Connectivity Metrics Research Articles

High-order brain interactions in ketamine during rest and task: a double-blinded cross-over design using portable EEG on male participants

Ketamine is a dissociative anesthetic that induces a shift in global consciousness states and related brain dynamics. Portable low-density EEG systems could be used to monitor these effects. However, previous evidence is almost null and lacks adequate methods to address global dynamics with a small number of electrodes. This study delves into brain high-order interactions (HOI) to explore the effects of ketamine using portable EEG. In a double-blinded cross-over design, 30 male adults (mean age = 25.57, SD = 3.74) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Ketamine induced an increase in redundancy in brain dynamics (copies of the same information that can be retrieved from 3 or more electrodes), most significantly in the alpha frequency band. Redundancy was more evident during resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine’s HOI and experiences of derealization. Ketamine appears to increase redundancy and HOI across psychometric measures, suggesting these effects are correlated with alterations in consciousness towards dissociation. In comparisons with event-related potential (ERP) or standard functional connectivity metrics, HOI represent an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations between electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes.

Permutation conditional mutual information to quantify TMS-evoked cortical connectivity in disorders of consciousness

Objective. To improve the understanding and diagnostic accuracy of disorders of consciousness (DOC) by quantifying transcranial magnetic stimulation (TMS) evoked electroencephalography connectivity using permutation conditional mutual information (PCMI). Approach. PCMI can characterize the functional connectivity between different brain regions. This study employed PCMI to analyze TMS-evoked cortical connectivity (TEC) in 154 DOC patients and 16 normal controls, focusing on optimizing parameter selection for PCMI (Data length, Order length, Time delay). We compared short-range and long-range PCMI values across different consciousness states—unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and normal (NOR)—and assessed various feature selection and classification techniques to distinguish these states. Main results. (1) PCMI can quantify TEC. We found optimal parameters to be Data length: 500 ms; Order: 3; Time delay: 6 ms. (2) TMS evoked potentials (TEPs) for NOR showed a rich response, while MCS patients showed only a few components, and UWS patients had almost no significant components. The values of PCMI connectivity metrics demonstrated its usefulness for measuring cortical connectivity evoked by TMS. From NOR to MCS to UWS, the number and strength of TEC decreased. Quantitative analysis revealed significant differences in the strength and number of TEC in the entire brain, local regions and inter-regions among different consciousness states. (3) A decision tree with feature selection by mutual information performed the best (balanced accuracy: 87.0% and accuracy: 83.5%). This model could accurately identify NOR (100.0%), but had lower identification accuracy for UWS (86.5%) and MCS (74.1%). Significance. The application of PCMI in measuring TMS-evoked connectivity provides a robust metric that enhances our ability to differentiate between various states of consciousness in DOC patients. This approach not only aids in clinical diagnosis but also contributes to the broader understanding of cortical connectivity and consciousness.

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

AbstractComplex natural fracture networks typically consist of multiple clusters, whose connectivity is rarely quantified. Therefore, for each identified fracture network, we propose a connectivity metric that accounts for individual fracture clusters and their interactions. This metric evaluates contributions from all fracture clusters, considering their relative sizes and interactions among the isolated clusters, which in turn depend on the hydraulic conductance of the interconnecting rock matrix. Furthermore, we investigate how the system connectivity depends on fracture sealing, alterations of central clusters, and cluster linkage. Fracture sealing strongly impacts overall fracture connectivity, with 5 percent of sealed fractures reducing connectivity by 20 percent. The connectivity reduction is small when transitioning the central cluster from the largest to the smallest one. However, the largest cluster significantly contributes to overall connectivity, while the smallest one contributes minimally. Natural fracture networks increase connectivity by linking more clusters, with heterogeneity and anisotropy playing pivotal roles.

Childhood obesity's impact on cognition and brain connectivity worsens with low family income.

Childhood obesity and its adverse health consequences have risen worldwide, with low socioeconomic status increasing the risk in high-income countries like the US. Understanding the interplay between childhood obesity, cognition, socioeconomic factors, and the brain is crucial for prevention and treatment. Using data from the ABCD study, we investigated how body mass index (BMI) relates to brain structural and functional connectivity metrics. Obese/overweight children (n = 2,356) were more likely to live in poverty and exhibited lower cognitive performance compared to normal weight children (n = 4,754). Higher BMI was associated with multiple brain measures that were strongest for lower longitudinal diffusivity in corpus callosum, increased activity in cerebellum, insula, and somatomotor cortex, and decreased functional connectivity in multimodal brain areas, with effects more pronounced among children from low-income families. Notably, nearly 80% of the association of low income and 70% of the association of impaired cognition on BMI were mediated by higher brain activity in somatomotor areas. Increased resting activity in somatomotor areas and decreased structural and functional connectivity likely contribute to the higher risk of overweight/obesity among children from low-income families. Supporting low-income families and implementing educational interventions to improve cognition may promote healthy brain function and reduce the risk of obesity.

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes.

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

White matter connectivity and social functioning in survivors of pediatric brain tumor

ObjectiveSurvivors of pediatric brain tumors (SPBT) are at risk for social deficits, fewer friendships, and poor peer relations. SPBT also experience reduced brain connectivity via microstructural disruptions to white matter from neurological insults. Research with other populations implicates white matter connectivity as a key contributor to poor social functioning. This case-controlled diffusion-weighted imaging study evaluated structural connectivity in SPBT and typically developing controls (TDC) and associations between metrics of connectivity and social functioning.MethodsDiffusion weighted-imaging results from 19 SPBT and 19 TDC were analyzed using probabilistic white matter tractography. Survivors were at least 5 years post-diagnosis and 2 years off treatment. Graph theory statistics measured group differences across several connectivity metrics, including average strength, global efficiency, assortativity, clustering coefficient, modularity, and betweenness centrality. Analyses also evaluated the effects of neurological risk on connectivity among SPBT. Correlational analyses evaluated associations between connectivity and indices of social behavior.ResultsSPBT demonstrated reduced global connectivity compared to TDC. Several medical factors (e.g., chemotherapy, recurrence, multimodal therapy) were related to decreased connectivity across metrics of integration (e.g., average strength, global efficiency) in SPBT. Connectivity metrics were related to peer relationship quality and social challenges in the SPBT group and to social challenges in the total sample.ConclusionsMicrostructural white matter connectivity is diminished in SPBT and related to neurological risk and peer relationship quality. Additional neuroimaging research is needed to evaluate associations between brain connectivity metrics and social functioning in SPBT.

Local adaptation of Pinus leiophylla under climate and land use change models in the Avocado Belt of Michoacán.

Climate change and land use change are two main drivers of global biodiversity decline, decreasing the genetic diversity that populations harbour and altering patterns of local adaptation. Landscape genomics allows measuring the effect of these anthropogenic disturbances on the adaptation of populations. However, both factors have rarely been considered simultaneously. Based on a set of 3660 SNPs from which 130 were identified as outliers by a genome-environment association analysis (LFMM), we modelled the spatial turnover of allele frequencies in 19 localities of Pinus leiophylla across the Avocado Belt in Michoacán state, Mexico. Then, we evaluated the effect of climate change and land use change scenarios, in addition to evaluating assisted gene flow strategies and connectivity metrics across the landscape to identify priority conservation areas for the species. We found that localities in the centre-east of the Avocado Belt would be more vulnerable to climate change, while localities in the western area are more threatened by land conversion to avocado orchards. Assisted gene flow actions could aid in mitigating both threats. Connectivity patterns among forest patches will also be modified by future habitat loss, with central and eastern parts of the Avocado Belt maintaining the highest connectivity. These results suggest that areas with the highest priority for conservation are in the eastern part of the Avocado Belt, including the Monarch Butterfly Biosphere Reserve. This work is useful as a framework that incorporates distinct layers of information to provide a more robust representation of the response of tree populations to anthropogenic disturbances.

Quantum Dot-Fab' Conjugates as Compact Immunolabels for Microtubule Imaging and Cell Classification.

Antibodies and their conjugates of fluorescent labels are widely applied in life sciences research and clinical pathology. Among diverse label types, compact quantum dots (QDs) provide advantages of multispectral multiplexing, bright signals in the deep red and infrared, and low steric hindrance. However, QD-antibody conjugates have random orientation of the antigen-binding domain which may interfere with labeling and are large (20-30 nm) and heterogeneous, which limits penetration into biospecimens. Here, we develop conjugates of compact QDs and Fab' antibody fragments as primary immunolabels. Fab' fragments are conjugated site-specifically through sulfhydryl groups distal to antigen-binding domains, and the multivalent conjugates have small and homogeneous sizes (∼12 nm) near those of full-sized antibodies. Their performance as immunolabels for intracellular antigens is evaluated quantitatively by metrics of microtubule labeling density and connectivity in fixed cells and for cytological identification in fixed brain specimens, comparing results with probes based on spectrally-matched dyes. QD-Fab' conjugates outperformed QD conjugates of full-sized antibodies and could be imaged with bright signals with 1-photon and 2-photon excitation. The results demonstrate a requirement for smaller bioaffinity agents and site-specific orientation for the success of nanomaterial-based labels to enhance penetration in biospecimens and minimize nonspecific staining.

Combining prior and post‐release data while accounting for dispersal to improve predictions for reintroduction populations

AbstractAttempts to reintroduce species to managed areas may be compromised by dispersal into the surrounding landscape. Therefore, decisions regarding the selection and ongoing management of reintroduction areas require predicting dispersal as well as the survival and reproduction rates of the species to be reintroduced. Dispersal can potentially be measured directly by tracking animals, but this is often impractical. However, dispersal can also be inferred from re‐sighting surveys done within reintroduction areas if such data are available from multiple areas with varying connectivity to the surrounding landscape, allowing apparent survival and recruitment to be modelled as a function of connectivity metrics. Here, we show how data from 10 previous reintroductions of a New Zealand passerine, the toutouwai (Petroica longipes), were used to predict population dynamics at a predator‐controlled reintroduction area with high connectivity, and predictions then updated using post‐release data. Bayesian hierarchical modelling of the previous data produced prior distributions for productivity, adult survival and apparent juvenile survival rates that accounted for random variation among areas as well as rat density and connectivity. The modelling of apparent juvenile survival as a function of connectivity allowed it to be partitioned into estimates of survival and fidelity. Bayesian updating based on post‐release data produced posterior distributions for parameters that were consistent with the priors but much more precise. The prior data also allowed the recruitment rate estimated in the new area to be partitioned into separate estimates for productivity, juvenile survival and juvenile fidelity. Consequently, it was possible to not only estimate population growth under current management, but also predict the consequences of reducing the scale or intensity of predator control, facilitating adaptive management. The updated model could then be used to predict population growth as a function of the connectivity and predator control regime at proposed reintroduction areas while accounting for random variation among areas.

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients.

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

Revisiting the small-world property of co-enrollment networks: A network analysis of hybrid course delivery strategies

Infectious disease outbreaks bring new challenges to campus operations at institutions of higher education (IHEs). At the beginning of the COVID-19 pandemic, as a precaution against COVID-19 transmission on their campuses, many IHEs shifted to entirely remote courses in the Spring 2020 semester. As these institutions prepared for the Fall 2020 semester, their administrations evaluated the extent to which remote courses should continue. While remote courses may reduce the number of students who interact on campus and thus mitigate the risk of disease spread, there may be academic and financial downsides. MethodologyUsing de-identified enrollment, course schedule, and student profile data from the Fall 2019 semester at Georgia Institute of Technology (Georgia Tech), we construct a co-enrollment network, which is a representation of student connections through courses. In this co-enrollment network, we observe the small-world property in which the average path length among reachable student pairs is short and nearly all students are connected to each other in the main component of the network through multiple independent paths. We also observe high connectivity between different majors. Then, we apply various hybrid instructional mode strategies to this co-enrollment network and use network connectivity and in-person instruction metrics to compare these strategies from health and academic perspectives, by their impacts on various groups of students, and based on the trade-off between reducing network connectivity and delivering in-person instruction. ResultsAlthough these strategies result in reduced network connectivity, they do not remove the small-world property. When comparing the strategies in terms of reducing network connectivity, we find three different strategy types to be the most favorable. Even under these strategies, the percentage of students with in-person courses that could be exposed rises quickly with initial infection rates. When comparing the strategies in terms of delivering in-person instruction, the most favorable strategies select a fixed percentage of courses for each subject to be delivered remotely. We then consider the impacts of these strategies on various groups of students to evaluate if there may be disparate impacts across these groups. In general, we find that strategies with more remote courses result in less disparate impacts on network connectivity and in-person instruction across these groups. In addition, we generally see less in-person instruction for lower-level students (i.e., first-year students and sophomores) compared to upper-level students. Regarding the trade-off between reducing network connectivity and delivering in-person instruction, the only strategy that is dominated under all scenarios in this study is the 1000s and 4000s only strategy, which keeps only the 1000- and 4000-level (i.e., first-year and senior level, respectively) courses in-person. The reason for this is that even though the university offers less in-person instruction due to this strategy's large quantity of remote courses, this strategy results in more connected networks because the 1000- and 4000-level courses tend to be large in size, the 1000-level courses are highly central, and the first-year students and seniors have high connectivity. ImportanceBy quantitatively comparing network connectivity and in-person instruction for these various strategies, campus administrators can weigh the physical health, academic performance, and mental health of students, along with the financial well-being of their academic institutions when responding to infectious disease outbreaks.

Performance evaluation of six mesh router replacement methods for wireless mesh networks: A comparison study for small and middle scale networks considering two islands distribution of mesh clients

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM.

Building pondscapes for amphibian metapopulations.

The success of ponds constructed to restore ecological infrastructure for pond-breeding amphibians and benefit aquatic biodiversity depends on where and how they are built. We studied effects of pond and landscape characteristics, including connectivity, on metapopulation dynamics of 12 amphibian species in Switzerland. To understand the determinants of long-term occupancy (here summarized as incidence), environmental effects on both colonization and persistence should be considered. We fitted dynamic occupancy models to 20years of monitoring data on a pond construction program to quantify effects of pond and landscape characteristics and different connectivity metrics on colonization and persistence probabilities in constructed ponds. Connectivity to existing populations explained dynamics better than structural connectivity metrics, and simple metrics (distance to the nearest neighbor population, population density) were useful surrogates for dispersal kernel-weighted metrics commonly used in metapopulation theory. Population connectivity mediated the persistence of conservation target species in new ponds, suggesting source-sink dynamics in newly established populations. Population density captured this effect well and could be used by practitioners for site selection. Ponds created where there were 2-4 occupied ponds within a radius of ∼0.5km had >3.5 times higher incidence of target species (median) than isolated ponds. Species had individual preferences regarding pond characteristics, but breeding sites with larger (≥100m2) total water surface area, that temporarily dried, and that were in surroundings with maximally 50% forest benefitted multiple target species. Pond diversity will foster amphibian diversity at the landscape scale.

Tumors Affect the Metabolic Connectivity of the Human Brain Measured by 18 F-FDG PET.

18 F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity. Forty-three patients with left hemispheric tumors and 18 F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test ( P < 0.05). Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC ( P < 0.001), except assortativity and betweenness centrality ( P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right ( P < 0.01), with the exception of betweenness centrality ( P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics ( P < 0.001) than temporal tumors, but lower betweenness centrality ( P = 4.5 -7 ). Patients with high KPS showed higher distance local efficiency ( P = 0.01), rich club coefficient ( P = 0.0048), clustering coefficient ( P = 0.00032), betweenness centrality ( P = 0.008), and similarity ( P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses. Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness – A systematic review and meta-analysis

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60–0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70–0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70–0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

Habitat connectivity of threatened ungulate species in a native savanna landscape of northern South America

AbstractThe native savanna ecosystem of the Orinoquia region is the habitat of 50% of the wild ungulate species reported for Colombia. Over the last 20 years, this high species diversity has been strongly threatened by the human transformation of the natural land cover causing connectivity loss of the habitats. The Orinoquia region lacks a biological connectivity analysis with a multi-species approach involving species groups that are representative of the savanna ecosystem such as the ungulates. Understanding the spatial distributions of suitable areas and the main habitats that act as primary habitats for ungulate species in these landscapes is fundamental for the design of conservation strategies. We use an occurrence dataset for lowland tapir (Tapirus terrestris), white-tailed deer (Odocoileus virginianus), and white-lipped peccary (Tayassu pecari) in the development of species’ potential distribution models, binarization process, and morphological spatial pattern analysis. This information was used for the modeling of dispersal corridors connecting the core habitats of the ungulate focal species using the randomized shortest path algorithm and quantifying the weighted global connectivity metrics. Our results suggest an integral corridor with potential least-cost dispersal routes between the native savanna landscape on the middle basins of the savanna rivers and the Meta River. These areas associated with the fluvial dendritic systems are connected, while the core habitats in the eastern part of the savanna landscape are disconnected. We discuss how the application of such knowledge on the spatial ecology of ungulate focal species might improve the management of the metapopulations in the Orinoquia region.

Multi-objective path planning for multi-UAV connectivity and area coverage

In this paper, we assume that a team of drones equipped with sensing and networking capabilities explore an unknown area via onboard sensors for surveillance, monitoring, target search or data collection purposes and deliver the sensed data to a ground control station (GCS) over multi-hop links. We propose a multi-drone path planner that jointly optimizes area coverage time and connectivity among the drones. We propose a novel connectivity metric that includes not only percentage connectivity of the drones to GCS, but also the maximum duration of consecutive time that the drones are disconnected from the GCS. To solve this optimization formulation, we propose a multi-objective evolutionary algorithm with novel operations. We use our solver to test single, two and many objective path planning problems and compare our Pareto-optimal solutions to benchmark weighted-sum based solutions. We show that as opposed to the single solution that weighted-sum methods provide based on prior information from the user, the proposed evolutionary multi-objective optimizers can provide a diverse set of solutions that cover a range of mission time and connectivity performance illustrating the trade-off between these conflicting objectives. The end-user can then choose the best path solution based on the mission priorities during operation.

Assessing the Impact of Hypercapnic Stimulation on Brain Connectivity Metrics During Functional Magnetic Resonance Imaging

Assessing the Impact of Hypercapnic Stimulation on Brain Connectivity Metrics During Functional Magnetic Resonance Imaging

Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips.In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles.While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency.This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems.