Miracle-dz/UniFAR-FTUs huggingface dataset:
Recherche de la symétrie tétraédrique dans le noyau 156Gd par spectroscopie γ

Des calculs theoriques utilisant la methode de champ moyen ont suggeree l'existence des formes du noyau avec la symetrie tetraedrique et/ou octraedrique dans la region des terres rares au voisinage des noyaux 156Gd et 160Yb. Dans les noyaux avec une symetrie tetraedrique pure, des transitions intra-bande E2 a bas spin dans des bandes de parite negative disparaissent ou sont tres faibles. Ce travail est dedie a une recherche experimentale de la symetrie tetraedrique dans le noyau 156Gd. Une experience a ete faite a Jyvaskyla avec le multi-detecteur JUROGAM, sur la reaction de fusion - evaporation 154Sm(, 2n)156Gd. L'analyse des donnees, a etabli les rapports d'embranchement de deux bandes de parite negative et a permis, par distribution angulaire, de connaitre la nature d'une nouvelle transition. Les rapports d'embranchement obtenus sont comparables avec ceux des experiences precedentes et quelques limites superieures ont ete determinees. L'absence de transitions a bas spin (I$^{pi}<9$) dans la bande de parite negative a spin impair a ete confirmee. Ces resultats ont renforce l'hypothese d'une symetrie tetraedrique dans le noyau 156Gd. La spectroscopie gamma est l'outil majeure utilise dans ce travail. Les principes ainsi qu'une etude de simulation realiste sont detailles dans ce manuscrit. La simulation avec des evenements realistes a ete faite pour comparer la fonction de reponse de deux types de multidetecteurs EUROBALL et AGATA. Les resultats montrent que sous certain conditions la phase demonstrateur d'AGATA peut etre utilisee pour la recherche d'evenements rares.
Chirality in Transition Metal Chemistry: Molecules, Supramolecular Assemblies and Materials

Preface. Foreword. 1. Introduction. 2. Chirality and Enantiomers. 2.1. Chirality. 2.2. Enantiomers and Racemic Compounds. 2.3. Absolute Configurations and System Descriptors. 2.4. Physical Properties of Enantiomers and Racemics. 2.5. Principles of Resolution and Preparation of Enantiomers. 2.6. Summary. 3. Some Examples of Chiral Organometallic Complexes and Asymmetric Catalysis. 3.1. Chirality at Metal Half-sandwich Compounds. 3.2. Chiral-at-metal Complexes in Organic Synthesis. 3.3. Asymmetric Catalysis by Chiral Complexes. 3.4. Summary. 4. Chiral Recognition in Organometallic and Coordination Compounds. 4.1. Octahedral Metal Complexes with Helical Chirality. 4.2. Chiral Recognition Using the Chiral Anion Strategy. 4.3. Brief Introduction to DNA Discrimination by Octahedral Polypyridyl Metal Complexes. 4.4. Summary. 5. Chirality in Supramolecular Coordination Compounds. 5.1. Self-assembly of Chiral Polynuclear Complexes from Achiral Building Units. 5.2. Chirality Transfer in Polynuclear Complexes: Enantioselective Synthesis. 5.3. Summary. 6. Chiral Enantiopure Molecular Materials. 6.1. General considerations. 6.2. Conductors. 6.3. Metallomesogens. 6.4. Porous Metalorganic Coordination Networks (MOCN). 6.5. Molecular Magnets. 6.6. Chiral Surfaces. 6.7. Summary. Index.
A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-Type Spatial Pattern Recognition in Wafer Bin Maps

Statistical quality control in semiconductor manufacturing hinges on effective diagnostics of wafer bin maps, wherein a key challenge is to detect how defective chips tend to spatially cluster on a wafer—a problem known as spatial pattern recognition. Recently, there has been a growing interest in mixed-type spatial pattern recognition—when multiple defect patterns, of different shapes, co-exist on the same wafer. Mixed-type spatial pattern recognition entails two central tasks: (1) spatial filtering, to distinguish systematic patterns from random noises; and (2) spatial clustering, to group filtered patterns into distinct defect types. Observing that spatial filtering is instrumental to high-quality mixed-type pattern recognition, we propose to use a graph-theoretic method, called adjacency-clustering, which leverages spatial dependence among adjacent defective chips to effectively filter the raw wafer maps. Tested on real-world data and compared against a state-of-the-art approach, our proposed method achieves at least 46% gain in terms of internal cluster validation quality (i.e., validation without external class labels), and about 5% gain in terms of Normalized Mutual Information—an external cluster validation metric based on external class labels. Interestingly, the margin of improvement appears to be a function of the pattern complexity, with larger gains achieved for more complex-shaped patterns.
Shear Bands in Materials Processing: Understanding the Mechanics of Flow Localization From Zener's Time to the Present

Shear banding is a material instability in large strain plastic deformation of solids, where otherwise homogeneous flow becomes localized in narrow micrometer-scale bands. Shear bands have broad implications for materials processing and failure under dynamic loading in a wide variety of material systems ranging from metals to rocks. This year marks 75 years since the publication of Zener and Hollomon's pioneering work on shear bands (Zener and Hollomon, J Appl. Phys., 15, 22–32, 1944), which is widely credited with drawing the attention of the mechanics community to shear bands and related localization phenomena. Since this landmark publication, there has been significant experimental and theoretical investigation into the onset of shear banding. Yet, given the extremely small length and time scales associated with band development, several challenges persist in studying the evolution of single bands, postinitiation. For instance, spatiotemporal development of strain fields in the vicinity of a band, crucial to understanding the transition from localized flow to fracture, has remained largely unexplored. Recent full-field displacement measurements, coupled with numerical modeling, have only begun to ameliorate this problem. This article summarizes our present understanding of plastic flow dynamics around single shear bands and the subsequent transition to fracture, with special emphasis on the postinstability stage. These topics are covered specifically from a materials processing perspective. We begin with a semihistorical look at some of Zener's early ideas on shear bands and discuss recent advances in experimental methods for mapping localized flow during band formation, including direct in situ imaging as well as ex situ/postmortem analyses. Classical theories and analytical frameworks are revisited in the light of recently published experimental data. We show that shear bands exhibit a wealth of complex flow characteristics that bear striking resemblance to viscous fluid flows and related boundary layer phenomena. Finally, new material systems and strategies for reproducing shear band formation at low speeds are discussed. It is hoped that these will help further our understanding of shear band dynamics, the subsequent transition to fracture, and lead to practical “control” strategies for suppressing shear band-driven failures in processing applications.
Sliding Window Integrated Weighting Algorithm for Positioning Indoor Active Objects

The modern industry necessitates gaining the real-time location of indoor objects to trigger related services and enhance efficiency. To devise effective localization algorithms concerning various metrics under different scenarios becomes much essential. Bluetooth Low Energy (BLE) comes to be a popular positioning technology, attributing to its low cost, low energy consumption, and ubiquity. In this work, the Sliding Window Integrated Mean-Max Weighting (SWIMMING) algorithm based on BLE has been proposed to locate moving objects indoors. The maximum value of signal strength within a timeslot, which attracts less attention in previous work, is regarded as one of the key features in our model. Moreover, Gaussian Processes (GPs) are used to filter raw data in advance so as to mitigate the effect of signal multipath fading. The warehouse scene is set as the background here for experiments since an actual requirement derived from the practice drives detecting the location of workers for operations advancement. BLE 5.0 is configured as the infrastructure for this test, which is new to the market and shows more stable signal transmission. A quantitative comparison with applying Kalman Filter, neural network and other approaches has been carried out. And the results demonstrate that our algorithm takes advantage of high location accuracy, cost-effective computational energy consumption, and ease of deployment. The SWIMMING algorithm achieves errors less than 1.5m 90% of the time under such dynamic conditions. Besides, other practical experiences are disclosed as a reference for future research.
Context-aware similarity of GPS trajectories

ABSTRACT Measuring similarity of GPS trajectories has attracted a lot of attention in recent years. As a result, multiple trajectory similarity measures have been developed and are used in a wide set of applications which aim to extract meaningful information from large collections. In this paper, we focus on some of the most popular measures and study how they all can be adapted to use contextual information. We experiment using the buildings in an urban setting as the context and demonstrate how it impacts the similarity values. Experiments show that routes rank differently in terms of similarity in the presence of context which can have serious implications in applications such as trajectory search and clustering similar trajectories.
Crystal Structure of Macrocalyxin J

The title compound,(1α,6β,11β,14α)-1,7:6,20-diepoxy-6,11-dihydroxy-6,7-seco-ent-kaur-16-ene-7,15-dione-14-acetate (macrocalyxin J),is a diterpenoid which was isolated from the leaves of Rabdosia macrocalyx and characterized by single-crystal X-ray diffraction. It cry-stallizes in orthorhombic,space group P212121 with a=9.3608(8),b=14.9787(12),c=15.5750(13) ,Z=4,V=2183.8(3) 3,C22H30O9,Mr=438.46,Dc=1.334 g/m3,μ(MoKα)=0.103 mm1,F(000)=936,the final R=0.0532 and wR=0.1262 for 2252 observed reflections (I 2σ(I)). In the molecule,three six-membered rings adopt chair,boat and slightly distorted boat conformations,respectively,while both five-membered rings have approximate envelope conformations.
Critical instability at moving keyhole tip generates porosity in laser melting

Driving the pores away The formation of “keyholes” (vapor-filled depressions) during additive manufacturing leads to porosity, which degrades alloy performance, especially fracture properties, and remains a big challenge for the 3D printing of metals. Zhao et al. used high-speed x-ray imaging to take a detailed look at how keyhole formation connects to porosity in a titanium alloy. They found that instability at the keyhole tip drives pores away to get trapped in the solidification front. Understanding this process and the operating parameters under which it occurs provides a roadmap for avoiding porosity and building high-quality metal parts. Science, this issue p. 1080 Pores trapped during additive manufacturing are connected to acoustic waves generated by keyhole instability. Laser powder bed fusion is a dominant metal 3D printing technology. However, porosity defects remain a challenge for fatigue-sensitive applications. Some porosity is associated with deep and narrow vapor depressions called keyholes, which occur under high-power, low–scan speed laser melting conditions. High-speed x-ray imaging enables operando observation of the detailed formation process of pores in Ti-6Al-4V caused by a critical instability at the keyhole tip. We found that the boundary of the keyhole porosity regime in power-velocity space is sharp and smooth, varying only slightly between the bare plate and powder bed. The critical keyhole instability generates acoustic waves in the melt pool that provide additional yet vital driving force for the pores near the keyhole tip to move away from the keyhole and become trapped as defects.
Optimization Model Supply Chain Network Design Determine Location-Allocation the Terminal Cathode Factory

Pati has a high certainty of raw material supply but Pati salt farmers experience quality and price problems. Currently, there is another potential high-value salt, namely as an energy source (Cathode battery). To solve this problem, the cathode terminal was built by considering the existing sand. Supply chain network design (SCND) is one of the most important strategic decisions in supply chain management. This paper presents novel research on the network design of a simplified single product multi farming and multi-terminal for salt raw material in cathode battery. The proposed optimization model minimized cost to determine the location of the cathode battery terminal by using Lingo Sofware. This paper will explain when the terminal opened and any farmer delivery salt raw material. This study develops a mathematic model for making a decision cathode battery terminal opened by mixed-integer linear programming (MILP). The result shows terminal 1 (Z1) receives a greater supply of raw materials and the least received supply from farming is terminal 3 (Z3).
An Autonomous Cognitive Empathy Model Responsive to Users’ Facial Emotion Expressions

Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user’s affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans’ emotions and replying to them appropriately. The proposed model continuously detects the affective states of a user based on facial expressions and generates desired, either parallel or reactive, empathic behaviors that are already adapted to the user’s personality. Users’ affective states are detected using a stacked autoencoder network that is trained and tested on the RAVDESS dataset. The overall proposed empathic model is verified throughout an experiment, where different emotions are triggered in participants and then empathic behaviors are applied based on proposed hypothesis. The results confirm the effectiveness of the proposed model in terms of related social and friendship concepts that participants perceived during interaction with the robot.
FastPath_MP

In this article, we present FastPath_MP, a novel low-overhead and energy-efficient storage multi-path architecture that leverages FPGAs to operate transparently to the main processor and improve the performance and energy efficiency of accessing storage devices. We prototyped FastPath_MP on both Arm-FPGA Zynq 7000 SoC and Zynq UltraScale+ MPSoC and evaluated its performance against standard microbenchmarks as well as the real-world in-memory Redis database. Our results show that FastPath_MP achieves up to 82% lower latency, up to 12× higher throughput, and up to 10× more energy efficiency against the baseline storage path of the Linux kernel.
Cognitive Style and Information Visualization—Modeling Users Through Eye Gaze Data

Information visualizations can be regarded as one of the most powerful cognitive tools to significantly amplify human cognition. However, traditional information visualization systems have been designed in a manner that does not consider individual user differences, even though human cognitive abilities and styles have been shown to differ significantly. In order to address this research gap, novel adaptive systems need to be developed that are able to (1) infer individual user characteristics and (2) provide an adaptation mechanism to personalize the system to the inferred characteristic. This paper presents a first step toward this goal by investigating the extent to which a user's cognitive style can be inferred from their behavior with an information visualization system. In particular, this paper presents a series of experiments that utilize features calculated from user eye gaze data in order to infer a user's cognitive style. Several different data and feature sets are presented, and results overall show that a user's eye gaze data can be used successfully to infer a user's cognitive style during information visualization usage.
Inactivation Efficacy of 405 nm LED Against Cronobacter sakazakii Biofilm

The objectives of this study were to evaluate the inactivation efficacy of a 405-nm light-emitting diode (LED) against Cronobacter sakazakii biofilm formed on stainless steel and to determine the sensitivity change of illuminated biofilm to food industrial disinfectants. The results showed that LED illumination significantly reduced the population of viable biofilm cells, showing reduction of 2.0 log (25°C), 2.5 log (10°C), and 2.0 log (4°C) between the non-illuminated and LED-illuminated groups at 4 h. Images of confocal laser scanning microscopy and scanning electron microscopy revealed the architectural damage to the biofilm caused by LED illumination, which involved destruction of the stereoscopic conformation of the biofilm. Moreover, the loss of biofilm components (mainly polysaccharide and protein) was revealed by attenuated total reflection Fourier-transformed infrared spectroscopy, and the downregulation of genes involved in C. sakazakii biofilm formation was confirmed by real time quantitative PCR analysis, with greatest difference observed in fliD. In addition, the sensitivity of illuminated-biofilm cells to disinfectant treatment was found to significantly increased, showing the greatest sensitivity change with 1.5 log reduction between non-LED and LED treatment biofilms in the CHX-treated group. These results indicated that 405 nm LED illumination was effective at inactivating C. sakazakii biofilm adhering to stainless steel. Therefore, the present study suggests the potential of 405 nm LED technology in controlling C. sakazakii biofilms in food processing and storage, minimizing the risk of contamination.
Metal speciation and bioavailability in aquatic systems

Transport of Metals Across Membranes (K. Simkiss & M. Taylor). Interactions between Trace Metals and Aquatic Organisms: A Critique of the Free-Ion Activity Model (P. Campbell). Environmental Organometallic Chemistry of Mercury, Tin and Lead: Present Status and Perspectives (E. Pelletier). Problems in Trace Metal Speciation Modeling (D. Turner). Trace Metal Speciation of Labile Chemical Species in Natural Water: Electrochemical Methods (A. Mota & M. Correia dos Santos). Trace Metal Speciation of Labile Chemical Species in Natural Waters and Sediments: Non-Electrochemical Approaches (S. Apter & G. Batley). Chromatographic Approaches to Trace Element Speciation of Non-Labile Chemical Species (W. Marshall & G.-M. Momplaisir). The Trophic Transfer of Metals in Marine Systems (N. Fisher & J. Reinfelder). Biological Factors Involved in Metal Concentrations Observed in Aquatic Organisms (W. Langston & S. Spence). Metal Detoxification in Aquatic Organisms (A. Mason & K. Jenkins). Prediction of Metal Toxicity in Nature from Bioassays: Limitations and Research Needs (S. Luoma). Index.
Promoting Protein Secondary Structure Prediction by Multi-output Model

The secondary structure of protein has three-state and eight-state classification standards, and there exist certain correspondences and commonalities between the two. In order to make full use of the consistent relationship to improve protein secondary structure prediction, an ensemble method named TSAES is introduced, which enables the three-classification and eight-classification to correct each other to attain more reasonable. Based on the same considerations, the final Multi-Output Simultaneous Prediction deep model shorted for MOSP is further proposed. It first mines the feature scale of the protein sequence, and then leverages a multi-layer temporal convolution networks to effectively extract the short-range dependence and long-term information of the sequence. High-level feature representation is finally used to optimize the outputs of three-state and eight-state classifications simultaneously. Experimental results demonstrate that 38relation-aware methods proposed here averagely enhanced 0.73%, 0.48% for Q3 and Q8 accuracy upon 38relation-agnostic baseline model. Though our work, the effectiveness of relation utilization had been verified, hence a novel valuable thinking angle except model innovation when making accurate prediction have been provided. Moreover, the proposed methods could be easily transplanted to other models, exhibiting strong feasibility and practicality.
Gestational Diabetes Is Uniquely Associated With Altered Early Seeding of the Infant Gut Microbiota

Gestational diabetes mellitus (GDM) is a worldwide public health problem affecting up to 27% of pregnancies with high predictive values for childhood obesity and inflammatory diseases. Compromised seeding of the infant gut microbiota is a risk factor for immunologic and metabolic diseases in the offspring; however, how GDM along with maternal obesity interact to alter colonization remains unknown. We hypothesized that GDM individually and in combination with maternal overweight/obesity would alter gut microbial composition, diversity, and short-chain fatty acid (SCFA) levels in neonates. We investigated 46 full-term neonates born to normal-weight or overweight/obese mothers with and without GDM, accounting for confounders including cesarean delivery, lack of breastfeeding, and exposure to antibiotics. Gut microbiota in 2-week-old neonates born to mothers with GDM exhibited differences in abundance of 26 microbial taxa; 14 of which showed persistent differential abundance after adjusting for pre-pregnancy BMI. Key pioneering gut taxa, including potentially important taxa for establishing neonatal immunity, were reduced. Lactobacillus, Flavonifractor, Erysipelotrichaceae, and unspecified families in Gammaproteobacteria were significantly reduced in neonates from mothers with GDM. GDM was associated with an increase in microbes involved in suppressing early immune cell function (Phascolarctobacterium). No differences in infant stool SCFA levels by maternal phenotype were noted; however, significant correlations were found between microbial abundances and SCFA levels in neonates. Our results suggest that GDM alone and together with maternal overweight/obesity uniquely influences seeding of specific infant microbiota in patterns that set the stage for future risk of inflammatory and metabolic disease.
Application of Wickerhamomyces anomalus in Simulated Solid-State Fermentation for Baijiu Production: Changes of Microbial Community Structure and Flavor Metabolism

Wickerhamomyces anomalus is conducive to the synthesis of ester compounds in brewing the Chinese liquor Baijiu; esters are crucial for the quality of Baijiu. In this study, simulated solid-state fermentation for Baijiu production was used to explore whether artificial addition of W. anomalus could improve the flavor substance in Baijiu, and the underlying mechanisms. Two experimental groups were studied, in which W. anomalus Y3604 (Group A) and YF1503 (Group B) were added, respectively; in the control group (Group C), no W. anomalus was added. Adding strain Y3604 increased the content of esters in fermentation samples, especially ethyl acetate and ethyl caproate, and reduced the content of higher alcohols. Adding strain YF1503 had little effect on the ester content but decreased the content of higher alcohols. The diversity and abundance of prokaryotic genera in Group A and B samples were similar, but there were some differences compared with Group C. The correlations of genera in Group A or B samples were simple compared with group C. Although the predominant eukaryotic genera in the three groups were consistent, the abundance of each gene varied among groups. Based on our findings, bioaugmentation of Baijiu fermentation with W. anomalus will change the ethyl acetate content and cause changes in the levels of other flavor substances. We suggest that the changes in flavor substances caused by the addition of W. anomalus are mainly due to changes in the microbial community structure that result from the addition of W. anomalus.