Time Space Research Articles

Articulating environmental sustainability dynamics with space-time cube

Conceptually, environmental sustainability involves maintaining crucial environmental functions while considering both present and future development. However, existing methods for expressing environmental sustainability are mainly derived from a steady state with minimal spatial explicitness. Furthermore, the environmental impact of certain events may exhibit a lag, particularly in basins. Here, we propose a framework that employs a space-time cube to articulate environmental sustainability. This cube can visualize the environment's evolution over time, identify hot and cold spots in space, and concurrently determine underlying influencing factors via spatial regression analysis. Unlike traditional methods, the space-time cube incorporates not only spatial dimensions but also temporal dimensions. We applied this framework to China's upper Han River basin, using the Remote Sensing Ecological Index (RSEI) as an indicator of environmental sustainability. It enabled us to chart the basin's ecological trajectory with spatial and temporal explicitness from 1990 to 2020. Our findings reveal that climate change (represented by temperature and precipitation changes) and human activities (represented by nighttime light) were the main factors driving changes in environmental sustainability from 2000 to 2020 in the basin. Therefore, our proposed spatial-temporal integration framework proves to be an efficient tool in articulating environmental sustainability.

Manning Coefficient Effect on the Shallow Water Flow Equation

This work will seek to solve shallow water equations, using numerical integration methods, to solve partial differential equations (PDEs) and subsequently present a brief physical interpretation of the solutions. The derivation of the equations is demonstrated, along with the explanation of each physical quantity present. The discretization of non-conservative shallow water equations is carried out using the method of finite differences advanced in time and central in space (FTCS), in explicit form. With the deductions, different Manning coefficients will be addressed in the solutions, verifying their effects.

Considerations on sentiment of social network posts as a feature of destructive impacts

Nowadays, people spend a lot of time in the information space, communicating within various social platforms. Content of those platforms can influence people’s feelings and personalities, which is especially relevant for young people. In this research, we made an attempt to prove this hypothesis. For the experiment, we selected the VKontakte social network and analysed users profiles together with the results of the psychological tests passed by them. The goal of the experiment was to find correlations between the information provided within the social network communities and the users’ personalities. Moreover, in this paper, we made an attempt to enhance the results of the classifier accuracy using the sentiment analysis. The experiments were conducted to test the sentiment analysis models, to analyse the proposed feature based on posts’ sentiment, and test the classifier for the detection of the potentially destructive impacts. The analysis of the correlation of the proposed feature with the communities that have potentially destructive impacts on anxiety is conducted. The analysis of the obtained results is provided. During the experiments, the authors found out that consideration of the posts’ sentiment allows increasing accuracy of the classifier for anxiety destructive impacts on 12.24 %. Additionally, we analysed the relationship between the user sentiments metric and destructiveness. We confirmed that the assessment of the user’s posts’ sentiment can be used to compile his psychological characteristics and determine possibility of destructiveness.

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

Space Security and the Law of International Space Cooperation: The Need for Leadership and Coordination

In recent times space exploration and technological advancements hold prominent importance, the merger of space security and international collaboration must be paid attention too. Whereby, during the recent advancements in space technologies which include the proliferation of satellite and emergence of various space programs, the non-commercial and sustainable utilization of space must be taken care of. This paper explores and access the methodologies adopted and put into motion by nation having a well-established space program to address the difficulties faced while maintaining the space security along with ongoing space explorations. This research study will also pay emphasis towards analysing the already existing legal framework which directly governs various outer space activities, international organizations, role of governments and private industry players towards shaping of governance in outer space. In order to build trust and furthering the shared objectives in outer space and resolution of disputes among nation states, emphasis on how significant and valued international cooperation and diplomacy can prove to be. The various risks factors such as deliberate actions which are testing of anti-satellite weapons and dispersion of space debris in high volumes that the assets in space encounter are explored in this research. This study also discusses about case studies that have impacted the outer space and space explorations and other space missions positively as well as negatively in past instances. This study concludes the necessity of proactive leadership and teamwork in addressing the issue of space security and the legislation of international space cooperation. Roles of significant stakeholders, implementation of future course of actions with necessary changes to guide and provide policymakers in understanding the complexity of space reforms for peaceful utilization of outer space for both current and future human race.

Contact with nature may be a remedy for loneliness: A nationally representative longitudinal cohort study

BackgroundNature prescribing – a written recommendation by a health professional for a person to spend more time in nature for health reasons – is being heralded by some as a potential solution to loneliness, defined as a felt deprivation of meaningful connection, companionship, and camaraderie. However, such recommendations currently lack evidence on how much time in natural environments is needed to tackle loneliness and over what timescale. MethodsGeneral, emotional, and social loneliness were measured using De Jong Gierveld's 6-item scale in a nationally representative sample of 3043 Australian participants at baseline (October 2020), with 2421 and 2123 completing follow-ups at 4 months (February 2021) and 16 months (February 2022), respectively. Multilevel logistic regressions examined the odds of change in loneliness status between baseline and follow-up in relation to hours spent in nature in the 7 days prior to the baseline survey, adjusting for confounding factors that influence time spent in nature and the risk of feeling lonely. FindingsRelief from general loneliness in those experiencing it at baseline was 22.4% at month 4 and 29.4% at month 16. The equivalent findings for relief from social loneliness and emotional loneliness were 21.2% and 24.6%, and 34.2% and 41.2% at months 4 and 16, respectively. Incidence of loneliness at 4 months was 21.8% (general), 26.1% (social), and 11.8% (emotional), and at 16 months was 22.7% (general), 25.6% (social), and 12% (emotional). At each time point, the prevalence of loneliness was lower among participants with more time in nature. Just 1–2 h per week in nature (versus <1) was associated with relief from social loneliness at 4 months (Odds Ratio [OR] 1.69, 95%CI 1.10, 2.65) and at 16 months (OR 2.10, 95%CI 1.34, 3.30). Higher odds of relief from emotional loneliness were observed at 3–4 h spent in nature at 4 months (OR 1.84, 95%CI 1.11, 3.06) and at 16 months (OR 1.82, 95%CI 1.09, 3.07). Incidence of loneliness was not associated with nature contact. InterpretationIncreasing time in nature may an effective non-medical prescription for people who are lonely to find durable relief within a relatively short space of time. Findings from this longitudinal study can inform further investigation through randomised trials of co-designed nature prescriptions targeting relief from loneliness in vulnerable populations.

Eigenbin compression for reducing photon-counting CT data size.

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M<N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data. The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with<5% change in mean values and a small noise penalty (mean change in noise of<12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with<5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in<5% change in CNR in the VMIs. The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

A fourth order Runge-Kutta type of exponential time differencing and triangular spectral element method for two dimensional nonlinear Maxwell's equations

In this paper, we study a numerical scheme to solve the nonlinear Maxwell's equations. The discrete scheme is based on the triangular spectral element method (TSEM) in space and the exponential time differencing fourth-order Runge-Kutta (ETDRK4) method in time. TSEM has the advantages of spectral accuracy and geometric flexibility. The ETD method involves exact integration of the linear part of the governing equation followed by an approximation of an integral involving the nonlinear terms. The RK4 scheme is introduced for the time integration of the nonlinear terms. The stability region of the ETDRK4 method is depicted. Moreover, the contour integral in the complex plan is utilized and improved to compute the matrix function required by the implementation of ETDRK4. The numerical results demonstrate that our proposed method is of exponential convergence with the order of basis function in space and fourth order accuracy in time.

Positivity preserving and unconditionally stable numerical scheme for the three-dimensional modified Fisher–Kolmogorov–Petrovsky–Piskunov equation

This paper introduces a numerical approach for the practical solution of the modified Fisher–Kolmogorov–Petrovsky–Piskunov equation that describes population dynamics. The diffusion term and nonlinear term is based on the operator splitting method and interpolation method, respectively. The analytic proof of the discrete maximum principle and positivity preserving for the numerical algorithm is demonstrated. Numerical solution calculated using the proposed method remains stable without blowing up, which implies that the proposed method is unconditionally stable. Numerical studies show that the proposed method is second-order convergence in space and first-order convergence in time. The performance and applicability of the proposed scheme are studied through various computational tests that present the effects of model parameters and evolution dynamics.

Chronotopes of Transnational Literacies: How Youth Live and Imagine Social Worlds in their Digital Media Practices

AbstractThis study explores the transnational literacies that contribute to creating online social connections among migrant youth that may support their development and navigation of life across countries. We examine how social connectedness is semiotically constructed through time–space framing among two youth of Chinese descent who were participants of a larger study of the digital media practices of young people in an urban high school. We build on the concept of chronotope (Bakhtin, 1981) and scholarship of transnational literacies to understand the space–time dimensions of digital literacy practices, specifically how youth create the spatiotemporal context or frame of reference for their actions and positionings with others across geographical distances. Using a case study approach with data from interviews and observations, we analyzed how the youth depicted their transnational social relations within particular time–space frames. Qualitative and discourse analyses of the youths' narrative accounts show that they positioned themselves in proximity with their transnational peers within particular time–space contexts that supported their actions and emotions, and how they made sense of events, issues, practices, and their own identities as transnational individuals. The youths' ideas of schooling and education, and attunement to nationalistic ideologies, were developed through multiple chronotopes in their social connections across countries. This study offers implications for how an attention to the transnational spatiotemporal frame of youths' discourse and narrative may give us a more nuanced understanding of the subjectivity, knowledge, and perspective they derive from their experiences of movement and connection across borders.

Three-Layer Artificial Neural Network for Pricing Multi-Asset European Option

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time–space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN’s relative error is about 0.8%. This method can be extended into multi-layer ANN-q(q&gt;3) and extended into American options.

Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster.

Decision-making in animals often involves choosing actions while navigating the environment, a process markedly different from static decision paradigms commonly studied in laboratory settings. Even in decision-making assays in which animals can freely locomote, decision outcomes are often interpreted as happening at single points in space and single moments in time, a simplification that potentially glosses over important spatiotemporal dynamics. We investigated locomotor decision-making in Drosophila melanogaster in Y-shaped mazes, measuring the extent to which their future choices could be predicted through space and time. We demonstrate that turn-decisions can be reliably predicted from flies' locomotor dynamics, with distinct predictability phases emerging as flies progress through maze regions. We show that these predictability dynamics are not merely the result of maze geometry or wall-following tendencies, but instead reflect the capacity of flies to move in ways that depend on sustained locomotor signatures, suggesting an active, working memory-like process. Additionally, we demonstrate that fly mutants known to have sensory and information-processing deficits exhibit altered spatial predictability patterns, highlighting the role of visual, mechanosensory, and dopaminergic signaling in locomotor decision-making. Finally, highlighting the broad applicability of our analyses, we generalize our findings to other species and tasks. We show that human participants in a virtual Y-maze exhibited similar decision predictability dynamics as flies. This study advances our understanding of decision-making processes, emphasizing the importance of spatial and temporal dynamics of locomotor behavior in the lead-up to discrete choice outcomes.

Elastic MSM: A Fast, Elastic and Modular Preprocessing Technique for Multi-Scalar Multiplication Algorithm on GPUs

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed.In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively.From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.

Evaluating the strength of subgrade soil for pavement design: an analysis using support vector regression model optimized by bayesian algorithm

Determining the strength of subgrade soil, crucial for flexible pavement design, traditionally relies on time-consuming and labour-intensive California Bearing Ratio (CBR) tests. The limitations of linear and non-linear regression-based prediction methods have led to an increased importance of soft computing techniques in providing flexible and consistent CBR predictions for practical engineering. This paper proposes a hybrid model that combines the Bayesian Optimization algorithm (BOA) with support vector regression (SVR) to predict CBR for subgrade soil in pavement design. A database of 238 soil test datasets from northern Algeria was utilised to develop the BOA-SVR model. Using the BOA technique to optimize hyperparameters can improve the model’s performance. The developed hybrid model closely matched experimental results, with a coefficient of determination of 89% and a mean squared error of 5.77, indicating high predictive accuracy. Comparative analysis shows that the BOA-SVR model performs better than conventional and other machine learning models in predicting CBR in tested subgrade soils, in terms of accuracy and robustness. Finally, the optimal subset of features from the initial dataset combined with the BOA-SVR model is determined using an exhaustive feature selection method that includes cross-validation. The results of the experiment indicate that the accuracy of the hybrid model that has been developed is improved by a subset of only seven features from the twelve initial features, this allows for the reduction of computing time and storage space.

Series form solutions of time–space fractional Black–Scholes model via extended He-Aboodh algorithm

The objective of the current study is analyze linear and nonlinear time–space fractional Black–Scholes models via modified homotopy perturbation method (m-HPM). In current investigation, memory effects in financial markets are explored through fractional derivative in Caputo sense. The effectiveness of proposed methodology is checked numerically by finding residual errors and presented in tables. These tables also provide a benchmark for the comparison with already existing results in literature. Furthermore, solutions are graphically analyzed via 3D and contour plots across a range of parameters under varying market conditions. Analysis confirms the efficiency of m-HPM for predicting solutions of time–space fractional Black–Scholes models. The current study can contribute in understanding the applications of fractional calculus in finance, and can be a valuable computational tool for pricing financial derivatives in fractional environments.

Biomedical, Biopsychosocial, and Biocognitive: A Point-to-Point Comparison of Models in Psychiatry

Mainstream psychiatry claims scientific status, relying on two essentially incompatible models to justify that claim. The first, the reductionist biomedical model, is based on the physicalist ontology that there is nothing in the universe beyond matter and energy interacting in the time–space matrix. The second, the biopsychosocial model, is dualist in that it allows room for causally significant psychological events to play a part in the etiology of mental disorders. More recently, the biocognitive model, a formal model of dualist interaction, offers a third path. This article sets out criteria for models and shows that neither the biomedical nor the biopsychosocial model satisfy those criteria, not least because they do not exist in a form recognizable as a scientific model. The biocognitive model is the only model available today that can provide a valid basis for psychiatry as a nonreductionist science.

Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI?

Recent advancements in chatbots have provided students and academics with a new mode of how knowledge can be sourced and composed. Within a very short space of time, students and academics have flocked to use ChatGPT and other Generative Artificial Intelligence (GAI) platforms owing to their capable responses. Additionally, apart from the generative chatbots (such as ChatGPT and Gemini), AI writing tools for paraphrasing, summarising, and co-writing have also become capable and increasingly prevalent to such a degree that the public is spoilt for choice. Having conducted tests on popular chatbots and AI writing tools, it became clear that while programs like Turnitin are developing new algorithms to detect plagiarism and AI-generated content, the initial findings from this study suggest that this may be an increasingly difficult task. These tests were published on YouTube, and within a few weeks, the evidence garnered tens of thousands of views as students and educators seemed uncertain about the strengths, weaknesses, and legalities of these AI tools. What is clear is that we have passed the tipping point, and AI assistance is no longer just a grammar fixer. The implications of this are concerning and far-reaching, as plagiarism is already a significant problem in universities. This position paper reports on tests conducted using Turnitin software and AI writing tools such as ChatGPT and QuillBot. These real-world tests support the paper’s position that it is becoming increasingly difficult to determine what constitutes original work in a world of GAI. The aim of this article is to provide evidence that educators who rely on similarity checking and AI detectors in their current form may inadvertently be supporting plagiarism rather than reducing it. A new method of academic plagiarism detection is proposed, utilizing large language models to generate and track ideas, thereby serving as an idea database. The proposed method focuses on the "understanding" of the work rather than on text similarity.

Use of the Perturbing Sphere Method for the Estimation of Radiofrequency Coils' Efficiency in Magnetic Resonance Applications: Experience from an Electromagnetic Laboratory.

Radiofrequency (RF) transmitter and receiver coils are employed in in magnetic resonance (MR) applications to, respectively, excite the nuclei in the object to be imaged and to pick up the signals emitted by the nuclei with a high signal-to-noise ratio (SNR). The ability to obtain high-quality images and spectra in MR strongly depends on the RF coil's efficiency. Local coil efficiency can be estimated with magnetic field mapping methods evaluated at a fixed point in space. Different methods have been described in the literature, divided into electromagnetic bench tests and MR techniques. In this paper, we review our experience in designing and testing RF coils for MR in our electromagnetic laboratory with the use of the perturbing sphere method, which permits coil efficiency and magnetic field mapping to be estimated with great accuracy and in a short space of time, which is useful for periodic coil quality control checks. The method's accuracy has been verified with simulations and workbench tests performed on RF coils with different surfaces and of different volumes. Furthermore, all the precautions taken to improve the measurement sensitivity are also included in this review, in addition to the various applications of the method that have been described over the last twenty years of research in our electromagnetic laboratory.

Factors Influencing Customer Satisfaction in Online Shopping Among Non-Resident Students of UiTM Cawangan Terengganu

People nowadays prefer to do their purchasing online. Customers like to purchase on the internet because it is more convenient than traditional shopping, which is often associated with anxiety, crowdedness, traffic jams, limited time, and parking space, among other things. In Malaysia, the number of internet users has increased, and online shopping has become a growing trend. Therefore, this study will look into the elements that influence customer satisfaction with online buying among university students. Product quality, brand, and shopping experience are three aspects that contributed to this study. The survey approach was utilized in this study, and 364 respondents answered questionnaires. Questionnaires were distributed to non-resident university students to determine the elements that influence their decision to shop online. The researcher employed both the online survey and manual method to obtain data. Statistical Package for Social Science (SPSS) and Microsoft Excel were used to analyze the data gathered. According to the findings, product quality, brand, and shopping experience all have a significant impact on non-resident university students' satisfaction with online shopping at UiTM Cawangan Terengganu. In summary, the findings of this study are essential for building a successful, sustainable, and competitive business. They provide actionable insights that can lead to improved customer retention, increased revenue, and a stronger brand reputation.

Electric vehicle charging load prediction based on graph attention networks and autoformer

AbstractWith the widespread popularity of electric vehicles in the domestic market, large‐scale electric vehicle user data has been collected and stored. Highly accurate user‐level charging load prediction has a wide range of application scenarios and great business value. However, most existing EV load prediction methods are modelled from the charging station perspective, ignoring the user's travel habits and charging demand. Therefore, this paper proposes a temporal spatial neural network based on graph attention and Autoformer to predict electric vehicle charging load. Firstly, the urban map of Wuhan is rasterized. Then, driving and charging data from the user level are aggregated into the raster module according to the time sequence, and a spatio‐temporal graph data structure of user travel trajectory is constructed. Finally, the temporal spatial neural network is used to construct the EV charging load prediction model from the user's perspective. The experimental results show that, compared with other baseline prediction methods, the proposed method effectively improves the accuracy of the EV charging load prediction model by fully exploiting the distribution of EV user clusters in time and geographic space.