Search results for: medial temporal atrophy

Authors: Eiji Nakamachi, Tsuyoshi Eguchi, Sayo Yamamoto, Yusuke Morita, H. Sakamoto

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In this study, we aim to reveal the relationship between the meniscal tearing and the articular cartilage injury of knee joint by using the dynamic explicit finite element (FE) method. Meniscal injuries reduce its functional ability and consequently increase the load on the articular cartilage of knee joint. In order to prevent the induction of osteoarthritis (OA) caused by meniscal injuries, many medical treatment techniques, such as artificial meniscus replacement and meniscal regeneration, have been developed. However, it is reported that these treatments are not the comprehensive methods. In order to reveal the fundamental mechanism of OA induction, the mechanical characterization of meniscus under the condition of normal and injured states is carried out by using FE analyses. At first, a FE model of the human knee joint in the case of normal state – ‘intact’ - was constructed by using the magnetron resonance (MR) tomography images and the image construction code, Materialize Mimics. Next, two types of meniscal injury models with the radial tears of medial and lateral menisci were constructed. In FE analyses, the linear elastic constitutive law was adopted for the femur and tibia bones, the visco-hyperelastic constitutive law for the articular cartilage, and the visco-anisotropic hyperelastic constitutive law for the meniscus, respectively. Material properties of articular cartilage and meniscus were identified using the stress-strain curves obtained by our compressive and the tensile tests. The numerical results under the normal walking condition revealed how and where the maximum compressive stress occurred on the articular cartilage. The maximum compressive stress and its occurrence point were varied in the intact and two meniscal tear models. These compressive stress values can be used to establish the threshold value to cause the pathological change for the diagnosis. In this study, FE analyses of knee joint were carried out to reveal the influence of meniscal injuries on the cartilage injury. The following conclusions are obtained. 1. 3D FE model, which consists femur, tibia, articular cartilage and meniscus was constructed based on MR images of human knee joint. The image processing code, Materialize Mimics was used by using the tetrahedral FE elements. 2. Visco-anisotropic hyperelastic constitutive equation was formulated by adopting the generalized Kelvin model. The material properties of meniscus and articular cartilage were determined by curve fitting with experimental results. 3. Stresses on the articular cartilage and menisci were obtained in cases of the intact and two radial tears of medial and lateral menisci. Through comparison with the case of intact knee joint, two tear models show almost same stress value and higher value than the intact one. It was shown that both meniscal tears induce the stress localization in both medial and lateral regions. It is confirmed that our newly developed FE analysis code has a potential to be a new diagnostic system to evaluate the meniscal damage on the articular cartilage through the mechanical functional assessment.

Keywords: finite element analysis, hyperelastic constitutive law, knee joint injury, meniscal tear, stress concentration

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Ibrahim M. I. Hamoda, Ahmed R. Z. Baghdadi, Mohammed K. Mohamed, Nawal A. Abu-Shady

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Background: Most of patients with vestibular dysfunction suffering from balance disorders, Abnormality in balance increase effort and exertion which affect the independency, so this study might be a guide in managing balance problem and consequently improve walking with less exertion and maximum function. Purpose: to analyze and discuss the effect of transcranial magnetic therapy on balance in patients with vestibular dysfunction. Methods: forty subjects from both sexes were classified to divided randomly into two equal groups; Group I study group: this group received transcranial magnetic therapy, with a selected physical therapy program for improving balance and vestibular disorders (Balance training, Cawthorne-Cooksey Exercises) and group II (control group): this group received a selected physical therapy program as group I without transcranial magnetic therapy. This treatment procedure will be applied three times weekly for three months. The mean age was 54.53±3.44 and 55.33±2.32 years and BMI 35.7±3.03 and 35.73±1.03 kg/m2 for group I and II respectively. The Biodex Balance System, Berge balances scale (BBS) and brain MRI were used for assessment. Assessments were conducted before and after treatment. The treatment program for group I included balance training, Cawthorne-Cooksey Exercises and pulsed magnetic therapy (Parameters used in the program of 20 minutes, Intensity 2 gausses, Frequency 1 Hz). This selected program was done in approximately one hour every other day for three month. The treatment program group II Patients received the same program as group A without transcranial magnetic therapy. Results: The One-way ANOVA revealed that there were no significant differences in BBS scores, overall balance index, Anterior / posterior balance index, Medial / lateral balance index and dynamic limits of stability between both groups. Moreover, the BBS scores increased and overall balance index, Anterior / posterior balance index, Medial / lateral balance index and dynamic limits of stability decreased significantly after treatment in group I and II compared with before treatment. Interpretation/Conclusion: Adding pulsed magnetic therapy to balance training, Cawthorne-Cooksey Exercises has no effect on static and dynamic balance in patients with balance problems due to benign positional paroxysmal vertigo.

Keywords: balance, transcranial magnetic therapy, vestibular dysfunction, biomechanic

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This study evaluates the surface water quality in the Kebir Rhumel Basin by analyzing hydrochemical parameters. To assess spatial and temporal variations in water quality, we applied the Water Quality Index (WQI), Mann-Kendall (MK) trend analysis, and hierarchical cluster analysis (HCA). Monthly measurements of eleven hydrochemical parameters were collected across eight stations from January 2016 to December 2020. Calcium and sulfate emerged as the dominant cation and anion, respectively. WQI analysis indicated a high incidence of poor water quality at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khalifa (SK), where 89.5%, 90.6%, 78.2%, and 62.7% of samples, respectively, fell into this category. The MK trend analysis revealed a significant upward trend in WQI at Oued Boumerzoug (ON) and SK stations, signaling temporal deterioration in these areas. HCA grouped the dataset into three clusters, covering approximately 22%, 30%, and 48% of the months, respectively. Within these clusters, specific stations exhibited elevated WQI values: GR and ON in the first cluster, OB and SK in the second, and AS, BH, El Milia (EM), and Hammam Grouz (HG) in the third. Furthermore, approximately 38%, 41%, and 38% of samples in clusters one, two, and three, respectively, were classified as having poor water quality. These findings provide essential insights for policymakers in formulating strategies to restore and manage surface water quality in the region.

Keywords: surface water quality, water quality index (WQI), Mann-Kendall Trend Analysis, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Geng-Gui Wang, Chia-Hao Chang, Jee-Cheng Wu

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A shoreline is a line where a body of water and the shore meet. It provides economic and social security to coastal habitations. However, shorelines face multiple threats due to both natural processes and man-made effects because of disasters, rapid urbanization, industrialization, and sand deposition and erosion, etc. In this study, we analyzed multi-temporal satellite images of the Yilan coast, Taiwan from 1978 to 2016, using the United States Geological Survey (USGS) Digital Shoreline Analysis System (DSAS), weather information (as rainfall records and typhoon routes), and man-made construction project data to explore the causes of shoreline changes. The results showed that the shoreline at Yilan coast is greatly influenced by typhoons and anthropogenic interventions.

Keywords: shoreline change, multi-temporal satellite, digital shoreline analysis system, DSAS, Yi-Lan coast

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Authors: Gabriella Notarianni Burk

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The present study investigates the Aspect Hypothesis (AH) in Italian child language in the production of frog story narratives from the CHILDES database. The AH is based on the assumption that children initially encode aspectual and lexical distinctions rather than temporal relations. Children from a variety of first languages have been shown to mark past initially with achievements and accomplishments (telic predicates) and in later stages with states and activities (atelic predicates). Aspectual distinctions in Romance languages are obligatorily and overtly encoded in the inflectional morphology. In Italian the perfective viewpoint is realized by the passato prossimo, which expresses a temporal and aspectual meaning of pastness and perfectivity, whereas the imperfective viewpoint in the past tense is realized by the imperfetto. The aim of this study is to assess the role of lexical aspect in the acquisition of tense and aspect morphology and to understand if Italian children’s mapping of aspectual and temporal distinctions follows consistent developmental patterns across languages. The research methodology aligns with the cross-linguistic designs, tasks and coding procedures previously developed in the frog story literature. Results from two-factor ANOVA show that Italian children (age range: 4-6) exhibited a statistically significant distinction between foregrounded perfective and backgrounded imperfective marking. However, a closer examination of the sixty narratives reveals an idiosyncratic production pattern for Italian children, whereby the marking of imperfetto deviates from the tenets of AH and emerges as deictic tense to entail completed and bounded events in foreground clauses. Instances of ‘perfective’ uses of imperfetto were predominantly found in the four-year old narratives (25%). Furthermore, the analysis of the perfective marking suggests that morphological articulation and diatopic variation may influence the child production of formal linguistic devices in discourse.

Keywords: actionality, aspect, grounding, temporal reference

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Authors: Mads Schultz, Galice Hoarau, Marvin Choquet

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Members of the genus Calanus are key species in the North Atlantic and Arctic marine ecosystems due to their vast abundance and their ability to accumulate high amounts of lipid. As a link between primary producers and higher trophic levels, the temporal presence of each Calanus species is important in a time of changing communities and northward distribution shifts. This study focused on the temporal niches of the sympatric species Calanus helgolandicus, Calanus finmarchicus, Calanus glacialis, and Calanus hyperboreus in Skjerstad fjord, a Norwegian fjord (67˚14’N, 14 ˚44’E). Three depth intervals were sampled monthly over a year, targeting copepodite stages of the genus Calanus. Species determination was carried out genetically using insertion/deletion markers. In addition, during the reproductive season (Jan-May), weekly samples of the upper 50 meters of the water column targeting nauplii and 5 depth intervals targeting copepodites were collected. Nauplii samples were sorted into two groups (NI-NIII and NIV-NVI), and species were genetically identified. Specimens from stage CIV to adults from each depth interval of copepodite sampling were photographed in order to generate a supporting timeline of visual traits, including gonad maturation stage, presence of stomach content, and total lipid content. The most abundant species were Calanus finmarchicus and Calanus glacialis, followed by Calanus hyperboreus. These species were present in the water column throughout the year, whereas Calanus helgolandicus, the least abundant species, was only present during the summer and autumn period. Each species showed distinct temporal niches, with Calanus finmarchicus occupying the upper 50 meters longer than any of the other species. Calanus hyperboreus dominates in abundance early in the spring but are outnumbered by Calanus glacialis and Calanus finmarchicus after spring bloom sets in. In Skjerstad fjord, Calanus hyperboreus is a clear capital breeder with a long period of nauplii presence before the spring bloom. Calanus glacialis and Calanus finmarchicus both utilize income breeding, with Calanus glacialis developing to the larger nauplii stages quicker than Calanus finmarchicus, but also having a shorter reproduction period. Indeed, the “traditional Arctic” species Calanus hyperboreus and Calanus glacialis appear to end their reproduction period earlier than the North Atlantic Calanus finmarchicus.

Keywords: calanus, depth distribution, reproduction, stage composition, temporal niches

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Authors: Sofia Matoug, Amr Abdel-Dayem

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This paper presents a comprehensive survey of recent research studies to segment and classify brain MR (magnetic resonance) images in order to detect significant changes to brain ventricles. The paper also presents a general framework for detecting regions that atrophy, which can help neurologists in detecting and staging Alzheimer. Furthermore, a prototype was implemented to segment brain MR images in order to extract the region of interest (ROI) and then, a classifier was employed to differentiate between normal and abnormal brain tissues. Experimental results show that the proposed scheme can provide a reliable second opinion that neurologists can benefit from.

Keywords: Alzheimer, brain images, classification techniques, Magnetic Resonance Images MRI

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Authors: Zeina Merabi, Arij Dao

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The temporal order of information processing in the brain is an important code in many acoustic signals, including speech, music, and animal vocalizations. Despite its significance, surprisingly little is known about its underlying cellular mechanisms and network manifestations. In the songbird telencephalic nucleus HVC, a subset of neurons shows temporal combination sensitivity (TCS). These neurons show a high temporal specificity, responding differently to distinct patterns of spectral elements and their combinations. HVC neuron types include basal-ganglia-projecting HVCX, forebrain-projecting HVCRA, and interneurons (HVC¬INT), each exhibiting distinct cellular, electrophysiological and functional properties. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different wiring scenarios, aiming to explore possible neural mechanisms that orchestrate the combination sensitivity property exhibited by HVCX, as well as replicating in vivo firing patterns observed when TCS neurons are presented with various auditory stimuli. The ionic and synaptic currents for each class of neurons that are presented in our networks and are based on pharmacological studies, rendering our networks biologically plausible. We present for the first time several realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The different networks highlight neural mechanisms that could potentially help to explain some aspects of combination sensitivity, including 1) interplay between inhibitory interneurons’ activity and the post inhibitory firing of the HVCX neurons enabled by T-type Ca2+ and H currents, 2) temporal summation of synaptic inputs at the TCS site of opposing signals that are time-and frequency- dependent, and 3) reciprocal inhibitory and excitatory loops as a potent mechanism to encode information over many milliseconds. The result is a plausible network model characterizing auditory processing in HVC. Our next step is to test the predictions of the model.

Keywords: combination sensitivity, songbirds, neural networks, spatiotemporal integration

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Authors: Belinda Crissman

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Serious mental disorder is greatly overrepresented in prisoners compared to the general community, with consequences for prison management, recidivism and the prisoners themselves. Incarcerated individuals with psychotic disorders experience insufficient detection and treatment and higher rates of suicide in custody. However direct evidence to explain the overrepresentation of individuals with psychosis in prisons is sparse. The current study aimed to use a life course criminology perspective to answer two key questions: 1) What is the temporal relationship between psychosis and offending (does first mental health contact precede first recorded offence, or does the offending precede the mental health diagnosis)? 2) Are there key temporal points or system contacts prior to incarceration that could be identified as opportunities for early intervention? Data from the innovative Queensland Linkage project was used to link individuals with their corrections, health and relevant social service systems to answer these questions.

Keywords: mental disorder, crime, life course criminology, prevention

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Authors: Sikiru Ademola Adewale, Joe Thomas, Bolanle Hafiz Matti, Tosin Ige

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This project demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.

Keywords: decoder, encoder, many-to-many mapping, video captioning, 2-gram BLEU

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This research aims to present a surface water quality assessment of hydrochemical parameters in the Kebir Rhumel Basin, Algeria. The water quality index (WQI), Mann–Kendall (MK) test, and hierarchical cluster analysis (HCA) were used in oder to understand the spatio-temporal distribution of the surface water quality in the study area. Eleven hydrochemical parameters were measured monthly at eight stations from January 2016 to December 2020. The dominant cation in the surface water was found to be calcium, followed by sodium, and the dominant anion was sulfate, followed by chloride. In terms of WQI, a significant percentage of surface water samples at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khlifa (SK) exhibited poor water quality, with approximately 89.5%, 90.6%, 78.2%, and 62.7%, respectively, falling into this category. Mann–Kendall trend analysis revealed a significantly increasing trend in WQI values at stations Oued Boumerzoug (ON) and SK, indicating that the temporal variation of WQI in these stations is significant. Hierarchical clustering analysis classified the data into three clusters. The first cluster contained approximately 22% of the total number of months, the second cluster included about 30%, and the third cluster had the highest representation, approximately 48% of the total number of months. Within these clusters, certain stations exhibited higher WQI values. In the first cluster, stations GR and ON had the highest WQI values. In the second cluster, stations Oued Boumerzoug (OB) and SK showed the highest WQI values, while in the last cluster, stations AS, BH, El Milia (EM), and Hammam Grouz (HG) had the highest mean WQI values. Also, approximately 38%, 41%, and 38% of the total water samples in the first, second, and third clusters, respectively, were classified as having poor water quality. The findings of this study can serve as a scientific basis for decision-makers to formulate strategies for surface water quality restoration and management in the region.

Keywords: surface water, water quality index (WQI), Mann Kendall (MK) test, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: Qingjin Liu, Jinpeng Niu, Kangjia Chen, Jiao Li, Huafu Chen, Wei Liao

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Functional connectomics is essential in cognitive science and neuropsychiatry, offering insights into the brain's complex network structures and dynamic interactions. Although neuroimaging has uncovered functional connectivity issues in Major Depressive Disorder (MDD) patients, the dynamic shifts in connectome topology and their link to gene expression are yet to be fully understood. To explore the differences in dynamic connectome topology between MDD patients and healthy individuals, we conducted an extensive analysis of resting-state functional magnetic resonance imaging (fMRI) data from 434 participants (226 MDD patients and 208 controls). We used multilayer network models to evaluate brain module dynamics and examined the association between whole-brain gene expression and dynamic module variability in MDD using publicly available transcriptomic data. Our findings revealed that compared to healthy individuals, MDD patients showed lower global mean values and higher standard deviations, indicating unstable patterns and increased regional differentiation. Notably, MDD patients exhibited more frequent module switching, primarily within the executive control network (ECN), particularly in the left dorsolateral prefrontal cortex and right fronto-insular regions, whereas the default mode network (DMN), including the superior frontal gyrus, temporal lobe, and right medial prefrontal cortex, displayed lower variability. These brain dynamics predicted the severity of depressive symptoms. Analyzing human brain gene expression data, we found that the spatial distribution of MDD-related gene expression correlated with dynamic module differences. Cell type-specific gene analyses identified oligodendrocytes (OPCs) as major contributors to the transcriptional relationships underlying module variability in MDD. To the best of our knowledge, this is the first comprehensive description of altered brain module dynamics in MDD patients linked to depressive symptom severity and changes in whole-brain gene expression profiles.

Keywords: major depressive disorder, module dynamics, magnetic resonance imaging, transcriptomic

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Authors: Samane Eftekhari Ranjbar

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Diabetes mellitus, a metabolic disorder characterized by elevated blood glucose levels, ranks among the leading causes of adult mortality. This study investigates the impact of an eight-week high-intensity interval training (HIIT) program combined with sodium nitrite supplementation on TNF- α, MURF1, and PI3K in a type 2 diabetes rodent model. Elevated TNF-α levels have been associated with insulin resistance, while MURF1 and PI3K play roles in muscle atrophy and insulin signaling pathways, respectively. In this experimental study, 15 eight-week-old rats from the Sara Laboratory Center in Tabriz were assigned to one of five groups: healthy control, diabetic control, diabetic with sodium nitrite supplementation, diabetic with eight weeks of intermittent exercise, and diabetic with eight weeks of interval training plus sodium nitrite supplementation. The HIIT protocol was designed to span eight weeks, with five weekly sessions at specified intensities and durations. Sodium nitrite, known for its vasodilatory and cytoprotective properties, was administered via injection. The findings revealed that the HIIT program and sodium nitrite supplementation influenced the examined biomarkers. ANOVA test outcomes indicated statistically significant differences in TNF- α (P=0.001), MURF1 (P=0.001), and PI3K (P=0.001) concentrations among the various groups. The healthy control group exhibited substantially decreased TNF- α, and MURF1 levels, as well as elevated PI3K levels compared to the diabetic control group. The exercise group, in conjunction with sodium nitrite supplementation, demonstrated a significant rise in PI3K levels (P=0.001) and a decline in TNF- α levels (P=0.018) relative to the diabetic control group. These results suggest that the combined intervention may help improve insulin sensitivity and reduce inflammation. However, MURF1 levels, which are related to muscle atrophy, showed no significant difference (P=0.24). In conclusion, in type 2 diabetic rats, an eight-week high-intensity interval training program with sodium nitrite supplementation does not affect MURF1 levels but does influence PI3K and TNF- α levels. This combination may hold potential for improving insulin sensitivity and reducing inflammation in type 2 diabetes patients, warranting further investigation and potential translation to human clinical trials.

Keywords: high-intensity interval training, sodium nitrate supplementation, type 2 diabetes, tumor necrosis factor-alpha, phosphatidylinositol-3-kinase, muscle RING-finger protein-1

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

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Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

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Authors: Md. Jahoor Alam

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Recent studies suggest that Glucose controls several lifesaving pathways. Glucose molecule is reported to be responsible for the production of ROS (reactive oxygen species). In the present work, a p53-MDM2-Glucose model is developed in order to study spatiotemporal properties of the p53 pathway. The systematic model is mathematically described. The model is numerically simulated using high computational facility. It is observed that the variation in glucose concentration level triggers the system at different states, namely, oscillation death (stabilized), sustain and damped oscillations which correspond to various cellular states. The transition of these states induced by glucose is phase transition-like behaviour. Further, the amplitude of p53 dynamics with the variation of glucose concentration level follows power law behaviour, As(k) ~ kϒ, where, ϒ is a constant. Further Stochastic approach is needed for understanding of realistic behaviour of the model. The present model predicts the variation of p53 states under the influence of glucose molecule which is also supported by experimental facts reported by various research articles.

Keywords: oscillation, temporal behavior, p53, glucose

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Authors: Hui Luo, Zhenhong Li, Zhen Dong

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The analysis of multi-temporal InSAR (MTInSAR) such as persistent scatterer (PS) and small baseline subset (SBAS) techniques usually relies on temporal/spatial phase unwrapping (PU). Unfortunately, it always fails to unwrap the phase for two reasons: 1) spatial phase jump between adjacent pixels larger than π, such as layover and high discontinuous terrain; 2) temporal phase discontinuities such as time varied atmospheric delay. To overcome these limitations, a least-square based PU method is introduced in this paper, which incorporates baseline-combination interferograms and adjacent phase gradient network. Firstly, permanent scatterers (PS) are selected for study. Starting with the linear baseline-combination method, we obtain equivalent 'small baseline inteferograms' to limit the spatial phase difference. Then, phase different has been conducted between connected PSs (connected by a specific networking rule) to suppress the spatial correlated phase errors such as atmospheric artifact. After that, interval phase difference along arcs can be computed by least square method and followed by an outlier detector to remove the arcs with phase ambiguities. Then, the unwrapped phase can be obtained by spatial integration. The proposed method is tested on real data of TerraSAR-X, and the results are also compared with the ones obtained by StaMPS(a software package with 3D PU capabilities). By comparison, it shows that the proposed method can successfully unwrap the interferograms in urban areas even when high discontinuities exist, while StaMPS fails. At last, precise DEM errors can be got according to the unwrapped interferograms.

Keywords: phase unwrapping, time series, InSAR, urban areas

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Authors: Tanu Aneja, Harsha Malaviya

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Capturing pose information in cricket shots poses several challenges, such as low-resolution videos, noisy data, and joint occlusions caused by the nature of the shots. In response to these challenges, we propose a CondDGConv-based framework specifically for cricket shot prediction. By analyzing the spatial-temporal relationships in batsman shot sequences from an annotated 2D cricket dataset, our model achieves a 97% accuracy in predicting shot types. This performance is made possible by conditioning the graph network on batsman 2D poses, allowing for precise prediction of shot outcomes based on pose dynamics. Our approach highlights the potential for enhancing shot prediction in cricket analytics, offering a robust solution for overcoming pose-related challenges in sports analysis.

Keywords: action recognition, cricket. sports video analytics, computer vision, graph convolutional networks

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Nicholas Freer

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Rhythmic complexity in progressive metal is a developing area of analysis, particularly the interpretation of hyper-metric time spans as hierarchically significant rhythmic units of compositional organisation (Pieslak 2007, Charupakorn 2012, Capuzzo 2018, Calder 2018, Lucas 2018, Hannan 2020). This paper adds to this developing area by considering the relationships between the concepts of tactus, metric imposition, polymeter and rhythmic parallax in the Meshuggah composition “Do Not Look Down”. By considering an architectonic rhythmic framework within “Do Not Look Down” as the controlling organisation mechanism, an exploration of the interaction between distinct rhythmic layers and the composition’s formal segmentation and harmony (as riffs), reveals a pervasive structural misalignment between these elements. By exhibiting how Meshuggah’s manipulations of rhythmic complexities deliberately blur structural boundaries, creating misalignments in a flat approach to temporal partitioning (Nieto 2014), rhythmic characteristics of Meshuggah and the genre of Djent are exposed.

Keywords: hypermeter, rhythmic parallax, meshuggah, temporal partitioning

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Yuchien Lin

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This study attempted to investigate the changes in electroencephalography (EEG) and emotion regulation following eight-week inward-attention meditation training program. The subjects were 24 adults without meditation experiences divided into meditation and control groups. The quantitatively analyzed changes in psychophysiological parameters during inward-attention meditation, and evaluated the emotion scores assessed by the State-Trait Anxiety Inventory (STAI), the Positive and Negative Affect Schedule (PANAS), and the Emotion Regulation Scale (ERS). The results were found: (1) During meditation, significant EEG increased for theta-band activity in the frontal and the bilateral temporal areas, for alpha-band activity in the left and central frontal areas, and for gamma-band activity in the left frontal and the left temporal areas. (2) The meditation group had significantly higher positive affect in posttest than in pretest. (3) There was no significant difference in the changes of EEG spectral characteristics and emotion scores in posttest and pretest for the control group. In the present study, a unique meditative concentration task with a constant level of moderate mental effort focusing on the center of brain was used, so as to enhance frontal midline theta, alpha, and gamma-band activity. These results suggest that this mental training allows individual reach a specific mental state of relaxed but focused awareness. The gamma-band activity, in particular, enhanced over left frontoparietal area may suggest that inward-attention meditation training involves temporal integrative mechanisms and may induce short-term and long-term emotion regulation abilities.

Keywords: meditation, EEG, emotion regulation, gamma activity

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Authors: Hsiao-Lan Wang, Chun-Han Chiang, I-Chen Chen

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In Chinese Mandarin, lexical tones play an important role to provide contrasts in word meaning. They are pitch patterns and can be quantified as the fundamental frequency (F0), expressed in Hertz (Hz). In this study, we aim to investigate the influence of frequency discrimination on Chinese children’s performance of reading abilities. Fifty participants from 3rd to 4th grades, including 24 children with reading difficulties and 26 age-matched children, were examined. A serial of cognitive, language, reading and psychoacoustic tests were administrated. Magnetoencephalography (MEG) was also employed to study children’s auditory sensitivity. In the present study, auditory frequency was measured through slide-up pitch, slide-down pitch and frequency-modulated tone. The results showed that children with Chinese reading difficulties were significantly poor at phonological awareness and auditory discrimination for the identification of frequency-modulated tone. Chinese children’s character reading performance was significantly related to lexical tone awareness and auditory perception of frequency-modulated tone. In our MEG measure, we compared the mismatch negativity (MMNm), from 100 to 200 ms, in two groups. There were no significant differences between groups during the perceptual discrimination of standard sounds, fast-up and fast-down frequencies. However, the data revealed significant cluster differences between groups in the slow-up and slow-down frequencies discrimination. In the slow-up stimulus, the cluster demonstrated an upward field map at 106-151 ms (p < .001) with a strong peak time at 127ms. The source analyses of two dipole model and localization resolution model (CLARA) from 100 to 200 ms both indicated a strong source from the left temporal area with 45.845% residual variance. Similar results were found in the slow-down stimulus with a larger upward current at 110-142 ms (p < 0.05) and a peak time at 117 ms in the left temporal area (47.857% residual variance). In short, we found a significant group difference in the MMNm while children processed frequency-modulated tones with slow temporal changes. The findings may imply that perception of sound frequency signals with slower temporal modulations was related to reading and language development in Chinese. Our study may also support the recent hypothesis of underlying non-verbal auditory temporal deficits accounting for the difficulties in literacy development seen developmental dyslexia.

Keywords: Chinese Mandarin, frequency modulation sweeps, magnetoencephalography, mismatch negativity, reading difficulties

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Authors: Xiyao Shan, Pavlos Evangelidis, Adam Kositsky, Naoki Ikeda, Yasuo Kawakami

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The human triceps surae (two bi-articular gastrocnemii and one mono-articular soleus) have aponeuroses in the posterior and anterior aspects of each muscle, where the anterior aponeuroses of the gastrocnemii adjoin the posterior aponeurosis of the soleus, possibly contributing to the intermuscular force transmission between gastrocnemii and soleus. Since the mechanical behavior of these aponeuroses at different knee- and ankle-joint positions remains unclear, the purpose of this study was to clarify this through observations of the localized changes in passive stiffness of the posterior aponeuroses, muscle belly and adjoining aponeuroses of the medial gastrocnemius (MG) induced by different knee and ankle angles. Eleven healthy young males (25 ± 2 yr, 176.7 ± 4.7 cm, 71.1 ± 11.1 kg) participated in this study. Each subject took either a prone position on an isokinetic dynamometer while the knee joint was fully extended (K180) or a kneeling position while the knee joint was 90° flexed (K90), in a randomized and counterbalanced order. The ankle joint was then passively moved through a 50° range of motion (ROM) by the dynamometer from 30° of plantar flexion (PF) to 20° of dorsiflexion (DF) at 2°/s and the ultrasound shear-wave velocity was measured to obtain shear moduli of the posterior aponeurosis, MG belly, and adjoining aponeuroses. The main findings were: 1) shear modulus in K180 was significantly higher (p < 0.05) than K90 for the posterior aponeurosis (across all ankle angles, 10.2 ± 5.7 kPa-59.4 ± 28.7 kPa vs. 5.4 ± 2.2 kPa-11.6 ± 4.1 kPa), MG belly (from PF10° to DF20°, 9.7 ± 2.2 kPa-53.6 ± 18.6 kPa vs. 8.0 ± 2.7 kPa-9.5 ± 3.7 kPa), and adjoining aponeuroses (across all ankle angles, 17.3 ± 7.8 kPa-80 ± 25.7 kPa vs. 12.2 ± 4.5 kPa-52.4 ± 23.0 kPa); 2) shear modulus of the posterior aponeuroses significantly increased (p < 0.05) from PF10° to PF20° in K180, while shear modulus of MG belly significantly increased (p < 0.05) from 0° to PF20° only in K180 and shear modulus of adjoining aponeuroses significantly increased (p < 0.05) across the whole ROM of ankle both in K180 and K90. These results suggest that different knee-joint positions can affect not only the bi-articular gastrocnemius but also influence the mechanical behavior of aponeuroses. In addition, compared to the gradual stiffening of the adjoining aponeuroses across the whole ROM of ankle, the posterior aponeurosis became slack in the plantar flexed positions and then was stiffened gradually as the knee was fully extended. This suggests distinct stiffening for the posterior and adjoining aponeuroses which is joint position-dependent.

Keywords: aponeurosis, plantar flexion and dorsiflexion, shear modulus, shear wave elastography

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Authors: Diamant Irene, Dar Tamar

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Burnout, conceptualized within the framework of stress research, is to a large extent a result of a threat on resources of time or a feeling of time shortage. In reaction to numerous tasks, deadlines, high output, management of different duties encompassing work-home conflicts, many individuals experience ‘time pressure’. Time pressure is characterized as the perception of a lack of available time in relation to the amount of workload. It can be a result of local objective constraints, but it can also be a chronic attribute in coping with life. As such, time pressure is associated in the literature with general stress experience and can therefore be a direct, contributory burnout factor. The present study examines the relation of chronic time pressure – feeling of time shortage and of being rushed, with another central aspect in subjective temporal experience - time perspective. Time perspective is a stable personal disposition, capturing the extent to which people subjectively remember the past, live the present and\or anticipate the future. Based on Hobfoll’s Conservation of Resources Theory, it was hypothesized that individuals with chronic time pressure would experience a permanent threat on their time resources resulting in relatively increased burnout. In addition, it was hypothesized that different time perspective profiles, based on Zimbardo’s typology of five dimensions – Past Positive, Past Negative, Present Hedonistic, Present Fatalistic, and Future, would be related to different magnitudes of chronic time pressure and of burnout. We expected that individuals with ‘Past Negative’ or ‘Present Fatalist’ time perspectives would experience more burnout, with chronic time pressure being a moderator variable. Conversely, individuals with a ‘Present Hedonistic’ - with little concern with the future consequences of actions, would experience less chronic time pressure and less burnout. Another temporal experience angle examined in this study is the difference between the actual distribution of time (as in a typical day) versus desired distribution of time (such as would have been distributed optimally during a day). It was hypothesized that there would be a positive correlation between the gap between these time distributions and chronic time pressure and burnout. Data was collected through an online self-reporting survey distributed on social networks, with 240 participants (aged 21-65) recruited through convenience and snowball sampling methods from various organizational sectors. The results of the present study support the hypotheses and constitute a basis for future debate regarding the elements of burnout in the modern work environment, with an emphasis on subjective temporal experience. Our findings point to the importance of chronic and stable temporal experiences, as time pressure and time perspective, in occupational experience. The findings are also discussed with a view to the development of practical methods of burnout prevention.

Keywords: conservation of resources, burnout, time pressure, time perspective

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Authors: Aurore Cauquis, Philippe Heinrich, Mario Ricchiuto, Audrey Gailler

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In this talk, we look for an accurate time scheme to model the propagation of waves. Several numerical schemes have been developed to solve the extended weakly nonlinear weakly dispersive Boussinesq Equations. The temporal schemes used are two Lax-Wendroff schemes, second or third order accurate, two Runge-Kutta schemes of second and third order and a simplified third order accurate Lax-Wendroff scheme. Spatial derivatives are evaluated with fourth order accuracy. The numerical model is applied to two monodimensional benchmarks on a flat bottom. It is also applied to the simulation of the Algerian tsunami generated by a Mw=6 seism on the 18th March 2021. The tsunami propagation was highly dispersive and propagated across the Mediterranean Sea. We study here the effects of the order of temporal discretization on the accuracy of the results and on the time of computation.

Keywords: numerical analysis, tsunami propagation, water wave, boussinesq equations

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Authors: Navya Saira George, Patroba Achola Odera

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This study aims to determine spatio-temporal expansion of Kochi City, situated on the west coast of Kerala State in India. Remote sensing and GIS techniques have been used to determine land use/cover and urban expansion of the City. Classification of Landsat images of the years 1973, 1988, 2002 and 2018 have been used to reproduce a visual story of the growth of the City over a period of 45 years. Accuracy range of 0.79 ~ 0.86 is achieved with kappa coefficient range of 0.69 ~ 0.80. Results show that the areas covered by vegetation and water bodies decreased progressively from 53.0 ~ 30.1% and 34.1 ~ 26.2% respectively, while built-up areas increased steadily from 12.5 to 42.2% over the entire study period (1973 ~ 2018). The shift in land use from agriculture to non-agriculture may be attributed to the land reforms since 1980s.

Keywords: Geographical Information Systems, Kochi City, Land use/cover, Remote Sensing, Urban Sprawl

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Authors: A. Er-Raki, D. Hartmann, J. P. Belaud, S. Negny

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This paper has a double purpose: firstly, a literature review of the life cycle analysis (LCA) and secondly a comparison between conventional (static) LCA and multi-level dynamic LCA on the following items: (i) inventories evolution with time (ii) temporal evolution of the databases. The first part of the paper summarizes the state of the art of the static LCA approach. The different static LCA limits have been identified and especially the non-consideration of the spatial and temporal evolution in the inventory, for the characterization factors (FCs) and into the databases. Then a description of the different levels of integration of the notion of temporality in life cycle analysis studies was made. In the second part, the dynamic inventory has been evaluated firstly for a single nuclear plant and secondly for the entire French nuclear power fleet by taking into account the construction durations of all the plants. In addition, the databases have been adapted by integrating the temporal variability of the French energy mix. Several iterations were used to converge towards the real environmental impact of the energy mix. Another adaptation of the databases to take into account the temporal evolution of the market data of the raw material was made. An identification of the energy mix of the time studied was based on an extrapolation of the production reference values of each means of production. An application to the construction of the French nuclear power plants from 1971 to 2000 has been performed, in which a dynamic inventory of raw material has been evaluated. Then the impacts were characterized by the ILCD 2011 characterization method. In order to compare with a purely static approach, a static impact assessment was made with the V 3.4 Ecoinvent data sheets without adaptation and a static inventory considering that all the power stations would have been built at the same time. Finally, a comparison between static and dynamic LCA approaches was set up to determine the gap between them for each of the two levels of integration. The results were analyzed to identify the contribution of the evolving nuclear power fleet construction to the total environmental impacts of the French energy mix during the same period. An equivalent strategy using a dynamic approach will further be applied to identify the environmental impacts that different scenarios of the energy transition could bring, allowing to choose the best energy mix from an environmental viewpoint.

Keywords: LCA, static, dynamic, inventory, construction, nuclear energy, energy mix, energy transition

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