Assignment Approach Research Articles

Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2.

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions in proteins are parts of the sequence that do not form ordered, folded structures under typical physiological conditions. In the original implementation of RRD, Rosetta ab initio models were generated, and disordered regions were predicted based on residue scores (disordered residues typically exist in regions of unfavorable scores). In this work, we show that by (i) replacing the ab initio modeling with AF (using the same scoring and disorder assignment approach) and (ii) updating the score function, the predictiveness improved significantly. Residues were better ranked by the order/disorder, evidenced by an improvement in receiver operating characteristic area-under-the-curve from 0.69 to 0.78 on a large (229 protein) and balanced data set (relatively even ordered versus disordered residues). Finally, the binary prediction accuracy also improved from 62% to 74% on the same data set. Our results show that the combined AF-RRD approach was as good as or better than all existing methods by these metrics (AF-RRD had the highest prediction accuracy).

Development and Validation of Two Intact Lumbar Spine Finite Element Models for In Silico Investigations: Comparison of the Bone Modelling Approaches

Finite element (FE) analyses contribute to a better understanding of the human lumbar spine’s biomechanics and serve as an effective predictive tool. This study aims to present the development of two L1–L5 FE models using literature-based (LBM) and patient-specific (PSM) bone material assignment approaches. The geometry of the lumbar spine was developed based on quantitative computed tomography scans. The LBM and the PSM were compared under pure and combined loads. Various biomechanical parameters were investigated to validate the models. The total range of motion of the LBM in pure flexion-extension, lateral bending, and axial rotation were 30.9°, 29°, and 13.7°, respectively, while for the PSM, it was 31.6°, 28.6°, and 14.1°. The required computational time of the PSM to complete against pure and combined loads were 12.1 and 16.6 times higher on average compared to the LBM. This study demonstrated that both models agree with experimental and in silico results, although the cumulative distribution of the stress and characterization of strain values showed a noteworthy difference between the two models. Based on these findings, the clinically-focused biomechanical FE studies must perceive the differences in internal mechanical parameters and computational demand between the different bone modelling approaches.

A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment

This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors to reach a consensus on the values of common decision variables. A sub-problem receives proposed values for common decision variables from all adjacent sub-problems and incorporates them into its own offered values by weighted averaging and enforcing a gradient step to minimize its objective function. Then, the updated values are projected onto the feasible region of the sub-problems. The algorithm finds high quality solutions in all tested scenarios with a finite number of iterations. The algorithm is tested on a case study network under different demand levels and finds solutions with at most a 5% optimality gap.

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments.

Multi-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as for molecular ecology and biomonitoring applications in different sectors from academic research to regulatory agencies and industry. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are often used to process animal metabarcode markers from the same study. MetaWorks provides a harmonized processing environment, pipeline, and taxonomic assignment approach for demultiplexed Illumina reads for all biota using a wide range of metabarcoding markers such as 16S, ITS, and COI. A Conda environment is provided to quickly gather most of the programs and dependencies for the pipeline. Several workflows are provided such as: taxonomically assigning exact sequence variants, provides an option to generate operational taxonomic units, and facilitates single-read processing. Pipelines are automated using Snakemake to minimize user intervention and facilitate scalability. All pipelines use the RDP classifier to provide taxonomic assignments with confidence measures. We extend the functionality of the RDP classifier for taxonomically assigning 16S (bacteria), ITS (fungi), and 28S (fungi), to also support COI (eukaryotes), rbcL (eukaryotes, land plants, diatoms), 12S (fish, vertebrates), 18S (eukaryotes, diatoms) and ITS (fungi, plants). MetaWorks properly handles ITS by trimming flanking conserved rRNA gene regions as well as protein coding genes by providing two options for removing obvious pseudogenes. MetaWorks can be downloaded from https://github.com/terrimporter/MetaWorks and quickstart instructions, pipeline details, and a tutorial for new users can be found at https://terrimporter.github.io/MetaWorksSite.

A crash feature-based allocation method for boundary crash problem in spatial analysis of bicycle crashes

In conventional safety analysis, traffic and crash data are often aggregated at the geographical units like census tracts, street blocks, and traffic analysis zones, which are often delineated by roads and other physical entities. A considerable proportion of crashes may occur at or near the boundary of geographical units. Such the crashes, also known as boundary crashes, can correlate with the explanatory variables of neighboring geographical units, regardless of the spatial proximity. This could then bias the parameter estimation of crash frequency model. In this study, a novel data-driven approach is developed for the allocation of boundary crashes. For example, crash severity and bicyclist characteristics are considered in the crash feature-based allocation. An illustrative case study based on built environment, population, traffic and bicycle crash data from 289 Lower Layer Super Output Areas (LSOAs) of London in the period 2017–2019 was conducted. Results indicate that high matching percentages of boundary crash allocation can be achieved. Furthermore, prediction performances, in terms of root mean square error (RMSE) and mean absolute error (MAE), of the crash frequency models based on the proposed crash feature-based allocation method is superior, compared to that based on conventional boundary crash allocation methods like half-and-half and iterative assignment approaches. Last but not least, more influencing factors that affect the bicycle crash frequency at macroscopic level can be identified. Findings should be indicative to the spatial safety analysis for different geographical configurations.

Optimal waypoint assignment for designing drone light show formations

Advancements in technology and new drone policies have boosted the adoption of drones by various industries. With active research and development, ingenious applications using drones are being developed. One such innovative and creative application of swarm drones is the drone light show. Light shows are generally performed outdoors in a dedicated clear open air space, which attracts the usage of drones for the execution. A fleet of multiple UAVs illuminates the night sky with picturesque performances in a synchronized manner. Use of drones in light shows provides an eco-friendly and reusable alternative for unique ways of advertisements, celebrations, storytelling, and entertainment. To choreograph the performance of swarm drones means allocating waypoints to each drone for every formation and the complexity of designing increases with the number of drones. An intelligent optimal assignment system is much required to allocate waypoints to drones for each transition in order to create formations. We propose a static swarm-intelligence-based assignment approach named Constrained Hungarian Method for Swarm Drones Assignment (CHungSDA) for optimally assigning multi-UAVs to waypoints. This approach uses Hungarian algorithm as a base with added constraints specific to the targeted application. Several simulations of the proposed approach — carried out for different designs have shown promising results. The work will be of best reference to the UAV manufacturers for their functional design of UAVs and development of Ground Control Station Software to communicate with multiple UAVs, along with other companies that aim to create visual illustrations using UAVs.

Rapid and reliable RNA resonance assignment by combining chemical and enzymatic stable isotope labeling

In this work a rapid RNA assignment approach by combining chemical and enzymatic 13C and 15N stable isotope labeling is introduced. We exemplify the assignment strategy for imino N1H1 purine and N3H3 pyrimidine and aromatic C6H6 pyrimidine, C8H8 purine and C2H2 adenine resonances for a non-coding RNA comprising 66 nucleotides. The assignment strategy is based on position specific labeling by chemical solid phase synthesis and dilute stable isotope 13C/15N-labeling by mixing labeled and commercially available unlabeled RNA phosphoramidites. The assignment process is further facilitated by nucleotide specific labeling using T7 RNA polymerase in vitro transcription with in house produced atom specific 13C labeled ribonucleotide triphosphates. The approach is fast with a total NMR measurement time of only 22 h and also competitive in terms of costs as compared to the standard methodology relying on in vitro transcription using 2H, 15N and 13C/15N uniformly labeled ribonucleotide triphosphates. Furthermore, the assignment procedure revealed a slow exchange process on the NMR chemical shift time scale in the 66 nt non-coding RNA with possible biological implications in the regulation of bacterial competence.

Assuring quality and waiting time in real-time spatial crowdsourcing

With the rapid development of mobile devices, spatial crowdsourcing has become an important way to collect data. Task assignment is an important aspect of spatial crowdsourcing. How to improve the quality of the results and decrease the travel distance has been extensively studied in recent years. Existing studies often assume that moving speed is constant or real-time road network information is known. In this paper, the travel time is predicted based on historical data. A framework for time-prediction-based task assignment approach in spatial crowdsourcing (TP-TASC) is proposed. Firstly, a prediction model based on the light gradient boosting machine (LightGBM) is used to predict the travel time of workers with the consideration of the spatial features, the temporal features, and the climate features. Secondly, a heuristic algorithm is proposed to assign the spatial crowdsourcing tasks to appropriate workers. When a task is assigned to a worker, the payment of the worker is also determined automatically. Finally, the simulation experiments based on a real-world taxi-hailing dataset show that the proposed method can not only effectively minimize the task requesters’ waiting time, but also maximize the results’ quality.

A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making

Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.

Dual-zone material assignment method for correcting partial volume effects in image-based bone models

In image-based finite element analysis of bone, partial volume effects (PVEs) arise from image blur at tissue boundaries and as a byproduct of geometric reconstruction and meshing during model creation. In this study, we developed and validated a material assignment approach to mitigate partial volume effects. Our validation data consisted of physical torsion testing of intact tibiae from N = 20 Swiss alpine sheep. We created finite element models from micro-CT scans of these tibiae using three popular element types (10-node tetrahedral, 8-node hexahedral, and 20-node hexahedral). Without partial volume management, the models over-predicted the torsional rigidity compared to physical biomechanical tests. To address this problem, we implemented a dual-zone material model to treat elements that overlap low-density surface voxels as soft tissue rather than bone. After in situ inverse optimization, the dual-zone material model produced strong correlations and high absolute agreement between the virtual and physical tests. This suggests that with appropriate partial volume management, virtual mechanical testing can be a reliable surrogate for physical biomechanical testing. For maximum flexibility in partial volume management regardless of element type, we recommend the use of the following dual-zone material model for ovine tibiae: soft-tissue cutoff density of 665 mgHA/cm3 with a soft tissue modulus of 50 MPa (below cutoff) and a density-modulus conversion slope of 10,225 MPa-cm3/mgHA for bone (above cutoff).

Design of decentralized proportional–integral proportional–retarded controllers in discrete-time domain for two-input two-output processes

In this paper, a decentralized proportional–integral proportional–retarded (PI-PR) controller design method is proposed for two-input two-output (TITO) systems in discrete-time domain. The well-known dominant pole assignment (DPA) approach is used as the basis of the proposed approach. The controller design starts with the decoupling of a given TITO system into two sub-systems and continues with the design of proportional–integral–retarded (PIR) controllers for each sub-system, respectively. The feasible discrete PIR controller parameter set is obtained through the Nyquist stability criterion by considering the desired closed-loop performance specifications. The obtained PIR controllers are then implemented using a PI-PR control structure to avoid poor performance of the closed-loop system (CLS) transient response, which can be caused by the controller zeros. Moreover, a case study is presented to show the performance of the PI-PR controller in a simulation environment. It is shown that the proposed control structure provides a satisfactory performance when compared with the other proportional–integral–derivative (PID) control methods from the literature.

Operability assessment and systematic PI control of a class of Extractive Dividing Wall Distillation Columns: Case of ethanol dehydration

In this work, the practical control capability assessment and the input-output pairing in the design of a control system for a class of Extractive Dividing Wall Distillation Columns is addressed through a parameter called Operability Index. The configured control system is equipped with PI conventional controllers, which all of them are simultaneously and systematically tuned through relationships coming from a pole-stable assignment approach. By considering the bioethanol dehydration as a case of study, known for its difficulty in achieving high purities, the control system’s performance is illustrated. The advantage of this control system design way lies on: (1) the Operability Index smartly provides the physical perception of the control effort that the input will likely perform to cause a change to the related output, (2) a systematic tuning framework that sidestep the typical procedure in distillation columns of tuning the controllers in a sequential way, and (3) the simplest calculations on the basis of the simplest parameters that can be identified in a complex process as a distillation column is: static gains, nominal value of the inputs and the one of the outputs, and settling times.

Recidivism prevention for impaired driving: Longitudinal 5-year outcomes from Quebec's severity-based intervention assignment program

IntroductionDriving while impaired by alcohol (DWI) is a persistent problem. Tailoring intervention modality to client risk and needs (i.e., risk/needs) is posited to both reduce recidivism more efficiently than uniform approaches and circumvent overtreatment or undertreatment. DWI drivers in Quebec must participate in a severity-based intervention assignment program to be relicensed, but like most tailoring programs it has yet to undergo systematic scrutiny. The current longitudinal cohort study tests two main hypotheses underpinning this approach: 1) drivers classified at higher recidivism risk based on their arrest characteristics (DWIR) show poorer outcomes over up to 5-years postassessment compared to drivers classified at lower risk (DWIF); and 2) for both DWIR and DWIF groups, assignment of drivers with greater risk/needs to intensive intervention (II) will be advantageous for reducing recidivism risk compared to assignment into brief intervention (BI) for those with lower risk/needs. MethodsDrivers who entered the program from 2012 to 2016 were followed to the end of 2018 (N = 37,612). Survival analysis examined the predictive validity of the initial classification into DWIR or DWIF groups for documented recidivism over a follow-up of up to 5 years. Logistic regression discontinuity evaluated the relative outcomes of drivers who were assigned to either BI or II. The study explored interaction effects between classification and intervention assignment with age and sex. ResultsIn line with the hypothesis, the average hazard of recidivism was 58 % greater in DWIR drivers compared to DWIF drivers. In both DWIF and DWIR drivers, assignment of drivers with greater risk/needs to II was associated with reduced recidivism compared to assignment of drivers with lower risk/needs to BI, with 57 % and 35 % decreased probability of recidivism, respectively. Younger age was more strongly associated with recidivism risk in DWIF drivers than in DWIR drivers. ConclusionsThe current study found that Quebec's severity-based intervention assignment approach accurately identifies DWI drivers who: i) by their arrest characteristics pose a greater risk for recidivism, which may require expeditious exposure to preventative countermeasures; and ii) as a function of their greater risk/needs, benefit from assignment to more intensive intervention to mitigate their recidivism risk.

Dynamic Resource Allocation With Decentralized Multi-Task Assignment Approach for Perimeter Defense Problem

In this article, a dynamic resource allocation with decentralized multi-task assignment (DREAM) approach using a dynamic sized team of defenders is presented to protect a perimeter of a high-security area from heterogeneous intruders. In the DREAM approach, the spatio-temporal problem of neutralizing the heterogeneous intruders is converted into a decentralized time-ordered multi-task assignment problem. A single-step dynamic resource allocation algorithm based on the computed assignments determines an optimal number of defenders required. The DREAM approach adds more defenders to the team from the reserve stations or removes the excess defenders from the team such that it always utilizes minimal resources for protecting convex territory. A trajectory computation algorithm converts the optimal multi-task assignments to trajectories for the defenders. The working of the DREAM approach for a typical perimeter defense problem is illustrated using a simulated convex territory protection problem. Based on the results from the ablation study, it is seen that one needs to deploy a higher number of defenders in the team for a better success rate to handle highly maneuvering intruders. Furthermore, a higher defender-to-intruder speed ratio helps in better resource utilization. These results clearly indicate that the DREAM approach can protect any convex territory efficiently with minimal resources.

Combining electro- and magnetoencephalography data using directional archetypal analysis.

Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.

A review on anchor assignment and sampling heuristics in deep learning-based object detection

Deep learning-based object detection is a fundamental but challenging problem in computer vision field, has attracted a lot of study in recent years. State-of-the-art object detection methods rely on the selection of positive samples and negative samples, i.e., called sample assignment, and the definition of a useful set for training, i.e., called sample sampling heuristics. This paper presents a comprehensive review of the advanced anchor assignment and sampling approaches in deep learning-based object detection. Each problem is classified and analyzed systematically. According to the problem-based taxonomy, we identify the advantages and disadvantages of each problem in-depth and present open issues regarding the current methods. Furthermore, this paper also reviews the new trends in solving object detection that has not been discussed during the last two years. To track the latest research, a webpage related to the above problems is provided, which is available at https://github.com/VoXuanThuy/ObjectDetectionReview.

Spatial pricing of ride-sourcing services in a congested transportation network

We investigate the impacts of spatial pricing for ride-sourcing services in a Stackelberg framework considering traffic congestion. In the lower level, we use combined distribution and assignment approaches to explicitly capture the interactions between drivers’ relocation, riders’ mode choice, and all travelers’ routing decisions. In the upper level, a single transportation network company (TNC) determines spatial pricing strategies to minimize imbalance in a two-sided market. We show the existence of the optimal pricing strategies for locational imbalance minimization, and propose effective algorithms with reliable convergence properties. Furthermore, the optimal pricing is unique and can be solved in a convex reformulation when waiting time is small compared to travel time. We conduct numerical experiments on different scales of transportation networks with different TNC objectives to generate policy insights on how spatial pricing could impact transportation systems.

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands.

Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multireference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized octahedral mononuclear transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that the MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of the MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions.

Improved Dual Attention for Anchor-Free Object Detection.

In anchor-free object detection, the center regions of bounding boxes are often highly weighted to enhance detection quality. However, the central area may become less significant in some situations. In this paper, we propose a novel dual attention-based approach for the adaptive weight assignment within bounding boxes. The proposed improved dual attention mechanism allows us to thoroughly untie spatial and channel attention and resolve the confusion issue, thus it becomes easier to obtain the proper attention weights. Specifically, we build an end-to-end network consisting of backbone, feature pyramid, adaptive weight assignment based on dual attention, regression, and classification. In the adaptive weight assignment module based on dual attention, a parallel framework with the depthwise convolution for spatial attention and the 1D convolution for channel attention is applied. The depthwise convolution, instead of standard convolution, helps prevent the interference between spatial and channel attention. The 1D convolution, instead of fully connected layer, is experimentally proved to be both efficient and effective. With the adaptive and proper attention, the correctness of object detection can be further improved. On public MS-COCO dataset, our approach obtains an average precision of 52.7%, achieving a great increment compared with other anchor-free object detectors.

Study on Fixed and Dynamic Spectrum Access Models for Cellular Networks

Radio frequency spectrum is a scarce resource, hence its effective and efficient utilization for contemporary and future technologies is of paramount importance. This paper mainly focuses on analysing conventional models for spectrum management, such as command and control and market-based models, and their inefficacy to serve the upcoming technological demands globally. Generally outlined observations concerning spectrum mismanagement include lesser availability of unassigned spectrum and under-utilization of spectrum allocated to passive users. Due to the considerable urge for flexible spectrum assignment framework models and policies, new spectrum assignment approaches, such as Dynamic Spectrum Access, are investigated. Moreover, models such as Licensed Shared Access (LSA), which evolved from Europe, and Citizen Bands Radio Service (CBRS) from North America are also evaluated in this paper as potential choices for future spectrum management in Pakistan. In the aforesaid models, spectrum is shared among multiple users as per time, place and dimensions, keeping in view the security and priority of incumbent and licensed users. In addition, the efficiency of spectrum utilization and economic advantages of these models have also been analysed. Lastly, LSA and CBRS are studied comparatively, and spectrum management suggestions are made for effective implementation in Pakistan.