Temporal View Research Articles

Towards a transformative model of circular economy for SMEs

This study develops a transformative conceptual model for small and medium-sized enterprises (SMEs) from emerging markets, using a multilevel perspective (MLP) framework. The transformative model highlights four phases through which circular economy (CE) practices present their transformative characteristics. Our model maps the transition and interactions of the four phases, three systems at three levels of analysis: macro-, meso- and micro-level. The distinctive contribution of our model is that it presents a temporal view of how firms evolve towards a CE and actors’ vital role and agentic resources in implementing transformative change. Following the four stages of transition: emerging, filtering, stabilizing, and transforming, our transformative model of SMEs focuses on the adoption of the CE approach by analyzing macro-, meso- and micro-level influences to achieve sustainable development and offer a solid foundation for future studies. We develop several testable propositions with implications for practice for SMEs and for entrepreneurship theory in delivering sustainable innovation.

On the circulation, water mass distribution, and nutrient concentrations of the western Chukchi Sea

Abstract. Substantial amounts of nutrients and carbon enter the Arctic Ocean from the Pacific Ocean through the Bering Strait, distributed over three main pathways. Water with low salinities and nutrient concentrations takes an eastern route along the Alaskan coast, as Alaskan Coastal Water. A central pathway exhibits intermediate salinity and nutrient concentrations, while the most nutrient-rich water enters the Bering Strait on its western side. Towards the Arctic Ocean, the flow of these water masses is subject to strong topographic steering within the Chukchi Sea with volume transport modulated by the wind field. In this contribution, we use data from several sections crossing Herald Canyon collected in 2008 and 2014 together with numerical modelling to investigate the circulation and transport in the western part of the Chukchi Sea. We find that a substantial fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. This water then contributes to the high-nutrient waters of Herald Canyon. The bottom of the canyon has the highest nutrient concentrations, likely as a result of addition from the degradation of organic matter at the sediment surface in the East Siberian Sea. The flux of nutrients (nitrate, phosphate, and silicate) and dissolved inorganic carbon in Bering Summer Water and Winter Water is computed by combining hydrographic and nutrient observations with geostrophic transport referenced to lowered acoustic Doppler current profiler (LADCP) and surface drift data. Even if there are some general similarities between the years, there are differences in both the temperature–salinity and nutrient characteristics. To assess these differences, and also to get a wider temporal and spatial view, numerical modelling results are applied. According to model results, high-frequency variability dominates the flow in Herald Canyon. This leads us to conclude that this region needs to be monitored over a longer time frame to deduce the temporal variability and potential trends.

Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.

Understanding the intrinsic patterns of human brain is important to make inferences about the mind and brain-behavior association. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity without the effect of vascular confounds. The blood oxygenated level-dependent (BOLD) signal of functional MRI (fMRI) reveals the spatial and temporal brain activity across different brain regions. However, it is unclear how to associate the high temporal resolution Electrophysiological measures with high spatial resolution fMRI signals. Here, we present a novel interpretable model for coupling the structure and function activity of brain based on heterogeneous contrastive graph representation. The proposed method is able to link manifest variables of the brain (i.e. MEG, MRI, fMRI and behavior performance) and quantify the intrinsic coupling strength of different modal signals. The proposed method learns the heterogeneous node and graph representations by contrasting the structural and temporal views through the mind to multimodal brain data. The first experiment with 1200 subjects from Human connectome Project (HCP) shows that the proposed method outperforms the existing approaches in predicting individual gender and enabling the location of the importance of brain regions with sex difference. The second experiment associates the structure and temporal views between the low-level sensory regions and high-level cognitive ones. The experimental results demonstrate that the dependence of structural and temporal views varied spatially through different modal variants. The proposed method enables the heterogeneous biomarkers explanation for different brain measurements.

A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting

Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real world. Any instance of MTS is generated from a hybrid dynamical system and their specific dynamics are usually unknown. The hybrid nature of such a dynamical system is a result of complex external attributes, such as geographic location and time of day, each of which can be categorized into either spatial attributes or temporal attributes. Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view. Moreover, from each of these two views, we can partition the set of data samples of MTS into disjoint forecasting tasks in accordance with their associated attribute values. Then, samples of the same task will manifest similar forthcoming pattern, which is less sophisticated to be predicted in comparison with the original single-view setting. Considering this insight, we propose a novel multi-view multi-task (MVMT) learning framework for MTS forecasting. Instead of being explicitly presented in most scenarios, MVMT information is deeply concealed in the MTS data, which severely hinders the model from capturing it naturally. To this end, we develop two kinds of basic operations, namely task-wise affine transformation and task-wise normalization, respectively. Applying these two operations with prior knowledge on the spatial and temporal view allows the model to adaptively extract MVMT information while predicting. Extensive experiments on three datasets are conducted to illustrate that canonical architectures can be greatly enhanced by the MVMT learning framework in terms of both effectiveness and efficiency. In addition, we design rich case studies to reveal the properties of representations produced at different phases in the entire prediction procedure.

IEEE VAST Challenge 2021 Winner: Visual Analytics for Spatial-Temporal Situation Awareness.

IEEE VAST Challenge 2021 provides fruitful data to test the visual analytics capability of participants. We summarize our work in this article. Trajectory data and consumption data contain a lot of information, such as consumption patterns, behavior characteristics, and so on. The information can provide favorable clues for law enforcement departments to crack a case about missing employees. We designed a visual analytics system called Sundial for spatio-temporal situation awareness with multidata fusion. It contains three views, that is, consumption view, temporal behavior view, and spatial-temporal map. With the system, analysts can effectively identify the consumption and behavior patterns of employees, and detect the suspicious activities and informal or formal relationships. Through case analysis, we illustrated how to use the system and obtain effective information.

A three-wave longitudinal study on the relation between commuting strain and somatic symptoms in university students: exploring the role of learning-family conflicts

BackgroundWe examine the role of learning-family conflicts for the relation between commuting strain and health in a sample of medical university students. The first goal of the study was to investigate the mediating role of learning-family conflicts. The second goal was to extend the temporal view on relations between study variables. Therefore, we differentiated long-term systematic change among variables over a period of two-years from a dynamic perspective with repeated commuting events on the individual level of analyses.MethodsWe applied a multilevel research design and collected survey data from 128 medical students on three points in time (N = 339 measurement points). Participants informed about commuting strain, learning-family conflicts, somatic symptoms, as well as commuting distance and time.ResultsBayesian multilevel analyses showed that results differed with regard to level of analysis: while learning-family conflicts mediated the relation between commuting strain and somatic symptoms on a systematic aggregation-level perspective of analysis (indirect effect estimatebetween = 0.13, SE = .05, 95% CI [0.05; ∞), Evidence Ratio = 250.57), this was not the case on the dynamic event perspective (indirect effect estimatewithin = 0.00, SE = 0.00, 95% CI [− 0.01; ∞), Evidence Ratio = 0.84).ConclusionsWe demonstrated that learning-family conflicts explain why commuting may have unfavorable effects on health for medical students. We also showed that it is the long-term commuting experience that is related to health complaints and not the single commuting event. This means that short-term deviations from general levels of commuting strain do not cause somatic symptoms, but general high levels of commuting strain do instead.

Postmortem brain donations vs premortem surgical resections for glioblastoma research: viewing the matter as a whole.

There have been limited improvements in diagnosis, treatment, and outcomes of primary brain cancers, including glioblastoma, over the past 10 years. This is largely attributable to persistent deficits in understanding brain tumor biology and pathogenesis due to a lack of high-quality biological research specimens. Traditional, premortem, surgical biopsy samples do not allow full characterization of the spatial and temporal heterogeneity of glioblastoma, nor capture end-stage disease to allow full evaluation of the evolutionary and mutational processes that lead to treatment resistance and recurrence. Furthermore, the necessity of ensuring sufficient viable tissue is available for histopathological diagnosis, while minimizing surgically induced functional deficit, leaves minimal tissue for research purposes and results in formalin fixation of most surgical specimens. Postmortem brain donation programs are rapidly gaining support due to their unique ability to address the limitations associated with surgical tissue sampling. Collecting, processing, and preserving tissue samples intended solely for research provides both a spatial and temporal view of tumor heterogeneity as well as the opportunity to fully characterize end-stage disease from histological and molecular standpoints. This review explores the limitations of traditional sample collection and the opportunities afforded by postmortem brain donations for future neurobiological cancer research.

The phenotype of recovery VII: Delay discounting mediates the relationship between time in recovery and recovery progress

IntroductionSubstance use disorders (SUDs) remain challenging maladies to treat in the United States and impose significant societal costs. Despite these challenges, a significant number of individuals endorse being in recovery from SUD. The scientific understanding of SUD recovery has evolved to include not only improvements in substance use but also improvements in personal wellness and psychosocial functioning. The devaluation of future rewards (delay discounting; DD) is broadly associated with SUD inception and outcomes. We sought to investigate the relationship between DD, time in recovery, and recovery progress. MethodsWe conducted an online assessment of 127 individuals in recovery from SUD who the study recruited via the International Quit and Recovery Registry (IQRR). The research team obtained measures of recovery progress via the Addiction Recovery Questionnaire (ARQ) and the Treatment Effectiveness Assessment (TEA). Additionally, the study collected measures of DD, time in recovery, and endorsement of abstinence in recovery (i.e., requiring abstinence vs. not). We utilized linear regression to test for associations among these variables and performed a mediation analysis to test the role of DD in mediating the relationship between time in recovery and measures of recovery progress. ResultsTime in recovery was positively associated with the ARQ (p < .001) and TEA (p < .001). Furthermore, an individual's delay discounting rate mediated the relationship between time in recovery and ARQ/TEA. Of the participants, 66% endorsed recovery requiring total abstinence from alcohol and drugs. Last, through an exhaustive model selection, the study did not find an individual's endorsement of abstinence in recovery to be a primary predictor of recovery progress. ConclusionsThis study presents evidence that, for individuals in recovery, the temporal view (i.e., focus on immediate vs. future rewards) is a significant influence on recovery progress. Additionally, an individual's endorsement of abstinence in recovery was not significantly associated with recovery progress, suggesting the importance of a holistic view of SUD recovery. These findings contribute to the understanding of recovery as a multidimensional process and provide further support for DD as a behavioral marker of addiction.

Identifying Spatial and Temporal Characteristics of Land Surface Albedo Using GF-1 WFV Data

Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by “double peaks and a single valley”, with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou.

TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION

Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

How Actors Effect Shifts in Strategic Trajectories: The Role of Temporal Nexus Events

We apply a temporal view to an event at the intersection of two activity streams in a naval design and construction company. This view shows how, at this event, actors revived a solution that had been proposed several years before within one stream by using it to address an imminent problem in a second stream, which eventually produced a shift in the company’s strategic trajectory. The temporal orientation of the event’s activity contrasted with the temporal orientations of the two intersecting activity streams. The temporal orientations of the two intersecting streams proved nevertheless vital for the event’s generation of the strategy shift. We suggest the label “temporal nexus events” for these intersectional events. We propose a process model for how actors reach back in time along activity streams while drawing upon those streams to implement novel solutions that harbor the potential for a strategic shift.

2-D regional short-term wind speed forecast based on CNN-LSTM deep learning model

Short-term wind speed forecast is of great importance to wind farm regulation and its early warning. Previous studies mainly focused on the prediction at a single location but few extended the task to 2-D wind plane. In this study, a novel deep learning model was proposed for a 2-D regional wind speed forecast, using the combination of the auto-encoder of convolutional neural network (CNN) and the long short-term memory unit (LSTM). The 12-hidden-layer deep CNN was adopted to encode the high dimensional 2-D input into the embedding vector and inversely, to decode such latent representation after it was predicted by the LSTM module based on historical data. The model performance was compared with parallel models under different criteria, including MAE, RMSE and R2, all showing stable and considerable enhancements. For instance, the overall MAE value dropped to 0.35 m/s for the current model, which is 32.7%, 28.8% and 18.9% away from the prediction results using the persistence, basic ANN and LSTM model. Moreover, comprehensive discussions were provided from both temporal and spatial views of analysis, revealing that the current model can not only offer an accurate wind speed forecast along timeline (R2 equals to 0.981), but also give a distinct estimation of the spatial wind speed distribution in 2-D wind farm.

Reinforcer pathology in cocaine use disorder: Temporal window determines cocaine valuation

AimsThe Experimental Medicine Approach offers a unique perspective to determine clinical behavior change by engaging a target underlying the cause of a disorder. The present work engaged a novel target of addiction, Reinforcer Pathology, in two studies to test changes in behavior among individuals with cocaine use disorder. MethodsIn Study 1, n = 44 participants engaged the temporal window with episodic future thinking (EFT), a positive prospection exercise. Changes in temporal view and cocaine valuation were tested using delay discounting and behavioral economic demand, respectively. Additionally, a computational model assessed the relative reliance on the near- and far-sighted systems during EFT. In Study 2, n = 71 engaged the temporal window with a negatively-valenced hurricane scenario to test the opposite effects on window length and cocaine valuation. ResultsResults demonstrated systematic and symmetrical engagement of the behavioral target. Study 1 robustly replicated previous work, wherein EFT lengthened the temporal window and decreased cocaine valuation. Moreover, EFT increased the weighting of the modeled far-sighted system, increasing the relative impact of long-term discounting decisions. Study 2 produced opposite outcomes, shortened temporal window and increased cocaine valuation. ConclusionsThis approximately equal and opposite reaction to the manipulations supports reinforcer pathology theory and implicates the temporal window over which rewards are valued as a target to be pushed and pulled to produce clinically meaningful behavior change. Using the Experimental Medicine Approach as a guide, future work should identify new potential interventions to engage reinforcer pathology and use the clinically relevant outcomes as a litmus test for mechanism.

Temporalities of Israel/Palestine: Culture and Politics

This article charts a relational history of Palestinian and Israeli temporalities. Probing the interplay of political and cultural discourses, I show how while literature and film are indices of the temporal views that inform political action, they also work to expand those views. What are the key temporal concepts of Zionism and Palestinian thought, and how have they been negotiated in literary and cinematic works from the 1940s to the present? How have major political developments influenced temporal attitudes and cultural discourse? What emergent temporalities are at play in the current political climate? To address these questions, I bring insights from the social sciences into dialogue with close readings of Hebrew and Arabic works. After delineating what I call the syntagmatic model of Israeli and Palestinian temporalities, I discuss the fluid and nonlinear temporalities that are increasingly salient in Palestinian cultural production.

Current methodologies and algorithms used to identify and quantify pollutants in sub-basins: A review

Over the past few years, water quality has been threatened and is vulnerable to various pollutants and climate variables. The deteriorating state of water resources/bodies has been further exacerbated by the impacts of climate change patterns in Southern Africa. Therefore, modelling and predicting the quality of water in sub-basins has become important in controlling water pollution. Remote sensing techniques gained popularity over the past few years as these techniques have been used to monitor water quality parameters such as suspended sediments, chlorophyll, temperature and other parameters in surface water bodies. Furthermore, optical and thermal sensors on aircrafts and satellites provide both spatial and temporal information needed to monitor changes in water quality parameters, for the development of management practices which seek to improve the quality of water, at sub-basin level. Thus, the integration of remotely sensed data, geographical information system (GIS), machine learning technologies and in-situ measurements provide valuable tools to monitor the impacts of climate change on water quality. According to literature cited in this paper, measurements and collection of water samples for subsequent laboratory analyses are currently used to evaluate water quality, not only in the South African context but in other developing countries as well. While such measurements are accurate for a point in time and space, they do not give either the spatial or temporal view of water quality needed for accurate assessments and management of water bodies. Hence, the need for and purpose of this study, to explore and review current methodologies and algorithms used to identify microbial and other pollutants that have increased above standard thresholds in sub-basins. Key words: Water quality, remote sensing, modeling, algorithms, pollutants.

Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic.

The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is ≈ ±2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease’s evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

Trick of the Light Through the Cornea

A 70-year-old man with aqueous-deficient dry eye presented for regular examination. Diffuse illumination slit-lamp imaging revealed an abnormal dark lesion in the nasal-inferior iris of his left eye (Fig A). The temporal view of the nasal-inferior cornea revealed a delle (a localized saucer-shaped corneal thinning due to dehydration). Light passing through the concave lens caused by the delle was diffused, resulting in a dark lesion on the iris underneath (Fig B; right). The nasal view to avoid the optical artifact by the delle demonstrated no dark lesion (Fig B; left), which was confirmed by cross-sectional anterior-segment OCT imaging (Fig C).

Child Occupant Safety: Towards Implementation of Child Restraint System Law in Malaysia

&#x0D; &#x0D; &#x0D; The World Health Organization (WHO) has consistently recommended that all countries regardless of their income add the requirement for Child Restraint System (CRS) to their traffic regulations in order to promote road safety. Malaysia is adopting the CRS regulation in 2020. This paper aims to discuss the readiness and mapping of the roadmap towards the implementation of the law. In a temporal view of road crashes, CRS was part of 'during-crash' passive safety components i.e. to provide protection during the crash impact phase. This analysis includes the CRS benefits and issues, related road safety strategic programmes, the current legal framework, and local research findings based on observational studies. CRS usage and acceptance are on the rise, although still at a nascent stage. This situation can be considered as 'below satisfactory compliance level', hence a more inclusive solution is needed, including the introduction of technology, to influence or compel parents to ensure their children are always secured by CRS while on the road.&#x0D; &#x0D; &#x0D;

Genome-Wide Identification, Comparison, and Expression Analysis of Transcription Factors in Ascidian Styela clava.

Tunicates include diverse species, as they are model animals for evolutionary developmental biology study. The embryonic development of tunicates is known to be extensively regulated by transcription factors (TFs). Styela clava, the globally distributed invasive tunicate, exhibits a strong capacity for environmental adaptation. However, the TFs were not systematically identified and analyzed. In this study, we reported 553 TFs categorized into 60 families from S. clava, based on the whole genome data. Comparison of TFs analysis among the tunicate species revealed that the gene number in the zinc finger superfamily displayed the most significant discrepancy, indicating this family was under the highly evolutionary selection and might be related to species differentiation and environmental adaptation. The greatest number of TFs was discovered in the Cys2His2-type zinc finger protein (zf-C2H2) family in S. clava. From the point of temporal view, more than half the TFs were expressed at the early embryonic stage. The expression correlation analysis revealed the existence of a transition for TFs expression from early embryogenesis to the later larval development in S. clava. Eight Hox genes were identified to be located on one chromosome, exhibiting different arrangement and expression patterns, compared to Ciona robusta (C. intestinalis type A). In addition, a total of 23 forkhead box (fox) genes were identified in S. clava, and their expression profiles referred to their potential roles in neurodevelopment and sensory organ development. Our data, thus, provides crucial clues to the potential functions of TFs in development and environmental adaptation in the leathery sea squirt.

Analyzing the Research Evolution in Response to COVID-19

In order to understand how these studies are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with the extracted information, the research evolution was analyzed from the temporal, spatial, and topic perspectives. From a temporal view, in the three months after the emergence of COVID-19, the number of published papers showed an obvious growth trend, and it showed a relatively stable cyclical trend in the later period, which is basically consistent with the development of COVID-19. Spatially, most of the authors who participated in related research are concentrated in the United States, China, Italy, the United Kingdom, Spain, India, and France. At the same time, with the continuous spread of COVID-19 in the world, the distribution of the number of authors has gradually expanded, showing to be correlated with the severity of COVID-19 at a spatial scale. From the perspective of topic, the early stage of COVID-19 emergence, the related research mainly focused on the origin and gene identification of the virus. After the emergence of the pandemic, studies related to the diagnosis and analysis of psychological health, personal security, and violent conflict are added. Meanwhile, some categories are most closely related to the control and prevention of the epidemic, such as pathology analysis, diagnosis, and treatment; epidemic situation and coping strategies; and prediction and assessment of epidemic situation. In most time periods, the majority of studies focused on these three categories.