Temporal Approach Research Articles

Synthesis of recovery patterns in microbial communities across environments

BackgroundDisturbances alter the diversity and composition of microbial communities. Yet a generalized empirical assessment of microbiome responses to disturbance across different environments is needed to understand the factors driving microbiome recovery, and the role of the environment in driving these patterns.ResultsTo this end, we combined null models with Bayesian generalized linear models to examine 86 time series of disturbed mammalian, aquatic, and soil microbiomes up to 50 days following disturbance. Overall, disturbances had the strongest effect on mammalian microbiomes, which lost taxa and later recovered their richness, but not their composition. In contrast, following disturbance, aquatic microbiomes tended away from their pre-disturbance composition over time. Surprisingly, across all environments, we found no evidence of increased compositional dispersion (i.e., variance) following disturbance, in contrast to the expectations of the Anna Karenina Principle.ConclusionsThis is the first study to systematically compare secondary successional dynamics across disturbed microbiomes, using a consistent temporal scale and modeling approach. Our findings show that the recovery of microbiomes is environment-specific, and helps to reconcile existing, environment-specific research into a unified perspective.FPVwQXB65-N4idcpgwJZ6_Video

Assessment of postoperative pain, dysesthesia, and weather sensitivity after pterional and temporal neurosurgical approaches.

Many neurosurgical approaches require incision of the temporal muscle (TM). Consequently, patients often report reduced opening of the mouth, facial asymmetry, numbness, and pain after lateral craniotomies. A systematic assessment of these postoperative subjective complaints is lacking in the literature. Therefore, in this study, the authors evaluate subjective complaints after pterional, frontolateral-extended pterional, or temporal craniotomy using a 6-item questionnaire. They examine the association of these subjective complaints with the extent of the mobilization of the TM. The questionnaire assessed complaints about limited opening of the mouth, pain in the mastication muscles, facial asymmetry, sensory deficits in the temporal region, weather sensitivity, and headache. Eligible patients with benign intracranial processes operated on using lateral cranial approaches between 2016 and 2019 were included. The questionnaire was answered before surgery (baseline) and 3 and 15 months after surgery. Surgeons documented the extent of TM incision. Among the 55 patients in this study, all complaints apart from headache showed an increase at a statistically significant rate at 3 months postoperatively, that is, limited mouth opening (p < 0.0001), pain in the mastication muscles (p < 0.0001), an impression of asymmetry in the mastication muscles (p = 0.0002), sensory disturbances in the temporal region (p < 0.0001), and weather sensitivity (p < 0.001). Only pain in the mastication muscles showed a relevant decrease at 15 months postsurgery (p = 0.058). The extent of the mobilized TM was associated with pain in the mastication muscles at 3 months (p = 0.0193). Subjective complaints in patients following lateral craniotomy can be detected. As the extent of the mobilized TM relevantly influenced pain in the mastication muscles, the authors conclude that one should sparsely mobilize the TM. Furthermore, a neurosurgeon should be aware and warn the patient of subjective postoperative complaints and inform the patient about their natural course.

PERFORMANCE EVALUATION OF TEMPORAL MODEL AND SPATIAL CORRELATION METHODS FOR PERSISTENT SCATTERER DETERMINATION IN LANDSLIDE PRONE AREAS

Abstract. The Philippines is no stranger to natural hazards. Aside from flooding, landslides emerge as a primary contributor to damage dealt by natural hazards. Traditional methods for landslide monitoring require on-site field measurements, which makes them resource-intensive and time-consuming. Moreover, the frequency of updating of existing landslide hazard maps is limited by resources and manpower. This research introduces the application of remote sensing technology, specifically Persistent Scatter Interferometry (PSI), as a complementary tool for updating hazard maps. PSI enables the identification of stable ground points, eliminating the need for labor-intensive fieldwork and facilitating assessments of potential slope instability. Two distinct PSI processing methods are evaluated in the study; the Temporal Model approach and the Spatial Correlation model approach. Findings reveal that the Temporal Model approach successfully detected 11,647 Persistent Scatterers (PS) points, while the Spatial Correlation approach identified 272,614 PS points. Furthermore, the Spatial Correlation approach demonstrated its capability to detect PS points within high landslide susceptibility areas. Consequently, the results highlight the suitability of the Spatial Correlation approach for detecting PS points, particularly in landslide-prone regions, offering support to local landslide hazard monitoring systems. This research supports the initiative to a more efficient and cost-effective approach to maintaining up-to-date landslide susceptibility maps in the Philippines in enhancing disaster preparedness and mitigation efforts.

Return to work and psychosocial trajectories after breast cancer: a longitudinal and sequential approach.

We aimed to describe the psychosocial adjustments according to return to work (RTW) trajectories in breast cancer survivors (BCS) using a sequential and temporal approach. We used BCS data included from February 2015 to April 2016 in the Longitudinal Study on Behavioural, Economic and Sociological Changes after Cancer (ELCCA) cohort. RTW trajectories were identified using the sequence analysis method followed by a clustering. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and the EORTC quality of life questionnaire was used at inclusion and all follow-up visits to assess Health-Related Quality of Life (HRQoL). Fifty-two BCS were included in the study among whom four clusters of RTW trajectories were identified and labeled: slow RTW (N = 10), quick RTW (N = 27), partial RTW (N = 8), and part-time work (N = 7). Quick and slow RTW clusters showed slightly lower baseline mean levels of anxiety and higher levels of HRQoL. In the 4years following diagnosis, BCS in the quick RTW cluster tended to report higher HRQoL in terms of functioning and less symptoms of pain and fatigue while those in the partial RTW cluster showed a lower HRQoL on almost all dimensions. All clusters showed an increase in pain and fatigue symptoms until 6months followed by a tendency to recover baseline levels. The results of this study suggest that BCS who return to full-time work (slow and quick RTW patterns) recover better than patients who return to part-time work (partial and part-time RTW patterns).

Supplier diversity journey: an empirical investigation

Purpose This paper aims to investigate how purchasing organizations implement supplier diversity (SD) initiatives over time. Design/methodology/approach A multiple case study approach was conducted. Data were collected through in-depth interviews with participants from purchasing organizations, intermediary organizations and diverse suppliers. Findings The research suggests that the SD journey encompasses three different, but interrelated stages before full implementation is achieved: structuring, operation and adaptation. The findings also provide evidence that SD implementation in Brazil is highly influenced by the lack of a consistent knowledge base and the lack of legitimized intermediary organizations. Research limitations/implications Using a temporal approach to understand how different practices suggested by the literature have been managed by practitioners over time, this study contributes to the understanding of the path to effective SD implementation and how intra- and interorganizational context influences this journey. Practical implications By identifying which practices should be adopted during different phases of SD implementation and proposing ways to overcome some of the inherent challenges, managers can better plan and allocate resources for the adoption of a successful SD initiative. Social implications This research demonstrates how organizations can promote diversity and reduce social and economic inequalities by buying from diverse suppliers. Originality/value Using a temporal approach, the research empirically investigates how different purchasing organizations have implemented and managed the known practices and dealt with the challenges faced when trying to adopt SD.

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Road traffic anomaly detection is vital for reducing the number of accidents and ensuring a more efficient and safer transportation system. In highways, where traffic volume and speed limits are high, anomaly detection is not only essential but also considerably more challenging, given the multitude of fast-moving vehicles, often observed from extended distances and diverse angles, occluded by other objects, and subjected to variations in illumination and adverse weather conditions. This complexity has meant that human error often limits anomaly detection, making the role of computer vision systems integral to its success. In light of these challenges, this paper introduces MEDAVET - a sophisticated computer vision system engineered with an innovative mechanism that leverages spatial and temporal structures for high-precision traffic anomaly detection on highways. MEDAVET is assessed in its object tracking and anomaly detection efficacy using the UA-DETRAC and Track 4 benchmarks and has its performance compared with that of an array of state-of-the-art systems. The results have shown that, when MEDAVET’s ability to delimit relevant areas of the highway, through a bipartite graph and the Convex Hull algorithm, is paired with its QuadTree-based spatial and temporal approaches for detecting occluded and stationary vehicles, it emerges as superior in precision, compared to its counterparts, and with a competitive computational efficiency.

Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.

Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation patterns based on visual stimuli using fMRI in two patients with unique aura triggers. Both patients underwent separate fMRI sessions during the ictal and interictal periods. The Gaussian Process Classifier (GPC) was used to differentiate these periods by employing a machine learning temporal embedding approach and spatiotemporal activation patterns based on visual stimuli. When restricted to visual and occipital regions, GPC had an improved performance, with accuracy rates for patients A and B of roughly 86-90% and 77-81%, respectively (p < 0.01). The algorithm effectively differentiated visual stimulation and rest periods and identified times when aura symptoms manifested, as evident from the varying predicted probabilities in the GPC models. These findings contribute to our understanding of the role of visual processing and brain activity patterns in migraine with aura and the significance of temporal embedding techniques in examining aura phenomena. This finding has implications for diagnostic tools and therapeutic techniques, especially for patients suffering from aura symptoms.

Endoscopic Evacuation of Putaminal Hemorrhage Using the Trans-Middle Temporal Gyrus Approach: Technical Notes and Case Presentations.

The development of minimally invasive endoscopic neurosurgery has enabled widespread application of endoscopic surgery via the ipsilateral transfrontal approach for putaminal hematoma evacuation. However, this approach is unsuitable for putaminal hematomas that extend into the temporal lobe. We adopted the endoscopic trans-middle temporal gyrus approach, instead of the conventional surgical approach, for the management of these complicated cases and determined its safety and feasibility. Twenty patients with putaminal hemorrhage underwent surgical treatment at the Shinshu University Hospital between January 2016 and May 2021. Of these, two patients with left putaminal hemorrhage that extended into the temporal lobe underwent surgical treatment using the endoscopic trans-middle temporal gyrus approach. The procedure entailed the use of a thinner transparent sheath to reduce the technique's invasiveness, a navigation system to determine the location of the middle temporal gyrus and the sheath's trajectory, and an endoscope with a 4K camera for higher image quality and utility. The sylvian fissure was compressed superiorly using our novel "port retraction technique" (i.e., by tilting the transparent sheath superiorly) to avoid damage to the middle cerebral artery and Wernicke's area. The endoscopic trans-middle temporal gyrus approach allowed sufficient hematoma evacuation and hemostasis under endoscopic observation without any surgical complexities or complications. The postoperative course was uneventful in both patients. The endoscopic trans-middle temporal gyrus approach for putaminal hematoma evacuation helps avoid damage to normal brain tissue, which could result from the wide range of motion of the conventional technique, particularly when the hemorrhage extends to the temporal lobe.

Tourism dreams in rubble: Mass demolition and the reconfiguration of growth coalitions within China's ecological civilization

Amidst the mounting interest in China's Ecological Civilization (EC) campaign, this paper examines its ground-level implementation and its influences on fostering eco-conscious urban governance. Employing a temporal approach to scrutinize the change in local priorities over time, this paper conducts a detailed case study of Dali, a tourist destination in Southwest China. Environmental protection has escalated in this city over the last decade, manifesting in diverse measures adopted by the local government, including the demolition of hundreds of buildings in the core conservation zone of the lake Erhai. This paper demonstrates how the campaign of EC has strengthened environmental efforts locally, while emphasizing that local compliance relies on heightened oversight and financial support from the central government. Moreover, this paper argues that, despite resembling a degrowth strategy in terms of rhetoric and short-term effects, EC-led demolition serves as a spatio-temporal fix that has helped the local government to address both ecological and political imperatives, with growth coalitions being reconfigured. Overall, this paper contributes to scholarly discussions on the impacts of the EC campaign, expands the comprehension of the dynamic process of greening urban governance, and spotlights the analytical prowess of the demolition lens in such studies.

Prokaryotic communities of the French Polynesian sponge Dactylospongia metachromia display a site-specific and stable diversity during an aquaculture trial

Dynamics of microbiomes through time are fundamental regarding survival and resilience of their hosts when facing environmental alterations. As for marine species with commercial applications, such as marine sponges, assessing the temporal change of prokaryotic communities allows us to better consider the adaptation of sponges to aquaculture designs. The present study aims to investigate the factors shaping the microbiome of the sponge Dactylospongia metachromia, in a context of aquaculture development in French Polynesia, Rangiroa, Tuamotu archipelago. A temporal approach targeting explants collected during farming trials revealed a relative high stability of the prokaryotic diversity, meanwhile a complementary biogeographical study confirmed a spatial specificity amongst samples at different longitudinal scales. Results from this additional spatial analysis confirmed that differences in prokaryotic communities might first be explained by environmental changes (mainly temperature and salinity), while no significant effect of the host phylogeny was observed. The core community of D. metachromia is thus characterized by a high spatiotemporal constancy, which is a good prospect for the sustainable exploitation of this species towards drug development. Indeed, a microbiome stability across locations and throughout the farming process, as evidenced by our results, should go against a negative influence of sponge translocation during in situ aquaculture.

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

Development of unsteady reduced-order methods for multi-row turbomachinery flow simulation based on the computational fluids laboratory three-dimensional solver

Rotor–stator interaction (RSI) is an inherent phenomenon in multi-row turbomachinery. Unsteady reduced-order methods, such as the harmonic balance (HB) method and the space-time gradient (STG) method, have been proposed to capture RSI with fewer computational resources compared to fully unsteady simulation. In this study, the steady mixing-plane method, the HB method, and the STG method are implemented into the open-source external computational fluid laboratory three-dimensional (CFL3D) flow solver to gain the ability to predict turbomachinery flows based on solving Reynolds-averaged Navier–Stokes equations. Additionally, a rotation interpolation approach for adjacent blades is implemented for the unsteady multi-row turbomachinery simulation. For the HB method, the phase-lag periodic conditions and the temporal interpolation approach between two adjacent blade rows are integrated into CFL3D. Then, the steady mixing-plane method, the HB method, the STG method, and the fully unsteady simulation method are conducted on a quasi-three-dimensional radial slice and a three-dimensional geometry of the National Aeronautics and Space Administration Stage-35 compressor. Both the transient and time-averaged flowfield predicted by the reduced-order methods are compared with the unsteady simulations. Results indicate that the STG method and the HB method can accurately simulate the unsteady flow with better predictions of RSI impact. For the HB method, accurate prediction of transient unsteady flow requires a minimum of seven harmonics, whereas the time-averaged flow requires only five harmonics. Additionally, a quantitative assessment of computational speed is conducted, revealing that the HB method with seven harmonics achieved a speed 28 times faster than the fully unsteady simulation.

A thermostable and inhibitor resistant β-glucosidase from Rasamsonia emersonii for efficient hydrolysis of lignocellulosics biomass.

The present study reports a highly thermostable β-glucosidase (GH3) from Rasamsonia emersonii that was heterologously expressed in Pichia pastoris. Extracellular β-glucosidase was purified to homogeneity using single step affinity chromatography with molecular weight of ~ 110kDa. Intriguingly, the purified enzyme displayed high tolerance to inhibitors mainly acetic acid, formic acid, ferulic acid, vanillin and 5-hydroxymethyl furfural at concentrations exceeding those present in acid steam pretreated rice straw slurry used for hydrolysis and subsequent fermentation in 2G ethanol plants. Characteristics of purified β-glucosidase revealed the optimal activity at 80°C, pH5.0 and displayed high thermostability over broad range of temperature 50-70°C with maximum half-life of ~ 60h at 50°C, pH 5.0. The putative transglycosylation activity of β-glucosidase was appreciably enhanced in the presence of methanol as an acceptor. Using the transglycosylation ability of β-glucosidase, the generated low cost mixed glucose disaccharides resulted in the increased induction of R. emersonii cellulase under submerged fermentation. Scaling up the recombinant protein production at fermenter level using temporal feeding approach resulted in maximal β-glucosidase titres of 134,660units/L. Furthermore, a developed custom made enzyme cocktail consisting of cellulase from R. emersonii mutant M36 supplemented with recombinant β-glucosidase resulted in significantly enhanced hydrolysis of pretreated rice straw slurry from IOCL industries (India). Our results suggest multi-faceted β-glucosidase from R. emersonii can overcome obstacles mainly high cost associated enzyme production, inhibitors that impair the sugar yields and thermal inactivation of enzyme.

A temporal approach to online discussion during disasters: Applying SIR infectious disease model to predict topic growth and examining effects of temporal distance

A temporal approach to online discussion during disasters: Applying SIR infectious disease model to predict topic growth and examining effects of temporal distance

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation.

The role of enterprise risk management in enabling organisational resilience: a case study of the Swedish mining industry

This study empirically examines the role of enterprise risk management (ERM) in developing and maintaining resilience resources and capabilities that are necessary for an organisation’s strategic transformation towards sustainability. Data was collected through 25 semi-structured interviews, one non-participant observation, and secondary sources in the context of a Swedish mining company undergoing a high-risk strategic transformation towards full decarbonisation. Following the temporal bracketing approach (Langley in Academy of Management Review 24:691–70, 1999) and employing thematic analysis (Gioia in Organizational Research Methods 16:15–31), the data was structured and analysed according to three phases from 2012 to 2023. The findings show: first, different ERM practices, such as risk governance frameworks, risk culture, risk artefacts, and risk awareness, influence resilience resources and capabilities. Second, the evolution of risk management practices from traditional risk management to ERM is an ongoing developmental process to ensure that risk management continues to be aligned with the company’s strategy. Third, in tandem with strategic changes, resilience in terms of resources and capabilities emerges over time and develops through a series of events, gradually enhancing the company’s ability to manage risks and uncertainties associated with multidimensional sustainability challenges. These results contribute to the ERM literature that follows the dynamic capability approach and also focuses on the relationship between ERM and strategy by adding more detailed empirical evidence from the risk management literature in relation to resilience resources and capabilities. Additionally, the results contribute to the resilience literature that follows a developmental perspective.

Predictive Control for Unknown Dynamics With Observation Loss: A Temporal Game-Theoretic Approach

This paper is concerned with a model-free predictive control problem on systems with unknown dynamics. Different from existing predictive control, the predictive feedback strategy is designed to take control inputs to cover the infinite horizon when the system exists the observation loss. To predict future control inputs, a temporal game-theoretic approach is presented to model such a predictive control issue as an optimization problem and ensure the optimality of the system performance. Moreover, a predictive algebraic Riccati equation (PARE) is constructed to solve such an optimization problem. By leveraging offline datasets and the real-time data of state and input, a data-driven parallel computational framework is developed to iteratively solve the PARE. In this way, the prior knowledge of the systems is avoided and the computational complexity of the proposed algorithm is reduced. Finally, numerical and practical examples are presented to show the applicability of the proposed results.

A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis

Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best fit to univariate time series only, and have not sufficiently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a M ulti- S cale D ecomposition MLP- Mixer , which learns to explicitly decompose and represent the input time series in its different layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks, we demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.

A set of ecosystem service indicators for European grasslands based on botanical surveys

AbstractBackgroundGrasslands provide a wide range of ecosystem services (ESs). However, there is currently no method for easily diagnosing the level of ESs produced. Our aim was to develop ES indicators based on botanical surveys, which are readily available data and integrative of grassland spatiotemporal variability.MethodsBased on academic knowledge and expertise, we identified several simple vegetation criteria that we aggregated using a multicriteria analysis tool to construct indicators of the level of ESs provided by grasslands. In this study, the indicators were calculated from over 2000 botanical surveys spread over a wide biogeographical gradient.ResultsAnalyses of correlation between the various indicators show that “forage supply” and “diversity conservation” were not correlated. “Forage availability” and “nitrogen availability for the vegetation” were positively linked together and negatively linked to the robustness of the plant community to extreme events. A temporal approach highlights that the “biodiversity conservation” score decreased from 1970 to 2010 and that “nitrogen availability for the vegetation” was lower in 1970 and 1980 than in 2000 and 2010.ConclusionsThese results show that our aggregation method based on a large data set of botanical surveys could be appropriate for studying temporal dynamics of ESs.

Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction?

Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction?