Connectivity Parameters Research Articles

A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models.

Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.

INFORMATION SYSTEMS PROACTIVE PROTECTION SUITE AGAINST NETWORK RECONNAISSANCE

The usage of known protection tools in information systems, including cryptographic ones, does not allow ensuring the confidentiality of information about its composition, structure and functioning algorithms, due to the fact that modern network technologies require addressable information in the service headers of the transmitted message packets. Strict dependence of information systems configurations on the quality requirements for the architecture, as well as established security policies set by regulators, causes them to have the properties of static, homogeneous and deterministic network parameters. This gives the adversary a number of indisputable advantages to non-compromising conduct network reconnaissance, high reliability of its results over a long period of time, as well as advance (planned) formation and application of the optimal set of tools to implement computer attacks. In this regard, there is a need to develop security technologies that replace static parameters of information systems with the dynamic ones. The analysis of existing technologies in the subject area under consideration showed a number of their inherent disadvantages, consisting in high resource intensity, insufficient performance and narrowness of the scope. In order to solve this problem, the author proposed a new technical solution that allows to level the disadvantages of known analogues and surpasses them by a number of criteria. The technical shape of the suite, consisting of three interconnected subsystems, that allows to mask information directions, the parameters of local area networks and manage the parameters of network connections with network reconnaissance tools, is presented and justified.

The Influence of Spatial Smoothing Kernel Size on the Whole-brain Dynamic Functional Network Connectivity and Meta-state Parameters.

In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated. Here, we sought to identify the effects of spatial smoothing on brain dynamics by performing dynamic functional network connectivity (dFNC) and meta-state analysis, a unique approach capable of examining a higher-dimensional temporal dynamism of whole-brain functional connectivity. Gaussian smoothing kernel with different widths at half of the maximum of the height of the Gaussian (4, 8, and 12 mm FWHM) were used during preprocessing prior to the group independent component analysis (ICA) with a relatively high model order of 75. dFNC was conducted using the sliding-time window approach and k-means clustering algorithm. Meta-state dynamics method was performed by reducing the number of windowed FNC correlations using principal components analysis (PCA), temporal and spatial ICA and k-means. Results revealed robust effects of spatial smoothing on the connectivity dynamics of several network pairs including a variety of cognitive/attention networks in a connectivity state with the highest occurrence (FDR corrected-p < 0.01). Meta-state analyses indicated significant changes in meta-state metrics including the number of meta-states, meta-state changes, meta-state span, and the total distance. These changes were particularly pronounced when we compared resting state data smoothed with 8 vs. 12 mm FWHM. Our preliminary findings give insights into the effects of spatial smoothing kernel size on the dynamics of functional connectivity and its consequences on meta-state parameters. It also provides further indication of the importance of evaluating variance associated with preprocessing steps on analysis outcomes.

Multi‐stream adaptive spatial‐temporal attention graph convolutional network for skeleton‐based action recognition

Skeleton-based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achieve significant performance in skeleton-based action recognition. However, existing GCN-based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher-order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi-stream adaptive spatial-temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial-temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi-stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state-of-the-art performance on both the NTU-RGBD and Kinetics-Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.

Imbalanced basal ganglia connectivity is associated with motor deficits and apathy in Huntington’s disease

The gating of movement depends on activity within the cortico-striato-thalamic loops. Within these loops, emerging from the cells of the striatum, run two opponent pathways—the direct and indirect basal ganglia pathways. Both are complex and polysynaptic, but the overall effect of activity within these pathways is thought to encourage and inhibit movement, respectively. In Huntington’s disease, the preferential early loss of striatal neurons forming the indirect pathway is thought to lead to disinhibition, giving rise to the characteristic motor features of the condition. But early Huntington’s disease is also associated with apathy, a loss of motivation and failure to engage in goal-directed movement. We hypothesized that in Huntington’s disease, motor signs and apathy may be selectively correlated with indirect and direct pathway dysfunction, respectively.We used spectral dynamic casual modelling of resting-state functional MRI data to model effective connectivity in a model of these cortico-striatal pathways. We tested both of these hypotheses in vivo for the first time in a large cohort of patients with prodromal Huntington’s disease. Using an advanced approach at the group level we combined parametric empirical Bayes and Bayesian model reduction procedures to generate a large number of competing models and compare them using Bayesian model comparison. With this automated Bayesian approach, associations between clinical measures and connectivity parameters emerge de novo from the data.We found very strong evidence (posterior probability > 0.99) to support both of our hypotheses. First, more severe motor signs in Huntington’s disease were associated with altered connectivity in the indirect pathway components of our model and, by comparison, loss of goal-direct behaviour or apathy, was associated with changes in the direct pathway component.The empirical evidence we provide here demonstrates that imbalanced basal ganglia connectivity may play an important role in the pathogenesis of some of commonest and disabling features of Huntington’s disease and may have important implications for therapeutics.

Feedforward Neural Network Controller for Quadrotor in the Presence of Payload and Wind Disturbances

Recently, Artificial Neural Networks (ANN) have been used in broad research fields such as control system engineering. Inspired by the human brain system, this algorithm uses the learning process to improve system performance using the training dataset. The ANN algorithm is based on a weighted connection between the neurons to deal with the controller design. These connection parameters represent one of the crucial elements necessary to apply the learning process efficiently. However, finding the connection parameters becomes problematic when the controlled system is complex, nonlinear, and subjected to external disturbances. This work uses the most available ANN architecture called the Feedforward Neural Network to stabilize the quadrotor system in reference trajectory tracking when subjected to model payload and Dryden turbulent wind. A Deterministic Algorithm (DA) trains the FNN weight and bias without initialization with another training method to improve the control performance. Besides, three simulation tests are performed to control the quadrotor system. The first simulation uses classical PID to collect the necessary training data for the learning process. Secondly, the DA algorithm trains the FNN parameters to design the controllers necessary to stabilize the quadrotor in-flight tests. The third simulation uses the most common training method: the Levenberg-Marquardt Backpropagation (LMB) algorithm to tune the FNN parameters. The final simulation tests these control methods' efficiency when the quadrotor system is subjected to the model payload and the Dryden turbulent wind. A comparison test between the proposed DA-based feedforward neural network, the classical PID, and the LMB-based feedforward neural network is performed in the nominal case (without disturbance) and in the case of adding disturbance. The test result is obtained using Matlab software and demonstrates much accuracy and efficacity of the proposed DA-based FNN controller compared to the other methods. This result considers the proposed control approach an additional innovative method-based neural network to stabilize the quadrotor's Unmanned Aerial Vehicles (UAV).

Analysis of the functional EEG network in an Ecuadorian schizophrenia sample

Background and objectivesHigher mental functions depend on global functional coordination of the brain. Our aim was to study the baseline condition and modulation of functional networks in a previously unevaluated clinical population, compare the results with a population from another country, and analyze their relationship with cognitive functioning. MethodsWe evaluated the functioning of brain networks by EEG in 24 patients with schizophrenia and 32 healthy Ecuadorian controls. EEG recordings were made at rest and while performing a P300 task. Small world (SW), Path Length (PL), clustering coefficient (CLC) and connective strength (CS) values were calculated in both conditions. The values obtained were compared between groups, with the results of Spanish patients, and the relationship between the connective parameters and the cognitive performance of the participants was analyzed. ResultsHigher PL, CLC and CS values were identified in patients diagnosed with schizophrenia compared to controls (in basal condition) and lower SW values in this same condition. Ecuadorian patients obtained higher values than Spanish patients in the PL and CLC parameters and lower values for the SW parameter, despite these differences, the pattern of alteration in both samples followed the same trend. Finally, the alteration of CS, SW, CLC and PL parameters at baseline was related to cognitive performance. ConclusionThe connective alterations identified in Ecuadorian schizophrenic patients are consistent with those found in another sample with different genetic, environmental and cultural conditions. In addition, these alterations were associated with worse performance in different cognitive domains.

Maladaptive compensation of right fusiform gyrus in developmental dyslexia: A hub-based white matter network analysis

Cognitive theories have been proposed to clarify the causes and symptoms of dyslexia. However, correlations between local network parameters of white matter connectivity and literacy skills remain poorly known. An unbiased hypothesis-free approach was adopted to examine the correlations between literacy symptoms (reading and spelling) and hub-based white matter networks' connectivity parameters [nodal degree fractional anisotropy (FA) values] of 90 brain regions based on Anatomical Atlas Labels (AAL) in a group of French children with dyslexia aged 9–14 years. Results revealed that the higher the right fusiform gyrus's (FFG) nodal degree FA values, the lower the reading accuracy for words and pseudowords in dyslexic children. The results indicate that the severity of word/pseudoword reading symptoms in dyslexia relates to a white matter network centered around the right FFG. The negative correlation between right FFG network connectivity and reading accuracy, in particular pseudoword reading accuracy, suggests that right FFG represents a maladaptive compensation towards a general orthography-to-phonology decoding ability in developmental dyslexia.

Cyclic connectivity index of fuzzy incidence graphs with applications in the highway system of different cities to minimize road accidents and in a network of different computers.

A parameter is a numerical factor whose values help us to identify a system. Connectivity parameters are essential in the analysis of connectivity of various kinds of networks. In graphs, the strength of a cycle is always one. But, in a fuzzy incidence graph (FIG), the strengths of cycles may vary even for a given pair of vertices. Cyclic reachability is an attribute that decides the overall connectedness of any network. In graph the cycle connectivity (CC) from vertex a to vertex b and from vertex b to vertex a is always one. In fuzzy graph (FG) the CC from vertex a to vertex b and from vertex b to vertex a is always same. But if someone is interested in finding CC from vertex a to an edge ab, then graphs and FGs cannot answer this question. Therefore, in this research article, we proposed the idea of CC for FIG. Because in FIG, we can find CC from vertex a to vertex b and also from vertex a to an edge ab. Also, we proposed the idea of CC of fuzzy incidence cycles (FICs) and complete fuzzy incidence graphs (CFIGs). The fuzzy incidence cyclic cut-vertex, fuzzy incidence cyclic bridge, and fuzzy incidence cyclic cut pair are established. A condition for CFIG to have fuzzy incidence cyclic cut-vertex is examined. Cyclic connectivity index and average cyclic connectivity index of FIG are also investigated. Three different types of vertices, such as cyclic connectivity increasing vertex, cyclically neutral vertex and, cyclic connectivity decreasing vertex, are also defined. The real-life applications of CC of FIG in a highway system of different cities to minimize road accidents and a computer network to find the best computers among all other computers are also provided.

Взаємозв’язки порушень біохімічного й імунологічного гомеостазу у хворих на ювенільний ревматоїдний артрит з урахуванням тривалості захворювання

Актуальність. Ювенільний ревматоїдний артрит (ЮРА) належить до довічних патологічних станів. Тому актуальними є дослідження, що вивчають закономірності його розвитку, функціонування метаболічних систем, розвиток дистрофічних змін на тлі запального процесу. Мета. Вивчення особливостей метаболізму сполучнотканинних структур відповідно до стану імунного гомеостазу на етапах еволюції ЮРА у хворих дитячого віку. Матеріали та методи. Вивчено особливості структурного складу компонентів сполучної тканини, вмісту мікроелементів, імунологічного гомеостазу у дітей із ЮРА. Проаналізовано 355 клінічних спостережень хворих на ЮРА з урахуванням ретро- і проспективних досліджень за 2012–2016 роки. Клінічна група становила 117 хворих на суглобову форму віком від 2 до 18 років (у середньому 6,0 ± 0,5 року). Результати. Дослідження імунного статусу визначило зниження рівня Т-загальних лімфоцитів (р &lt; 0,001), Т-лімфоцитів-хелперів (р &lt; 0,001), Т-супресорів/цитотоксичних (р &lt; 0,001), підвищення імунорегуляторного індексу (р &lt; 0,05), зменшення бактерицидної активності нейтрофілів (р &lt; 0,001), збільшення показників тесту з нітросинім тетразолієм і зниження показника індексу стимуляції нейтрофілів (р &lt; 0,001), підвищення рівня циркулюючих імунних комплексів (р &lt; 0,001). Аналіз умісту прозапальних цитокінів довів підвищення рівня інтерлейкіну-1 (р &lt; 0,05) та інтерлейкіну-6. Визначено залежність імунологічних показників від тривалості хвороби. Встановлено підвищення вмісту хондроїтин-4- та -6-сульфатів, кератан- і дерматансульфатів крові. Найбільші відхилення відзначались на перших етапах розвитку хвороби, що, безперечно, пов’язано з активністю патологічного процесу до призначення протизапального лікування або на перших етапах його отримання. У віддалені періоди захворювання відбувається зменшення рівня загальних глікозаміногліканів, хондроїтин-4- та -6-сульфатів, сповільнення екскреції уронових кислот (рt &lt; 0,05). Аналіз змін у вмісті неорганічних компонентів залежно від тривалості хвороби виявив незначне зниження втрати організмом фосфору у період 3–5 років ЮРА. Аналіз відхилень імунобіохімічних співвідношень на різних етапах еволюції патологічного процесу виявив зміни кількості та характеру взаємозв’язків між показниками сполучнотканинного метаболізму й імунологічного гомеостазу. Висновки. Висока активність захворювання є основним предиктором розвитку дистрофічних змін, що підтверджується численними кореляційними взаємозв’язками показників обміну сполучної тканини та імунологічних параметрів. Найбільш несприятливими періодами є перші 1,5 року і через 3–5 років від початку захворювання. Виявлені зміни є підставою для проведення медикаментозної корекції біохімічного складу сполучнотканинних структур з метою профілактики розвитку вторинних дистрофічних змін в уражених суглобах у дітей з ЮРА, а також можуть відображати недостатню ефективність проведеного лікування та збереження субклінічної активності запалення.

Altered Effective Connectivity within an Oculomotor Control Network in Unaffected Relatives of Individuals with Schizophrenia.

The ability to rapidly stop or change a planned action is a critical cognitive process that is impaired in schizophrenia. The current study aimed to examine whether this impairment reflects familial vulnerability to schizophrenia across two experiments comparing unaffected first-degree relatives to healthy controls. First, we examined performance on a saccadic stop-signal task that required rapid inhibition of an eye movement. Then, in a different sample, we investigated behavioral and neural responses (using fMRI) during a stop-signal task variant that required rapid modification of a prepared eye movement. Here, we examined differences between relatives and healthy controls in terms of activation and effective connectivity within an oculomotor control network during task performance. Like individuals with schizophrenia, the unaffected relatives showed behavioral evidence for more inefficient inhibitory processes. Unlike previous findings in individuals with schizophrenia, however, the relatives showed evidence for a compensatory waiting strategy. Behavioral differences were accompanied by more activation among the relatives in task-relevant regions across conditions and group differences in effective connectivity across the task that were modulated differently by the instruction to exert control over a planned saccade. Effective connectivity parameters were related to behavioral measures of inhibition efficiency. The results suggest that individuals at familial risk for schizophrenia were engaging an oculomotor control network differently than controls and in a way that compromises inhibition efficiency.

Equivalent hydraulic conductivity, connectivity and percolation in 2D and 3D random binary media

The equivalent hydraulic conductivity (Keq) relates the spatial averages of flux and head gradient in a block of heterogeneous media. In this article, we study the influence of connectivity on Keq of media samples composed of a high conductivity (k+) and a low conductivity (k−) facies. The k+ facies is characterized by a proportion p, and also by two connectivity parameters: a connectivity structure type (no, low, intermediate, high), and a correlation integral scale lc.The probability distribution of Keq, and the critical value of p at which percolation occurs (pav), are studied as a function of these connectivity parameters. The distribution of log(Keq) is Gaussian in all cases, so the results are presented in terms of the geometric mean (〈Keq〉) and the variance (σlog(Keq)2).Both quantities show a data collapse if expressed as a function of p−pav (for the variance σlog(Keq)2, notably, even if 2D and 3D data are plotted together). In 3D, when a connectivity structure exists, Keq is always greater than when no structure exists, and increases (while pav decreases) as lc increases. The same is observed in 2D, except for the low connectivity structure type (i.e. when the k+ facies is disconnected), that shows an unprecedented behaviour: Keq is greater in the absence of structure, and decreases (pav increases) as lc increases. Our results show that any influence of connectivity on Keq is well accounted for simply by a shift in the percolation threshold pav, and then, suggest that Keq is controlled mainly by the proximity to percolation.

Surrogate models for endplate beam-column connections with shape memory alloy bolts

This paper presents the development and validation of moment-rotation backbone curve parameters for self-centering steel endplate connections with superelastic SMA bolts. In terms of influential design factors, surrogate models of response parameters are proposed using detailed finite element models of self-centering beam-column connections and statistical analysis of response parameters. The predictive surrogate models are useful for characterizing the connection backbone and self-centering response, which is essential for the performance-based design and assessment of extended endplate connections equipped with SMA bolts. Through a verification study, the accuracy of predictions using the proposed models is confirmed. The proposed surrogate models are used to assess the fragility of SMA-based beam-column connections. The results demonstrate that endplate beam-column connections with SMA bolts can provide an acceptable level of deformation capacity. Furthermore, it is found that larger diameter SMA bolts and smaller depth beam sections can reduce the probability of experiencing SMA bolt fracture by 31% and 77%, respectively.

Introduction of Trichoderma spp. biocontrol strains against Sclerotinia sclerotiorum (Lib.) de Bary change soil microbial community composition in common bean (Phaseolus vulgaris L.) cultivation

Biological control represents a promising alternative to maintain the viability and productivity of different agricultural crops. In this work, dry bean (Phaseolus vulgaris L.) was grown in a field contaminated with Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold disease. We analyzed the impact of the application of antagonistic strains of Trichoderma harzianum ESALQ-1306 and Trichoderma asperellum BRM-29104 on the edaphic native microbiota and endophytic fungal community. The structure of the fungal and bacterial communities was evaluated by next generation sequencing of the 28S and 16S rRNA genes, respectively. The results showed that the introduction of the strains changed the composition of the community without significantly affecting the parameters of abundance and alpha diversity (Anova P > 0.05). Ascomycota (54.8%) and Actinobacteria (37.2%) were predominant in the analyzed communities. Hill's diversity profiles of the treatments were similar in terms of diversity and dominance, being higher in a 90-day period and in the control treatment. NMDS and UPGMA analysis show that treatments of different lengths have low similarity in their composition, however, treatments where Trichoderma spp. were inoculated were relatively similar to the control. T. harzianum ESALQ-1306 promoted a greater quantity of indicator OTUs, 17 in total. T. harzianum ESALQ-1306 and T. asperellum BRM-29104 alter the size (1140 and 449 nodes respectively), connectivity parameters such as average degree (79.28 and 22.29 links respectively) and predominant topological roles in co-occurrence networks, but they do not alter the richness or abundance of orthologous genes. Cultivation time and plant organs were the factors that most influenced the structure of the microbial community. Trichoderma harzianum ESALQ-1306 and T. asperellum BRM-29104 promote the biological control of white mold without deleterious effects on the edaphic and endophytic bean communities.

Increased functional interaction within frontoparietal network during working memory task in major depressive disorder.

Abnormal fronto‐parietal activation has been suggested as a neural underpinning of the working memory (WM) deficits in major depressive disorder (MDD). However, the potential interaction within the frontoparietal network during WM processing in MDD remains unclear. This study aimed to examine the role of abnormal functional interactions within frontoparietal network in the neuropathological mechanisms of WM deficits in MDD. A total of 40 MDD patients and 47 demographic matched healthy controls (HCs) were included. Functional magnetic resonance imaging and behavioral data were collected during numeric n‐back tasks. The psychophysiological interaction and dynamic causal modelling methods were applied to investigate the connectivity within the frontoparietal network in MDD during n‐back tasks. The psychophysiological interaction analysis revealed that MDD patients showed increased functional connectivity between the right inferior parietal lobule (IPL) and the right dorsolateral prefrontal cortex (dlPFC) compared with HCs during the 2‐back task. The dynamic causal modelling analysis revealed that MDD patients had significantly increased forward modulation connectivity from the right IPL to the right dlPFC than HCs during the 2‐back task. Partial correlation was used to calculate the relationship between connective parameters and psychological variables in the MDD group, which showed that the effective connectivity from right IPL to right dlPFC was correlated negatively with the sensitivity index d’ of WM performances and positively with the depressive severity in MDD group. In conclusion, the abnormal functional and effective connectivity between frontal and parietal regions might contribute to explain the neuropathological mechanism of working memory deficits in major depressive disorder.

Predicting subclinical psychotic-like experiences on a continuum using machine learning

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants:A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters;Regularisation of spatiotemporal maps of event-related potentials.Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

Effectiveness of vegetation cover pattern on regulating soil erosion and runoff generation in red soil environment, southern China

Coupling vegetation cover pattern with soil erosion and runoff generation positions a multi-discipline hotspot. Due to the widely occurred patchy vegetation cover and water-limited environment, regulation of vegetation cover pattern on runoff and soil erosion in arid and semiarid environment was addressed widely. In tropical and subtropical environment, patchy vegetation cover pattern also widely occurred in human-dominated landscapes. However, the effect of vegetation cover pattern on runoff generation and sediment yield and its dependence on rainfall characteristics attract little attention, and are unclear. Correspondingly, this study addresses two relevant issues: (1) the rainfall-dependent capacity of vegetation cover pattern to mitigate runoff and soil erosion; (2) rainfall-runoff and rainfall-erosion regimes mitigated by vegetation cover pattern. Eighteen 10 m-by-5 m plots were constructed for the three-year (2015–2017) observation in a humid and red-soil-covered environment in southern China. The results show that vegetation cover pattern affected the response of runoff generation and soil erosion to rainfall. The soil loss was more readily to be regulated by the fraction and spatial pattern of vegetation cover than runoff; the potential of the fraction of vegetation cover in regulating soil loss positively response to rainfall depth and rainfall intensity; and rising rainfall depth led to a weakened relationship between vegetation coverage and runoff volume. Our results illustrated that the increased rainfall intensity promotes the regression relationship between vegetation coverage and runoff volume. In this humid environment, vegetation cover pattern regulates the response of runoff and sediment discharge to rainfall characteristics; and the effectiveness of vegetation cover pattern on regulation runoff and sediment discharge was rainfall-dependent. This study indicates that including connectivity parameters in quantifying the spatial pattern of vegetation may lead to different understanding of the effectiveness of vegetation cover on hydrology and soil erosion.

An extensive survey on the Internet of Drones

The Internet of Drones (IoD) recently gained momentum due to its high adaptability to a wide variety of complex scenarios. Indeed, Unmanned Aerial Vehicles (UAVs) can successfully be employed in different applications, such as agriculture, search and rescue missions, surveillance systems, mission-critical services, among others, thanks to some technological and practical advantages: high mobility, capability to extend wireless coverage areas, or ability to reach places inaccessible to humans. Moreover, the employment of drones promisingly improves the performance parameters of different network architectures, e.g., reliability, connectivity, throughput, and delay. Nevertheless, the adoption of networks of drones gives rise to several issues related to the intrinsic unreliability of the wireless medium, the duration of batteries, and the high mobility degree, which may cause frequent topology changes. Also security and privacy issues need to be properly investigated. This explains the very large number of works produced in the recent literature on IoD-related topics. With respect to other surveys on IoD-related topics, the goal of the present work is to categorize the multifaceted aspects of IoD, proposing a classification approach of the IoD environment that develops along two main directions. The IoD is initially introduced according to the possible applications and reference operating scenarios. On top of that, this work discussed with its applicability with a dedicated focus on the economical/social implications that the adoption of drones at scale may have. At a macroscopic level, it follows the structure of the Internet protocol stack, starting from the physical layer and extending to the upper layers, without neglecting cross-layer and optimization approaches. At a finer level, all the most relevant works belonging to each layer of the stack are further classified, according to the different issues that propose a peculiar challenges for each layer, and highlighting the most relevant differences with the other surveys present in literature. To provide a deeper insight, the present work embraces many facets of the IoD, with a thorough examination of privacy and security considerations. Finally, a discussion on the main research challenges and possible future directions is carried out, focusing on both open issues and the most promising technologies that deserve to be further developed in the IoD field.

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.

REGARDING THE QUALITY OF ASSEMBLY OF THE SCREW CONNECTIONS OF THE MOUNTING ASSEMBLY OF BALL BEARINGS

Application of method of vibrodiagnostics is based on the presence of cross-correlation dependences of dynamic descriptions of all knots from contact pressure in connection through the change of inflexibility of joints. The change of inflexibility of separate elements causes the change of the resulted inflexibility of knot, and at the same time and characteristic it to dynamic descriptions of the artificially created oscillation field: frequencies and amplitudes of resonance vibrations, indexes of damping, phase correlations. There is a task of determination of method of control of the most responsible technological frame-clamping operations at making of such objects: quality of contact connections, ball-bearing knots, fixing of previous pull of ball-bearings and control of isotropicness, inflexibility and dissipative descriptions of the system of pendant of rotor at all stages of drafting, including in the functional state. The primary objective of this work is research of question of influence of non-perfect resiliency and different inflexibility of the supporting bearings on the dynamic reaction of object at external oscillation excitation, determination of estimation of the different inflexibility bearings, determination of accordance of axial inflexibility of ball-bearings supports, them by a basic value. The dynamic model of knot and certain functions of influence of parameters of the system is considered on high-quality indexes. The dynamic model of precision rotor system (PRS) is investigated. Vibration tests at the stage of assembling the structure of the ORS showed that in the process of assembling the elements change the natural frequencies of the system and the quality factor of the oscillating circuits. The resulted dependences allow giving an estimation degree of non-linearity of resilient description and, as a result, different inflexibility of bearings supports as a result of oscillation researches. The sensitivity functions of the design parameters of the complex press-threaded connection in relation to the vibration dynamic parameters – frequencies and amplitudes of resonant oscillations – do not differ qualitatively from those obtained previously.