Spatiotemporal Research Articles

Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model

In view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) models. In this study, a convolutional neural network (CNN) model is used to process two-dimensional image data in both time and frequency domains, and a convolutional core attention mechanism is introduced to extract spatial features, such as peaks, cliffs, and waveforms, from the samples. A long short-term memory (LSTM) network is embedded in the output processing of the convolutional neural network (CNN) to analyze the long-sequence variation characteristics of rolling bearing vibration signals and enable long-term time series prediction by capturing long-term dependencies in the sequence. In addition, a gated recurrent unit (GRU) is used to refine long-term time series predictions, providing local fine-tuning and improving the accuracy of fault diagnosis. Using a dataset obtained from Case Western Reserve University (CWRU), the average accuracy of CNN-LSTM-GRU fault vibration is greater than 99%, and its superior performance in a noisy environment is demonstrated.

Integrating Temporal Dimensions in Circularity of the Built Environment Analysis of Two Flemish Industrial Parks

This manuscript explores how incorporating temporal dimensions into built environment research can promote a more circular society, adding societal improvements to efficiency-driven measures closing waste or material cycles. The current circularity approaches in industrial environments mainly focus on short-term innovations reducing resource extraction and waste, overlooking long-term circularity potentials of natural resource management such as living soils as a basis for all life. This study addresses this gap by investigating, analyzing, and drawing interplays between regenerative soil cycles and business development cycles in two Flemish industry parks, Kortrijk-Noord and Haasrode. Using diachronic mapping, a qualitative design and action research tool, the study aims to generate a space–time composite of soil and business cycles, integrating archival research, interviews, and policy document reviews. This method visually captures interplays between geology, land valuation, and economic development, demonstrating that integrating soil and business cycles can suggest new pathways for site-specific circular practices on Flemish industry parks, which can inform site-specific project frameworks for circular built environments. As such, the research advocates a paradigm shift in industry park (re)development, from product and material innovation within a ‘time is money’ framework to an integrated ‘time is life’ approach, where time’s historical and social dimensions are part of circular landscape development.

Anxiety in digital consumer culture: a psychoanalytic reflection on the emergence of a reactive subjectivity

ABSTRACT Anxiety is widely understood to be a harmful consequence of engagement with digital technologies such as social media. Although digital life is studied by cultural consumer researchers, its psychic effects are not well explored. We focus on anxiety as a psychosocial effect of digital consumer culture by elaborating on Teresa Brennan’s theorisation of the connection between technological development, subject formations, and space–time relations. We propose four psychosocial trends that propel anxiety, illustrated by composite narratives: objectification, immediacy, disembodiment, and estrangement. Our narratives emphasise a spatio-temporal shift in how the subject relates to itself and others, conditioning the emergence of a reactive subjectivity driven by self-examination and modification rather than relationality. This offers an understanding of how digital technologies shape subject formations to be anxious, thereby attending to capital interests invested in propelling a reactive state of subjectivity at the expense of the spontaneous, relational experience of being human.

African Swine Fever in Saxony—Disease Dynamics

African swine fever (ASF) emerged in Germany in 2020. A few weeks after the initial occurrence, infected wild boar were detected in Saxony. In this study, data from wild boar surveillance in Saxony were analyzed. The analysis focused on the eastern districts of the state, where the disease likely spread through infected wild boar. Additionally, data from Meissen, located approximately 65 km further west, were examined. In Meissen, the disease emerged one year later, and a human-mediated introduction was suspected. To evaluate the progression of ASF in the two study areas over time, data from active and passive surveillance were analyzed both descriptively and using a Bayesian space–time model. Prevalence estimates were calculated for wild boar testing positive for the ASF virus and for ASF-specific antibodies. Higher prevalence estimates were observed in the eastern districts, indicating a stronger viral load, consistent with patterns seen in other European regions. Over time, seroprevalence increased, suggesting an accumulation of surviving wild boar. The findings provide important insights into the epidemiology of ASF in wild boar over time. They complement the existing knowledge and support targeted ASF control measures. This is particularly significant, as ASF continues to spread across Europe rather than being successfully eradicated.

Ultrafast synthesis and binder‐free fabrication of a monolithic metal–organic framework for efficient carbon capture

AbstractAchieving rapid synthesis alongside efficient shaping without sacrificing high porosity and crystallinity poses significant challenges for metal–organic frameworks (MOFs) in practical applications. Here, we report an ultrafast, scalable method for preparing an ultramicroporous MOF at room temperature. This method achieves a space–time yield significantly higher than conventional MOF synthesis by orders of magnitude. As a result of strongly promoted crystal nucleation by careful selection of solvent and metal source, the MOF material is produced in a gel state offering both high crystallinity and processability. This allows for the binder‐free fabrication of monolithic adsorbents with predesigned macro shapes and sizes. Owing to its narrowly distributed pore size and high‐density open metal sites, the monolithic adsorbent demonstrates top‐tier selectivity for CO2/N2 (>200) and CO2/CH4 separations. The performance sets a new benchmark among current MOF xero‐ or aerogel monoliths. Breakthrough experiments further verify its robust ability for carbon capture under dynamic conditions.

Exploring citizens’ preference for on-demand valet parking service under uncertain parking process: a Bayesian estimation approach

ABSTRACT In major global cities, parking problems have intensified owing to increased private vehicle usage and decreased availability of parking spaces. To address this issue, researchers have explored effective parking management policies based on the parking behaviour of citizens. This study investigates citizens’ preferences for an emerging solution called on-demand valet parking (OVP) through a stated preference (SP) survey conducted in the Seoul Metropolitan Area, South Korea. The survey included time-related uncertainties that occur during self-parking and OVP, such as searching time for a parking space, waiting time for the valet to arrive, and time spent on handing over the vehicle to the valet. Using mixed logit models with Bayesian estimation, this study analyzes individual-level parameters to understand heterogeneity in preferences. The results indicate that citizens prioritize parking costs and time, and that uncertainty in the parking process significantly influences their preferences. Moreover, while the average elasticity suggests that certain attributes are relatively elastic, individual-level analysis revealed significant heterogeneity in responsiveness. Policymaking and service designs based on population-level parameters may overlook this heterogeneity. Cluster analysis further elucidates this heterogeneity by identifying four distinct types of decision-makers based on their sensitivity to various attributes, such as walking time and uncertainty. These insights can guide more personalized and effective service enhancements, ensuring that the specific needs of different driver groups are met. These findings underscore the importance of considering individual preferences and uncertainties in parking behaviour for effective transportation policy and service provision. Further research can explore decision-rule heterogeneity to better understand citizen behaviour in the context of parking choices.

Improving productivity of citramalate from CO2 by Synechocystis sp. PCC 6803 through design of experiment

BackgroundCyanobacteria have long been suggested as an industrial chassis for the conversion of carbon dioxide to products as part of a circular bioeconomy. The slow growth, carbon fixation rates, and limits of carbon partitioning between biomass and product in cyanobacteria must be overcome to fully realise this industrial potential. Typically, flux towards heterologous pathways is limited by the availability of core metabolites. Citramalate is produced in a single enzymatic step through the condensation of the central metabolites pyruvate and acetyl-CoA; improvements in citramalate productivity can, therefore, be used as a measure of overcoming this limitation. Furthermore, citramalate is a useful biomaterial precursor and provides a route to renewable methyl methacrylate and poly(methyl methacrylate), which is often traded as Perspex or Plexiglas.ResultsHere, we describe a phenomenon where the concerted optimisation of process parameters significantly increased citramalate production in Synechocystis sp. PCC 6803. Design of experiment principles were used to determine the optima for each parameter and the interplay between multiple parameters. This approach facilitated a ~ 23-fold increase in citramalate titre from initial unoptimised experiments. The process of scale-up from batch cultures to 0.5, 2, and 5 L photobioreactors is described. At the 2-L scale, citramalate titres from carbon dioxide reached 6.35 g/L with space–time yields of 1.59 g/L/day whilst 5-L PBRs yielded 3.96 ± 0.23 g/L with a productivity of 0.99 ± 0.06 g/L/day. We believe the decrease in productivity from 2-L to 5-L scale was likely due to the increased pathlength and shading for light delivery reducing incident light per cell. However, changes in productivity and growth characteristics are not uncommon when scaling up biotechnology processes and have numerous potential causes.ConclusionsThis work demonstrates that the use of a process parameter control regime can ameliorate precursor limitation and enhance citramalate production. Since pyruvate and/or acetyl-CoA give rise to numerous products of biotechnological interest, the workflow presented here could be employed to optimise flux towards other heterologous pathways. Understanding the factors controlling and thus increasing carbon partitioning to product will help progress cyanobacteria as part of a carbon–neutral circular bioeconomy. This is the first study using design of experiment to optimise overall carbon fixation rate and carbon partitioning to product, with the goal of improving the performance of a cyanobacterium as a host for biological carbon capture.

New space–time view and methods to evaluating satellite and reanalysis meteorological and hydrological datasets: the case study of the Upper Yellow River Basin, China

New space–time view and methods to evaluating satellite and reanalysis meteorological and hydrological datasets: the case study of the Upper Yellow River Basin, China

Spatiotemporal patterns of youth isolation and loneliness in the US: a geospatial analysis of Crisis Text Line data (2016–2022)

In 2021, the US Surgeon General issued a national advisory citing an epidemic of isolation and loneliness. Even before the onset of the COVID-19 pandemic, approximately half of people in the US reported experiencing measurable levels of loneliness. Despite localized and select cross-sectional studies highlighting even higher increases in isolation/loneliness during the COVID-19 pandemic, additional research is needed, particularly for youth and young adults. This work examines patterns of isolation/loneliness across the US from 2016 to 2022 among individuals aged 24 and younger. Our study leverages a unique dataset, Crisis Text Line, which provides complete spatiotemporal coverage of crisis conversations in the US. We conducted a geospatial analysis using Kuldroff’s Space–Time SatScan to identify statistically significant clustering of elevated isolation/loneliness-related conversations. The statistical significance of spatiotemporal clusters was determined using Monte Carlo simulations (n = 9999). Results demonstrated local relative risk as high as 1.47 in high-risk populations in Southern, Midwest, and Atlantic states, indicating areas where the actual case count is 147% of the expected cases (p value < 0.01) from May to July 2020. Results also identified co-occurrence of isolation/loneliness and other crises concerns, including depression/sadness, anxiety, and multiple suicidality indicators, with higher rates among racial/ethnic minority, transgender and gender diverse, and younger individuals. This work makes a unique contribution to the literature by elucidating spatiotemporal disparities in isolation/loneliness among young people, providing much-needed knowledge as to where future public health interventions are immediately needed.

Tuning Flame Spray Pyrolysis for Variation of the Crystallite Size in Cu/ZnO/ZrO2 and its Influence on the Performance in CO2‐to‐Methanol Synthesis

AbstractCO2 hydrogenation to methanol (MeOH) is a key transformation in the Power‐to‐liquid concept, which aims to store energy in chemical energy carriers and chemicals. Cu/ZnO/ZrO2 (CZZ) shows great promise due to its enhanced stability in the presence of water, a critical by‐product when utilizing CO2‐based feedstocks. The structure‐sensitivity of this reaction, especially for particle sizes below 10 nm and in three‐component systems, remains highly debated. Herein, we systematically prepared a series of CZZ catalysts by flame spray pyrolysis (FSP) to vary the crystallite size and to study its effect on methanol synthesis in this three‐component system. FSP enabled us to maintain a fixed Cu/Zn/Zr ratio close to the commercial composition (61/29/10 atomic ratio), while varying the precursor feed rate. This resulted in variation in the crystallinity. The characterization by X‐ray diffraction and electron microscopy revealed an increase in crystallite size with rising feed rate for Cu and t‐ZrO2, whereas ZnO remained mostly unaffected. The testing of the materials in methanol synthesis uncovered an increase in performance, higher space time yield and MeOH selectivity, with decreasing crystallite size for two (Cu, t‐ZrO2) of its three components. The increased selectivity with smaller sizes might be attributed to an increase in interfacial sites.

Exploring the Big Bang with Femtoscopy

Exploring the fundamental constituents of the matter around us and in the Universe, as well as their interactions, is among the premier goals of physics. Investigating ultrarelativistic collisions in particle accelerators has delivered answers to these questions many times in the past decades. In this article we focus on the research aimed at recreating the matter that filled the Universe in the first microsecond after the Big Bang – but this time in collisions of heavy ions. In particular, we discuss the technique called femtoscopy, which provides us with a tool to understand the space–time structure of particle creation in heavy-ion collisions. We use Lévy-stable distributions to investigate this structure and explore its dependence on particle momentum and collision energy.

A dehydration membrane reactor towards highly efficient LPG synthesis via CO2 hydrogenation

In recent years, CO2 hydrogenation has garnered increasing attention as a promising avenue towards the conversion of captured CO2 to valuable liquid chemicals, including CH3OH, CH3OCH3, and liquified petroleum gas (LPG). However, chemical conversion of CO2 has proven to be immensely challenging, resulting in low CO2 conversion (<35 %) and product yields (<15 %). H2O, unavoidably evolved during CO2 hydrogenation, imposes both thermodynamic and kinetic penalties on CO2 hydrogenation reactions via adsorption on catalyst active sites and promotion of undesirable reaction pathways. In this work, to greatly facilitate direct conversion of CO2 to LPG over a bifunctional Cu/Zn/Zr on Al2O3 (CZZA)/Pd-β-zeolite catalyst, a dehydration membrane reactor (MR) featuring a H2O-conducting Na+-gated nanochannel membrane was studied. Before the incorporation of the Na+-gated membrane, the CZZA/Pd-β-zeolite was unable to produce LPG at 280–310 °C and 14 bar, and the CO2 conversion was limited to < 35 %. In stark contrast, the MR exhibited CO2 conversion and LPG yield of 72.1 % and 51.7 %, respectively. The operating conditions of the MR were systematically investigated to determine the maximum achievable performance resulting in CO2 conversion and LPG yield as high as 90.2 % and 60.5 %, respectively. Furthermore, the LPG space–time yield (STY) at the optimized conditions was near that of the best-performing traditional reactors (TRs) in the literature but at 3 times and 8 times increased CO2 conversion and time factor (W/F), respectively. The MR in this work demonstrates the synergistic efficacy of combined H2O removal and CH3OH consumption towards highly efficient hydrogenation of CO2 for LPG production.

Mimetic Weyl geometric gravity

We consider a mimetic type extension of the Weyl geometric gravity theory, by assuming that the metric of the space–time manifold can be parameterized in terms of a scalar field, called the mimetic field. The action of the model is obtained by starting from a conformally invariant gravitational action, constructed, in Weyl geometry, from the square of the Weyl scalar, the strength of the Weyl vector, and an effective matter term, respectively. After linearizing the action in the Weyl scalar by introducing an auxiliary scalar field, we include the mimetic field, constructed from the same auxiliary scalar field used to linearize the action, via a Lagrange multiplier. The conformal invariance of the action is maintained by imposing the trace condition on the effective matter energy–momentum tensor, built up from the ordinary matter Lagrangian, and some specific functions of the Weyl vector, and the scalar field, respectively, thus making the matter sector of the action conformally invariant. The field equations are derived by varying the action with respect to the metric tensor, the Weyl vector field, and the scalar field, respectively. We investigate the cosmological implications of the Mimetic Weyl geometric gravity by considering the dynamics of an isotropic and homogeneous Friedmann–Lemaitre–Robertson–Walker FLRW Universe. The generalized Friedmann equations of the model are derived, and their solutions are obtained numerically for a dust filled Universe. Moreover, we perform a detailed comparison of the predictions of the considered model with a set of observational data for the Hubble function, and with the results of the ΛCDM standard paradigm. Our results indicate that the present model give a good description of the observational data, and they reproduce almost exactly the predictions of the ΛCDM scenario. Hence, Mimetic Weyl geometric gravity can be considered a viable alternative to the standard approaches to cosmology, and to the gravitational phenomena.

Characterization of Subdaily Rainfall Events over Central Africa: Duration, Intensity, Amount, and Spatial Scale of the Storm Types

Abstract Using half-hourly rainfall data from 14 automated weather stations over central Africa, rainfall events [≥0.1 mm (30 min)−1] characteristics are explored. A total of 10 096 wet events (WEs) were identified and classified into six storm types (STs), mostly discretized by their duration and intensity. ST 1 is very short (&lt;1.5 h) with low rainfall intensities over a small area and contributes the least to the total rainfall (7%) but is by far the most frequent (70% of the WEs). ST 2 is short (∼1.5 h) and sudden with very intense rains and of medium spatial scale (&lt;200 km). ST 3 is short and of medium scale too but with low intensities and rainfall amounts. ST 4 and ST 5 are of large scale and long (∼3–4 h), with high rainfall amounts so that they contribute the most to the total rainfall (29% and 20%). Last, ST 6 is the largest, longest, and rainiest, although moderately intense. A complementary classification is performed on lagged gridded rainfall fields from IMERG to document the possible space–time evolution of the rainfall field during the life cycle of WEs. Four spatial types are identified. Spatial type 1 gathers the most frequent, less intense ones. Spatial type 2 is far less frequent but pictures westward-moving rainfall patterns, probably associated with mesoscale convective systems. Two spatial types (3 and 4) are related to high-intensity near-stationary rainfall events, respectively, located to the southwest and northwest of the stations. ST 4 is mostly present at stations close to the Atlantic Ocean.

Abrupt X-to-O-wave structural field transition in presence of anomalous dispersion

All linear, propagation-invariant, paraxial pulsed beams are spatiotemporally X-shaped (conical waves) in the absence of group-velocity dispersion (GVD) or in the presence of normal GVD. It is known, however, that such conical waves become O-shaped in the presence of anomalous GVD, resulting in a field profile that is circularly symmetric in space and time. To date, experiments generating conical waves in which the wavelength of a high-energy pump laser is tuned across the zero-dispersion wavelength of a nonlinear medium have not revealed the expected X-to-O-wave structural field transition. We report here an unambiguous observation of a fixed-central-wavelength X-to-O-wave structural field transition occurring in linear dispersion-free wave packets in the anomalous GVD regime, without needing to change the sign or magnitude of the GVD. Instead, by tuning the group velocity of a space–time wave packet (STWP) across a threshold value that we call the “escape velocity,” we observe an abrupt transition in the STWP from an O-shaped to an X-shaped spatiotemporal profile. This transition is associated with an abrupt change in the associated spatiotemporal spectrum of the STWP: from closed elliptical spatiotemporal spectra below the escape velocity to open hyperbolic spectra above it. These results may furnish new opportunities for engineering the phase-matching conditions in nonlinear and quantum optics.

Selective CO2 hydrogenation to methanol over a novel ternary In-Co-Zr catalyst

To address the challenges of global warming and energy shortages, the technology to convert CO2 into methanol is of great significance Indium-cobalt catalysts have attracted widespread attention due to their significant activity and excellent stability. Herein, In-Co and In-Co-Zr catalyst rich in In2O3 were prepared through a citric acid-assisted co-precipitation method. The results show that the In-Co-Zr catalyst can significantly enhance the activity for hydrogenating CO2 to methanol, achieving a CO2 conversion rate of 17.4 % and a methanol selectivity of 77.7 % under the conditions (5.0 MPa, 310 °C, GHSV = 10.28 l·gcat-1·h-1, H2/CO2 ratio of 4). At a GHSV of 48 l·gcat-1·h-1, the space–time yield of methanol was 0.79 gMeOH·gcat-1·h-1, and the catalyst exhibited stable catalytic activity over a continuous reaction period of 120 h. It was revealed that the incorporation of ZrO2 could significantly improve methanol selectivity of In-Co catalyst. Moreover, an increased oxygen vacancy and strong interaction of the In-Co-Zr catalysts are conducive to improving the CO2 adsorption strength and capacity. It is expected that this novel ternary In-Co-Zr catalyst could inspire new opportunities for designing and developing multi-component hybrid catalysts for highly active, stable and selective CO2 hydrogenation to methanol

Numerical solutions for space–time conformable nonlinear partial differential equations via a scientific machine learning technique

Numerical solutions for space–time conformable nonlinear partial differential equations via a scientific machine learning technique

Influence of alumina on the performance of Ag/ZnO based catalysts for carbon dioxide hydrogenation

We study silver-zinc oxide type catalysts with and without the addition of alumina and perform structural analysis and activity tests for the hydrogenation of carbon dioxide. Adding alumina has a dispersing effect on the zinc oxide without structurally altering the silver phase. An alumina-surface enriched ZnO/Al2O3 phase is observed with an increased surface reducibility. Ag/ZnO has a high selectivity towards carbon monoxide (63 ± 12 %) and methane (24 ± 3 %) and low selectivity towards methanol (13 ± 0.5 %). Operando infrared (SSITKA-FTIR) and mass spectrometric product detection indicate methane formation via an adsorbed carbon monoxide (COads) intermediate. The selectivity changes gradually with increasing alumina content, up to 80 ± 3 % toward methanol, and 20 ± 4 % carbon monoxide without methane detection, combined with a tripling of the space time yield to 0.65 ± 0.02mmolMeOH*gcat-1*h−1 at 250 °C and 30 bar. Kinetic analysis suggests that the selectivity change originates from hindering the CO-pathway, while the formate pathway leading to methanol remains active.

Dynamic Seat Management Algorithm for Optimizing Sleeper Train Capacity

Abstract: In this paper, we present a Dynamic Day-Night Seat Allocation Algorithm designed to optimize the utilization of sleeper train berths during long-duration journeys. The algorithm dynamically reallocates berths based on real-time conditions, optimizing seat usage for daytime sitting and nighttime sleeping. The objective is to enhance passenger comfort, prioritize elderly and family passengers for lower berths, and maximize train capacity through intelligent seat management. Our approach incorporates greedy algorithms to efficiently assign passengers to the most appropriate berth based on time of day and available space.

CESE Schemes for Solar Wind Plasma MHD Dynamics

Magnetohydrodynamic (MHD) numerical simulation has emerged as a pivotal tool in space physics research, witnessing significant advancements. This methodology offers invaluable insights into diverse space physical phenomena based on solving the fundamental MHD equations. Various numerical methods are utilized to approximate the MHD equations. Among these, the space–time conservation element and solution element (CESE) method stands out as an effective computational approach. Unlike traditional numerical schemes, the CESE method significantly enhances accuracy, even at the same base point. The concurrent discretization of space and time for conserved variables inherently achieves higher-order accuracy in both dimensions, without the need for intricate higher-order time discretization processes, which are often challenging in other methods. Additionally, this scheme can be readily extended to multidimensional cases, without relying on operator splitting or direction alternation. This paper primarily delves into the remarkable progress of CESE MHD models and their applications in studying solar wind, solar eruption activities, and the Earth’s magnetosphere. We aim to illuminate potential avenues for future solar–interplanetary CESE MHD models and their applications. Furthermore, we hope that the discussions presented in this review will spark new research endeavors in this dynamic field.