Time Estimation Research Articles

Comparative analysis of chloroplast genomes and phylogenetic relationships in the endemic Chinese bamboo Gelidocalamus (Bambusoideae)

IntroductionGelidocalamus Wen is a small yet taxonomically challenging genus within the Arundinarieae tribe. Recent molecular studies have suggested it may not be monophyletic. However, limited species sampling and insufficient molecular marker information have resulted in poorly resolved phylogenetic relationships within this genus.MethodsThe complete chloroplast genomes covering all 16 species and one variant of Gelidocalamus were sequenced, and comparative analyses were conducted. Phylogenetic analyses were performed using different molecular markers, including chloroplast data, the nuclear ribosomal DNA (nrDNA) repeats region, and 29 mitochondrial protein-coding genes. Additionally, the divergence times of Gelidocalamus were estimated to reveal their evolutionary history.ResultsThe plastomes of Gelidocalamus ranged in size from 139,500 bp to 139,801 bp, with a total of 137 identified genes, including 90 protein-coding genes, 39 tRNA genes, and 8 rRNA genes. The size of the nrDNA repeats ranged from 5,802 bp to 5,804 bp. Phylogenetic analysis based on chloroplast data revealed that Gelidocalamus is polyphyletic, with different subclades distributed within the IV and V clades. However, phylogenetic analysis based on nrDNA and mitochondrial genes did not effectively resolve the relationships within the genus.DiscussionComparative analysis of chloroplast genomes indicated that Gelidocalamus shares a high degree of similarity with closely related genera in terms of chloroplast genome collinearity, codon usage bias, and repetitive sequences. Divergence time estimation suggests that it is a relatively young group, with all members appearing successively over the past four million years. The complex phylogenetic patterns may arise from the rapid radiation of Arundinarieae. This study provides a preliminary foundation for further in-depth research on the phylogeny, genomic structural features, and divergence times of this genus.

Turning Research Ideas into Reality: A Guide to Developing a Simulated Research Protocol using Administrative Data

Objectives Demonstrate the translation of a research idea into a protocol with an analytic plan, including discussion on requirements for administrative data (AD) analysis, e.g., governance, time estimation. Foster discussion on processes for conducting AD research to enable learning and collaboration. ApproachParticipants were guided through three sections, and each alternated between presentations, group participation and discussion sessions. An overview of the Manitoba Centre for Health Policy (MCHP) Data Repository was presented to use as the framework for the AD discussion. Our chosen topic of discussion was: “How do health history and family history affect risk of poor outcomes for people with diabetes?” In small groups, participants discussed issues arising from translating the idea into a research plan. For example, define a cohort of people with diabetes, examine cardiovascular events as the outcome using survival analysis, and control for demographics, treatment compliance and family history of diabetes and cardiovascular events. We evaluated the feasibility of using AD to measure these variables. We discussed a method to estimate the time to conduct this proposed using AD, i.e., time for programming and analysis. Finally, we discussed data approvals and requirements when conducting research using AD. Conclusions and ImplicationsWe discussed the benefits and cautions of using AD and shared knowledge regarding research opportunities and obstacles across jurisdictions to identify commonalities and areas of opportunity for growth. The workshop was informative and interactive. It generated much discussion and was well-received among participants, with many asking for further information after the workshop concluded.

Integrating arrival time estimation in truck scheduling: an explorative study in grocery retailing

ABSTRACT This paper studies a new managerial approach that integrates predictive trucks’ estimated time of arrival (ETA) into truck scheduling to deal with the truck arrival time uncertainty. The approach exploits ETA to reschedule trucks by the up-to-date information and aims to minimize the trucks’ waiting time. The approach was applied in a real case. Results quantified the impact of adopting ETA for truck scheduling, also showing how the ETA accuracy and peak truck arrival rate affect the total trucks’ waiting time. The paper enriches both truck scheduling literature, investigating a new managerial approach to deal with truck arrival time uncertainty, which greatly threatens the synchronization of logistics flows, and Industry 4.0 literature, showing how real-time data availability can change traditional decision-making processes. Insights for the approach application are discussed, together with the economic, environmental, and social benefits by supply chain actors (i.e. carriers, warehouse managers, and drivers).

SOFTWARE COST EFFORT AND TIME ESTIMATION USING DRAGONFLY WHALE LION OPTIMIZED DEEP NEURAL NETWORK

Effective software development depends on the exact estimation of effort, time, cost, and customer satisfaction. Software project management requires an accurate evaluation of software development's effort, time, and cost, often underestimated or overestimated. So far, methodology has yet to accurately and reliably estimate the cost of software development. To overcome this issue, this paper proposed a constructive rapid application development model based on software cost effort and time estimation approach (CORADMO-based CETA) for accurate software cost estimation. The data requirements, cost drivers, constraints, and priorities are given as input to the fuzzy inference system (FIS). The processed output, such as effort, time, and cost for the nominal plan, shortest schedule plan, and least cost plan, is computed in the FIS. To reduce the effort, time, and cost, the output is optimized by dragonfly whale lion optimization (DWLO), which provides the best-estimated effort, time, and cost as an output for software development. The proposed CORADMO-based CETA model is tested in the NASA 93 dataset using MATLAB. The performance of the CORADMO-based CETA method is measured in terms of Pred (25 %), magnitude of relative error, and mean magnitude of relative error, attaining the values of 80.72 %, 87.94 %, and 98.13 %, respectively. Finally, the CORADMO-based CETA model justifies the suitability of dragonfly whale lion optimization with the proposed fuzzy logic.

Rapid Zooplankton Assessment: Evaluating a Tool for Ecosystem‐Based Fisheries Management in the Large Marine Ecosystems of Alaska

ABSTRACTEcosystem‐based fisheries management (EBFM) remains an aspirational goal for management throughout the world. One of the primary limitations of EBFM is the incorporation of basic lower trophic level information, particularly for zooplankton, despite the importance of zooplankton to fish. The generation of zooplankton abundance estimates requires significant time and expertise to generate. The rapid zooplankton assessment (RZA) is introduced as a tool whereby nontaxonomic experts may produce rapid zooplankton counts shipboard that can be applied to management in near real time. Zooplankton are rapidly counted shipboard and placed into three broad groups of zooplankton relevant to higher trophic levels: large copepods (> 2 mm), small copepods (< 2 mm), and euphausiids. A Bayesian, hierarchical linear regression modeling approach was used to validate the relationship between RZA abundances and laboratory‐processed abundances to ensure the rapid method is a reliable indicator. Additional factors likely to impact the accuracy of the RZA abundance predictions were added to the initial regression model: RZA sorter, survey, season, and large marine ecosystem (Bering Sea, Chukchi/Beaufort Sea, and Gulf of Alaska). We tested models that included the random effect of sorter nested within survey, which improved fits for both large copepods (Bayes R2 = 0.80) and euphausiids (Bayes R2 = 0.84). These factors also improved the fit for small copepods when the fixed effect of season was also included (Bayes R2 = 0.65). Additional RZA data were used to predict laboratory‐processed abundances for each zooplankton category and the results were consistent with model training data: large copepods (Bayes R2 = 0.80), small copepods (Bayes R2 = 0.64), and euphausiids (Bayes R2 = 0.88). The Bayesian models were therefore able to predict laboratory‐processed abundances with an associated error when accounting for these fixed and random effects. To demonstrate the utility of zooplankton data in management, zooplankton time series from the Bering Sea shelf were shown to vary in relation to warm and cold conditions. This variability impacted commercially important fish, notably Walleye Pollock (Gadus chalcogrammus), and these time series were used by managers using a risk table approach. The RZA method provides a rapid zooplankton population estimation in near real time that can be applied to the management process quickly, thus helping to fill a gap in EBFM.

Implementation of preamble based GFDM prototype for robust 5G systems

AbstractGeneralized frequency division multiplexing (GFDM) is a flexible block‐structured multi‐carrier scheme recently proposed for next‐generation wireless communication systems. There are various approaches suggested for its analysis and implementation via simulations but testing in real‐time environments is not heavily investigated. This paper carries out the real‐time implementation of the GFDM system utilizing software‐defined radio (SDR) by emphasizing mainly channel estimation and synchronization. Symbol timing, frequency offset, and channel estimate algorithms are applied using a windowed preamble with two identical halves to satisfy low egress noise requirements. Time and frequency estimation is evaluated in terms of residual offsets along with symbol error rate over frequency selective channels. This algorithm is extended to a preamble composed of multiple identical parts. This facilitates a large frequency estimation range at the cost of complexity. For practical validation of the above concepts, the National Instruments (NI) universal software radio peripheral (USRP) 2953R is employed as hardware and it is interfaced with LabVIEW.

Differences in time perception in patients with obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a condition that occurs due to complete (apnea) and partial (hypopnea) obstruction in the upper airways during sleep. Hypoxia is one of the key factors contributing to the development of symptoms of obstructive sleep apnea and OSA-related diseases. The present study aimed to evaluate time perception differences between patients with OSA and healthy individuals, as well as among different OSA severity groups. Twenty severe OSA, twenty moderate OSA, twenty mild OSA patients, and twenty healthy volunteers without OSA were included in the study. Scales were administered to the participants. Time perception tests were administered to evaluate perceptual timing. In the paced motor timing test, a difference was observed between the OSA ( +) group and the OSA (-) group. In the Time Estimation Test, a difference was observed between the OSA ( +) group and the OSA (-) group and their subgroups. The internal clock works slower in the OSA ( +) group. When subgroups were compared based on the degree of OSA, the internal clock worked slower as we transitioned from the OSA (-) group to the severe OSA group. It is considered that as you move from the OSA (-) group to the severe OSA group, the switch between pacemaker and accumulator is disrupted due to the decrease in attention. Recurrent hypoxia observed in OSA may alter the perception of time by affecting attention.

Do morphometric data improve phylogenetic reconstruction? A systematic review and assessment

BackgroundIsolating phylogenetic signal from morphological data is crucial for accurately merging fossils into the tree of life and for calibrating molecular dating. However, subjective character definition is a major limitation which can introduce biases that mislead phylogenetic inferences and divergence time estimation. The use of quantitative data, e.g., geometric morphometric (GMM; shape) data can allow for more objective integration of morphological data into phylogenetic inference. This systematic review describes the current state of the field in using continuous morphometric data (e.g., GMM data) for phylogenetic reconstruction and assesses the efficacy of these data compared to discrete characters using the PRISMA-EcoEvo v1.0. reporting guideline, and offers some pathways for approaching this task with GMM data. A comprehensive search string yielded 11,123 phylogenetic studies published in English up to Oct 2023 in the Web of Science database. Title and abstract screening removed 10,975 articles, and full-text screening was performed for 132 articles. Of these, a total of twelve articles met final inclusion criteria and were used for downstream analyses.ResultsPhylogenetic performance was compared between approaches that employed continuous morphometric and discrete morphological data. Overall, the reconstructed phylogenies did not show increased resolution or accuracy (i.e., benchmarked against molecular phylogenies) as continuous data alone or combined with discrete morphological datasets.ConclusionsAn exhaustive search of the literature for existing empirical continuous data resulted in a total of twelve articles for final inclusion following title/abstract, and full-text screening. Our study was performed under a rigorous framework for systematic reviews, which showed that the lack of available comparisons between discrete and continuous data hinders our understanding of the performance of continuous data. Our study demonstrates the problem surrounding the efficacy of continuous data as remaining relatively intractable despite an exhaustive search, due in part to the difficulty in obtaining relevant comparisons from the literature. Thus, we implore researchers to address this issue with studies that collect discrete and continuous data sets with directly comparable properties (i.e., describing shape, or size).

A new exceptionally preserved sawfly fossil (Hymenoptera: Pergidae) and an evaluation of its utility for divergence time estimation and biogeography

A new exceptionally preserved sawfly fossil (Hymenoptera: Pergidae) and an evaluation of its utility for divergence time estimation and biogeography

Sequence comparison of the mitochondrial genomes of five caridean shrimps of the infraorder Caridea: phylogenetic implications and divergence time estimation

BackgroundThe Caridea, affiliated with Malacostraca, Decapoda, and Pleocyemata, constitute one of the most significant shrimp groups. They are widely distributed across diverse aquatic habitats worldwide, enriching their evolutionary history. In recent years, considerable attention has been focused on the classification and systematic evolution of Caridea, yet controversies still exist regarding the phylogenetic relationships among families.MethodsHere, the complete mitochondrial genome (mitogenome) sequences of five caridean species, namely Heterocarpus sibogae, Procletes levicarina, Macrobrachium sp., Latreutes anoplonyx, and Atya gabonensis, were determined using second-generation high-throughput sequencing technology. The basic structural characteristics, nucleotide composition, amino acid content, and codon usage bias of their mitogenomes were analyzed. Selection pressure values of protein-coding genes (PCGs) in species within the families Pandalidae, Palaemonidae, and Atyidae were also computed. Phylogenetic trees based on the nucleotide and amino acid sequences of 13 PCGs from 103 caridean species were constructed, and divergence times for various families within Caridea were estimated.ResultsThe mitogenome of these five caridean species vary in length from 15,782 to 16,420 base pairs, encoding a total of 37 or 38 genes, including 13 PCGs, 2 rRNA genes, and 22 or 23 tRNA genes. Specifically, L. anoplonyx encodes an additional tRNA gene, bringing its total gene count to 38. The base composition of the mitogenomes of these five species exhibited a higher proportion of adenine-thymine (AT) bases. Six start codons and four stop codons were identified across the five species. Analysis of amino acid content and codon usage revealed variations among the five species. Analysis of selective pressure in Pandalidae, Palaemonidae, and Atyidae showed that the Ka/Ks values of PCGs in all three families were less than 1, indicating that purifying selection is influencing on their evolution. Phylogenetic analysis revealed that each family within Caridea is monophyletic. The results of gene rearrangement and phylogenetic analysis demonstrated correlations between these two aspects. Divergence time estimation, supported by fossil records, indicated that the divergence of Caridea species occurred in the Triassic period of the Mesozoic era, with subsequent differentiation into two major lineages during the Jurassic period.ConclusionsThis study explored the fundamental characteristics and phylogenetic relationships of mitogenomes within the infraorder Caridea, providing valuable insights into their classification, interspecific evolutionary patterns, and the evolutionary status of various Caridea families. The findings provide essential references for identifying shrimp species and detecting significant gene rearrangements within the Caridea infraorder.

Bolus tracking from pulsed x-ray projections: A feasibility study using a five-dimensional cardiac CT contrast dynamics model.

Cardiac computed tomography (CT) exams are some of the most complex CT exams due to the need to carefully time the scan when the heart chambers are near the peak contrast concentration. With current "bolus tracking" and "timing bolus" techniques, after contrast medium is injected, a target vessel or chamber is scanned periodically, and images are reconstructed to monitor the opacification. Both techniques have opportunities for improvement, such as reducing the contrast medium volume, the exam time, the number of manual steps, and improving the robustness of correctly timing the peak opacification. The objective of our study is to (1) develop a novel autonomous cardiac CT clinical workflow to track contrast bolus dynamics directly from pulsed x-ray projections, (2) develop a new five-dimensional virtual cardiac CT data generation tool with programmable cardiac profiles and bolus dynamics, and (3) demonstrate the feasibility of projection-domain prospective bolus tracking using a neural network trained and tested with the virtual data to find the contrast peak. In our proposed workflow, pulsed mode projections (PMPs) are acquired with a wide-open collimator under sparse view conditions (monitoring phase). Each time a new PMP is acquired, the neural network is used to estimate the contrast enhancement inside the target chambers. To train such a network, we introduce a new approach to generate clinically realistic virtual scan data based on a five-dimensional cardiac model, by synthesizing user-defined contrast bolus dynamics and patient electrocardiogram profiles. In this study, we investigated a scenario with one single PMP per rotation. To find the optimal PMP view angle, 20 angles were explored. For each angle, 300 virtual exams were generated from 115 human subject datasets and divided into training, validation, and testing groups. Twenty neural networks were trained and evaluated in total to find the optimal network. Finally, a simple bolus peak time estimation algorithm was developed and evaluated by comparing to the ground truth bolus peak time. To evaluate the accuracy of a bolus time-intensity curve estimated by the network, the cosine similarity between the estimation and the ground truth was computed. The cosine similarity was larger than 0.97 for all projection angles. A view angle corresponding to the x-ray tube at 30 degrees from vertical (left-anterior of subject) showed the lowest errors. The amplitude of the estimated bolus curves (in Hounsfield Units) was not always correctly predicted, but the shape was accurately predicted. This resulted in an RMSE of 1.23s for the left chambers and 0.78s for the right chambers in the contrast peak time estimation. In this study, we proposed an innovative real-time way to predict the contrast bolus peak in cardiac CT as well as an innovative approach to train a neural network using virtual but clinically realistic data. Our trained network successfully estimated the shape of the time-intensity curve for the target chambers, which led to accurate bolus peak time estimation. This technique could be used for autonomous diagnostic cardiac CT to trigger a diagnostic scan for optimal contrast enhancement.

Influence of fat on hepatic T2 relaxation time estimation: a preliminary investigation.

Influence of fat on hepatic T2 relaxation time estimation: a preliminary investigation.

Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: https://github.com/Xuan-1998/LPSim

Satellite measurement of forest disturbance, recovery and deposit distribution following explosive volcanic eruptions

The characteristics and extent of forest damage, and the subsequent patterns of recovery, reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using satellite measurements of vegetation damage and recovery patterns, following the 2015 explosive eruption of Calbuco, Chile, we assess the impact on surrounding temperate forests and how areas impacted by different deposit types recover post-eruption. The Calbuco eruption resulted in tephra deposition over hundreds of square kilometres, pyroclastic flows extending 6 km and lahars extending 15 km. We explore NDVI derived from optical imagery (June 2013–May 2023) as well as radar backscatter and phase coherence (October 2014–June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40 cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28 km3. Vegetation recovery rates relate to initial impact type and intensity, but also local topography, aspect and altitude. Our results demonstrate a novel application of optical and radar satellite remote sensing to determine eruption extents and magnitudes through vegetation disturbance. We show that measuring vegetation disturbance, particularly in remote and densely vegetated environments, can help refine field-based analyses in inaccessible or intensely damaged zones.

Pd-anchored VSe2 for glucose sensing: Prediction from first principle simulations

Accurate detection of glucose molecule is clinically important for treatment of diabetes. The sensor should efficiently detect the glucose molecule with suitable response time. The conductivity of the sensor material thus plays a vital role in fast and precise sensing. Layered vanadium di-selenide (VSe2) is a transition metal dichalcogenide (TMDC) with metallic characteristics. The current study is to portray a theoretical exhaustive investigation about enhancement in effectual detection of glucose using VSe2 decorated with transition metals (TM) such as Ag and Pd. We have performed extensive DFT simulations to relate adsorption energy of glucose with pristine and decorated VSe2. Introduction of TM to pristine VSe2 was found to increase the adsorption energy of glucose which increases the sensitivity of metal- anchored VSe2. Comprehensive partial density of states (PDOS) and total density of states (TDOS) analysis has been carried out to analyse orbital interaction and qualitative charge transfer which has also been measured using Bader charge analysis. In addition to that, permanency of the best decorated system has been verified through molecular dynamics calculation and modality of recurrent serviceability has been determined by means of recovery time estimation. This is crucial since as on today, most of the available glucose detectors are meant for single usage. Our theoretically derived findings clearly point to the prediction that TM-decorated layered VSe2 can be an efficient glucose sensor, if practically developed in forthcoming times. The obtained results will thus potentially be beneficial for experimentalists for scheming and evolving VSe2-based glucose sensors.

Cognitive correlates of circadian rhythm and sleep-wake behaviour in chronic obstructive pulmonary disease patients

ABSTRACT Chronic obstructive pulmonary disease (COPD) patients often experience reduced physical activity, sleep disturbances, and cognitive impairment. However, reports on measurement of rest-activity rhythm and sleep-wake behavior and their impact on cognitive functions in COPD patients are limited. This study aimed to objectively measure circadian rhythms (rest-activity and ambient illuminance) and sleep behaviors in clinically stable COPD patients and their relationship with cognitive functions. The study involved 65 male COPD patients and 50 age-matched controls, monitored over 3–7 days using actigraphy. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) followed by short interbal time estimation via time production and reproduction with reaction time measurement using TimeProd software. Findings indicated significant disruptions in circadian rhythms in COPD patients, characterized by lower mesor, amplitude, and autocorrelation coefficients compared to controls. Patients also reported poorer sleep quality and higher sleep fragmentation, with 85.7% displaying cognitive impairment. Notably, longer time estimations, increased variability in task performance, and slower reaction times suggested cognitive deterioration. Positive correlations emerged between rhythm parameters (amplitude and circadian quotient) and cognitive performance metrics. This highlights the relevance of circadian and sleep disturbances in COPD, suggesting that addressing these rhythms could help mitigate cognitive decline, potentially through chronotherapeutic strategies.

Estimation of vessel link-level travel time distribution: A directed network-driven approach

Accurate vessel travel time estimation is essential for operational efficiency and route optimization. Despite the prevalent use of the automatic identification system (AIS) in garnering multifaceted real-time data, ambiguities and inaccuracies in travel time predictions persist. It leads to planning uncertainties, inefficient resource allocations, and heightened operational costs in maritime logistics. To addresses these issues, this paper proposed a nuanced method to enhance the precision and reliability of vessel travel time estimations. Firstly, a directed maritime network is constructed by extracting essential information from AIS-based historical vessel trajectories. This lays the foundation for the subsequent analytical processes. Secondly, the non-parametric kernel density estimation (KDE) is applied to this constructed network, enabling the estimation of vessel travel time distributions across various network links. The non-parametric KDE is used in combination with AIS data, which improves the specificity and accuracy of the travel time estimations at the link level. Finally, this paper employs cellular automata (CA) simulations to validate the accuracy of the KDE-based estimations. Comparison between the simulation results and real-world data reveals a high degree of accuracy in the proposed method, confirming its applicability and effectiveness in estimating vessel travel times.

Genetic Structure of Monochamus alternatus (Hope) in Qinling-Daba Mountains and Expansion Trend: Implications for Pest Prevention and Management.

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, severely threatens global pine forests. Monochamus alternatus is the primary vector of B. xylophilus in East Asia. Understanding the population structure and evolutionary forces of vector insects is critical for establishing effective PWD management strategies. The present work explores the genetic structure and phylogenetic relationships of 20 populations of M. alternatus from the Qinling-Daba Mountains (QDM) in China using the mitochondria DNA dataset, supplemented by ecological niche modeling (ENM). All M. alternatus populations were categorized into three phylogeographic clusters (Clade A, Clade B, and Clade C), with Clade A and Clade B corresponding to the western and eastern QDM, respectively. The results of divergence time estimation concur with environmental changes induced by Quaternary glacial climate oscillations in QDM of China. M. alternatus populations exhibited significant genetic differentiation, with expansion in their population size. Ecological niche modeling (ENM) demonstrated that precipitation and temperature significantly influence the distribution of M. alternatus and the species is anticipated to grow into higher latitude and higher altitude regions in the future. In a nutshell, exploring the genetic structure and evolutionary dynamics of M. alternatus can provide valuable insights into the prevention and occurrence of B. xylophilus. These findings also serve as a reference for research on population differentiation and phylogeography of other species in QDM and adjacent areas.

GT-TTE: Modeling Trajectories as Graphs for Travel Time Estimation

GT-TTE: Modeling Trajectories as Graphs for Travel Time Estimation

Urban Traffic Identification by Comparing Machine Learning Algorithms

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%.