In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

In this paper we will present a simplified approach for extracting the ground level – a digital terrain model (DTM) – from the surface provided in a digital surface model (DSM). Most existing algorithms try to find the ground values in a digital surface model. Our approach works the opposite direction by detecting probable above ground areas. The main advantage of our approach is the possibility to use it with incomplete DSMs containing much no data values which can be e.g. occlusions in the calculated DSM. A smoothing or filling of such original derived DSMs will destroy much information which is very useful for deriving a ground surface from the DSM. Since the presented approach needs steep edges to detect potential high objects it will fail on smoothed and filled DSMs. After presenting the algorithm it will be applied to a test area in Salzburg and compared to a terrain model freely available from the Austrian government.

The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; 2) The data, when communicated, are vulnerable to security and privacy issues; 3) The communication of the continuously collected data is not only costly but also energy hungry; 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks.This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

This paper discusses wind tunnel test results aimed at advancing active flow control technology to increase the aerodynamic efficiency of an aircraft during take-off. A model of the outer section of a representative civil airliner wing was equipped with two-stage fluidic actuators between the slat edge and wing tip, where mechanical high-lift devices fail to integrate. The experiments were conducted at a nominal take-off Mach number of M = 0.2. At this incidence velocity, separation on the wing section, accompanied by increased drag, is triggered by the strong slat edge vortex at high angles of attack. On the basis of global force measurements and local static pressure data, the effect of pulsed blowing on the complex flow is evaluated, considering various momentum coefficients and spanwise distributions of the actuation effort. It is shown that through local intensification of forcing, a momentum coefficient of less than c_μ = 0.6% suffices to offset the stall by 2.4°, increase the maximum lift by more than 10%, and reduce the drag by 37% compared to the uncontrolled flow.

The problem of quantifying the vulnerability of graphs has received much attention nowadays, especially in the field of computer or communication networks. In a communication network, the vulnerability measures the resistance of the network to disruption of operation after the failure of certain stations or communication links. If we think of a graph as modeling a network, the average lower 2-domination number of a graph is a measure of the graph vulnerability and it is defined by ${\gamma }_{2av}\left(G\right)=\frac{1}{\left|V\left(G\right)\right|}{\sum }_{v\in V\left(G\right)}{\gamma }_{2v}\left(G\right)$, where the lower 2-domination number, denoted by ${\gamma }_{2v}\left(G\right)$, of the graph G relative to v is the minimum cardinality of 2-domination set in G that contains the vertex v. In this paper, the average lower 2-domination number of wheels and some related networks namely gear graph, friendship graph, helm graph and sun flower graph are calculated. Then, we offer an algorithm for computing the 2-domination number and the average lower 2-domination number of any graph G.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

Introduction: Musculoskeletal dysfunctions happen to be the most common reason for referral to physiotherapy and manual therapy services. Therapists may use several articular and/or soft tissue concepts/approaches to evaluate and treat such dysfunctions that may include integration of myofascial system. Despite the research in this area spanning more than three decades, the role played by fascia has not received its duly deserved attention, owing to the lack of definitive research evidence. The concept of ‘fascial connectivity’ evolved two decades ago from a simple anatomical hypothesis called ‘myofascial meridians’. Since then it has been widely researched, as conceptually it makes more sense for functional movements than ‘single-muscle’ theory. Researchers have been exploring its existence and role in musculoskeletal dysfunctions and clinicians continue to practice based on anecdotal evidence. This narrative review attempts to gather available evidence, in order to support and facilitate further research that can enhance evidence based practice in this field. Methods: A search of most major databases was conducted with relevant keywords that yielded 272 articles as of December 2019. Thirty five articles were included for final review with level of evidence ranging from 3b to 2a (as per Center of Evidence Based Medicine’s scoring). Results: Findings from cadaveric, animal and human studies supports the claim of fascial connectivity to neighboring structures in the course of specific muscle-fascia chains that may have significant clinical implications. Current research (level 2) supports the existence of certain myofascial connections and their potential role in the manifestation of musculoskeletal dysfunctions and their treatment. Conclusion: Although these reviews and trials yield positive evidence for the objective reality/existence of fascial connectivity and continuity, several aspects need further exploration and in-depth analysis, which could not be evidenced entirely in this review. Manual and physical therapists may utilize the concept of fascial connectivity as a convincing justification to deal with clinical problems, but need to remain vigilant that functional implications are still being investigated.

This paper presents an approach to the Kirchhoff-Love plate theory (KLPT) using the finite difference method (FDM). The KLPT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally in the case of KLPT, the finite difference approximations are derived based on the fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection surface. The FOPH is made for the fourth and third derivative of the deflection surface while the SOPH is made for its second and first derivative; this leads to a 13-point stencil for the governing equation. In addition, the boundary conditions and not the governing equations are applied at the plate edges. In this paper, the FOPH was made for all of the derivatives of the deflection surface; this led to a 25-point stencil for the governing equation. Furthermore, additional nodes were introduced at plate edges and at positions of discontinuity (continuous supports/hinges, incorporated beams, stiffeners, brutal change of stiffness, etc.), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at the plate edges and to satisfy the boundary and continuity conditions. First-order analysis, second-order analysis, buckling analysis, and vibration analysis of plates were conducted with this model. Moreover, plates of varying thickness and plates with stiffeners were analyzed. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. In first-order, second-order, buckling, and vibration analyses of rectangular plates, the results obtained in this paper were in good agreement with those of well-established methods, and the accuracy was increased through a grid refinement.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

Green spaces in residential community is important, yet understudied, feature as an urban ecological system. While large urban parks and remnant wildlands in urban areas tend to receive a public attention from conservation and management perspectives, less is known for the importance of spatial and ecological characteristics of the community-scale landscapes. This study investigates natural elements in four planned communities in Phoenix metropolitan area, Arizona, two of which represent conventional type of neighborhood and the other two exemplify the community development with a proclaimed vision of sustainability. These distinct type of communities, which capture variations in age, location, open space type, and a cross-section of housing density, are compared with regard to landscape connectivity as a means of gauging ecological condition for community sustainability. Using Geographical Information System and landscape connectivity indices, each community’s landscape features including size, physical connectedness and ecological potential were examined. Furthermore, a questionnaire survey was performed to examine the perceptional differences between the two types of community residents. The findings demonstrate that the green spaces in conventional communities are more physically connected than the counterparts, but the naturalness and ecological quality manifested by the amount of the land that may serve as potential urban desert habitats were higher in the sustainable communities. The results of the survey indicated that the respondents inhabiting in sustainable communities possess a higher level of satisfaction than the people in conventional type of community due mainly to the amount of, easy access to, and perceived ecological values of, green spaces in their neighborhoods and surrounding areas. The study concludes that careful community design with ecological consideration can help create sustainable communities which can benefit both site-scale ecosystems and perceived human well-being.

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

The potential of Thai industrial product design is still inferior to those of leading competitors in world market that give more importance on the design during their product development to increase their competitive edges on commercial scale. The product design is very important part for sustainable growth in this industry. Thus, this research aims at investigating footwear design strategies for Thai footwear industry to be excellence in world market. The research has been designed with the mixed method of both qualitative and quantitative study. The quantitative data were collected through semi-structure interview from 500 designers who presented their designs to join the award competition. The results revealed that the footwear design strategies consisted of 4 factors, i.e. 1) design, 2) market analysis, 3) innovation, and 4) information technology. This paper utilizes the method of Structural Equation Modeling (SEM) to establish a strategies model for competitive advantage in Thai footwear industry. The analysis results indicated that the footwear design strategies model could help make more effective policies and organization strategies for enterprises and designers to develop themselves to be excellence in world market.

The aim of this expository article is to present recent developments in the centuries-old discussion on the interrelations between several types of domination in graphs. However, the novelty even more prominent in the newly discovered simplified presentations of several older results. Domination can be seen as arising from real-world application and extracting classical results as first described by this article.The main part of this article, concerning a new domination and older one, is presented in a narrative that answers two classical questions: (i) To what extend must closing set be dominating? (ii) How strong is the assumption of domination of a closing set? In a addition, we give an overview of the results concerning domination. The problem asks how small can a subset of vertices be and contain no edges or, more generally how can small a subset of vertices be and contain other ones. Our work was as elegant as it was unexpected being a departure from the tried and true methods of this theory that had dominated the field for one fifth a century. This expository article covers all previous definitions. The inability of previous definitions in solving even one case of real-world problems due to the lack of simultaneous attentions to the worthy both of vertices and edges causing us to make the new one. The concept of domination in a variety of graphs models such as crisp, weighted and fuzzy, has been in a spotlight. We turn our attention to sets of vertices in a fuzzy graph that are so close to all vertices, in a variety of ways, and study minimum such sets and their cardinality. A natural way to introduce and motivate our subject is to view it as a real-world problem. In its most elementary form, we consider the problem of reducing waste of time in transport planning. Our goal here is to first describe the previous definitions and the results, and then to provide an overview of the flows ideas in their articles. The final outcome of this article is twofold: (i) Solving the problem of reducing waste of time in transport planning at static state; (ii) Solving and having a gentle discussions on problem of reducing waste of time in transport planning at dynamic state. Finally, we discuss the results concerning holding domination that are independent of fuzzy graphs. We close with a list of currently open problems related to this subject. Most of our exposition assumes only familiarity with basic linear algebra, polynomials, fuzzy graph theory and graph theory.

Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications not only require meeting performance targets but also meeting strict reliability and resilience constraints due to operations in harsh and hostile environments. Numerous research articles have been proposed, but not all of these include full specifications. Most of these tend to compare their architecture with other existing CPUs, GPUs, or other reference research. This implies that the performance results of the articles are not comprehensive. Thus, this work lists the three key features in the specifications such as computation ability, power consumption, and the area size of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. We introduce the actual evaluation results showing the trend in edge AI accelerator design about key performance metrics to guide designers on the actual performance of existing edge AI accelerators’ capability and provide future design directions and trends for other applications with challenging constraints.

Conservation scientists recognize that additional protected areas are needed to maintain biological diversity and ecological processes. As regional conservation planners embark on recommending additional areas for protection in formal conservation reserves, it is important to evaluate candidate lands for their role in building a resilient protected areas system of the future. Here, we evaluate North Carolina’s Mountain Treasures with respect to their (i) ecological integrity, (ii) role in connecting existing core protected areas, (iii) potential to diversify the ecosystem representation of reserves, and (iv) role in maintaining hotspots of biologically-rich areas not well protected. Mountain Treasures represent a citizen inventory of roadless areas and serve as candidates for elevated levels of conservation protection on U.S. federal lands. We compared Mountain Treasures to other candidate lands throughout the country to evaluate their potential national significance. While the Mountain Treasures tended to be more impacted by human modifications than other roadless areas, they are as important as other roadless areas with respect to their role in connecting existing protected areas and diversifying representation of ecosystems in conservation reserves. However, Mountain Treasures tended to have a much higher biodiversity priority index than other roadless areas leading to an overall higher composite score compared to other roadless areas. Our analysis serves as an example of how using broad-scale datasets can help conservation planners assess the national significance of local areas.

Rapid growth of IoT applications and their interference in our daily lives led to many different IoT devices which generates enormous data. The IoT devices’ resources are very limited, so storing and processing IoT data in the devices is very inefficient. Several resources of cloud-computing are efficiently used to handle some IoT resources issues. While using resources in the cloud centers cause some other issues, like latency in the IoT applications, which are time-critical. Thus, the technology of edge cloud has evolved recently. This technology permits storage and data processing at the network edge. This paper studies edge computing in-depth for timeless sensitive devices in IoT. In-depth, cutting-edge IoT computing systems (ECAs-IoT) are evaluated and characterized in this paper according to numerous criteria, such as information placement, improvisation facilities, reliability, and data visualization. Moreover, according to distinctive properties, the paper aims at comparing each structure in detail. The paper also highlights the significant limitations of the new ECAs-IoT and recommends solutions to them. The studies also introduce and propose solutions to some of the most important restrictions of the current ECAs-IoT. Consequently, in the edge computing domain, this survey outlines the IoT implementations. Lastly, with the use of IoT implementations for ECAs-IoT, the paper suggests four distinct scenarios.

One of the biggest queries in cognitive sciences is the emergence of consciousness from matter. Modern neurobiological theories of consciousness propose that conscious experience is the result of interactions between large-scale neuronal networks in the brain, traditionally described within the realm of classical physics. Here, we propose a generalized connectionist framework in which the emergence of “conscious networks” is not exclusive of large brain areas, but can be identified in sub-cellular networks exhibiting non-trivial quantum phenomena. The essential feature of such networks is the existence of strong correlations in the system (classical or quantum coherence) and the presence of an optimal point at which the system’s complexity is maximized, expressed either by maximization of the information content in large scale functional networks or by achieving optimal efficiency through the quantum Goldilock effect.

This paper presents technical solutions to increase Industry 4.0 maturity. Within the “5G-Enabled Manufacturing” project, a 5G network has been deployed at the shop floor to enable fast and scalable connectivity. This network was used to connect a grinding machine to a private cloud to enable visibility and transparency of the production data, which is the basis for Industry 4.0 and smart manufacturing. Results indicate a present lack of commercially available product independent solutions for interconnection and data transfer despite the availability of open standards and well-documented demonstrator projects. The solution is described and discussed regarding technical interoperability, focusing on the system layout, communication standards, and open systems. From the discussion, it is derived that manufacturing end-users are in need to expand and further internalize knowledge on future information and communication technologies to reduce their dependency on equipment and technology providers.

Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the IoT hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support AI applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program.

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

This article explores the relationship between green energy and cryptocurrencies in the sustainable energy finance sector. The research findings contribute to our understanding of the application of green economy practice, enabling investors in financial markets, policymakers, and stakeholders to make informed decisions and develop specific strategies. Adopting the green economy paradigm makes it possible to promote collaboration and innovation by integrating ethical and responsible principles that can improve the overall quality of processes and boost sustainable growth. Cryptocurrencies have been widely used as financial instruments over the last decade. Given the development of the cryptocurrency market and the growing awareness of greener and more energy-efficient tokens, the green economy has become a popular topic for understanding economic and political issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green energies. Therefore, this study examines the long- and short-term relationships between dirty cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), clean cryptocurrencies such as Cardano (ADA), Ripple (XRP), Stellar (XLM), and green energies such as ISE Clean Edge Global Wind Energy, S&P Global Clean Energy, S&P TSX Renewable Energy and Clean Technology, Solactive China Clean Energy, in the period from January 2020 to September 2023. The results show that diversification is key, with clean cryptocurrencies such as ADA, XLM and XRP offering diversification opportunities alongside "dirty" cryptocurrencies such as BTC and ETH. Although sustainable energy indices show mixed evidence in the long and short term, they remain relevant for those who focus on clean energy investments. It is also becoming increasingly relevant for investors in sustainable portfolios to assess their environmental impact, especially for energy-intensive cryptocurrencies, and it is advisable to explore sustainable blockchain technologies.

Topological indices are numerical parameters of a graph which characterize its topology and are usually graph invariant. In this paper, bounds for the Randić, general Randić, sum-connectivity, the general sum-connectivity and harmonic indices for tensor product of graphs are determined by using the combinatorial inequalities and combinatorial computing.

Many marine species use different habitats at different stages of their life cycle. Functional connectivity, the degree to which the seascape facilitates or impedes movement between habitat patches, is poorly studied in marine systems. We reviewed the scientific literature to explore the various barriers preventing functional connectivity between marine habitats and how the removal of these barriers may restore connectivity. To our knowledge, this is the first systematic review to investigate functional connectivity between life cycle habitats for a range of marine species. A total of 4,499 records were identified and screened, leaving 69 publications eligible for review. The results highlighted a range of distances between nursery and adult habitats that limited functional connectivity for a number of species, predominantly reef fishes. For some species, adults were absent on reefs >9km from the closest nursery habitat, suggesting a threshold for connectivity. Similarly, increased distance between spawning and settlement habitats decreased settling success of larvae of various taxa. Pelagic larval duration, seascape topography and climate change were also shown to impact functional connectivity during the larval phase. The removal and mitigation of barriers preventing functional connectivity, including dams and habitat fragmentation, restored connectivity between disconnected life cycle habitats, but the efficacy of these approaches differed between species and studies. The results of this review deepen our understanding of marine functional connectivity between life cycle habitats via larval, juvenile, and adult dispersal. These findings have implications for the design and management of marine reserve networks.

Electroencephalograms (EEGs) of children with reading disorder (RD) are characterized by higher power and coherence in slow frequencies (delta and theta bands) and lower power and coherence in the alpha band compared to EEGs of typically developed children. Neurofeedback (NFB) is useful for treating learning disorders by reinforcing the reduction of the theta/alpha ratio. This efficacy is supported by EEG power normalization and cognitive-behavioral improvement. To further explore brain changes in isolated areas, this study aimed to explore the effects of this NFB protocol on functional connectivity (coherence) in children with RD. Twenty children with RD and an abnormally high theta/alpha ratio underwent 30 sessions of NFB. After treatment, 90% of par-ticipants learned to decrease their theta/alpha ratio, and on average, children increased their reading accuracy and comprehension scores. Interhemispheric coherence diminished in the delta, theta and beta bands, mainly between frontopolar regions. Intrahemispheric coherence decreased in delta, theta and beta bands and increased in alpha band. The reduction in theta coherence be-tween the left frontal area and other regions of the left hemisphere had particular relevance for reading. These results suggest that this NFB treatment could positively impact reading-related functions in the brain networks of children with RD.

A novel domination invariant defined by Boutrig and Chellali in the recent: total vertex-edge domination. In this paper we obtain an improved upper bound of total vertex edge-domination number of a tree. If is a connected tree with order , then with and we characterize the trees attaining this upper bound. Furthermore we provide a characterization of trees with

Many various using of this new-born fuzzy model for solving real-world problems and urgent requirements involve introducing new concept for analyzing the situations which leads to solve them by proper, quick and ecient method based on statistical data. This gap between the model and its solution cause that we introduce nikfar domination in neutrosophic graphs as creative and eective tool for studying a few selective vertices of this model instead of all ones by using special edges. Being special selection of these edges aect to achieve quick and proper solution to these problems. Domination hasn't ever been introduced. So we don't have any comparison with another denitions. The most used graphs which have properties of being complete, empty, bipartite, tree and like stu and they also achieve the names for themselves, are studied as fuzzy models for getting nikfar dominating set or at least becoming so close to it. We also get the relations between this special edge which plays main role in doing dominating with other special types of edges of graph like bridges. Finally, the relation between this number with other special numbers and characteristic of graph like order are discussed.

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.

The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

In the study of QSAR/QSPR, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

Our knowledge on the plastic functions of the serotonin (5-HT) receptor subtype 7 (5-HT7R) in the brain physiology and pathology considerably advanced in the last few years. A wealth of data show that the 5-HT7R is a key player in the establishment and remodeling of neuronal cytoarchitecture during development and in the mature brain, and its dysfunction is linked to neuropsychiatric and neurodevelopmental diseases. The involvement of this receptor in synaptic plasticity is further demonstrated by data showing that its activation allows to rescue long term potentiation (LTP) and long term depression (LTD) deficits in various animal models of neurodevelopmental diseases. In addition, it is becoming clear that the 5-HT7R is involved in inflammatory intestinal diseases, possibly playing a role in the gut-brain axis, and modulates the function of immune cells. In this review, we will mainly focus on recent findings on this receptor’s role in the structural and synaptic plasticity of the mammalian brain, although we will also illustrate novel aspects highlighted in gut and immune system.

A growing number of studies focus on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing inter- and intra- subject variability of connectivity matrices as well as graph-theoretical measures in a large (n=1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected as opposed to directed methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra- subject variabilities in both directed and undirected connectomic measures.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

Rapid eye movement sleep behavior disorder (RBD) is often prodromal to Parkinson’s disease (PD). Thus there should be detectable in vivo functional signatures shared between RBD and PD that aid in disease classification. To assess common in-vivo phenotypes, resting state data was collected on a 3T clinical MRI platform and a novel functional connectivity magnetic resonance imaging (fcMRI) approach, which combined independent component analysis (ICA) and graph theory, was used to evaluate deficits in interconnectivity among 15 PD, 14 RBD and 13 control participants. Whole brain and network-level analyses revealed the largest deficits in network connectivity in PD compared with controls, with less severe differences between RBD and controls. Importantly, the network-level analysis demonstrated decreased network interconnectivity, with the greatest aberrant networks in PD, and a subset in RBD. Additionally, a disease classification algorithm predicted PD cases by being trained on RBD cases with 0.87 sensitivity and 0.68 specificity. The functional alterations in cortical networks in RBD extended beyond the brainstem. These findings demonstrate progressive reductions in connectivity between brain networks, with less severe deficits in RBD than PD. Moreover, RBD phenotypes can be used to predict PD status in a cross-sectional sample, which suggests RBD is an intermediate phenotype.

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behavior. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics, and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here we review and clarify the key concepts of Connectivity, Computation, Criticality, and Coherence — the 4C’s — and outline a potential role for metastability as a common denominator across these propositions. We analyze and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging (fMRI), to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.

Changes in the amount and the distribution of mean and turbulent quantities in the free shear layer wake of a 2D NACA 0012 airfoil and AR 4 NACA 0012 wing with passive segmented rigid trailing edge (TE) extensions was investigated at the University of Dayton Low Speed Wind Tunnel (UD-LSWT). The TE extensions were intentionally placed at zero degrees with respect to the chord line to study the effects of segmented extensions without changing the effective angle of attack. Force based experiments was used to determine the total lift coefficient variation of hte wing with seven segmented trailing edge extensions distributed across the span. The segmented trailing edge extensions had negligible effect of lift coefficient but showed measurable decrement in sectional and total drag coefficient. Investigation of turbulent quantities (obtained through Particle Image Velocimetry (PIV)) such as Reynolds stress, streamwise and transverse RMS in the wake, reveal a significant decrease in magnitude when compared to the baseline. The decrease in the magnitude of turbulent parameters was supported by the changes in coherent structures obtained through two-point correlations. Apart from the reduction in drag, the lower turbulent wake generated by the extensions has implications in reducing structural vibrations and acoustic tones.

Many manufacturing sectors require high surface finishing. After machining operations such as milling or drilling, undesirable burrs or insufficient edge finishing may be generated. For decades, many finishing processes have been handmade-basis; this fact is accentuated when dealing with complex geometries especially for high value-added parts. In recent years, it’s a tendency of trying to automate as far as possible this kind of processes, repeatability and time/money savings are main purposes. Based on that idea, the aim of this work is to check new tools and strategies for finishing aeronautical parts, especially critical engine parts made on Inconel 718, a very ductile nickel alloy. Automating edge finishing of chamfered holes is a complicated but really important goal. In this paper, flexible abrasive tools were used for this purpose. A complete study of different abrasive possibilities was carried out, mainly focusing on roughness analysis and final edge results obtained.

In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud and edge (fog) computing. We propose an early warning system which detects wild animals nearby road/rail with the help of wireless sensor networks and alerts passing vehicles of possible animal crossing. Additionally, we show that animals' detection at the earliest and the related processing, if possible, at sensors would reduce the energy consumption of edge devices and the end-to-end delay in notifying vehicles, as compared to the scenarios where raw sensed data needs to be transferred up the base stations or the cloud. At the same time, machine learning helps in classification of captured images at edge devices, and in predicting different time-varying traffic profiles-- distinguished by latency and bandwidth requirements-- at base stations, including animal appearance events at sensors, and allocating bandwidth in FiWi access networks accordingly. We compare three scenarios of processing data at sensor nodes, base stations and a hybrid case of processing sensed data at either sensors or at base stations, and showed that dynamic allocation of bandwidth in FiWi access networks and processing data at its origin leads to lowering the congestion of network traffic at base stations and reducing the average end-to-end delay.

Ecological connectivity, as a research method related to spatial ecology and conservation biology, has attracted increasing attention from researchers at home and abroad in recent years. Habitat connectivity, as a key link in ecological connectivity, is of great significance to promote offshore ecological restoration and protection. However, there has been less systematic research about habitat connectivity, which lacks corresponding theories and practices. Therefore, this paper discusses habitat connectivity from three aspects: (1) the concept of habitat connectivity is introduced and clarified, (2) the application of connectivity in artificial habitat and adjacent waters and its relationship with biodiversity conservation are reviewed and illustrated with examples, and (3) the future development trends of this research direction are summarized and prospected, in order to provide a scientific basis and useful reference for the related work of offshore restoration projects in China. Generally, this paper argues that an increase in human behavior irreversibly destroys the connectivity of marine habitats and threatens the temporal scales of biodiversity and ecosystem services. Therefore, the theoretical research results and practical experience of ecological connectivity should be fully applied to marine ecosystems, and the restoration of degraded ecosystems should be encouraged and supported in ways that promote natural recovery.

This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm. In short, a) it does not require manual annotation of images to learn how to count targeted pests and, b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server which configures its operation while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of hydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep-learning approaches (YOLO7/8, crowd counting, deep learning regression) and we open-source the code and a large database of Lepidopteran plant pests.

In an analogous manner as occurred during the development of a connected metabolism that at some point reached characteristics associated with what is called ‘life’ ―due mainly to a catalytic closure phenomenon when chemicals started to autocatalyze themselves forming a closed web of chemical reactions― it is here proposed that cognition and consciousness (or features associated with them) arose as a consequence of another type of closure within the nervous system, the brain especially. Proper brain function requires an efficient web of connections and once certain complexity is attained due to the number and coordinated activities of the brain cell networks, the emergent properties of cognition and consciousness take place. Seeking to identify main features of the nervous system organization for optimal function, it is here proposed that while catalytic closure yielded life, neuroglial closure produced cognition/consciousness.

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant health care burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PK) and pharmacodynamics (PD). Ocular quantitative systems pharmacology (QSP) models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based pharmacokinetic (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models employing molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eye and describe cellular systems, data repositories as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for post-operative fibrosis after glaucoma surgery.

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through Classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g. data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders / number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

Clinical work conducted over the last seventeen years at the EEG Institute in Los Angeles and by other neurofeedback providers around the world has demonstrated the utility of extending frequency-based neurofeedback deep into the infra-low frequency (ILF) region, using the method of endogenous neuromodulation described herein. The method is characterized by the absence of any overt reinforcements, which makes it possible to extend the clinical reach to extremely low frequencies. As the training frequency is lowered, the signal becomes more difficult to discriminate, and ultimately it can only be discerned by the brain itself, in the process of endogenous neuromodulation. The method emulates how the brain does skill learning in general: It must observe itself performing the skill, with feedback on its performance. While the immediate target of ILF neurofeedback is enhanced self-regulatory competence--with symptomatic relief and functional recovery the secondary consequences, progressive lowering of the target frequencies has led to improved outcomes in application to challenging dysfunctions such as episodic suicidality, migraine, seizures, and bipolar mood swings. The work has also yielded insights into how the frequency domain is organized. The training proceeds best at frequencies that are specific to each individual, and these are referred to as optimal response frequencies (ORFs). These frequencies differ for various placements but stand in two fixed relationships to one another, one that holds over the EEG spectral range, and another that holds over the entire ILF range. Training in the ILF region engages the dynamics of the glial-neuronal networks, which govern tonic, resting state regulation. The collective clinical experience with ILF neuromodulation within a large practitioner network supports the case for making protocol-based, individualized ‘homeodynamic’ regulation a therapeutic priority, particularly for our most impacted clinical populations: addiction, trauma formations, traumatic brain injury, and the dementias. The case is made for further outcome studies and foundational research.

Endemic freshwater fish from semiarid environments are among the most threated species in the world due to the water overexploitation and habitat fragmentation problems. Stepped or pool-type fishways are used worldwide to reestablish longitudinal connectivity and mitigate fish migration problems. Many of them are being installed or planned in rivers of semiarid environments, however, very few studies about fish passage performance through pool-type fishways has been carried out to date on these regions. The present work focuses on the passage performance of two potamodromous cyprinids endemic of these regions, with different ecological and swimming behavior: southern Iberian barbel (Luciobarbus sclateri) and Iberian straight-mouth nase (Pseudochondrostoma polylepis), in two of the most common types of stepped fishways: vertical slot and submerged notch with bottom orifice fishways. Experiments were carried out during the spawning season in the Segura River (South-Eastern Spain), using a PIT tag and antennas system. Ascent success was greater than 80%, with a median transit time lower than 17 minutes per meter of height in all trials and for both species and fishway types. Results show that both types of fishways, if correctly designed and built, provide interesting alternatives for the restoration of fish migration pathways on these regions.

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

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, spectral CT has emerged in the field of computed tomographies, utilizing detailed information from extracted spectral parameters of the specimen. Metal nanoparticle platforms enable effective payload delivery for this technique. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 functionalized gold nanoparticles (AuNPs) for tumor specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities, but not on corresponding normal tissue cells, predestining this target epitope for tumor-selective in vivo targeting. In vitro microscopical analysis revealed the presence of cmHsp70.1-AuNP in the cytosol of tumor cell lines, being internalized via the endosomal-lysosomal pathway. In tumor bearing mice the biodistribution as well as the intratumorally enrichment of AuNP were examined 24h after i.v. application, in vivo. In parallel to spectral CT analysis, histological analysis confirmed the presence of tumor cells. In contrast to control NP, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively in tumors of different preclinical mouse models. Furthermore, the biodistribution of AuNP, following i.v. injection, was analyzed by a machine-learning approach on digitalized slides. In summary, utilizing mHsp70 on tumor cells for guidance of cmHsp70.1 antibody functionalized nanoparticles enables sufficient enrichment and uniform distribution of AuNPs in mHsp70-expressing tumor cells, adequate for various microscopical imaging techniques and spectral-CT-based tumor delineation, in vivo.

Building outlines extracted from remote sensing images and raster maps often have irregular boundaries, redundant points, inaccurate positions, and unclear turns due to factors such as image quality, complexity of the surrounding environment, and extraction methods. This study proposes a regularization algorithm for right-angled polygon building outlines with jagged edges. First, the minimum bounding rectangle of the building outline is established and populated with a square grid based on the smallest visible length principle. An overlay analysis is then applied to the grid and original building to extract the turning points of the outline. Finally, the building orientation is used as a reference axis to sort the turning points and reconstruct the simplified building outline. Analysis of the experimental results shows that the proposed simplification method enhances the morphological characteristics of building outlines, such as parallelism and orthogonality, while considering simplification principles, such as the preservation of direction, position, area, and shape of the building. The proposed algorithm provides a new regularization method for building outlines with jagged edges.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1–3 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (13–21 Hz), and high beta (22–30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = −0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

The military areas in Sardinia are around 234 km2, which constitutes 59.97% of the national surface affected by military easements. This situation is due to its geographic centrality in the Mediterranean. This contribution evaluates the performance of the Local Coastline Plan (LCP) and the Site Management Plan of Community Interest (SCI) in conditions of military constraint. The case study is the Municipality of Villaputzu where an important coastal military easement and the use of the coast for recreational tourism purposes coexist together through specific planning, a consequence of institutional agreements between the Municipal Administration of Villaputzu and the Ministry of Defense. The evaluation of the congruence of the specific objectives of the LCP and the SCI shows how their combined action favors the environmental enhancement of Sardinia, contributing to the formation of ecosystem services, even in particular conditions arising from military easements. These are sites that pass from the status of "anti-commons" to "semi-commons". In fact, the military release process in Sardinia, together with the promiscuous military and civil use, activates unique governance policies of their kind that find a significant field of application in Sardinia to guarantee a sustainable renewal of economic development of the ‘semi-commons’ awaiting to become ‘commons’

To harness light-matter interaction at nano-/micro-scale better control tools have to be developed. Here, it is shown that by applying external electric and/or magnetic field, ablation of Si and glass under ultra-short (sub-1~ps) laser pulse irradiation can be controlled via the Lorentz force $\mathbf{F} = e\mathbf{E} + e[\mathbf{v}\times\mathbf{B}]$, where $\mathbf{v}$ is velocity of charge $e$, $\mathbf{E}$ is the applied electrical bias and $\mathbf{B}$ is the magnetic flux density. The external electric E-field was applied during laser ablation using suspended micro-electrodes above glass substrate with an air gap for the incident laser beam. The counter-facing Al-electrodes on Si surface were used to study debris formation patterns on Si. Debris was deposited preferentially towards the negative electrode in the case of glass and Si ablation. Also, an external magnetic field was applied during laser ablation of Si in different geometries and is shown to affect ripple formation. The vectorial nature of the Lorentz force widens application potential of surface modifications and debris formation in external E-/B-fields with potential applications in mass and charge spectroscopes.

This paper draws on data from a representative city-wide household food security survey of Nairobi conducted in 2017 to examine the importance of food remitting to households in contemporary Nairobi. The next section of the paper provides an overview of urbanization and the rapid growth of Nairobi which has led to growing socio-economic inequality, precarious livelihoods for the majority, and growing food insecurity, as context for the more detailed empirical analysis of food security and food remittances that follows. It is followed by a description of the survey methodology and sections analyzing the differences between migrant and non-migrant households in Nairobi. Attention then turns to the phenomenon of food remitting, showing that over 50% of surveyed households in the city had received food remittances in the previous year. The paper then uses multivariate logistic regression to identify the relationship between Nairobi household characteristics and the probability of receiving food remittances from rural areas. The findings suggest that there are exceptions to the standard migration and poverty-driven explanatory model of the drivers of rural-urban food remitting and that greater attention should be paid to other motivations for maintaining rural-urban connectivity in Africa.

The standard edge crush test (ECT) allows to determine the crushing strength of the corrugated cardboard. Unfortunately, this test cannot be used to estimate the compressive stiffness, which is an equally important parameter. It is because, any attempt to determine this parameter using current lab equipment quickly ends in a fiasco. The biggest obstacle is obtaining a reliable measurement of displacements and strains in the corrugated cardboard sample. In this paper, we present a method that not only allows to reliably identify the stiffness in the loaded direction of orthotropy in the corrugated board sample, but also the full orthotropic material stiffness matrix. The proposed method uses two samples: (a) traditional, cut crosswise to the wave direction of the corrugated core, and (b) cut at an angle of 45 degrees. Additionally, in both cases, an optical system with digital image correlation (DIC) is used to measure the displacements and strains on the outer surface of samples. The use of a non-contact measuring system allows to avoid using the measurement of displacements from the crosshead, which is burdened with a large error. Apart from the new experimental configuration, the article also proposes a simple algorithm to quickly characterize all sought stiffness parameters. The obtained results are finally compared with the results obtained in the homogenization procedure of the cross-section of the corrugated board. The results were consistent in both cases.

Trace metals can enter some natural regions with low human disturbance from atmospheric circulation, but little information is available regarding how the canopy can retained trace metals. Therefore, a representative sub-alpine spruce plantation was selected to investigate the net throughfall fluxes of eight trace metals (Fe, Mn, Cu, Zn, Al, Pb, Cd and Cr) of closed canopy and gap-edge canopy from August 2015 to July 2016. Over a one-year observational period, the annual fluxes of Al, Zn, Fe, Mn, Cu, Cd, Cr and Pb were 7.29 kg·ha^-1, 2.30 kg·ha^-1, 7.02 kg·ha^-1, 0.16 kg·ha^-1, 0.19 kg·ha^-1, 0.06 kg·ha^-1, 0.56 kg·ha^-1 and 0.24 kg·ha^-1, respectively, in the deposited precipitation. The annual net throughfall fluxes of these trace metals were 1.73 kg·ha^-1, 0.9 kg·ha^-1, 1.68kg·ha^-1, -0.032 kg·ha^-1, 0.04 kg·ha^-1, 0.018 kg·ha^-1, 0.093 kg·ha^-1 and 0.087kg·ha^-1, respectively, in the gap-edge canopy and -1.6 kg·ha^-1, 1.13 kg·ha^-1, 1.65 kg·ha^-1, -0.10 kg·ha^-1, 0.05 kg·ha^-1, 0.03 kg·ha^-1, 0.26 kg·ha^-1 and 0.15 kg·ha^-1, respectively, in the closed canopy. The closed canopy displayed a greater filter effect on the trace metals from precipitation than did the gap-edge canopy in the sub-alpine forest. In the rainy season, the net filtering ratio of trace metals ranged from -66%-89% in the closed canopy and from -52% to 25% in the gap-edge canopy. However, the net filtering ratio of all trace metals was greater than 50% in the closed canopy in the snowy season. Therefore, the results suggested that the most trace metals moving through the forest canopy are taken up rather than by rainfall leaching; moreover, the closed canopy can efficiently take up trace metals in the snowy season.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

This paper discusses Green Infrastructure, which can be considered a useful tool in the process of ensuring the sustainable development of urban structures in the Carpathian region. It allows for achieving a better quality of the environment of human life and healthy wildlife linkages. The element that supports defining information about the existing state of Green Infrastructure and its resources is the Green Infrastructure fragmentation coefficient based on edge effect calculations, which is the relation between the edge of the patch (circumference) to its surface area [1, 2]. With the use of model analysis of Green Infrastructure, it is possible to implement the provisions of the Carpathian Convention and coordinate planning documents that facilitate the sustainable development of spatial structures. Our study on the state of Green Infrastructure in rural areas of the Polish Carpathian Mountains is a source of knowledge about the quality of this area, its natural environment and fragmentation. Determining the territory’s Green Infrastructure fragmentation coefficient provides an opportunity for higher-precision studies and the detection of threats and integration of GI fragments and addressing proper solutions in conflict areas.

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D Cluster Variation Method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. Initial investigations with two different representative topographies (``scale-free-like'' and ``rich club-like'') produce interesting results when brought to a CVM free energy minimum. Additional phase space investigations, where one of these two parameters has been set to zero, identify useful parameter ranges. Careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid show that the computational results differ significantly from the analytic solution. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.

Increased connectivity between coastal lagoons and the sea is expected to entail a greater proportion of marine species in the former. Chetumal Bay, estuary of the Hondo river into the Caribbean, had a limited access to the sea until the opening of the Zaragoza Canal. We sought changes in the fish community from 1999-2001 (just after an expansion of the canal) to 2015-2018. The same fishing gear was used, in the same localities, during all seasons. Total fish abundance and mean local richness decreased, although total abundance increased in the polyhaline zone. Diversity was greater in the oligohaline zone in 1999-2001, but in the mesohaline zone in 2015-2018. Three guilds were absent in 2015-2018: medium-sized herbivores, large piscivores, and medium-sized planktivores. Abundance of small benthivores decreased by decade; medium-sized piscivores and small planktivores became more abundant in 2015-2018 in the polyhaline zone. These changes may be due to the opening of the channel, but illegal fishing outside the bay may explain the decrease in juveniles of large piscivores, and erosion in the innermost part may be destroying important habitats. Our findings can be a reference for similar situations, as coastal development and climate change interact and affect tropical estuaries.

In this article, the connections amid matroid and other notions have been studied. The structure of matroid could be a reflection of some other structure in lattice theory, group theory, other algebraic structure, graph theory, combinatorics and enumeration theory.

The US pacific northwest recorded its highest temperature in late June 2021. The three-day stretch of scorching heat had a devastating effect on not only the residents of the state, but also on the crops thus impacting the food supply-chain. It is forecasted that streaks of 100-degree temperatures will become common. Farmers will have to adapt to the changing landscape to preserve their crop yield and profitability. A research collaborative consisting of researchers and academicians in Eastern Washington led by a pioneering startup has setup a 16.9-acre Honeycrisp Apple Smart Orchard in Grandview, WA as a laboratory to study the environmental and plant growth factors in real-time using modern computational tools and techniques like IoT (Internet of Things), Edge and Cloud Computing, and Drone and LiDAR (Light Detection and Ranging) imaging. The computational analysis is used to develop guidelines for precision agriculture for orchard blocks to address plant growth issues scientifically and in a timely fashion. The analysis also helps in creating risk-mitigation strategies for severe weather events while helping prepare farmers to maximize crop yield and profitability per acre. I was fortunate to gain access to the terabytes of farm data related to the weather, soil, water, tree, and canopy health, to analyze and formulate recommendations for the farmers that can be adopted nationwide for different crops and weather conditions. This paper discusses the different streams of farm data that were analyzed (ex. soil moisture, soil water potential, and sap flow) and the development of the framework to use data to convert insights into actionable steps. For example, the use of sensors can inform a farmer that their level of soil water potential is below threshold in a specific patch of the orchard, prompting them to turn on irrigation for the patch instead of the whole orchard. I estimate that using an IoT-sensor-based decision framework discussed in this paper, growers can save up to 55% of their water costs for the season. Using these insights, farmers can better manage their irrigation resources and labor, thus maximizing their crop yield and profits.

We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (paranoid and depressive) will have distinct connectivity patterns and structural differences.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected with each other by an IoT network, along with a collection of enabling technologies such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, an increasing number of research proposals have been introduced over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

In recent years, pathoconnectomics emerged as an interesting framework for the investigation and better comprehension of disorders affecting the brain. Research in this field has used so far structural, functional, metabolic and genetic data, but limited attention was addressed to the possible role of vascularization. In the present work the following aspects making it a valuable candidate to pathoconnectomics investigation are discussed: i) The vascular system is by its nature a network, endowed with directionality information on the basis of circulation; ii) The current imaging techniques allow in vivo detection of the vascular system to a good level of detail; iii) The information extracted from this kind of data could interact in a meaningful way with the functional profile of the brain, being the BOLD effect in turn based on blood flow; iv) Further evidence could be found in support of the trophic failure hypothesis; v) Data about vascularization could allow to bring in the pathoconnectomics framework cardiovascular and metabolic disorders. We suggest, therefore, that the evaluation of vascular connectivity (which we propose to name “vesselomics”) could enhance the pathoconnectomics paradigm, and provide new elements towards the understanding of brain pathology.

This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ), bipolar disorder (BD) and the severity of these disorders. Toward this end, we have performed effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We have used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of 5 connections, which discriminate patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), fronto-parietal and dorsal attention network (middle frontal gyrus (MFG) corresponding to dorsolateral prefrontal cortex, frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, 4 out of the 5 connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.

Hydrochemical and geophysical data collected during a hydrological survey in September 2017, reveal patterns of small-scale hydrological connectivity in a small water track catchment, north-European Arctic. Elevated tundra patches underlain by sands were disconnected from the stream and stored precipitation water from previous months. At the catchment surface and in the water track thalweg, some circular hollows, from 0.2 to 0.4 m in diameter, acted as evaporative basins with low d-excess values, from 2 to 4‰. Other hollows were connected to shallow subsurface runoff, yielding d-excess values between 12 and 14‰. ‘Connected’ hollows yielded a 50% higher dissolved organic carbon (DOC) content, 17.5±5.3 mg/L, than the ‘disconnected hollows, 11.8±1.7 mg/L. Permafrost distribution across the landscape is continuous, but highly variable. Open taliks exist under fens and small hummocky depressions, as revealed by electric resistivity tomography surveys. Isotopic evidence supports upward subpermafrost groundwater migration through open taliks under water tracks and fens/bogs/depressions, and its supply to streams via shallow sub-surface compartment. Temporal variability of isotopic composition and DOC in water track and a major river system, the Vorkuta R., evidence the widespread occurrence of the described processes in the large river basin. Water tracks effectively drain the tundra terrain and maintain xeric veg-etation over the elevated inter-track tundra patches.

The development of novel coherent and brilliant sources, such as soft X-ray Free Electron Laser (FEL) and High Harmonic Generation (HHG) enables new ultrafast analysis of the electronic and structural dynamics of a wide variety of materials. Soft X-ray FEL delivers high brilliance beams with short pulse duration, high spatial coherence and photon energy tunability. In com-parison with FELs, HHG X-ray sources are characterized by a wide spectral bandwidth and few to sub-femtosecond pulses. The approach will lead to time-resolved reconstruction of molecular dynamics, shedding light on different photochemical pathways. The high peak brilliance of soft X-ray FELs facilitates investigations in nonlinear regime, while the broader spectral bandwidth of the HHG sources may provide the simultaneous probing of multiple components. Significant technical breakthroughs in these novel sources are under way to improve brilliance, pulse dura-tion, and to control spectral bandwidth, spot size, and energy resolution. Therefore, in the next few years, the new generation of soft X-ray sources combined with novel experimental tech-niques, new detectors, and computing capabilities will allow the study of several extremely-fast dynamics, such as vibronic dynamics. In the present review, we shortly discuss recent devel-opments in experiments, performed with soft X-ray FELs and HHG sources, operating near car-bon K-absorption edge, being a key atomic component in biosystems and soft materials. Differ-ent spectroscopy methods such as time-resolved pump-probe techniques, nonlinear spectrosco-pies and photoelectron spectroscopy studies have been addressed in an attempt of a better un-derstanding of fundamental physico-chemical processes

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long term illness. Out-of-the-body, non-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments has the potential to produce precise readings with comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81±0.97 and 0.86±0.61 beats/minute compared to a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore, reducing the data size by 40-140 times and requires 33% less power to process and transfer data. Further, we show that events hold enough information to reconstruct the original waveform, retaining pulse, and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies providing better user experience.

Small Shallow Lakes (SSL) support exceptionally high and original biodiversity, providing numerous ecosystem services. Their small size makes them especially sensitive to anthropic activities, that causes a shift to dysfunctional turbid states and induces loss of services and biodiversity. In this study we investigated the relationships between environmental factors and macrophyte communities. Macrophytes play a crucial role in maintaining functional clear states. Better understanding factors determining the composition and richness of aquatic plant communities in least-impacted conditions may be useful to protect them. We inventoried macrophyte communities and collected chemical, climatic and morphological data from 89 least-impacted SSL widely distributed in France. SSL were sampled across four climatic ecoregions, various geologies and elevations. Hierarchical cluster analysis showed a clear separation of four macrophyte assemblages strongly associated with mineralisation. Determinant factors identified by db-RDA analysis are, in order of importance, geology, distance from source (DIS, a proxy for connectivity with river hydrosystems), surface area, climate and hydroperiod (water permanency). Surprisingly, at country-wide scale, climate and hydroperiod filter macrophyte composition weakly. Geology and DIS are the major determinants of community composition, whereas surface area determines floristic richness. DIS is identified as determinant in freshwater lentic ecosystems for the first time.

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

The kind of set which is based on edges, is introduced. The analysis on this set is done in the matter of operation which are the classes of graphs. The general notion which is related to this concept, is up. The set of edges is seen in the matter of common vertex, entitled neighbor edges and the set of edges which has specific condition on the vertices of graphs, entitled ghost set. The kind of viewpoint when the edges are up so the kind of efforts to assign some notions which get the sensible result of edges which make sense about these two types of notions. Notions of having some attributes about vertices concerning edges and edges' attributes to get result about edges in the matter of vertices.

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

This paper presents an Edge-based color image segmentation derived from the method of Particle Motion in a Vector Image Fields (PMVIF) that could previously be applied only to monochrome images. Instead of using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields, directly computed from a color image, one parallel and another orthogonal to the edges, were used in the model to force a particle to move along the object edges. The normal compressive vector field is derived from the center-to-centroid vectors of local color distance images. Next, the edge vector field is derived by taking the normal compressive vector field, multiplied by differences of auxiliary image pixels to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012) and the Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison with those of the traditional PMVIF, Watershed, SLIC, K-means, Mean shift, and JSEG. The proposed method yields better RI, GCE, NVI, BDE, Dice coefficients, faster computation time, and noise resistance.

The consequences of manipulations in structure and amino acid composition of native cyclolinopeptide A (CLA) from linen seeds and its linear precursor on their biological activities and mechanisms of action are reviewed. The modifications included truncation of the peptide chain, replacement of amino acid residues with proteinogenic or non-proteinogenic ones, modifications of peptide bond, and others. The studies revealed changes in the immunosuppressive potency of these analogs investigated in a number of in vitro and in vivo experimental models, predominantly in rodents, as well as differences in their postulated mechanism of action. The modified peptides were compared with cyclosporine A and parent CLA. Some of the synthesized and investigated peptides show potential therapeutic usefulness.

Psychotic disorders occur as a result of pathobiochemical processes in the brain, which disrupt the central neurotransmission of molecules such as dopamine and glutamate. The dopamine hypothesis, adopted more than 2 decades ago, has repeatedly asserted its position as an etiopathogenetic substrate through the action of psychostimulants and neuroleptics on the mesolimbic and mesocortical systems, giving insight into the origin of positive and negative schizophrenic symptoms. On the other hand, cognitive impairments in schizophrenia remain not fully understood but are thought to be present during all stages of the disease, as well as in the prodromal the interictal and residual phases. Over the last decade, functional magnetic resonance imaging has focused on research of brain networks like the Default mode network, the Salience network and Central executive network, enabling a deeper understanding of cognitive deficits, as well as other phenomena such as disorganization of thought and behavior. The study of the nodes of these networks, such as the precuneus and insula, informs about their complex significant roles as structures responsible for important cognitive domains such as concentration, attention, ability to understand and reproduce information, as well as memory functions. It is suggested that the neurotransmission of dopamine and glutamate play a key role in these processes and their successful modulation in the correct brain regions through psychopharmacological and biomedical instrumental methods may lead to a significant reversal of conventional paradigms. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide the translation of scientific knowledge about the neural networks and the disruptions in and between different brain regions, into clinically applicable and effective therapeutic results in the management of severe psychotic disorders.

Amongst the advantages of using Worldwide Interoperability Microwave Access (WiMAX) technology at the last-mile level as access technology include an extensive range of 50 km Line of Sight (LOS), 5 to 15 km Non-Line of Sight and few infrastructure installations compared to other wireless broadband access technologies. Despite positive investments in ICT fibre infrastructure by developing countries, including Botswana, servicing end-users is subjected to high prices and service disparities. The alternative, the Wi-Fi hotspot initiative by the Botswana government, falls short as a solution for last-mile connectivity and access. This study used OPNET simulation Modeller 14,5 to investigate whether Botswana’s national broadband project could adopt WiMAX IEEE 802.16e as an access technology. Therefore, using the simulation method, this paper evaluates the WiMAX IEEE 802.16e/m over three subscriber locations in Botswana. The results obtained indicate that the deployment of the WiMAX IEEE 802.16e standard can solve most of the deployment issues and access at the last-mile level. Although the findings suggest that WiMAX IEEE 802.16e is more suitable for high-density areas, it could also solve rural areas’ infrastructure development challenges and provide the required high-speed connectivity access. However, unlike the Wi-Fi initiative, which requires more infrastructure deployment and less on institutional and regulatory frameworks, the deployment of WiMAX IEEE802.16e requires institutional and regulatory standards.

Aim. In the current study, we aimed at identifying resting-state brain networks, which are different in patients with depression compared to healthy individuals. Moreover, we analyzed the potential for clinical use of different network measures that could discriminate the two groups and thus help the diagnostic process. Method and subjects. We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed to investigate functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were made at the functional networks level, using false discovery rate method. Independent samples t-test was performed on the network measures mean values to reveal differences between HC and MDE groups. Permutation-based statistical testing was used to test the significance of the difference between the distributions of the network measures by nodes for HC and MDE groups. Linear discriminant analysis was used to differentiate between the groups. Results and discussion. Significant differences in FC between depressed patients and healthy controls were found with the most prominent changes encompassing within-region as well as between-region connectivity of occipital lobe areas such as precuneus (PreCu), cuneus (Cu), superior occipital gyrus (SOG), lingual gyrus (LG), fusiform gyrus (FG), cerebellum, along with limbic structures including the hippocampus (Hipp) and cingulate gyrus. Linear discriminant analysis demonstrated that the full connectivity matrices, as well as those with only the significant connections identified in advance, were the most precise in differentiating between depression and health. These measures reached precision levels of 97% and 94%, respectively. Conclusion. Our study delivered further evidence about impairment of functional connectivity networks in MDE that may contribute to differentiate patients with depression from healthy subjects.

[Background] Previous research achievements of the service-oriented tourist city network have often focused on the analysis of its geographical distribution and service role of the important cities instead of the connections and hierarchical tendencies between the whole city in a large region.[Method]Using big data approaches on the regional connections of 38 tourism organizations including famous hotels, air passenger transport, tourism service agencies across 63 most important tourist cities in China. Fuzzy c-means clustering analysis is used to define 8 city arena clusters. [Results]According to the distributions of connectivity between 63 cities, these eight clusters play different service functional roles in the urban tourism network at four hierarchies. With their “center-edge” memberships, these arena clusters are formed by the interweaving process of regional and hierarchical tourism service connections. The results include the analysis of the various service-oriented tourist city in China and point out the geography “gap” faced by network. [Conclusion] Service-oriented tourist cities need to find their hierarchies and positioning in the network scientifically to avoid blind development, to make regional urban tourism sustainable development.

Josephson junctions (JJs) in the presence of a magnetic field exhibit qualitatively different interference patterns depending on the spatial distribution of the supercurrent through the junction. In JJs based on two-dimensional topological insulators (2DTIs), the electrons/holes forming a Cooper pair (CP) can either propagate along the same edge or be split into the two edges. The former leads to a SQUID-like interference pattern, with the superconducting flux quantum $\phi_0$ (where $\phi_0=h/2e$) as a fundamental period. If CPs’ splitting is additionally included, the resultant periodicity doubles. Since the edge states are typically considered as being strongly localized, the critical current does not decay as a function of the magnetic field. Here we go beyond this approach and inspect a topological JJ in the tunneling regime featuring extended edge states. We consider the possibility that the two electrons of a CP propagate and explore the junction independently over length scales comparable with the superconducting coherence length. As a consequence of the spatial extension, we obtain a decaying pattern with different possible periods. In particular, we show that, if crossed Andreev reflections (CARs) are dominant and the edge states overlap, the resulting interference pattern features oscillations whose periodicity approaches $2\phi_0$.

The article focuses on derivation of simplified predictive models for identification of overall compressive stiffness and strength of corrugated cardboards. As a representative example an unsymmetrical 5-ply sample (with E and B flute) was used in this study. In order to exclude unreliable displacement measurement in the standard edge crush test, the virtual strain gauges are used. Video extensometry is employed here to collect measurements from the outer surfaces of the sample on both sides. Additional data allows for obtaining real force-displacement curves, which are used in the validation procedure. To emulate the experimental results, beside simple analytical model, also a 3D numerical model fully reflecting the geometry of the corrugated board, based on finite elements method was also built. In both cases a good agreement between the experimental results and the analytical and numerical calculations was observed. This proves that the proposed analytical model can be successfully used to determine the overall stiffness and compressive strength of the corrugated board, provided that the geometry and properties of all the layers of the board are known. The simple model presented in this work enables quick and reliable design and prototyping of new assemblies without the need to manufacture them.

The article presents a method of generating Key Performance Indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in frequency converter. The authors proved that using the proposed solution it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage Flux Control without encoder, vector Voltage Flux Control with encoder, vector Current Flux Control and Vector Current Flux Control with torque control). During the experiments values of current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque and the current rotational speed were measured and processed. Based on the data the level of the energy efficiency was determined for various control modes.

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

In this work, the problem is considered for eliminating a non-uniform rectilinear smearing of an image by mathematical and computer-based way, for example, a picture of several cars taken with a fixed camera and moving with different speeds. The problem is described by a set of 1-dimensional Fredholm integral equations (IEs) of the first kind of convolution type with a 1-dimensional point spread function (PSF) at uniform smearing and by a set of new 1-dimensional IEs of a general type (not convolution type) with a 2-dimensional PSF at non-uniform smearing. The problem is also described by one 2-dimensional IE of convolution type with a 2-dimensional PSF at uniform smearing and by a new 2-dimensional IE of a general type with a 4-dimensional PSF at non-uniform smearing. The problem for solving Fredholm IE of the first kind is ill-posed (unstable). Therefore, IEs of convolution type are solved by the Fourier transform (FT) method and Tikhonov's regularization (TR), and IEs of general type are solved by the quadrature/cubature and TR methods. Moreover, the magnitude of the image smear Δ is determined by the original “spectral method”, which increases the accuracy of image restoration. It is shown that the use of a set of 1-dimensional IEs is preferable to one 2-dimensional IE in the case of non-uniform smearing. In the inverse problem (image restoration), the Gibbs effect (the effect of false waves) in the image may occur. It can be edge or inner. The edge effect is well suppressed by the proposed technique “diffusing the edges". In the case of an inner effect, it is eliminated with difficulty, but the image smearing itself plays the role of diffusing and suppresses the inner Gibbs effect to a large extent. It is shown (in the presence of impulse noise in an image) that the well-known Tukey median filter can distort the image itself, and the Gonzalez adaptive filter also distorts the image (but to a lesser extent). We propose a modified adaptive filter. A software package was developed in MatLab and illustrative calculations are performed.

A simple undirected graph $\Gamma=(V_\Gamma,E_\Gamma)$ admits an $H$-covering if every edge in $E_\Gamma$ belongs to at least one subgraph of $\Gamma$ that isomorphic to a graph $H$. For any graph $\Gamma$ admitting $H$-covering, a total Labelling $\beta: V_\Gamma \cup E_\Gamma \longrightarrow \{1,2,\dots,p\}$ is called an $H$-irregular total $p$-labelling of $\Gamma$ if every two different subgraphs $H_1$ and $H_2$ of $\Gamma$ isomorphic to $H$ have distinct weights where the weight $w_\beta(K)$ of subgraph $K$ of $\Gamma$ is defined as $w_f(K):=\displaystyle \sum_{v\in V_K} f(v) + \sum_{e\in E_K} f(e)$. The smallest number $p$ for which a graph $\Gamma$ admits an $H$-irregular total $p$-labelling is called the total $H$-irregularity strength of $\Gamma$ and is denoted by $ths(\Gamma)$. In this paper, we determine the total $H$-irregularity strength of edge comb product of two graphs.

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.

In an era of global connectivity, language and literature education faces the challenge of navigating multilingualism, cultural diversity, and technological advancements. This paper explores the challenges and opportunities in language and literature education in the era of global connectivity. Through a qualitative research approach, interviews, focus group discussions, and classroom observations were conducted with 30 Arts and Sciences students and teachers. The findings reveal significant themes related to cultural diversity, multilingualism, and the integration of technology in language learning. The study emphasizes the importance of inclusive pedagogies, critical thinking development, and the incorporation of authentic and diverse literary works. Recommendations are provided for educators and policymakers to promote intercultural understanding and enhance language and literature education. The limitations of the study suggest areas for future research, including long-term language proficiency and diverse contextual considerations. Overall, this paper calls for ongoing innovation and collaboration to meet the evolving needs of language and literature education in an interconnected world.

AbstractIn the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from static functional to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.Impact StatementWhile functional connectivity has elucidated much MN properties with relation to age, disease, and behavior, effective connectivity has been shown to be useful in MN-informed therapies in adults. Thus, effective connectivity may have potential to impact childhood movement disorder therapies, given the lower to no patient demand.

Based on functional magnetic resonance imaging and multilayer dynamic network model, the quantified temporal stability of brain network has shown potentials in predicting altered brain functions. The present review focuses on summarizing current knowledge on the commonly-used measures of brain network’s temporal stability and the clinical research progress on them. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date that which measure is the best. The temporal stability of human brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, and data preprocessing/analyzing strategies, which should be considered in the clinical studies. Multiple common psychiatric diseases such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disease-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric diseases partly because of the divergent results, and further studies are warranted.

In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.