Time-varying Attributes Research Articles

Demand planning for building engineering products – A case study with transformer-based neural networks

Efficient demand planning holds critical significance for businesses. In this research, the authors investigate the applicability of the Temporal Fusion Transformer, a neural network-based model, to address demand planning challenges. Specifically, they explore the potential benefits of incorporating additional information related to product characteristics and sales channel types. The primary objective of this study is to assess the advantages gained by incorporating these supplementary variables. The dataset utilized in this analysis originates from a company predominantly engaged in the sale of building engineering products. The authors initially focus on static attributes such as product groupings and time-varying attributes such as sales channel variations. This paper’s contribution lies in its comprehensive case study, which applies the Temporal Fusion Transformer model to a real-world demand planning problem of the company, including all its specifications and customizations.

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.

Numerical study of the axial compression strength for nano-reinforced filament-wound riser considering curing residual stress

The present study is dedicated to the numerical analysis of progressive failure damage in multiangle nano-reinforced filament wound risers, with due consideration for process-induced residual stresses. Utilizing a multi-model coupling strategy, we introduce an integrated methodology predicated upon the time-varying material characteristics at a macroscopic scale. Initially, the process-induced residual stresses are anticipated via a sequential coupled numerical simulation methodology based on the time-varying attributes of the material. Subsequently, the curing residual stresses serve as the initial stress for the composite wound riser to prognosticate its axial compressive strength, grounded in the progressive damage model. Finally, the multiparametric effects on the strength properties of the composite risers are further investigated to reveal the intrinsic mechanism. The numerical findings evince that curing-induced residual stress retards both fiber and matrix failure while augmenting the axial compressive strength of the composite riser. Furthermore, the escalation in nanoparticle content diminishes the process-induced residual stresses. Additionally, the incorporation of 5 % nanoparticles yields an enhancement in the strength of the composite risers by approximately 16.26 % relative to those devoid of nanoparticles.

Least Square Estimation-Based Different Fast Fading Channel Models in MIMO-OFDM Systems

In cellular wireless communication systems, channel estimation (CE) plays a pivotal role as a crucial technique applied in orthogonal frequency division multiplexing (OFDM) modulation. CE utilizes a variety of methods, including decision-directed channel estimation, pilot-assisted channel estimation (PACE), and blind channel estimation. Among these options, PACE is widely favored for its remarkable stability and consistent superior performance. The idea of massive multiple-input multiple-output (MIMO) shows tremendous potential for the future of wireless communications. However, existing massive MIMO systems face challenges with their high computational complexity and intricate spatial structures, preventing efficient utilization of channel and sparsity features in these multiantenna systems. In communication channels, the signal received is often influenced by the characteristics of the channel and noise present at the receiver. To address this issue, an efficient dataset is utilized, employing the least square (LS) algorithm for minimization. OFDM is a commonly and widely used modulation method in communication systems utilized to specifically combat resonance fading in wireless channels. In wireless communication systems employing OFDM-MIMO, frequency selectivity and time-varying attributes due to multipath channels cause Intercarrier Interference (ICI) among symbols. Channel estimation is a vital aspect for mitigating the effects of fading channels. This investigation focuses on the application of a method examined in the study, which involves a block-type pilot symbol-assisted estimation technique for Rayleigh and Rician fading channel models. The research assesses the performance of the least square (LS) channel estimators in fast-fading channel models while employing various symbol mapping techniques focusing on bit error rate, throughput, and mean square error. The results indicate that the LS estimator exhibits excellent performance in Rayleigh and AWGN channels within the pedestrian A (PedA) model for both uplink and downlink scenarios. It outperforms the PedA model without channel estimation.

Airbnb pricing and term structure: A temporal analysis of omitted variable bias and repeat sales method as remedies

Many tourism studies leverage the hedonic price model to gauge tourists’ willingness to pay for diverse attributes of short-term rental properties. However, when this estimation is applied to the temporal analysis, it can be biased if variables varying with time, such as term structure effects in short-term rentals, are omitted. This paper introduces a repeat sales Airbnb ADR (average daily rate) index to track the change in quality-adjusted rentals of Airbnb properties over time in Auckland, New Zealand, while factoring in the term structure. The findings confirm that using repeat sales data from Airbnb listings can significantly mitigate the bias linked to time-varying attributes. Results demonstrate that when the term structure is not considered, the ADR calculated by the hedonic method may be overestimated by 0.2% per day of the tenancy term. The inventive Airbnb ADR repeat sales index enables the assessment of Airbnb rental trends, taking into account changes in the term structure of leases. This new index can potentially enhance Airbnb listings by incorporating the effects of lease term structures.

The rise of green energy metal: Could lithium threaten the status of oil?

Exploring the connectedness and time-varying attributes of conventional and new energy markets is crucial to the green-oriented transition of energy. This investigation performs bootstrap full-and sub-sample techniques to explore the correlation between the oil and lithium markets, and further probe whether lithium could threaten the status of oil. The empirical outcomes reveal that oil price (OP) exerts positive influences on lithium price (LP). High OP increases the lithium demand and its price through taking new energy vehicles as substitutes for conventional automobiles, indicating that lithium may threaten the status of oil under the oil bull market. But the above opinion could not be supported under the oil bear market, low OP lessens the attraction of new energy vehicles and then reduces lithium demand and LP. In turn, low LP negatively affects OP since oil is also a vital engine for economic development. Thus, these outcomes are consistent with the price correlation model, and lithium can only partially replace oil as a vehicle fuel under certain situations, the latter's status could not be threatened completely. Against a backdrop of urgent green transition and potential oil and lithium bubbles, these conclusions bring meaningful inspirations to the public, enterprises and countries.

RTGEN++: A Relative Temporal Graph GENerator

Graph management systems have become popular for storing and querying graph-oriented data, and they are often evaluated with benchmarks based on large-scale graphs. However, obtaining such graphs is difficult due to their limited public availability. Several graph generators have been developed to address this challenge, producing synthetic graphs with characteristics similar to real-world graphs, such as degree distribution, community structure, and diameter. Generating synthetic graphs with constantly changing topology has received less attention despite its importance in developing useful benchmarks for temporal graph systems. In this paper, we present RTGEN++, a temporal graph generator that supports two evolution models. The first model generates temporal graphs by controlling the evolution of their degree distributions, using optimal transport methods to minimize the transformation effort. We also extend our method in order to consider the community structure of the generated graphs. The second model allows one to control the number of added and removed graph entities, thus enabling the modeling of the evolution of real-world graphs in many use cases. Our generator also includes a decorator that adds types and time-varying attributes to nodes and edges, enhancing the generated graphs and aligning with data platforms that use the property graph model. We validate our approach with experiments that demonstrate the reliability of the generated graphs in approximating ground-truth parameters.

Identifying, exploring, and interpreting time series shapes in multivariate time intervals

We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing a single episode is a multivariate time series. To analyse collections of episodes, we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes. Each episode is thus represented by a combination of patterns. Using this representation, we apply visual analytics techniques to fulfil a set of analysis tasks, such as investigation of the temporal distribution of the patterns, frequencies of transitions between the patterns in episode sequences, and co-occurrences of patterns of different variables within same episodes. We demonstrate our approach on two examples using real-world data, namely, dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover.

Lymphocyte Kinetics is an Important Prognosticator in Predicting Survival Outcomes for Head and Neck Squamous Cell Carcinoma (HNSCC) Patients Using a Ransom Survival Forest (RSF) Model

<h3>Purpose/Objective(s)</h3> Lymphopenia is associated with poor outcomes in HNSCC, yet there is no consensus on how we can use lymphocyte metrics (LMs) dynamically in personalized treatment. We hypothesize that treatment-related lymphopenia (TRL) has greater prognostic significance vs. baseline lymphopenia (BL) in predicting OS & PFS, and that the time-varying attributes of lymphocyte metrics (LMs) have greater prognostic significance vs. static LMs. Given the number of covariates necessary to test our hypothesis, we need to move beyond traditional Cox regression in favor of a robust, machine learning model that is completely non-parametric, allows interactions & leads to lower risks to overfitting. <h3>Materials/Methods</h3> Eligibility criteria were newly diagnosed M0 HNSCC patients treated with neck RT ± chemo in 2015-19 with baseline absolute lymphocyte counts (ALC) and ≥ 1 on-treatment ALC data point. We retrospectively analyzed prospectively acquired data on 23 covariates: 6 baseline, 5 disease, 6 chemoRT & 6 LMs (baseline ALC, min ALC, max ALC decrease, baseline neutrophil-to-lymphocyte ratio or NLR, max NLR & max NLR increase). Overall, there was 1.4% missing data. We used random survival forest (RSF), an ensemble-based method for overall & progression-free survival analysis (OS & PFS). RSF is attractive because it handles missing data by proximity imputation, is not affected by multicollinearity, and provides internal validation of model fit using 37% of out-of-bag (OOB) data that do not participate in model training. Averaging over 1000 survival trees, we divided the OOB ensemble predictor scores into tertile risk groups. Recognizing the burden to analyze 23 covariates, we developed a simplified RSF model with 10 binary covariates using conditional inference tree & weighted risk scores using ranked variable importance (VIMP). We also studied pairwise interactions amongst covariates. <h3>Results</h3> 316 patients were eligible for analysis. Both full & simplified models showed show good predictive performance with survival AUCs 0.71-0.76 for OS & 0.66 – 0.72 for PFS. Two-year OS for low, intermediate, and high-risk groups was 94 vs 89 vs 70% in the full model (FM) and 96 vs 80 vs 65% in the simplified model (SM). Similarly, 2-year PFS was 89 vs 72 vs 62% (FM), and 88 vs 71 vs 49% (SM). Out-of-bag (OOB) prediction errors were 0.26 – 0.34. Consistent in both models, these 4 covariates contributed the most to survival outcomes: age, KPS, RT dose and max ALC decrease. Max ALC decrease was ranked #4 in relative variable importance (VIMP) score, indicating a greater influence on OS & PFS than HPV, chemo type/dose, and other LMs (range VIMP rank 6-20). Of note, GTV was more influential vs. T&N stage (rank 5-7 vs 9-22). <h3>Conclusion</h3> Our data suggests that TRL has a greater prognostic significance when compared with BL. Max ALC decrease has superior predictive performance vs other LMs. To our knowledge, this is the first study that highlights the time varying attributes of ALC as an important prognosticator for HNSCC outcomes.

Risk Connectedness Between Green and Conventional Assets with Portfolio Implications.

The increasing concerns of investors toward green bonds and their appealing nature of diversification has motivated the current research to study the risk connectedness between green and conventional assets spanning from August 2014 to December 2020. We first estimate the dynamic equi-correlations through DECO-GARCH. Next, we assess the dynamic and static risk connectedness in the median, extreme low, and extreme high quantiles arguing that spillovers vary across different time periods particularly during economically intense time periods. Finally, we analyzed the hedge ratio and hedge effectiveness between green bonds and other assets. We find that equi-correlations are intense during economic shocks such as the Shale oil crisis, Brexit, US interest rate hike, and COVID-19 pandemic. The volatility analysis at average, lower, and upper quantiles also validate time-varying attributes of green and conventional assets. Further, network figures of green and conventional assets identify potential diversification opportunities. Meanwhile, the hedge effectiveness indicates that green bonds are effective hedge for precious metals and cryptocurrencies. Our findings draw multiple implications for policymakers, green investors, financial market participants, and regulatory authorities regarding flight-to-safety during crisis times and maintaining a diverse portfolio to escape potential losses.

Dynamic Access Control and Trust Management for Blockchain-Empowered IoT

The Internet of Things (IoT), while providing comprehensive interconnection and ubiquitous services, poses security issues by enabling resources sharing among various devices from different untrusted authorities. Blockchain, as a distributed ledger, provides a traceable and verifiable platform to ensure the secure access control in IoT. The existing works based on blockchain may bring up intolerable computing overhead and delay to the lightweight IoT devices. In this article, we propose a dynamic and lightweight attribute-based access control framework for blockchain-empowered IoT, to achieve secure and fine-grained authorization. The proposed scheme allows access to resources by evaluating attributes, operations, and the environment relevant to a request. The access policy is executed through smart contract in blockchain for security and flexibility. To further adapt to IoT device constraints, we design a access control framework based on decentralized application (DApp), which can maintain tamper proof in a timely manner and be adapt to the delay-intolerant application. When delay-intolerant access is required, access can be allowed according to local replica of the blockchain, without a consensus of blockchain network. Considering the time-varying attributes of IoT devices, a trust management scheme is proposed based on the Markov chain to resist the security fluctuation caused by the vulnerability of IoT devices. In the experiments, we deploy our system prototype on Ethereum to evaluate the feasibility and effectiveness of the scheme. The results show the proposed scheme can achieve secure, high throughput, and flexible access control in IoT.

The exposure of the US tourism subsector stocks to global volatility and uncertainty factors

ABSTRACT In the face of uncertain circumstances and volatilities across tourism stocks, the current study attempts to investigate the connectedness of US tourism subsectors using time-varying vector autoregressions (TVP-VAR) technique for the period spanning 1 August 2008–31 August 2021. Moreover, we examined the influence of various financial and economic indicators to observe their impact on the connectedness of US tourism subsectors. Further, we employed Diebold and Yilmaz ([2012]. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66) connectedness approach to justify the robustness of our results. We find that hotels, transportation, and recreational are net transmitters of spillovers to the remaining US subsectors. We reported multiple time-varying attributes where the connectedness of US tourism subsectors was remarkably higher. In addition, the regression results reveal a positive and significant impact of financial and economic uncertainties on the connectedness. Finally, our findings were robust to the methodology employed, which substantially justified our estimator. We proposed numerous useful implications for policymakers, US government, tourism industry, investors, market players, and portfolio managers to benefit from the findings obtained.

Adaptive and Dynamic Security in AI-Empowered 6G: From an Energy Efficiency Perspective

Emerging AI-empowered services and techniques, such as connected vehicle, intelligent industry, and smart city, are forthcoming with the sixth generation (6G) cellular network to benefit daily life, industry, and society. However, the increasing integration of the 6G network with the physical world leads to a plethora of new scenarios that bring new challenges to 6G, especially from the security and energy aspects. In 5G networks, security solutions across all devices and base stations are configured with universal settings for certain types of attacks. This one-size-fit-all strategy no longer suits 6G security due to the higher diversity in device capabilities, service features, energy conditions, attack vulnerabilities, and other time-varying attributes. Since each scenario may have unique security requirements and energy availability, the selection and configuration of security strategies need to be optimized for 6G networks in an adaptive and dynamic manner. In this article, we explore 6G security from an energy efficiency perspective by balancing the tradeoff between security and energy consumption in various scenarios. Specifically, we first investigate the AI-empowered 6G network architecture with promising applications and visions. Then we identify the challenges for adaptive and dynamic security optimization in 6G from the aspects of heterogeneity, dynamics, and modeling complexity. To balance security-energy trade-off, we propose an optimization framework that provides customized recommendations of security strategy to different user devices and base stations. Finally, open issues are discussed on 6G security from an energy efficiency perspective.

Kerosene-water counter-current two-phase flow-structures varying flow-orientation in 15 mm diameter duct

Experimentations are conducted, and kerosene-water counter-current two-phase flow-structure in a duct of 15 mm diameter are identified, categorized, and characterized in five duct-orientations for different combinations of kerosene and water superficial velocities. Some unique counter-current liquid-liquid flow-structures, which have not yet been reported till date, are found through the present experimental investigation. Flow-structures’ interfacial distributions and highly time-varying attributes are captured using a high-resolution DSLR camera and a high-speed video camera, respectively, to distinguish and identify them. The subjective descriptions may confuse the observer. Thus, the objective interpretations of the flow-structures are collected as time-series current and voltage signals using a DPT and in-house fabricated conductivity probes, respectively. Statistical analyses of the time-series data are performed, and statistical parameters are extracted using MATLAB coding. The flow-structures are successfully parameterized and characterized in terms of the ‘non-parametric probability distribution (NPPD)’ plots and those statistical parameters of the objective signatures. These extracted NPPD plots and statistical parameters are found to be very useful for recognizing and distinguishing the individual flow-structures.

Quickest Flow Algorithms with Time-Varying Attributes

In many real-world situations, there are numerous network optimization problems where the network attributes depend on time. In this paper, we consider single-source single-sink discrete-time dynamic network flow problems. We review some algorithms for the quickest flow problems in two environments (to the network attributes): time-invariant and time-variant. This paper mainly focuses on the existing algorithms for a later one. In literature, most of the authors have made their objectives to determine the earliest arrival time paths along which a given amount of flow can be sent in the minimum time. Evacuation is the most recent research area of network optimization, where quickest flow models allow the estimation of the minimum time required to bring a given number of evacuees to safety.

Probabilistic analyses of structural dynamic response with modified Kriging-based moving extremum framework

Mechanical system is usually composed of multiple complex structures, which endure the combine action of multi-physical fields (e.g., flow field, thermal field, structural field, and so forth) during operation. Structural response, for instance, deformation, stress, strain, fatigue life, etc., have dynamic and time-varying attributes, their values play an important role in safeguarding function properly, and affects the safety of entire system. Probabilistic analyses (including reliability evaluation and sensitivity analysis) can effectively identify potential risk and improve the reliability level. The traditional way is to use Monte Carlo (MC) simulation to achieve probabilistic analyses of complex structures. However, the MC simulation needs to perform a large amount of calculations to complete the specified task analysis, the computational burden is too heavy for the reliability evaluation and sensitivity analysis of structural dynamic response. To efficiently perform probabilistic analyses of structural dynamic response, we therefore developed a surrogate model method, namely modified Kriging-based moving extremum framework (MKMEF, short for), absorbing extremum thought, moving least squares (MLS) technique, Kriging model and collaborative evolution genetic algorithm (CEGA). For this proposed MKMEF, the extremum thought is used to transform dynamic output response into extremum values within a time domain, and the MLS method is applied to obtain efficient samples to derive Kriging model. Besides, the CEGA is employed to replace gradient descent method to resolve maximum likelihood equation (MLE) and find the optimal hyperparameters of Kriging model. Furthermore, an aeroengine high-pressure turbine blisk is treated as a case study, the probabilistic analyses of radial running deformation is executed to validate the effectiveness of the proposed method by considering the fluid-thermal-solid interaction. The analytical results show that the reliability degree of turbine blisk is 99.78%, when the allowable value of radial running deformation is 2.5071 × 10−3 m (determined by 3 sigma levels), and the highest impact on the turbine blisk radial running deformation is gas temperature, followed by inlet velocity, rational speed, material density and outlet pressure. The comparison of methods including direct simulation, response surface method-based extremum thought (ERSM) and traditional Kriging model, shows that the developed MKMEF holds high-efficiency and high-accuracy for the probabilistic analyses of turbine blisk radial running deformation. The presented efforts provide a useful insight for assessing probabilistic analyses of structural dynamic response and enrich mechanical reliability theory.

The relative choice over destiny in a country’s long-run economic growth and economic affluence

This paper examines an in-depth and systematic review of why some nations are so rich, while others remain so poor taking into account temporal and spatial dynamics applied for economic growth covariants. Growth literature underscores direct and indirect causes for economic growth. Likewise, economic and non-economic dynamics are thoroughly examined for countries’ long-run economic growth and relative wealth accumulation. Endogenous growth theories emphasized that investment in human capital, innovation, and knowledge are major contributors to economic growth. Empirics confirmed that time-variant (such as well-established institutions and their prominent role in devising property rights and policies) gives more emphasis towards shaping a country’s long-run economic growth than time-invariant exogenous attributes (like geography). Meanwhile, some nations did not industrialize being geographically advantageous, and the location of the country does not exclusively determine the fate of the nation’s economic success. Moreover, state capacity is vital to determining relative wealth accumulation and economic prosperity. The incidence of routine war undermines the fiscal capacity and leads to an extractive form of government and weakens public and private investments, and this sets a country into undesirable outcomes. In a nutshell, time-varying attributes become more flexible to adjust the fate of countries’ economic growth and destiny. Furthermore, it requires an intense investigation of what governs a nation’s economic successes or failures focusing on country-specific concerns. It needs a close and continuous rectification to reconsider the country’s institutional setup and policy frameworks towards endogenously rooted economic growth and development for the relative economic affluence.

Decoding PPP Corrections From BDS B2b Signals Using a Software-Defined Receiver: An Initial Performance Evaluation

With the rapid development of China's BeiDou Navigation Satellite System (BDS), the application of real-time precise point positioning (RTPPP) based on BDS has become an active research area in the field of Global Navigation Satellite Systems (GNSS). BDS has provided the service of broadcasting RTPPP information. It indicates that BDS has become the second satellite system that provides RTPPP services, following Galileo among the GNSS, but work based on this direction has yet to be explored. Therefore, this paper evaluates the performance of precise point positioning (PPP) service using a software-defined receiver (SDR). An experiment was carried out to verify the feasibility of the SDR. The PPP-B2b signal was processed to obtain PPP service information, including orbit corrections, clock corrections, and differential code bias corrections. The time-varying attributes of these corrections of BDS and GPS are evaluated, and the integrity and stability of the PPP service were analyzed. The results show the PPP-B2b signal can stably provide PPP services for satellites in the Asia-Pacific region, including centimeter to decimeter-level orbit corrections and meter-level clock corrections for BDS satellites. At the same time, PPP services provide decimeter to meter-level orbit correction and meter-level clock correction for GPS satellites. Finally, detection tip for bitstream availability in SDR is proposed. Some content which is not defined in the official document, such as the PPP-B2b frame arrangement, various correction update cycles and the progress of PPP service are discussed.

Implementation of SExI–FS (Spatially Explicit Individual-based Forest Simulator) Model using UAV Aerial Photo Data Case Study: Jatinangor ITB Campus

Landscape architecture affected by interaction between built and natural environment such as vegetation. Nowadays, landscape architects are using 3D city models for simulations, which requires highly dynamic and time-varying attributes. 3D city modelling structure has been standardized by CityGML, although researches that are related to the storing of dynamic data had been conducted for the past years, it has not been supported by any standard until this very moment. In dynamizer, it is added as a data structure into a CityGML structure that is already existed, although the existing structure is a static one. Kolbe’s research on dynamic data using CityGML called dynamizer could use the spatial data in more dynamic way by changing its geometric, thematic, or appearance data, but its purpose is not specific for trees or vegetation. In this paper, a method of simulating the vegetation growth using SeXI-FS will be discussed to show the dynamic changes that happen in vegetation as part of the dynamic changes in landscape architecture. The result of this research will be used to address the importance of information on vegetation by studying its changes in Jatinangor ITB Campus and as initial research to build dynamizer in CityGML for landscape architecture.

Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D

Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.