Miracle-dz/UniFAR-FTUs huggingface dataset:
Diamond xenolith and matrix organic matter in the Sutter's Mill meteorite measured by C‐XANES

The Sutter's Mill (SM) meteorite fell in El Dorado County, California, on April 22, 2012. This meteorite is a regolith breccia composed of CM chondrite material and at least one xenolithic phase: oldhamite. The meteorite studied here, SM2 (subsample 5), was one of three meteorites collected before it rained extensively on the debris site, thus preserving the original asteroid regolith mineralogy. Two relatively large (10 μm sized) possible diamond grains were observed in SM2‐5 surrounded by fine‐grained matrix. In the present work, we analyzed a focused ion beam (FIB) milled thin section that transected a region containing these two potential diamond grains as well as the surrounding fine‐grained matrix employing carbon and nitrogen X‐ray absorption near‐edge structure (C‐XANES and N‐XANES) spectroscopy using a scanning transmission X‐ray microscope (STXM) (Beamline 5.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory). The STXM analysis revealed that the matrix of SM2‐5 contains C‐rich grains, possibly organic nanoglobules. A single carbonate grain was also detected. The C‐XANES spectrum of the matrix is similar to that of insoluble organic matter (IOM) found in other CM chondrites. However, no significant nitrogen‐bearing functional groups were observed with N‐XANES. One of the possible diamond grains contains a Ca‐bearing inclusion that is not carbonate. C‐XANES features of the diamond‐edges suggest that the diamond might have formed by the CVD process, or in a high‐temperature and ‐pressure environment in the interior of a much larger parent body.
SegMatic: A Deep Neural Network Learning Model for Semantic Segmentation

- Semantic segmentation enables vehicles to accurately identify and categorize objects in their surroundings, such as pedestrians, other cars, road signs, and obstacles. Semantic segmentation, object detection, and deep learning have emerged as critical pillars, enabling machines to perceive and understand the visual world with unprecedented precision. This paper introduces SegMatic, a novel deep-learning model specifically tailored to address the unique demands of autonomous vehicles. SegMatic is a novel deep model for semantic segmentation and precise object detection. This model harnesses the power of deep learning to transform raw images into pixel-wise semantic maps, providing detailed insights into object boundaries and category-specific regions. SegMatic employs a two-stage approach. It uses a modified U-Net as the first stage to extract feature maps. Mask R-CNN is used as the second stage for post-processing. Experiments are conducted on the Pascal VOC 2012 dataset. SegMatic outperformed traditional models with remarkable precision and pixel accuracy scores. It achieved superior results in both semantic segmentation and object detection. This success is evidenced by achieving a mIoU of 94.6 and PA of 95.7 across various object categories. These results substantiate the significance of SegMatic’s contributions to
Weakening of Winter North Atlantic Oscillation Signal in Spring Precipitation over Southern China

Abstract Abstract In this study, the relationship between the North Atlantic Oscillation (NAO) in winter (December-February) and the precipitation over southern China (SCP) in the following spring (March-May) was investigated. Results showed an interdecadal change, from strong to weak connection, in their connection. Before the early 1980s, they were highly correlated, with a strong (weak) winter NAO followed by an increased (decreased) spring SCP. However, after the early 1980s, their relationship was weakened significantly. This unstable relationship may be linked to the climatological change of East Asian jet. Before the early 1980s, the wave train along the Asian jet propagated the NAO signal eastward to East Asia and affected local upper-tropospheric atmospheric circulation. A strong NAO in winter led to an anomalous anticyclonic circulation at the south side of 30°N in East Asia in spring, resulting in an increase of SCP. In contrast, after the early 1980s, the wave train pattern along the Asian jet extended eastward due to strengthening of the climatological East Asian jet. Correspondingly, the NAO-related East Asian atmospheric circulations in the upper troposphere shifted eastward, thereby weakening the linkage between the spring SCP and the winter NAO.
Joint Optimization of Full-Duplex Relay Placement and Transmit Power for Multihop Ultraviolet Communications

Ultraviolet (UV) communication is a promising technology for civilian and military secure communication systems due to nonline-of-sight transmission, low background noise, and high local security. The full-duplex relay-assisted UV communications can achieve longer communication distances and higher efficiency of time–frequency utilization compared with direct UV communications. However, due to the strong scattering effect, serious interrelay-interference (IRI) is inevitably introduced in multihop full-duplex relay links. To mitigate the impacts of IRI, this article proposes an alternate iterative-Newton method (AINM) to optimize jointly the relay placement and transmit powers of each relay. We further propose a space-division coupled full-duplex relay configuration to reduce the influence of IRI. Numerical results show that the proposed AINM can significantly decrease the bit-error rate (BER); and the proposed space-division scheme can further decrease the BER but sacrifices about half achievable information rate. Besides, we demonstrate that, when the communication links are strong, the distance between adjacent relays should gradually decrease and the transmit power increase from the source node to the destination node. However, when the communication links are weak, each relay should adopt its maximum transmit power to achieve the minimum BER.
Vehicle detection in surveillance videos based on YOLOv5 lightweight network

The development of surveillance video vehicle detection technology in modern intelligent transportation systems is closely related to the operation and safety of highways and urban road systems. Yet, the current object detection network structure is complex, requiring a large number of parameters and calculations, so this paper proposes a lightweight network based on YOLOv5. It can be easily deployed on video surveillance equipment even with limited performance, while ensuring real-time and accurate vehicle detection. Modiﬁed MobileNetV2 is used as the backbone feature extraction network of YOLOv5, and DSC “depthwise separable convolution” is used to replace the standard convolution in the bottleneck layer structure. The lightweight YOLOv5 is evaluated in the UA-DETRAC and BDD100k datasets. Experimental results show that this method reduces the number of parameters by 95% as compared with the original YOLOv5s and achieves a good tradeoff between precision and speed.
Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking

Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization. In this paper, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuition that spatially aligned corresponding points (via 3D registration) tend to achieve consistent feature representations. Specifically, our method consists of two modules, including a tracking-specific nonlocal registration module and a registration-aided Sinkhorn template-feature aggregation module. The registration module targets at the precise spatial alignment between the template and search area. The tracking-specific spatial distance constraint is proposed to refine the cross-attention weights in the nonlocal module for discriminative feature learning. Then, we use the weighted SVD to compute the rigid transformation between the template and search area, and align them to achieve the desired spatially aligned corresponding points. For the feature aggregation model, we formulate the feature matching between the transformed template and search area as an optimal transport problem and utilize the Sinkhorn optimization to search for the outlier-robust matching solution. Also, a registration-aided spatial distance map is built to improve the matching robustness in indistinguishable regions (e.g., smooth surface). Finally, guided by the obtained feature matching map, we aggregate the target information from the template into the search area to construct the target-specific feature, which is then fed into a CenterPoint-like detection head for object localization. Extensive experiments on KITTI, NuScenes and Waymo datasets verify the effectiveness of our proposed method.
Transport Properties of Strongly Correlated Fermi Systems

Physicists are actively debating the nature of the quantum critical phase transition that determines the low-temperature properties of metals with heavy fermions. Important experimental observations of their transport properties incisively probe the nature of the quantum critical phase transition. In our short review, we consider the transport properties of strongly correlated Fermi systems like heavy fermion metals and high—Tc superconductors. Their transport properties are defined by strong inter-particle interactions, forming flat bands in these compounds. These properties do not coincide with those of conventional metals. Indeed, in contrast to the behavior of the transport properties of conventional metals, the strongly correlated compounds exhibit linear temperature resistivity ρ(T)∝T. We analyze the magnetoresistance and show that under the application of the magnetic field, it becomes negative. It is shown that near a quantum phase transition, when the density of the electronic states diverges, semiclassical physics remains applicable to describe the resistivity ρ of strongly correlated metals due to the presence of a transverse zero-sound collective mode, representing the phonon mode in solids. We demonstrate that when T exceeds the extremely low Debye temperature TD, the resistivity ρ(T) changes linearly with T since the mechanism of formation of the T-dependence ρ(T) is a similar electron-phonon mechanism, which predominates at high temperatures in ordinary metals. Thus, in the region of T-linear resistance, electron-phonon scattering leads to a lifetime of τ quasiparticles practically independent of the material, which is expressed as the ratio of the Planck constant ℏ to the Boltzmann constant kB, Tτ∼ℏ/kB. We explain that due to the non-Fermi-liquid behavior, the real part of the frequency-dependent optical conductivity σoptR(ω) exhibits a scaling behavior and demonstrates the unusual power law behavior σoptR(ω)∝ω−1, rather than the well-known one shown by conventional metals, σoptR(ω)∝ω−2. All our theoretical considerations are illustrated and compared with the corresponding experimental facts. Our results are in a good agreement with experimental observations.
UAV mission scheduling with completion time, flight distance, and resource consumption constraints

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.
Physics-Based Compact Modeling of the Transfer Current in III-V DHBTs with the Generalized Integral Charge Control Relation

Physics-based modeling of the transfer current of III-V DHBTs (Double Heterojunction Bipolar Transistors) using the Generalized Integral Charge-Control Relation (GICCR) is discussed. The impact of selected material properties on the transfer current is analyzed based on TCAD simulations and the GICCR. Based on the insights obtained from this analysis, an improved formulation of the transfer current is suggested and verified using measurement data of an InP/GaAsSb DHBT and an InP/InGaAs DHBT technology.
Potential of periosteal cells in bone and cartilage regeneration: a systematic review

Introduction: The unavailability of adequate human primary cells presents multiple challenges in terms of bone and cartilage regeneration and disease modeling experiments in vitro. Periosteal cells (PCs), which represent promising skeletal stem cell sources, could be a promising strategy in tissue engineering. The present study aimed to summarize the characteristics of PCs to investigate the efficacy of these cells in bone and cartilage regeneration in different models, paying special attention to the comparison of bone marrow stromal cells (BMSCs). Methods: A comprehensive literature search was conducted in Embase, PubMed/MEDLINE, Web of Science, and Scopus for articles published in English until April 2023. Only original researches in which PCs were employed for bone or cartilage regeneration experiments were included. Results: A total of 9140 references were retrieved. After screening the results, 36 publications were considered to be eligible for inclusion in the present literature review. Overall, PCs demonstrated beneficial bone and cartilage regenerative efficacy compared to the bare scaffold since almost all included studies reported positive results. The 9 studies assessing the differences in bone formation capacity between PCs and BMSCs indicated that PCs exhibited stronger in vivo osteogenic differentiation capabilities compared to BMSCs, while the other study demonstrated stronger chondrogenic potential of BMSCs. Discussion: PCs demonstrated beneficial to bone regenerative efficacy compared to the bare scaffold with a low risk of most studies included. However, the cartilage formation capacity of BMSCs still needs to be investigated due to the limited research available and the certain risk of bias. PCs exhibited higher osteogenic capabilities compared to BMSCs in combination with various scaffolds in vivo with good evidence. Further researches are needed to elucidate the comparative benefits of cartilage regeneration. Systematic Review Registration: https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42023411522, CRD42023411522.
ENCFIS: An Exclusionary Neural Complex Fuzzy Inference System for Robust Regression Learning

Robust learning, an emerging research topic in recent years, is a promising branch of advanced artificial intelligence. Robust learning models target mainly noisy and rough datasets, predominantly in situations where noises and outliers are hard to remove. In this article, the concept of robust learning is combined with complex fuzzy theory for the first time, proposing a novel neuro-fuzzy system ENCFIS with extensive adaptability to numerical regression problems, with or without noise. Simulation results indicate that such architecture has excellent performance on a dataset with massive (45%) label noises and on a distorted time-series dataset (25% corrupted). In addition, experimental results on a metallurgy dataset also show that the approximation performance of ENCFIS is not compromised for the increase in robustness, making it an ideal candidate for general industrial scenarios with weak noise but difficult data characteristics.
Prospecting for Hydrothermal Vents Using Moored Current and Temperature Data: Axial Volcano on the Juan de Fuca Ridge, Northeast Pacific*

Abstract Tidal and inertial currents and profuse hydrothermal discharge at recently erupted Axial Volcano, Juan de Fuca Ridge, cause relatively large and rapid temperature (T) changes in the near-bottom water column. Measurements show short-term T variations of as much as 0.13°C at 30 m and 0.18°C at 150 m above bottom and currents that have strong tidal components and means of 3–5 cm s−1. Locations and magnitudes of the hydrothermal sources leading to the observed T variations have been inferred via an inverse calculation. Results imply noncongruent source regions around the mooring site for plumes from low- and high-buoyancy flux sources. Water column and seafloor observations in the volcano’s caldera region generally support the distribution of source types and sites inferred. A high-buoyancy flux, ephemeral venting site, unexpected on the eastern shoulder of the volcano, is also indicated by the inverse calculation and supported by water-column survey data. Over the O(10 km2) calculation region, heat .
THE EFFECTIVENESS OF THE SOCIAL SECURITY PROVIDER (BPJS) PROGRAM IN HEALTH SERVICES AT SYARIFAH AMBAMI RATO EBU HOSPITAL, BANGKALAN REGENCY

The community's need for BPJS health services is very important. Syarifah Ambami Rato Ebu (Syamrabu) Bangkalan Hospital as a health provider to improve the effectiveness of health services for the people of Bangkalan district. Based on the Undang-Undang Nomor 24 Tahun 2011 concerning the Social Security Provider (BPJS), the government organizes a national health insurance program through BPJS Kesehatan to ensure that all Indonesians are covered by health insurance. The purpose of this study was to describe and analyze the Effectiveness of the Social Security Provider (BPJS) Program in Health Services. This study uses qualitative methods with data collection techniques of observation, interviews, and documentation. Data analysis techniques are data collection, data condensation, data presentation, and drawing conclusions. This research is located in Syarifah Ambami Rato Ebu Hospital, Bangkalan. The results of this study are the resources, funds, facilities, and infrastructure used are effective, the number and quality of services produced are effective, the time limit for producing goods or services is effective, and the way to do it is already effective. The conclusion of this study is that health services for BPJS Inpatient Installation (IRNA) B Bawah at Syarifah Ambami Rato Ebu Hospital, Bangkalan Regency have been effective but not optimal.
The Habitats Directive and port development in coastal zones: Experiences in safeguarding biodiversity

Abstract This paper outlines the implications of the Habitats Directive of the European Union for port extension projects. Descending from the ecosystem level to the species level it will also draw conclusions on what the successful and less successful elements in the practice of implementing the Habitats Directive requirements from a nature protection perspective are. The focus of the paper is on projects and plans for port extension in or in the proximity of designated sites and on species protection. On the basis of case studies of large ports in northwestern Europe, preliminary conclusions are formulated and recommendations for similar projects are presented.
THE EFFECTS OF TEACHER'S MOTIVATIONAL STRATEGIES ON EFL LEARNER'S SPEAKING ABILITY IN JUNIOR HIGH SCHOOL

Learners utilize their speaking ability to communicate, elicit an idea or point of view, convey their ideas and desires, and socialize oneself in order to interact with others. The ability to talk is used to evaluate English comprehension. On the other hand, many EFL learners' ability to use English for oral communication is still low, due to fear and shy. Therefore, EFL learners need motivation to strengthen their confidence. There is one method to achieve it that is teacher's motivational strategies. Thus, this study aims to determine the effect of teacher's motivational strategies on EFL learner's speaking ability. In achieving this goal, the researcher used mixed method research. For the quantitative phase, experimental study was utilized to assess the effect between variables X, the teacher's motivational strategies as an independent variable, and Y, the learner's speaking ability as a dependent variable. Meanwhile, the field notes observation is used in qualitative phase. The post-test of both the experimental and control groups is . 029 which is smaller than . 05. It is acceptable to assume that there is a significant difference between the experimental and control groups. The calculation shows that the teacher's motivational strategies increase the learner's speaking ability. From the field notes observation, it wshowed that they can participate actively in classroom when teachers are applying motivational strategies or treatment. It can conclude that teachers' motivational strategies increase learner's speaking ability in experimental group.
Comparative Analysis of Vision Transformer Models for Facial Emotion Recognition Using Augmented Balanced Datasets

Facial emotion recognition (FER) has a huge importance in the field of human–machine interface. Given the intricacies of human facial expressions and the inherent variations in images, which are characterized by diverse facial poses and lighting conditions, the task of FER remains a challenging endeavour for computer-based models. Recent advancements have seen vision transformer (ViT) models attain state-of-the-art results across various computer vision tasks, encompassing image classification, object detection, and segmentation. Moreover, one of the most important aspects of creating strong machine learning models is correcting data imbalances. To avoid biased predictions and guarantee reliable findings, it is essential to maintain the distribution equilibrium of the training dataset. In this work, we have chosen two widely used open-source datasets, RAF-DB and FER2013. As well as resolving the imbalance problem, we present a new, balanced dataset, applying data augmentation techniques and cleaning poor-quality images from the FER2013 dataset. We then conduct a comprehensive evaluation of thirteen different ViT models with these three datasets. Our investigation concludes that ViT models present a promising approach for FER tasks. Among these ViT models, Mobile ViT and Tokens-to-Token ViT models appear to be the most effective, followed by PiT and Cross Former models.