Project Model Research Articles

Research on Path Planning Problem of Smart Car Considering Kinematic Constraints

With the development of artificial intelligence, intelligent cars have gradually appeared in people's lives and brought many conveniences to people's lives and work. On the street, we can see many busy figures of unmanned delivery cars; In some factories, smart cars shuttle back and forth between product production processes for material distribution; In some hospitals, we can also find that some smart cars replace medical staff to undertake the task of sending medicines and tests. For intelligent cars, autonomous collision-free path planning and trajectory tracking of the planned path are the most basic and core tasks of intelligent vehicles. The existing path planning algorithms are mainly divided into search-based algorithms (Dijkstra, A*, etc.) and sample-based algorithms (PRM, RRT, etc.), and different types of algorithms have good performance in their respective applicable scenarios. However, these path planning algorithms all use the same simplification assumption, that is, the intelligent car is regarded as a freely moving point or sphere in the path planning task, and the driving experience in life tells us that the movement of the wheel-driven intelligent car is constrained by the turning radius of the vehicle, so the path planned by the simplified intelligent car for a point is often not suitable for the driving of the intelligent car in reality. Considering this constraint, this project models the kinematics of the intelligent trolley with four-wheel steering, and adds kinematic constraints to the path planning, so as to plan the path that conforms to the kinematics model of the intelligent trolley. The path planning algorithm proposed in this topic will be tested in ROS and Gazebo simulation environments, and compared with the traditional search-based A* algorithm, the results of multiple scenarios verify the effectiveness of the algorithm. Finally, this project summarizes and looks forward to this research, which has completed the construction of low-cost physical intelligent vehicles, and plans to complete the verification of the real vehicle function by combining the designed path planning algorithm with the real-time mapping and positioning system of the car in the future.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts’ predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models' predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency.

Integrating machine learning and network analytics to model project cost, time and quality performance

This study aims to connect project management, network science and machine learning in an accessible overview applied to a real original dataset. Based on an initial literature review of applicable project performance measures and attributes, relevant project data were collected through an online survey. The information was split into three categories, including the basic project measures (five attributes), project stakeholder network measures (seven attributes), and project complexity measures (seven attributes). In total, 70 responses were collected, and five machine learning approaches (i.e. support vector machine, logistic regression, k-nearest neighbour, random forest and extreme gradient boosting) were applied to model the relationships between project attributes, networks and the Iron Triangle of project cost, time and quality. The results confirm the expected trends affecting project performance and provide an example for the discussion of the applicability of integrated machine learning and network analytics approaches to modelling project performance. The article demonstrates in an accessible way a real case of integration of machine learning, network science and project management and suggests avenues for further research and applications in practice.

Evaluation of Convective Environments in the NARCliM Regional Climate Modeling System for Australia

Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate models can resolve the convective environments better than global models. Australia, especially the east and southeast parts of the continent, is a global hot spot for severe thunderstorms. This study evaluates the simulated convective environments from the New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) project based on the parameters of CAPE, CIN, 0–6-km vertical wind shear and storm severity. The ensemble regional downscaling is compared against the fifth-generation European Centre for Medium-range Weather Forecast Reanalysis (ERA5). The results show that although there are apparent biases (generally positive for CAPE and negative for CIN, and slightly overestimated vertical wind shear) in the downscaled storm parameters, the climatology of measures of storm severity over land, including their spatial patterns and seasonality, agree well with ERA5. These results have strong implication on the application of the climate projection to assess future convective environments in the region.

Template-based knowledge reuse method for generating high-speed railway virtual construction scenes

ABSTRACT Virtual construction has become an important approach to the high-quality development of high-speed railways, but existing methods have problems such as low efficiency in generating virtual construction scenes and the inability to reuse construction knowledge. To support the rapid visual representation of multiple types of construction processes and construction methods, a template-based knowledge reuse method is proposed. The method includes using a component-based modeling mode to build body structure models of a high-speed railway project and generate a 3D scene; decomposing the construction process and building a construction process knowledge base; establishing joint linkage models of construction machinery and forming a construction method knowledge template; and fusing multiple types of information according to a time sequence to visualize the construction process. Based on the template-based knowledge reuse method, a prototype system was developed, and virtual construction experiments were carried out. The results show that this method achieves the reuse of construction knowledge at different levels including construction machinery level, construction method level, and work site level. Compared with animation software for virtual construction, this method improves the production efficiency by 87%. Moreover, this method can provide a multilevel knowledge reuse scheme for diversified virtual construction.

MmWave Compound Link Budget Model of Dust and Humidity Effect

mmWave Path Loss (PL) Link Budget (LB) modeling considerations are based on many different factors. For instance, the third generation partnership project (3GPP) model is mainly based on the distance from an Access Point (AP) and the frequency of transmission as well as the transmission link budget situation. Furthermore, there are certain interesting models about the effects of dust and humidity on the mmWave propagation. These models had introduced the consequences of the humid and dusty environments without consideration for the additional mmWave transmission LB parameters. First of all, this paper introduces an average dust and humidity model based on statistical Z-test in order to overcome the variation in the results between the three chosen models for dust and humidity effect in the mmWave range. Secondly, it proposes LB compound model, that comprises 3GPP PL LB with an average dust and humidity model. This introduced compound model has been applied on RMa PL LB with and without the presence of dust and humidity. The simulation of the presented model has been applied for distinct distances from the AP and mmWave transmission frequency range from 0.5 to 30 GHz.

Heat Storage in the Upper Indian Ocean: The Role of Wind-Driven Redistribution

Abstract The heat content in the Indian Ocean has been increasing owing to anthropogenic greenhouse warming. Yet, where and how the anthropogenic heat is stored in the Indian Ocean have not been comprehended. Analysis of various observational and model-based datasets since the 1950s reveals a robust spatial pattern of the 0–700 m ocean heat content trend (ΔOHC), with enhanced warming in the subtropical southern Indian Ocean (SIO) but weak to minimal warming in the tropical Indian Ocean (TIO). The meridional temperature gradient between the TIO and SIO declined by 16.4% ± 7.5% during 1958–2014. The heat redistribution driven by time-varying surface winds plays a crucial role in shaping this ΔOHC pattern. Sensitivity experiments using a simplified ocean dynamical model suggest that changes in surface winds over the Indian Ocean, particularly those of the SIO, caused a convergence trend in the upper SIO and a divergence trend in the upper TIO. These wind changes primarily include the enhancements of westerlies in the Southern Ocean and the subtropical anticyclone in the SIO. Albeit with systematic biases, the ΔOHC pattern and surface wind changes simulated by phase 6 of the Coupled Model Intercomparison Project (CMIP6) models broadly resemble the observation and highlight the essence of external forcing in causing these changes. This heat storage pattern is projected to persist in the model-projected future, potentially impacting future climate.

An Improved HVDC Synchronous Firing Control Method Based on Order Switching and Phase Compensation

Synchronous reference frame phase-locked loop (SRF-PLL) is adopted in high-voltage direct current (HVDC) synchronous firing control to output the synchronous phase. The synchronous performance directly affects the accuracy of the firing pulse, and even affects whether the inverter can successfully commutate during AC asymmetric faults. Therefore, an improved HVDC synchronous firing control method based on order switching and phase compensation is proposed in this paper. The improved SRF-PLL has the functions of extracting the fundamental frequency positive sequence (FFPS) phase and switching the order. When the fault detection detects that a fault has occurred, PLL is converted from the second-order system to the first-order system, so as to shorten the out-of-lock time and make it quickly enter the resynchronization. Then, for the phase shift of commutation voltages caused by fault, the zero-crossing sampling method is used to detect the change of the commutation voltage phases, and the synchronous phase of each commutation voltage is compensated selectively. Finally, the improved method is tested in CIGRE HVDC benchmark model and Three Gorges-Shanghai (3GS) DC project model, respectively. The results show that the improved method has better effect on suppressing commutation failure.

Peningkatan Pemahaman dan Kemampuan Pemecahan Masalah Mahasiswa tentang Gizi dan Produktifitas Kerja Melalui Pendekatan Team Based Project dan Berdasar pada Evidence Based Learning di Industri Kerja

In this very dynamic period, universities must respond quickly and appropriately. Learning transformation is needed to be able to equip and prepare higher education graduates to become a superior generation. In sixteen weeks of meetings, many things can be explored in terms of learning methods, for example direct visits to agencies or companies, analyzing worker nutrition problems by reviewing case studies. This learning inovation to analyze problems and solve problems of nutrition and work productivity in this case juxtaposed with problem base learning (PjBL). This study uses a semi-experimental learning evaluation method with a project-based learning model, with the sample being 7th semester students who take lessons in nutrition and work productivity courses. The duration of this research is during the learning week, which is 16 weeks starting from the odd semester in 2021-2022. The form evaluation of learning outcomes is observational activities in the form of data collection from quiz questions and questionnaires. The application of this model was able to assess the level of student understanding of the work productivity nutrition course. The number of student satisfaction with the evidence based learning and teaching learning model with the Team based project model applied is very high with an average of above 75-85%, and through the results of FGD with student representatives saying that this method is more active, and they do not feel bored in lectures, especially in zoom meetings because they have many activities in learning that are centered on them, not only theoretical explanations from the lecturer.

Dynamics and Characteristics of Compound Heatwaves over Asia

The increasing frequency of heat waves in East Asia (EA) affects agriculture, water management, and people's livelihoods. In recent years, record-breaking heatwaves have occurred corresponding to extreme drought and are increasing in frequency. The two leading modes of heatwavesoverEA are closely related to dry conditions, but their temporal developments are somewhat different. The first major mode of heatwaves appears over northern EA, starting in early summer and lasting throughout the summer. The second mode of heatwaves occurs over central China and Korea and is closely related to negative precipitation anomalies during late summer and startsin July or August.This study investigated the quantitative feedback attribution of heatwave-related surface temperature anomalies using the coupled air-surface climate feedback-response analysis (CFRAM). The warming anomalies related tothe first mode of heatwaves are usually controlled by cloud, latent heat, and surface dynamics processes. It can be explained by reducing heat release from the surface to the atmosphere due to the lack of soil moisture under severe dry conditions.While surface warming related to the second mode of heatwaves is contributed by cloud feedback and atmospheric dynamic process. Reduction in cloud area associated with anticycloniccirculation anomalies induces increased insolation and it affects surface warming. However, the effect of humidity on high-temperature events has not yet been fully explored. Thus, this study identifiedcompound heatwaves that are described simultaneously with relative humidity conditions and suggested the future projections of two types of heatwaves over EA using phase six of the Coupled Model Intercomparison Project (CMIP6) model simulation. CMIP6 models projected intensification of dry heatwaves and increased moist heatwave days in response to projected increases in greenhouse gas concentrations.

From Speech to Text: Enhancing Descriptive Paragraph Writing with Unjuk Tutur‘s Learning Model

This study fills a gap in the literature by examining effectiveness of one specific speaking project named Unjuk Tutur (Show and Tell) learning model in improving the ability of seventh-grade students at SMPN 01 Simpang OKU Selatan to write descriptive paragraphs. The study used a one-group pretest-posttest design with a sample of 16 out of 119 students due to social restrictions from the government in response to COVID-19. Data was collected through a descriptive paragraph writing test, and the data was analyzed using statistical techniques. The results showed a significant improvement in students' ability to write descriptive paragraphs after using the Speaking Project learning model, with an average score increase from 66.67 on the pre-test to 78.13 on the post-test. The findings suggest that the Speaking Project learning model could be an effective tool for improving students' writing skills in descriptive paragraphs. The assessment criteria used to evaluate the students' descriptive paragraphs included vocabulary, sentence structure, and spelling. The implications of this study are significant for educators seeking to enhance students' writing abilities. The Speaking Project learning model, which focuses on simple communication skills, could be an effective approach to teaching writing skills to seventh-grade students. Moreover, the findings of this study are relevant to the wider context of Indonesian language learning in the field, where there is a lack of variety in learning methods and models. In conclusion, this study highlights the effectiveness of the Speaking Project model in improving students' descriptive writing skills in Indonesian language learning. Educators can benefit from this approach to enhance students' writing abilities.

Remote sensing data from Space for road image recognition in the forestry

Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.

Agro-climatic sensitivity analysis for sustainable crop diversification; the case of Proso millet (Panicum miliaceum L.).

Current agricultural production depends on very limited species grown as monocultures that are highly vulnerable to climate change, presenting a threat to the sustainability of agri-food systems. However, many hundreds of neglected crop species have the potential to cater to the challenges of climate change by means of resilience to adverse climate conditions. Proso millet (Panicum miliaceum L.), one of the underutilised minor millets grown as a rainfed subsistence crop, was selected in this study as an exemplary climate-resilient crop. Using a previously calibrated version of the Agricultural Production Systems Simulator (APSIM), the sensitivity of the crop to changes in temperature and precipitation was studied using the protocol of the Coordinated Climate Crop Modelling Project (C3MP). The future (2040-2069) production was simulated using bias-corrected climate data from 20 general circulation models of the Coupled Model Intercomparison Project (CMIP5) under RCP4.5 and 8.5 scenarios. According to the C3MP analysis, we found a 1°C increment of temperature decreased the yield by 5-10% at zero rainfall change. However, Proso millet yields increased by 5% within a restricted climate change space of up to 2°C of warming with increased rainfall. Simulated future climate yields were lower than the simulated yields under the baseline climate of the 1980-2009 period (mean 1707 kg ha-1) under both RCP4.5 (-7.3%) and RCP8.5 (-16.6%) though these changes were not significantly (p > 0.05) different from the baseline yields. Proso millet is currently cultivated in limited areas of Sri Lanka, but our yield mapping shows the potential for expansion of the crop to new areas under both current and future climates. The results of the study, indicating minor impacts from projected climate change, reveal that Proso millet is an excellent candidate for low-input farming systems under changing climate. More generally, through this study, a framework that can be used to assess the climate sensitivity of underutilized crops was also developed.

Оценка будущих температур воздуха Центральной Африки по сценариям проектов CMIP5 и CMIP6

Based on the results of the historical experiment of the CMIP5 and CMIP6 projects, the effectiveness of the scenario estimates (RCP/SSP1 2.6, RCP/SSP2 4.5 and RCP/SSP5 8.5) of the future air temperature for Central Africa for the IPSL and BCC global climate models is evaluated and the simulation results of these projects are compared with each other and with the observations of meteorological stations in the region, both for the historical experimental period and with the observations of recent years for the implementation of future climate projections. Based on historical experiment and future evaluations over the past 16 years, it has been determined that the CMIP6 project model versions are no more effective than the CMIP5 results when compared with observational data. Due to the fact that the differences between observational and modeling data are systematic, the results of scenario estimates were adjusted according to the principle of similarity of temperature growth rates for the historical observation period and the future scenario period untilthe end ofthe 21stcentury,which is divided into 3 intervalsfor estimating average values: 2011–2040, 2041–2070 and 2071–2100. Future air temperatures in Central Africawere estimated both forthe average values fortheterritory and forthetemperatures of individualweatherstations based on the adjusted scenario values, which differed in CMIP5 and CMIP6 by no more than 0.1°С. It was found that the average temperature increase over the territory in all months by the end of the 21st century reaches 2.0–2.3°C, and according to estimates at individual weather stations, spatial distributions of future temperatures were obtained, which show the greatest increase in the north of the region near the Sahel.

Minimizing Data Waste: Conservation in the Big Data Era

Minimizing Data Waste: Conservation in the Big Data Era

A Pedagogical Approach Using Digitalization and Heritage Building Information Modelling (HBIM) for a New Practice in Architecture and Archaeology

In the past years, architectural heritage was represented and described through 2D plans, sections, perspective drawings, pictures, and texts. But, in the last decades, with the development of digital tools such as 3D scanner laser and photogrammetry, new technologies have been introduced in the digital survey of architectural heritage and 3D modelling. In this regard, we noticed that students, architects, and archaeologists still do not use photogrammetry and lasergrammetry in their work. Some of them found those methods difficult and expensive. This paper aims to describe a methodology of digitizing architectural artefacts based on digital tools such as photogrammetry and lasergrammetry that allows representing realistic and accurate 3D models and then to explain the steps that lead to introducing photogrammetric model from Autodesk recap photo software in the Autodesk Revit software to have a Heritage Building Information Modeling (HBIM) project that is useful for architectural intervention on cultural heritage using digital platform allowing multidisciplinary collaboration in architectural projects. The field of experimentation is our teaching courses with the students at the national school of architecture and urban planning in Tunisia in different sites. The student’s results are promising in the new practice of Digital Architecture and Digital Archeology and the HBIM can be a conception aid design.

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Environmental noise modeling, quality control, verification, and validation concepts

Over the past 30 + year's noise modeling has developed in all engineering fields. ISO 9001 has developed detail simulation validation criteria's for variouse engineering fields. Engineering software application for environmental noise modeling are just one of many fields that offer advanced simulation options. Unfortunately, the validation of environmental noise simulation is still under development. With the increase of environmental noise projects the model validation between test and simulation is scrutinized by the public or in court. The presentation discusses the definition of Model, Mathematical Model, Computational Model and Conceptual Models and the definition of Verification, Validation and Updating. What are the tools for quality assurance (QA) for the noise modeling software, software verification and project quality control (QC)? What is the state of noise modeling standards in North America? What are the uncertainties and variabilities that the environmental noise engineer can control? Are we sure that we are modeling a worst-case scenario? What can consultants do to improve the quality of noise simulations.

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

Comparing Machine Learning Models to Determine Which is Most Effective at Detecting Brain Tumors

In this project, by using machine learning techniques to analyze various brain tumor scans, the goal was to determine which techniques are the most efficient and accurate in determining the presence of brain tumors. Specifically, the goal was to adequately process a dataset to determine if brain tumors can be detected with more than 75% accuracy using machine learning. The chosen dataset includes just over 250 axial MRI brain scans, thus providing a sufficient dataset to properly analyze. This research is most applicable to the healthcare field, specifically relating to the work done by neurologists and radiologists. If the most efficient and accurate way to detect brain tumors is not determined soon, individuals may have to continue waiting longer than necessary for their results. In this project, K-Nearest Neighbors, Decision Trees, and Muti-Layer Perceptron Models were compared to determine which machine learning algorithm is most effective at determining the presence of a brain tumor in an axial brain scan.