Location Prediction Research Articles

Predicting the subcellular location of prokaryotic proteins with DeepLocPro.

Protein subcellular location prediction is a widely explored task in bioinformatics because of its importance in proteomics research. We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms. DeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb. DeepLocPro compares favorably to the PSORTb 3.0 ensemble method, surpassing its performance across multiple metrics in our benchmark experiment. The DeepLocPro prediction tool is available online at https://ku.biolib.com/deeplocpro and https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/. Supplementary data are available at Bioinformatics online.

DEEP LEARNING-BASED CLASSIFICATION OF SEISMIC EVENTS USING WAVEFORM DATA

Artificial Intelligence (AI)-enhanced seismology has evolved to its tremendous capability in analyzing real-time seismic data. This supports various tasks such as earthquake detection, phase detection, epicentral location prediction, time prediction of future earthquakes, phase picking, first motion polarity, early warning systems etc. This is crucial for the management of emergency hazard response spectra and post-disaster activities.

Investigation of Novel Quinoline Derivatives Targeting Epidermal Growth Factor Receptors as Anticancer Agents a Computational Approach

Background: Newer chemical entities are created and synthesis has been made feasible by a variety of computer-aided drug design (CAAD) techniques. In addition to facilitating the visualisation of the ligand-target binding process, the application of in silico methodologies and structure-based drug design (SBDD) allows for the prediction of receptor affinities and significant binding pocket locations. Objective: The goal of the current study was to identify new quinoline derivatives by computational methods specially designed to bind the EGFR receptor in the treatment of breast cancer. Materials and Methods: ChemAxon Marvin Sketch 5.11.5 was used to create derivatives of quinolines. The admetSAR online web tools and SwissADME were utilised to forecast the toxicity and pharmacokinetic characteristics of several substances. A multitude of software programmes, such as Autodock 1.1.2, MGL Tools 1.5.6, Procheck, Protparam ExPasy tool, PyMOL, and Biovia Discovery Studio Visualizer v20.1.0.19295 were also employed to ascertain the ligand-receptor interactions between quinoline derivatives and the target receptor (PDB -5GNK). Result: Almost all components were shown to be less hazardous, orally consumable and to have the appropriate pharmacokinetic characteristics based on in silico study. All newly generated derivative compounds have higher docking scores when compared to the widely used medication sorafenib. Conclusion: Interactions with quinoline analogues boost binding energy and the number of Hbonds produced, making them a suitable place to start when creating compounds for further exploration. The quinoline moiety increases its potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive in vivo, in vitro, chemical-based, and pharma studies by medicinal chemists.

Non-stationary spatio-temporal modeling using the stochastic advection-diffusion equation

We construct flexible spatio-temporal models through stochastic partial differential equations (SPDEs) where both diffusion and advection can be spatially varying. Computations are done through a Gaussian Markov random field approximation of the solution of the SPDE, which is constructed through a finite volume method. The new flexible non-separable model is compared to a flexible separable model both for reconstruction and forecasting, and evaluated in terms of root mean square errors and continuous rank probability scores. A simulation study demonstrates that the non-separable model performs better when the data is simulated from a non-separable model with diffusion and advection. Further, we estimate surrogate models for emulating the output of a ocean model in Trondheimsfjorden, Norway, and simulate observations of autonomous underwater vehicles. The results show that the flexible non-separable model outperforms the flexible separable model for real-time prediction of unobserved locations.

Isolated Rib Response and Fracture Prediction for Young Mid-Size Male, Enabled by Population Specific Material Models and Rib Cross-Sectional Geometry.

Thorax injury remains a primary contributor to mortality in car crash scenarios. Although human body models can be used to investigate thorax response to impact, isolated rib models have not been able to predict age- and sex-specific force-displacement response and fracture location simultaneously, which is a critical step towards developing human thorax models able to accurately predict injury response. Recent advancements in constitutive models and quantification of age- and sex-specific material properties, cross-sectional area, and cortical bone thickness distribution offer opportunities to improve rib computational models. In the present study, improved cortical and trabecular bone constitutive models populated with age-specific material properties, age- and sex-specific population data on rib cross-sectional area, and cortical bone thickness distribution were implemented into an isolated 6th rib from a contemporary human body model. The enhanced rib model was simulated in anterior-posterior loading for comparison to experimental age- and sex-specific (twenty-three mid-size males, age range of 22- to 57-year-old) population force-displacement response and fracture location. The improved constitutive models, populated with age-specific material properties, proved critical to predict the rib failure force and displacement, while the improved cortical bone thickness distribution and cross-sectional area improved the fracture location prediction. The enhanced young mid-size male 6th rib model was able to predict young mid-size male 6th rib experimental force-displacement response and fracture location (overpredicted the displacement at failure by 35% and underpredicted the force at failure by 8% but within ±1 SD). The results of the present study can be integrated into full body models to potentially improve thorax injury prediction capabilities.

Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data.

During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts. This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction. To address instability of predictions caused by independent frame-by-frame assessment, we implemented a hard-voting-based post-processing algorithm that aggregates results from seven consecutive frames, improving the overall accuracy. Among the tested models, InceptionV3 demonstrated superior performance for still images, achieving an F1 score of 79.79%, precision of 80.57%, and recall of 80.08%. For video data, the InceptionResNetV2 model performed best, achieving an F1 score of 61.37%, precision of 73.08%, and recall of 57.21%. These results indicate that the deep learning models not only achieved high accuracy in position recognition for still images but also performed well on video data. Additionally, the post-processing algorithm effectively stabilized the predictions, highlighting its potential for real-time endoscopic applications. This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients.

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.

Bayesian optimized deep Q-network for diagnosing mine ventilation systems windage alteration fault targeting imbalanced data

Fault diagnosis of mine ventilation system is of great significance for mine safety production. Traditional machine learning algorithms have been widely applied in the field of mine ventilation systems windage alteration faults (WAFs) diagnosis, but these algorithms have poor intelligence and weak generalization ability. Therefore, this research constructs a WAFs diagnosis model (WAFs-BODQN) based on Bayesian optimized (BO) deep Q-network (DQN). Meanwhile, in order to solve the problem of poor performance of intelligent models when the dataset is imbalanced, a reinforcement learning reward function (KMeans-SDF) was designed, which combines the KMeans++ algorithm and spatial distance function (SDF). The WAFs-BODQN model with KMeans-SDF reward function has fault location diagnosis accuracies of 98.75 %, 97.50 %, and 95.00 % for mild, moderate, and extreme imbalanced datasets UB1, UB2, and UB3, respectively. This effectively avoids the "eccentricity" phenomenon of the intelligent model on majority class and achieves accurate prediction of fault locations. The accuracy of fault location diagnosis using the WAFs-BODQN model in simulation experiments and on-site empirical applications is higher than 95 %, demonstrating the intelligence, generalization, and feasibility of WAFs-BODQN model in the field of WAFs diagnosis in mine ventilation systems. By comparing the results with traditional machine learning and deep learning fault diagnosis models, it has been proven that the WAFs-BODQN model has advantages in intelligence and generalization ability. It is expected to establish a universal architecture for diagnosing WAFs, providing theoretical guidance and technical support for achieving intelligent mine ventilation.

Development of a gunshot location detection system based on acoustic data

Determination of sound source location is a crucial problem, especially for the assault threat detection in crowded areas or military applications. The chief objective of this study is to design a microphone array to detect the location of explosion type sound sources, such as gunshot, which have significant amplitudes when compared to the background noise levels. Furthermore, the time arrival difference algorithm is utilized for the determination of time arrival differences and sound source location. Evidently, for stationary microphones that are positioned close to each other, determination of time arrival differences with high accuracy is a significant problem. Thus, a significant effort is given to enhance the accuracy of time arrival difference predictions via proper sampling. The design process of the microphone array is conducted computationally, where the sound data received by the microphones are generated. The time arrival differences are then calculated numerically, which are used for the prediction of sound source location. The number of microphones, relative positions of the microphones are determined accordingly via digital experiments. Finally, the designed microphone array is built and experiments are conducted. In conclusion, it is observed that the numerical model successfully predicts the sound source location of the computational model.

Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks.

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

Prediction of Hydrate Formation in Supercritical CO2 Containing Impurities Long-Distance Transportation Pipelines with a Capacity of 0.36 Million t/a

Abstract Long-distance pipeline transportation is the most economical and feasible way to transport CO2. Hydrates can easily form during the transportation of supercritical CO2 containing impurities, leading to pipeline blockage. Therefore, accurate prediction of the deposition location and amount of hydrates in pipelines is crucial. This paper established a prediction model for hydrate growth in the long-distance CO2 transportation pipeline containing impurities. The phase equilibrium diagram and kinetic growth model were combined to simulate the formation behaviour of CO2 hydrate. The long-distance CO2 transportation pipeline of 0.36 million t/a in Yunlin was taken as an example. The results show that the volume of CO2 hydrate increased whereas the growth rate of CO2 hydrate gradually decreased with time. Hydrates begin to form in pipeline L11 at a distance of 35-44.3 km away from the inlet, whereas at a distance of 8.1 km away from the inlet for pipeline L12. The places with the highest amount of hydrate formation in both of the pipelines were at the end of pipelines. The area for hydrates formation in pipeline L12 was much longer than that in pipeline L11 due to the lower temperature. The cleaning cycle for pipeline L12 should be much shorter than that of pipeline L11. This paper will provide guidance for the operation of CO2 long distance pipelines.

Predictive modeling of flow characteristics in supersonic separators using machine learning

In the pursuit of cleaner energy sources, pre-processing fuel streams is crucial before distribution. Challenges arise during this process, leading to operational issues and diminished transmission efficiency. Among different technologies, supersonic separators are the ones that separate undesired components from the main gas stream more effectively. Precise forecasting of nozzle flow rate and shockwave location at a low cost can markedly enhance the efficiency of manufacturing and rating. In this research, the one-dimensional method has been developed to solve flow equations simultaneously by implementing equations of state. CFD has been used for the verification of the developed model, and the effect of different equations of state is investigated. Results show that the one-dimensional model significantly reduces the calculation time. Different EOSs result in up to 2.0 % and 3.5 % variation in the prediction of shock location and the mass flow rate, respectively.A machine learning methodology is employed to predict the location of shockwaves and the choked flow rate at an even lower cost. A dataset of 160 entries, generated using Peng Robinson's EOS, serves as the basis for this analysis. Prediction tasks are executed using Random Forest, k-nearest Neighbors, and Multilayer Perceptron models. While all three models demonstrate proficiency in data interpolation with R2 scores up to 0.9962 and 0.9951 for the shockwave location and mass flow rate, respectively, discerning their performance in extrapolation appears contingent on the specific case. In the case of extrapolation, Multilayer Perceptron and k-Nearest Neighbors show better match to one-dimensional results.

Relationship between optic nerve length and interoptic angle in the prediction of optic chiasm location

Introduction and objectivesThe sellar region is an area in the base of the skull that is among the most common sites for tumors of the central nervous system. Surgical interventions are currently performed via different routes. While the optic chiasm occupies its expected position in 70% of the population, it can deviate from this position. In such cases, surgery involving this region becomes more difficult as the known surgical routes are narrowed. Advance awareness of these variations can help surgeons to identify the optimal route for safe surgical intervention in the sellar region. By performing simple measurements of both the lengths of the optic nerves and the angle between them, a surgeon can predict the location of the chiasm. Materials and methodsTwenty specimens collected from autopsies performed at Bursa Forensic Medicine İnstitute were examined to determine the optic chiasm types and the relationships between the surrounding subchiasmal structures. ResultsAmong the 20 specimens, we found two prefixed (10%), 10 normo-fixed (50%), and eight postfixed chiasms (40%). The mean interoptic angle was 81.03 (±17.41)⁰. Prefixed chiasms had angles in the range 115.36°–124.76 ° (mean 120.06 [±6.65]⁰), normo-fixed chiasm angles were between 83.11° and 97.53 ° (mean 86.07 [±6.73]⁰), and postfixed chiasms ranged between 53.01 ° and 78.71 ° (mean 69.20 [±9.13]⁰). The length of the right optic nerve ranged between 6.95 and 13.83 mm (mean 10.25 [±1.81] mm), and the length of the left between 7.25 and 12.51 mm (mean 10.40 [±1.47] mm). Obtuse angles indicated that the chiasm was prefixed, and acute angles were indicative of a postfixed chiasm. There was a strong negative correlation between optic nerve lengths and the interoptic angle; thus, as the length of the nerves increases, the interoptic angle becomes more acute. ConclusionsWe have proposed a simple measurement of the optic nerve lengths and the angle between them to predict the relative location of the OC, which can be done easily on MRI.

Quasi-static crushing response of a novel triaxial isotropy mechanical metamaterial with dual-platform property

A novel triaxial isotropy origami metamaterial with dual-platform is proposed by combining the tachi tubes and the honeycomb structures. Crushing responses of the hexahedral metamaterial under quasi-static compression load are investigated through experimental tests and numerical simulations. Experimental and numerical results reveal that the hexahedral metamaterial sample shows three deformation modes. Meanwhile, the numerical predictions of deformation modes and locations agree very well with the experimental results. Moreover, the effect of aspect ratio, thickness-to-span ratio, angle on the deformation mode, peak stress, plateau stress of different stages, and specific energy absorption (SEAM, SEAV) is investigated. Finally, the proposed metamaterial is compared with traditional honeycomb. The numerical results demonstrate that the SEAM of the hexahedral metamaterial is 90.6% of the traditional honeycomb in the Z-direction. However, during X/Y-direction compression, the energy absorption capacity of the hexahedral metamaterial is 13.0 and 12.2 times that of the traditional honeycomb, respectively.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Electric Vehicle Next Charge Location Prediction

Electric Vehicle Next Charge Location Prediction

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

Potential nutrient, carbon and fisheries impacts of large-scale seaweed and shellfish aquaculture in Europe evaluated using operational oceanographic model outputs

Large-scale seaweed and shellfish aquaculture are increasingly being considered by policymakers as a source of food, animal feed and bioproducts for Europe. These aquacultures are generally thought to be low impact or even beneficial for marine ecosystems as they are ‘extractive’ – i.e., growing passively on foodstuff already available in seawater, and with potential habitat provision, fisheries, climate mitigation and eutrophication mitigation benefits. At some scale however, over-extraction of nutrients or chlorophyll could potentially have a negative effect on natural systems. Understanding the likely impacts of aquaculture production at scale is important to identify when safe limits are being approached. Taking seaweed aquaculture as the primary focus, this work uses operational oceanographic model outputs to drive prognostic growth models to predict the likely optimal distribution of seaweed farms across European waters to meet different production scenarios. A novel nutrient transport scheme is then used to model the interacting ‘footprints’ of nutrient drawdown from aquaculture facilities to demonstrate the likely spatial impact of large-scale aquaculture. Evaluation of both seaweed and shellfish contributions to CO2 balance under large scale production, and the potential impact on fisheries are also considered. The study finds that the impact of intensive seaweed aquaculture on nutrient availability could be significant where many farms are placed close together; but at the regional/basin scale even the highest level of production considered does not significantly impact total nutrient budgets. Seaweed aquaculture has the potential to extract large amounts of carbon dioxide, but the impact on carbon budgets depends on the end-use of the extracted seaweed. Shellfish aquaculture is a net source of CO2 due to the impact of calcification of shells on the carbonate system (i.e., alkalinity removal). However, gram-for-gram the CO2 impact of shellfish production is likely to be less than the impact of land-based meat production. Whilst operational oceanographic models are useful for taking a ‘broad brush’ approach to likely placement and impacts of aquaculture, reliable yield predictions for individual locations across European waters would require models integrating more physical and biogeochemical factors (wave environment, local currents, riverine inputs) at a finer scale than currently achievable.