Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling handling resources: robotic flowshops with circular layout and a practical railway problem. (Ordonnancement de ressources de transports : flow-shops robotisés circulaires et un problème pratique de gestion ferroviaire)

The first part of this work deals with cyclic production for throughput optimization in robotic flow-shops, where a robot is in charge of the material handling of parts. Robotic cells may have a linear or a circular layout. Most theoretical results for the linear layout do not hold for the circular layout. In particular, the problem of finding the best one part production cycle (1-cycle), which is a polynomial problem for linear additive cells, has been proved NP-hard for the corresponding circular configuration. We mainly focus on a special case of circular balanced cells, where the processing times are identical for all machines. After presenting tools for cyclic analysis in circular cells, we study necessary properties of efficient 1-cycles. These results allow to conclude on the best one part production cycle for any parameters in circular balanced cells up to 8 machines. However, we provide a counter-example to the classical 1-cycle conjecture, still open for this configuration. Then, we study the structure of efficient one part production cycles in arbitrarily large circular balanced cells. We introduce and study a new family of cycles based on this structure, and formulate a conjecture on its dominance over one part-production cycles, which would lead to a polynomial algorithm for finding the best 1-cycle for circular balanced cells. This structure allows to settle the best one part production cycle for cells with up to 11 machines. In a second part, we present work on an industrial problem of railway stock scheduling proposed by the French railway company in the context of the ROADEF/EURO competition. We propose a greedy algorithm for this problem combining the various aspects of trains handling inside a station.
Adaptation and acclimation both influence photosynthetic and respiratory temperature responses in Corymbia calophylla

Short-term acclimation and long-term adaptation represent two ways in which forest trees can respond to changes in temperature. Yet, the relative contribution of thermal acclimation and adaptation to tree physiological responses to temperature remains poorly understood. Here, we grew two cool-origin and two warm-origin populations of a widespread broad-leaved evergreen tree species (Corymbia calophylla (Lindl.) K.D. Hill & L.A. S.Johnson) from a Mediterranean climate in southwestern Australia under two growth temperatures representative of the cool- and warm-edge of the species distribution. The populations selected from each thermal environment represented both high and low precipitation sites. We measured the short-term temperature response of leaf photosynthesis (A) and dark respiration (R), and attributed observed variation to acclimation, adaptation or the combination of both. We observed limited variation in the temperature optimum (Topt) of A between temperature treatments or among populations, suggesting little plasticity or genetic differentiation in the Topt of A. Yet, other aspects of the temperature response of A and R were dependent upon population and growth temperature. Under cooler growth temperatures, the population from the coolest, wettest environment had the lowest A (at 25 °C) among all four populations, but exhibited the highest A (at 25 °C) under warmer growth temperatures. Populations varied in R (at 20 °C) and the temperature sensitivity of R (i.e., Q10 or activation energy) under cool, but not warm growth temperatures. However, populations showed similar yet lower R (at 20 °C) and no differences in the temperature sensitivity of R under warmer growth temperatures. We conclude that C. calophylla populations from contrasting climates vary in physiological acclimation to temperature, which might influence how this ecologically important tree species and the forests of southwestern Australia respond to climate change.
Complete mitochondrial genome of Hemiculter bleekeri bleekeri

Abstract Hemiculter bleekeri bleekeri is a typical freshwater species that are mainly distributed in the river basins in China. The biological information of this species is very limited. In this study, we determined the complete mitogenome sequence of H. bleekeri bleekeri. The complete mitogenome of H. bleekeri bleekeri is 16 6173 bp in length, which contains 22 transfer RNAs, 2 ribosomal RNAs, 13 protein-coding genes and 2 non-coding region, a rep region (33 bp) and a control region (D-loop). This work provides new information which is helpful for comprehending the molecular systematic, taxonomic status and evolutionary biology of this species.
Complete mitochondrial genome of Fistulifera solaris (Bacillariophycidae)

Abstract The complete mitochondrial DNA of pennate diatom Fistulifera solaris was determined. The circular mitochondrial genome is 39 648 bp in length with 28.1% GC content and contains 63 genes, including 33 protein-coding genes, three conserved open reading frame (ORF154, ORF192 and ORF251), 25 tRNA and 2 rRNA genes. All protein-coding genes have AUG as start codon. A group I intron was found in nad11 gene. Both the gene content and the structure of Fistulifera solaris mitochondrial genome are very similar to Berkeleya fennica mitogenome (KM886611). Phylogeny analysis indicate F. solaris a close genetic relationship with Berkeleya fennica. This new mitogenome will provide more useful information for exploration in diatoms diversity and evolution.
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

Modelling camera shake as a space-invariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera. In an effort to address these limitations, (i) we introduce a taxonomy of camera shakes, (ii) we build on a recently introduced framework for space-variant filtering by Hirsch et al. and a fast algorithm for single image blind deconvolution for space-invariant filters by Cho and Lee to construct a method for blind deconvolution in the case of space-variant blur, and (iii), we present an experimental setup for evaluation that allows us to take images with real camera shake while at the same time recording the space-variant point spread function corresponding to that blur. Finally, we demonstrate that our method is able to deblur images degraded by spatially-varying blur originating from real camera shake, even without using additionally motion sensor information.
Friendships in the air: Integrating social links into wireless network modeling, routing, and analysis

Social connections among network users have been well investigated as an additional opportunity in network design, such as in routing strategies and trusted networking. This paper presents a paradigm shift that explores the design and performance analysis of combining social links jointly with communication links to support message delivery in wireless networks. In a combined social and communication network, communication links are based on conventional wireless technologies (e.g., WiFi, Bluetooth) and social links are overlaid over a communication infrastructure (e.g., cellular network) that provides a complementary way for data transmission. The goal is to characterize the performance analytically when routing is designed by combining social and communication links. A distance discretization technique is applied to model the reliability and delay of the end-to-end message delivery, and a testbed is implemented with actual radios and real-world social network datasets to measure the performance of a heterogeneous network with social and communication links. The results presented via analysis and testbed experiments provide important insights on message delivery in a combined social and communication network and the developed analytical foundation enables network inference to improve the performance of message delivery.
Comprehensive analysis of area‐specific and time‐dependent changes in gene expression in the motor cortex of macaque monkeys during recovery from spinal cord injury

The present study aimed to assess the molecular bases of cortical compensatory mechanisms following spinal cord injury in primates. To accomplish this, comprehensive changes in gene expression were investigated in the bilateral primary motor cortex (M1), dorsal premotor cortex (PMd), and ventral premotor cortex (PMv) after a unilateral lesion of the lateral corticospinal tract (l‐CST). At 2 weeks after the lesion, a large number of genes exhibited altered expression levels in the contralesional M1, which is directly linked to the lesioned l‐CST. Gene ontology and network analyses indicated that these changes in gene expression are involved in the atrophy and plasticity changes observed in neurons. Orchestrated gene expression changes were present when behavioral recovery was attained 3 months after the lesion, particularly among the bilateral premotor areas, and a large number of these genes are involved in plasticity. Moreover, several genes abundantly expressed in M1 of intact monkeys were upregulated in both the PMd and PMv after the l‐CST lesion. These area‐specific and time‐dependent changes in gene expression may underlie the molecular mechanisms of functional recovery following a lesion of the l‐CST.
Administering atropine and ketamine before less invasive surfactant administration resulted in low pain scores in a prospective study of premature neonates

Less invasive surfactant administration (LISA) can avoid tracheal intubation for neonatal respiratory distress syndrome, but can be painful because it requires laryngoscopy. The aim of this study was to assess the efficacy and tolerance of intravenous atropine plus ketamine administration before LISA.
Review of dynamic contrast‐enhanced MRI: Technical aspects and applications in the musculoskeletal system

Although postcontrast imaging has been used for many years in musculoskeletal imaging, dynamic contrast enhanced (DCE) MRI is not routinely used in many centers around the world. Unlike conventional contrast‐enhanced sequences, DCE‐MRI allows the evaluation of the temporal pattern of enhancement in the musculoskeletal system, perhaps best known for its use in oncologic applications (such as differentiating benign from malignant tumors, evaluating for treatment response after neoadjuvant chemotherapy, and differentiating postsurgical changes from residual tumor). However, DCE‐MRI can also be used to evaluate inflammatory processes such as Charcot foot and synovitis, and evaluate bone perfusion in entities like Legg Calve Perthes disease and arthritis. Finally, vascular abnormalities and associated complications may be better characterized with DCE‐MRI than conventional imaging. The goal of this article is to review the applications and technical aspects of DCE‐MRI in the musculoskeletal system.
Using undiagnosed data to enhance computerized breast cancer analysis with a three stage data labeling method

A novel three stage Semi-Supervised Learning (SSL) approach is proposed for improving performance of computerized breast cancer analysis with undiagnosed data. These three stages include: (1) Instance selection, which is barely used in SSL or computerized cancer analysis systems, (2) Feature selection and (3) Newly designed ‘Divide Co-training’ data labeling method. 379 suspicious early breast cancer area samples from 121 mammograms were used in our research. Our proposed ‘Divide Co-training’ method is able to generate two classifiers through split original diagnosed dataset (labeled data), and label the undiagnosed data (unlabeled data) when they reached an agreement. The highest AUC (Area Under Curve, also called Az value) using labeled data only was 0.832 and it increased to 0.889 when undiagnosed data were included. The results indicate instance selection module could eliminate untypical data or noise data and enhance the following semi-supervised data labeling performance. Based on analyzing different data sizes, it can be observed that the AUC and accuracy go higher with the increase of either diagnosed data or undiagnosed data, and reach the best improvement (ΔAUC = 0.078, ΔAccuracy = 7.6%) with 40 of labeled data and 300 of unlabeled data.
The ontogeny of the female reproductive system in the parasitic castrator pea crab Calyptraeotheres garthi: Implications for its mating system

The knowledge of the mating system of pea crabs is still fragmentary as it remains dubious whether females copulate in the juvenile and free‐living ‘hard’ or in the obligatory symbiotic stages (adult stage ‘V’ or intermediate stages II to IV). To discriminate between these two possibilities, we analysed the female seminal receptacles, vagina and opercula, and the sperm content in different stages of the pea crab Calyptraeotheres garthi. Our histology and scanning electron microscopy results revealed that in the hard stage the seminal receptacle is simple without secretory epithelia, and vagina and opercula are not controlled by musculature. In stages II to IV, the seminal receptacles, vagina, and opercula are under development and these structures reach maturity in stage V. These results suggest that females become receptive in stage V and not during predating stages. We found no spermatozoa in SR of ‘hard’ and stage II to IV females while these structures were loaded of sperm in most stage V, indicating that females start to mate in stage V. Our results support the notion that males of C. garthi roam among hosts in search for sedentary stage V females, as predicted by Baeza and Thiel's ( ) model of mating systems for symbiotic crustaceans. Nevertheless, we failed to reveal whether females mate repeatedly: the accumulation of sperm in larger females might indicate occurrence of multiple copula or a high variability in male sperm transfer.
A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates

In this study, we assess the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007–09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks.
Potent Activities of Luliconazole, Lanoconazole, and Eight Comparators against Molecularly Characterized Fusarium Species

ABSTRACT A collection of clinical (n = 47) and environmental (n = 79) Fusarium isolates were tested against 10 antifungal drugs, including 2 novel imidazoles. Luliconazole and lanoconazole demonstrated very low geometric mean MIC values of 0.005 and 0.013 μg/ml, respectively, compared with 0.51 μg/ml for micafungin, 0.85 μg/ml for efinaconazole, 1.12 μg/ml for natamycin, 1.18 μg/ml for anidulafungin, 1.31 μg/ml for voriconazole, 1.35 μg/ml for caspofungin, 1.9 μg/ml for amphotericin B, and 4.08 μg/ml for itraconazole. Results show that these drugs are potential candidates for (topical) treatment of skin and nail infections due to Fusarium species.
Video retargeting with multi-scale trajectory optimization

Mobile devices are increasingly powerful in media storage and rendering. The prevalent request of decent video browsing on mobile devices is demanding. However, one limitation comes from the size and aspect constraints of display. To display a video on a small screen, rendering process probably undergoes a sort of retargeting to fit into the target display and keep the most of original video information. In this paper, we formulate video retargeting as the problem of finding an optimal trajectory for a cropping window to go through the video, capturing the most salient region to scale towards proper display on the target. To measure the visual importance of every pixel, we utilize the local spatial-temporal saliency (ST-saliency) and face detection results. The spatiotemporal movement of the cropping window is modeled in a graph where a smoothed trajectory is resolved by a Max-Flow/Min-Cut method in a global optimization manner. Based on the horizontal/vertical projections and a graph-based method, the trajectory estimation of each shot can be conducted within one second. Also, the process of merging trajectories is employed to capture more saliency in video. Experimental results on diverse video contents have shown that our approach is efficient and subjective evaluation shows that the retargeted video has gained desirable user satisfaction.
Robot pose estimation and navigation based on the understanding of laser landmarks in unknown environments

A pose estimation approach for the navigation of mobile robot in unknown environments is proposed. Compared with the existing mainstream SLAM, the proposed approach has a better generality without the help of odometers or motion models and it is completely based on external environment perceived by a laser sensor. In each of the laser frames, landmark points are first determined and they are then coded into a series of description matrixes. On this basis, by considering the landmarks matching in two neighboring laser frames, the pose change of the robot may be calculated, and the understanding of laser-based landmarks by geometric coding is achieved. The proposed approach is proved to be effective by the experiments of a tracked robot.
Patterns of Seismicity Associated with USGS Identified Areas of Potentially Induced Seismicity

A systematic review across U.S. Geological Survey (USGS) identified potentially induced seismic locations was conducted to discover seismic distance patterns and trends over time away from injection disposal wells. Previous research indicates a 10 km (6 miles) average where the majority of induced seismicity is expected to occur within individual locations, with some areas reporting a larger radius of 35 km (22 miles) to over 70 km (43 miles). This research analyzed earthquake occurrences within nine USGS locations where specified wells were identified as contributors to induced seismicity to determine distance patterns from disposal wells or outward seismic migration over time using established principles of hydrogeology. Results indicate a radius of 31.6 km (20 miles) where 90% of felt earthquakes occur among locations, with the closest proximal felt seismic events, on average, occurring 3 km (1.9 miles) away from injection disposal wells. The results of this research found distance trends across multiple locations of potentially induced seismicity.
Application of cuckoo search in water quality prediction using artificial neural network

Domestic and industrial pollution affected the water quality to a greater extent. Recent research studies have achieved reasonable success in predicting the water quality using several machine learning based techniques. In the current work, a proposed cuckoo search (CS) has been applied to improve the support in the classification process during the water quality prediction. The proposed model (NN-CS) gradually minimises an objective function; namely the root mean square error (RMSE) in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other well-established models, namely NN-GA (ANN trained with genetic algorithm) and NN-PSO (ANN trained with particle swarm optimisation) in terms of accuracy, precision, recall, f-measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-CS over the other models.
Heart sound segmentation using switching linear dynamical models

Localization of exact positions of the fundamental heart sounds (FHS) is an essential step towards automatic analysis of heart sound phonocardiogram (PCG) recordings, the automatic segmentation allows for data-driven classification of heart pathological events. Current approach using probabilistic models such as hidden Markov models (HMMs) has improved accuracy of heart sound segmentation. In this paper, we propose a switching linear dynamic system (SLDS) of piece-wise stationary autoregressive (AR) processes for segmenting the heart sounds into four fundamental components with distinct second order structure (auto-correlation). The SLDS is able to capture simultaneously both the continuous state-space in the hidden dynamics in PCG, and the regime switching in the dynamics using a discrete Markov chain. This overcomes limitation of HMMs which is based on a single-layer of discrete states. Compared to AR processes, the Gaussian mixture densities in HMM do not account for the temporal autorrelation structure in PCG which has one-to-one correspondence to frequency content a distinctive feature of HS components. We introduce three schemes for model estimation: (1) switching Kalman filter (SKF) model. (2) refinement by switching Kalman filter (SKS), and (3) fusion of SKF and the duration-dependent Viterbi algorithm (SKF-Viterbi). Results on a large PCG dateset of Physionet/Challenge 2016 shows SKF-Viterbi significantly outperforms SKF by improvement of segmentation accuracy from 71% to 84.2%.
Multi-Reference Video Coding Using Stillness Detection

Encoders of AOM/AV1 codec consider an input video sequence as succession of frames grouped in Golden-Frame (GF) groups. The coding structure of a GF group is fixed with a given GF group size. In the current AOM/AV1 encoder, video frames are coded using a hierarchical, multilayer coding structure within one GF group. It has been observed that the use of multilayer coding structure may result in worse coding performance if the GF group presents consistent stillness across its frames. This paper proposes a new approach that adaptively designs the Golden-Frame (GF) group coding structure through the use of stillness detection. Our new approach hence develops an automatic stillness detection scheme using three metrics extracted from each GF group. It then differentiates those GF groups of stillness from other non- still GF groups and uses different GF coding structures accordingly. Experimental result demonstrates a consistent coding gain using the new approach.