluid code is used to investigate the mutual interaction between edge plasma turbulence and neutrals recycling. A fluid neutrals model based on the assumption of a charge‐exchange‐dominated plasma‐neutral interaction has been implemented. Two simulations in circular geometry are compared: one without neutrals, where the particle flux is driven by a constant in flux from the core region, and the other one with neutrals recycling included in which the particle input to the system is self‐consistently injected by a gas puff from the midplane. The presence of the neutrals triggers three types of perturbations on the plasma: a local and non‐axisymmetric one driven by the gas puff, a global perturbation affecting both profiles and turbulence properties in the whole domain, and a local one in the vicinity of the limiter where recycling occurs. The largest effect of the inclusion of self‐consistent neutrals recycling is a large‐scale reorganization of the plasma profiles and turbulence properties due to the dissociation of particle and energy fluxes. These effects are expected to be more important at higher densities regimes or in diverted configuration. [ABSTRACT FROM AUTHOR]

Along with the significant growth of applications and facilities provided by the Internet of Things (IoT) in recent years, security challenges and related issues to privacy become considerable interest of researchers. On the other hand, the de facto IoT routing protocol for low-power and lossy networks called RPL is vulnerable to various types of routing attacks. Many researchers have investigated RPL security solutions focusing on effective detection of prevalent and destructive routing attacks such as blackhole attack, selective forwarding attack, rank attack and so on. Recent studies are proposing trust-based mechanisms with the aim of replacing traditional cryptography-based operations with lightweight security models in order to cover the inherent challenges of IoT devices, including energy and computational limitations. Therefore, in this paper, focusing on the problem of RPL vulnerability against well-known routing attacks, we have proposed a trust-based attack detection model, which investigates traffic behavior in different attack scenarios and detects malicious nodes relying on behavior deviation exactly at the same time as the start of any attack activity. Expected behavior is predicted by our learning model trained from the historical routing behavior pattern, using recurrent neural networks as a powerful deep learning method, which leads to attack detection with high-level accuracy and precision. Both mathematical analysis and simulation results on multiple RPL attack scenarios show clearly that the proposed trust-based defense mechanism is an effective approach capable of timely and precisely detection of routing behavior pattern deviation of malicious nodes exactly at the start time of the attack occurrence, which leads to attack detection and attacker identification based on trust scores extracted from the detected fluctuations between expected and real routing behavior patterns. [ABSTRACT FROM AUTHOR]

Texture segregation studies indicate that some types of textures are processed by edge-based and others by region-based mechanisms. However, studies employing nominally edge-based textures have found evidence for region-based processing mechanisms when the task was to detect rather than segregate the textures. Here we investigate directly whether the nature of the task determines if region-based or edge-based mechanisms are involved in texture perception. Stimuli consisted of randomly positioned Gabor micropattern texture arrays with five types of modulation: orientation modulation, orientation variance modulation, luminance modulation, contrast modulation and contrast variance modulation (CVM). There were four modulation frequencies: 0.1, 0.2, 0.4 and 0.8 cpd. Each modulation type was defined by three types of waveforms: sinewave (SN) with its smooth variations, square-wave (SQ) and cusp-wave (CS) with its sharp texture edges. The CS waveform was constructed by removing a sinewave from an equal amplitude square-wave. Participants performed two tasks: detection in which participants selected which of two stimuli contained the modulation and discrimination in which participants indicated which of two textures had a different modulation orientation. Our results indicate that threshold amplitudes in the detection task followed the ordering SQ < SN < CS across all spatial frequencies, consistent with detection being mediated by the overall energy in the stimulus and hence region based. With the discrimination task at low texture spatial frequencies and with CVM textures at all spatial frequencies the order was CS ≤ SQ with both < SN, consistent with being edge-based. We modeled the data by estimating the spatial frequency of a Difference of Gaussian filter that gave the largest peak amplitude response to the data. We found that the peak amplitude was lower for detection than discrimination across all texture types except for the CVM texture. We conclude that task requirements are critical to whether edges or regions underpin texture processing. [ABSTRACT FROM AUTHOR]

Background: Ultra‐high‐dose‐rate (UHDR) electron beams have been commonly utilized in FLASH studies and the translation of FLASH Radiotherapy (RT) to the clinic. The EDGE diode detector has potential use for UHDR dosimetry albeit with a beam energy dependency observed. Purpose: The purpose is to present the electron beam response for an EDGE detector in dependence on beam energy, to characterize the EDGE detector's response under UHDR conditions, and to validate correction factors derived from the first detailed Monte Carlo model of the EDGE diode against measurements, particularly under UHDR conditions. Methods: Percentage depth doses (PDDs) for the UHDR Mobetron were measured with both EDGE detectors and films. A detailed Monte Carlo (MC) model of the EDGE detector has been configured according to the blueprint provided by the manufacturer under an NDA agreement. Water/silicon dose ratios of EDGE detector for a series of mono‐energetic electron beams have been calculated. The dependence of the water/silicon dose ratio on depth for a FLASH relevant electron beam was also studied. An analytical approach for the correction of PDD measured with EDGE detectors was established. Results: Water/silicon dose ratio decreased with decreasing electron beam energy. For the Mobetron 9 MeV UHDR electron beam, the ratio decreased from 1.09 to 1.03 in the build‐up region, maintained in range of 0.98–1.02 at the fall‐off region and raised to a plateau in value of 1.08 at the tail. By applying the corrections, good agreement between the PDDs measured by the EDGE detector and those measured with film was achieved. Conclusions: Electron beam response of an UHDR capable EDGE detector was derived from first principles utilizing a sophisticated MC model. An analytical approach was validated for the PDDs of UHDR electron beams. The results demonstrated the capability of EDGE detector in measuring PDDs of UHDR electron beams. [ABSTRACT FROM AUTHOR]

Recently, learning classifier systems (LCS) have been used for a variety of Internet of Things (IoT) devices and multi-cloud services, including cloud-based centralized control of physical robots and actuators in continuous-time environments. Performance analysis of sensors, navigation of humanoid robots and intelligent control of rescue systems. In these systems, we can run evolutionary or intuitive algorithms on cloud servers to search the space of rules and simultaneously other learning processes to assign how to interact with the environment to the rules in the classification. Also, the problem of continuous congestion, traffic reduction, network routing and predicting traffic conditions in wireless networks is the main challenge facing these systems in real environments. Usually such systems are non-Markovian. Therefore, they need memory to save system states. This paper presents a framework for XCS-based memory-based LCS. In addition to identifying optimal rules in overlapping modes, the XCS architecture is equipped with a memory. Memory stores the most efficient classifier rules. These rules reduce the steps to reach the goal. In the first proposed method, only those rules that affect the moving motion are kept in memory. As the number of rules increases, some of them are deleted according to memory space. In the second proposed method, some features are added to the rules of this memory and the performance of the memory is optimized using evolutionary algorithms, which are used to remove the less used rules. The relative success of the proposed LCS architecture in solving well-known maze problems compared to the conventional XCS architecture confirms its efficiency in increasing the number of successes and reducing the steps to reach the goals. The results of this research are suggested as a suitable solution for reducing routing time, reducing network load in the problem of congestion and traffic in solving problems related to wireless networks. [ABSTRACT FROM AUTHOR]

Patient care, research, and decision-making are all aided by real-time medical data analysis in today's rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study's findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare. [ABSTRACT FROM AUTHOR]

This article draws on fieldwork in the West Bank (2007–2017) to understand how dispossession and ruination are not only spatialized, but engaged and remade. It examines experiences of a settler frontier in Faqu'a, a Palestinian village. People there have a visual vantage point onto the majority of the village lands, which have been expropriated by Israel. Following a demolition by Israel, Faqu'ans have transformed this frontier site from a place for home-building, first into a garbage dump and then, most recently, into a garden. Faqu'ans' shifting engagements with this site reveal that edgeness, which seems to characterize this location, is not a naturally occurring status. Edges are made. Faqu'ans periodically adjust their proximity to Faqu'a's eastern edge. By examining different people's engagements with one site over a long duration, what the article calls "absenting" comes into view as a primary means by which people adjust proximity to an edge. Proximity can be temporal, spatial, and affective. Absenting is a particularly useful technique under conditions of extreme duress, such as those arising out of life on a frontier that seeks one's disappearance. [ABSTRACT FROM AUTHOR]

LSTM has been presented to overcome the problem of the gradient vanishing and explosion. Tree-LSTM could improve the parallel speed of LSTM, and incorporate relevant information from dependency or syntax trees. Tree-LSTM can update gate and memory vectors from the multiple sub-units. Learning edge features can strengthen the expression ability of graph neural networks. However, the original Child-Sum Tree-LSTMs ignores edge features during aggregating the sub-nodes hidden states. To enhance node representation, we propose an interaction mechanism that can alternately updating nodes and edges vectors, thus the model can learn the richer nodes vectors. The interaction mechanism attaches the node embedding to its connected link at the first stage. Next, it superimposes the updated edge into the parent node once more. Repeat the above steps from bottom to top. We present five strategies during the alternant renewal process. Meanwhile, we adopt one constituent parser and one dependency parser to produce the diversified formats, and compare their performances in the experiment result. The proposed model achieves better performance than baseline methods on the BioNLP’09 and MLEE corpuses. The experimental results show that the simple event results are almost identical for each parser. But for complex events, Stanford Parser is better than MaltParser because of more frequent interactive behaviors. The different parsing formats have different results, and CoNLL’2008 Dependencies show competitive and superior performance for each parser.

Objectives Methods Results Conclusion Endoscopic ultrasound‐directed transgastric endoscopic retrograde cholangiopancreatography (ERCP; EDGE) is proposed as a less invasive alternative to laparoscopy‐assisted ERCP. However, postponing ERCP for 1–2 weeks to reduce the risk of lumen‐apposing metal stent (LAMS) migration may not be practical in urgent cases such as cholangitis, leading to increased procedural burden. This study aimed to assess the feasibility and safety of a single‐session EDGE utilizing a dedicated over‐the‐scope fixation device.A retrospective analysis of prospectively collected data from three referral centers was performed, including consecutive single‐session EDGE procedures with the Stentfix device, utilizing only 20 × 10 mm LAMS. The primary outcome was LAMS migration, and key secondary outcomes included adverse events and technical success.Twenty patients (mean age 59 [standard deviation (SD) ± 11.3] years, 65.0% female) with a predominantly classic Roux‐en‐Y gastric bypass history (90.0%, mini‐bypass 10.0%) underwent ERCP for indications such as common bile duct stones (60.0%), cholangitis (25.0%), or biliary pancreatitis (15.0%). No LAMS migration occurred, and technical success was achieved in 95.0%. Over a median follow‐up of 102 days (interquartile range [IQR] 24.8–182), two adverse events were reported (10.0%), comprising postprocedural pain (grade I) and post‐ERCP pancreatitis (grade II).While acknowledging potential contributions from LAMS orientation and stent caliber, our data suggest that utilizing a dedicated over‐the‐scope stent fixation device may effectively prevent LAMS migration during single‐session EDGE without the need for endoscopic suturing. [ABSTRACT FROM AUTHOR]

Chemical graph theory can be studied with the aid of mathematical tools called mpolynomials. M-Polynomials offer a potent tool for computing different topological indices associated with vertex degrees and analyzing degree-based structural information in graphs. By counting specific substructure types within them, they are able to encode information about the structure of molecules or networks. In this article, we have developed M-Polynomials with the help of different topological invariants such as first Zagreb (M1(β)), second Zagreb (M2(β)), second modified Zagreb (Mm 2 (β)), inverse sum (I(β)), harmonic index (H(β)) and Randic index (Rα0 (β)) for the molecular structures of Triangular oxide TOX(r), Regular triangular oxide RTOX(r), Triangular silicate TSL(r) & Regular triangular silicate RTSL(r) networks to introduce new closed formulas to get better understanding the applications of M-Polynomials and topological indices in mathematical chemistry especially in the field of QSAR and QSPR study with the help of some software like MATLAB. We have also discussed the graphical behaviors of the above-mentioned structures. [ABSTRACT FROM AUTHOR]

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks. [ABSTRACT FROM AUTHOR]

Cognitive network science has increased our understanding of how the mental lexicon is structured and how that structure at the micro-, meso-, and macro-levels influences language and cognitive processes. Most of the research using this approach has used single-layer networks of English words. We consider two fundamental concepts in network science—nodes and connections (or edges)—in the context of two lesser-studied languages (American Sign Language and Kaqchikel) to see if a single-layer network can model phonological similarities among words in each of those languages. The analyses of those single-layer networks revealed several differences in network architecture that may challenge the cognitive network approach. We discuss several directions for future research using different network architectures that could address these challenges and also increase our understanding of how language processing might vary across languages. Such work would also provide a common framework for research in the language sciences, despite the variation among human languages. The methodological and theoretical tools of network science may also make it easier to integrate research of various language processes, such as typical and delayed development, acquired disorders, and the interaction of phonological and semantic information. Finally, coupling the cognitive network science approach with investigations of languages other than English might further advance our understanding of cognitive processing in general. [ABSTRACT FROM AUTHOR]

Enormous data generated by IoT devices are handled in processing and storage by edge computing, a paradigm that allows tasks to be processed outside host devices. Task offloading is the movement of tasks from IoT devices to an edge or cloud server -where resources and processing capabilities are abundant-for processing, it is an important aspect of edge computing. This paper reviewed some task-offloading algorithms and the techniques used by each algorithm. Existing algorithms focus on either latency, load, cost, energy or delay, the deep reinforcement phase of a task offloading algorithm automates and optimizes the offloading decision process, it trains agents and defines rewards. Latency-aware phase then proceeds to obtain the best offload destination in order to significantly reduce the latency. [ABSTRACT FROM AUTHOR]

Cloud-edge healthcare systems aim to provide low-latency services to both doctors and patients by leveraging storage and computing capabilities located on hospital servers. However, these servers often need to be more trusted and possess limited computing resources, raising concerns regarding data integrity verification. In this paper, we introduce a blockchain-based solution to tackle this challenge. Our approach involves the development of a distributed data integrity verification method that eliminates the need for a third-party auditor. Data is segmented into smaller chunks and hashed to create a hash table, from which verification tags are generated using column-based techniques and a secret string derived from a pseudo-random function. Furthermore, we present a comprehensive blockchain-based data integrity auditing system, which includes mechanisms for verifying the frequency of verification and defining the structure of blocks. Additionally, we conduct a thorough security analysis to assess the resilience of our system against common attacks. Finally, we evaluate the effectiveness of our proposed system against two leading schemes in a simulated cloud-edge healthcare environment. Our results confirm that our system can ensure data integrity without compromising efficiency. [ABSTRACT FROM AUTHOR]

Navigation of mobile agents in unknown, unmapped environments is a critical task for achieving general autonomy. Recent advancements in combining Reinforcement Learning with Deep Neural Networks have shown promising results in addressing this challenge. However, the inherent complexity of these approaches, characterized by multi-layer networks and intricate reward objectives, limits their autonomy, increases memory footprint, and complicates adaptation to energy-efficient edge hardware. To overcome these challenges, we propose a brain-inspired method that employs a shallow architecture trained by a local learning rule for self-supervised navigation in uncharted environments. Our approach achieves performance comparable to a stateof-the-art Deep Q Network (DQN) method with respect to goal-reaching accuracy and path length, with a similar (slightly lower) number of parameters, operations, and training iterations. Notably, our self-supervised approach combines novelty-based and random walks to alleviate the need for objective reward definition and enhance agent autonomy. At the same time, the shallow architecture and local learning rule do not call for error backpropagation, decreasing the memory overhead and enabling implementation on edge neuromorphic processors. These results contribute to the potential of embodied neuromorphic agents utilizing minimal resources while effectively handling variability. [ABSTRACT FROM AUTHOR]

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has recently received a great deal of research interest due to its broad range of applications. One of the important layers in IoT applications is known as edge computing where resource-constrained devices at the edge form a simple type of network to sense required data. A more powerful edge device is responsible for collecting all sensed data to be transferred to the upper layers. A critical focus is therefore placed on maximum rate of data collection, requiring effective and intelligent solutions to coordinate the channel access of the devices. Medium access control (MAC) protocols take this responsibility as their design mission. Carrier-sense multiple access (CSMA) has been a baseline MAC scheme and many previous traditional networks utilized a CSMA-based solution. The motivation of this paper is to study the performance of a typical network at the edge through the CSMA theme. A practical network is constructed to assess the channel throughput performance via a commercially available radio transceiver. The practical performance observations indicate the suitability of the proposed CSMA-based solution. [ABSTRACT FROM AUTHOR]

The Visteon True Color Image Enhancement system currently allows either edge detection to be activated or disabled. This study proposes a third option of implementing a dynamic edge detection method which dynamically enhances symbology edges as a function of the ambient lighting condition. Under nighttime lighting conditions, edge enhancement is not desirable since image artifacts are introduced that are not required for image visibility. As the reflected ambient lighting conditions increase, it is desirable to increase the visibility of black borders around symbology. [ABSTRACT FROM AUTHOR]

Premise: Conservation policies typically focus on biodiversity hotspots. An alternative approach involves analyzing the evolutionary history of lineages in geographic areas along with their threat levels to guide conservation efforts. Mountains exhibit high levels of plant species richness and micro‐endemism, and biogeographic studies commonly point to recent and rapid evolutionary radiations in these areas. Using a nearly endemic clade of legumes, our study evaluates conservation prioritization approaches in the campo rupestre, a Neotropical ecosystem associated with mountaintops that is located between two biodiversity hotspots. Methods: We compared the EDGE and EDGE2 metrics, which combine the evolutionary distinctiveness and the extinction risk of a species in a single value. These metrics are compared with traditional metrics used to assess conservation priority, such as phylogenetic diversity. Results: The EDGE values reported are lower than those of other studies using this metric, mostly due to the prevalence of threatened species with short phylogenetic branch lengths (low values of evolutionary distinctiveness). Certain areas of campo rupestre with relatively high phylogenetic diversity and EDGE values do not correspond to areas with high species richness, agreeing with previous studies on biodiversity hotspots. Discussion: Our study highlights the necessity of conservation of the campo rupestres as well as advantages and disadvantages of using EDGE, EDGE2, and phylogenetic diversity for appropriate selection of conservation areas with rapid evolutionary radiations. The selection of the metrics will depend primarily on the life history of the focus group and the data availability, as well as the conservation approach. [ABSTRACT FROM AUTHOR]

In image analysis and processing, image segmentation is one of the most important functions. The outcomes of segmentation have such significance on all subsequent image analysis operations, covering object tracking and description, feature measurement, and even higher-level tasks like object recognition. Malicious code detection is becoming increasingly significant, and current models must be improved. Hence forth the Image segmentation in the field of Malware image classification is a significant task. The sectional structure or region of interest must be identified and extracted during the segmentation process so that it can be evaluated independently. There are various reviews stating the traditional approach of image segmentation in various fields. The necessity of image segmentation in malicious image is extracting data for classification using CNN is discussed. In this work we use malimg_paper_dataset_imgs with 9,339 malware images. Various segmentation techniques were used to enhance the malware images. Those enhanced malicious image were applied in CNN architecture and Mal_CNN for classification and a comparative result is been discussed. The malicious images in dataset after incorporating segmentation have achieved 95% of accuracy in CNN architecture and 97% with Mal_CNN. [ABSTRACT FROM AUTHOR]