Miracle-dz/UniFAR-FTUs huggingface dataset:
CoRe: Contrastive and Restorative Self-Supervised Learning for Surface Defect Inspection

Visual inspection technology based on deep learning has achieved great success in surface defect inspection tasks. Most existing works transfer the learned knowledge from the natural dataset (e.g., ImageNet dataset) into the target tasks. However, the paradigm is suboptimal for defect inspection tasks due to: 1) the inherent dataset gap between natural and defect images and 2) the misalignment of task objectives. The limitations make the model cannot learn a generalized visual representation. To address the above issues, a contrastive and restorative self-supervised learning framework (CoRe) is proposed to learn general representation for the defect inspection task. Specifically, to bridge the dataset gap, we pretrain the model on the related dataset with a similar feature distribution instead of the natural dataset, which facilitates the representation learning of defect inspection tasks. Subsequently, to mitigate the task misalignment, the proposed method combines contrastive and restorative learning to excavate features required for the defect inspection task. The experimental results on five surface defect datasets demonstrate that our method outperforms the existing self-supervised learning (SSL) works, typical supervised pretraining paradigm, and some specific defect inspection methods.
Active Josephson traveling wave antennae as prospective terahertz oscillators

A terahertz superconducting oscillators based on large amount of Josephson junction embedded in open system guiding traveling electromagnetic wave is theoretically considered and computer simulated. It is shown that such active Josephson antennae represent oscillator effectively radiated into open space with power scaled with the system size and number of junctions. Dynamics and directivity patterns of such Josephson antennae depending on bias current is investigated and it is shown that such oscillator with sufficiently large junction amount can be competitive with quantum cascade lasers. It is well known that the Josephson Effect can be used for the generation of high-frequency electromagnetic radiation. The frequency is limited only by the superconducting energy gap. For low-Tc superconductors the frequency is in the sub-THz range and for high-Tc superconductors it can be up to approximately 20 THz. But the emission power from a single Josephson junction is quite small. It can be considerably enhanced provided coherent contribution of many, 1 N ≫ , Josephson junctions to radiated electromagnetic field. For achieving a practically important mW level of emission power very large arrays containing
Efficient Package Delivery Task Assignment for Truck and High Capacity Drone

This paper investigates the task assignment problem for one truck and one drone to deliver packages to a group of customer locations. The truck, carrying a large number of packages, can only travel between a group of prescribed street-stopping/parking locations to replenish the drone with both packages and batteries. The drone can carry multiple packages simultaneously to serve customers sequentially within its limited operation range. The objective is to reduce the amount of time it takes the drone to deliver the necessary package to the last customer while taking into account its operation range and loading capacity. First, the package delivery task assignment problem is shown to be an NP-hard problem, which guides us to design heuristic task assignment algorithms. Secondly, based on graph theory, a lower bound on the minimum time for the drone to serve the last customer is achieved to approximately evaluate the performance of a task assignment algorithm. Third, several decoupled heuristic algorithms are designed to sequentially plan the routes for the drone and the truck. Two coupled heuristic algorithms, namely the improved nearest inserting algorithm and the improved minimum marginal-cost algorithm, are proposed to simultaneously plan the routes for the drone and the truck. Numerical simulations demonstrate that the improved minimum marginal-cost algorithm reduces the total service time by 14.93% and 14.06% on average compared with the existing decoupled two-phase algorithm TPA and the coupled greedy algorithm, respectively. In the best case, it reduces the total service time by 41.71% and 40.11% compared with the TPA and the coupled greedy algorithm, respectively.
On the theory prediction of R_K and radiative corrections

We review the theoretical prediction for the ratio $R_{K}$ which tests LFU in $B\to K\ell\ell$ with $\ell=e,\mu$. In particular we are interested in investigating the size of the uncertainty. To achieve this we perform a semi-analytical calculation of radiative corrections for the decay $B\to K\ell^{+}\ell^{-}$, and apply the results to $R_{K}$. We find out that the radiative corrections can be sizeable. However, applying the same cuts used in LHCb measurement, the overall effect of soft QED corrections diminishes. We perform also a comparison with PHOTOS, the Montecarlo (MC) tool used by LHCb to correct for QED correction. We find good agreement between our result and the MC with difference only at the at the per mill level. As an outlook we present also the effect of QED corrections on $R_{K^{*}}$, for which a measurement by LHCb is expected soon.
Serum Lipid Levels and Suicidal Ideation of Adults: A Cross-Sectional Study Using the Korea National Health and Nutrition Examination Survey

Cholesterol plays a crucial role in the brain, which suggests that changes in its concentration levels may have an impact on the central nervous system. To examine the association between serum lipid levels and suicidal ideation according to sex, we performed a cross-sectional study using data from the Korea National Health and Nutrition Examination Survey 2014–2018. A total of 13,772 adults 19 years or older were analyzed. The ninth item of the Patient Health Questionnaire was used to evaluate the suicidal ideation of participants. After sorting by sex, a complex logistic regression was performed to measure the association between serum lipid indicators and suicidal ideation. The analysis adjusted for age, body mass index, smoking, heavy drinking, regular exercise, household income, education level, dyslipidemia medication, depression, and chronic diseases. Compared to the intermediated category, the lowest range of low-density lipoprotein cholesterol (LDL-C; <100 mg/dL) was associated with increased suicidal ideation in men (odds ratio [OR] = 1.97; 95% confidence interval [CI]: 1.30–3.01). The association between lipid levels and suicidal ideation was not clear in women. We found an association between lower LDL-C levels and an increased risk of suicidal ideation among Korean men aged 19 years or older.
Semi Prime Ideals and Annihilators in

In this we introduce the concept of semi prime deal, sub maximal ideals in a Boolean like semi ring and obtain some properties of these ideals. Further annihilator of a set in a Boolean like semi ring is introduced and studies certain properties of these annihilators. Mathematics Subject Classification. 16Y30, 16 Y 60
Developing an automatic collector of runoff for studies using rainfall simulators

ABSTRACT Soil erosion studies using rainfall simulators are generally expensive and time consuming. Thus, the aim of this study was to develop a prototype of an automatic runoff collector, capable of real-time quantifying runoff volume and soil loss in field trials using a rainfall simulator. The used sensors were chosen based on the type A uncertainty computed from different volumes of water and concentrations of sediment. Through specific programming, the runoff volume, sediment concentrations and the time of occurrence of the collections corresponding to each 200 cm³ of runoff were recorded on a micro-SD card. The robustness of the calibration and the programming developed were also evaluated in the Arduino Mega® 2560 microcontroller. The pressure (PSI.420) and turbidity (ST100) sensors were selected for developing the prototype, which was evaluated in the field with the InfiAsper rainfall simulator. Then, the data collected automatically by the sensors were compared to those obtained by manual measurement. The automatic runoff collector equipped with the PSI.420 and ST100 sensors has potential to obtain and store runoff data, and it was effective in evaluating the erosion process, generating mean errors of 12.25 and 13.16% for runoff volume and soil loss, respectively. The proposed prototype has a low cost of manufacture, in addition to optimizing the collection of erosion data in studies with rainfall simulators.
Eukaryotic Translation Elongation Factor OsEF1A Positively Regulates Drought Tolerance and Yield in Rice

Drought is one of the most serious stresses affecting rice growth. Drought stress causes accelerated senescence, reduced fertility, and subsequent reductions in crop yield. Eukaryotic translation elongation factor EF1A is an important multifunctional protein that plays an essential role in the translation of eukaryotic proteins. In this study, we localized and cloned the OsEF1A gene in rice (Oryza sativa) in order to clarify its role in drought tolerance and yield. Subcellular localization revealed that it was mainly localized to the cell membrane, cytoskeleton and nucleus. Compared with the wild-type, OsEF1A overexpressing transgenic plants had significantly more tillers and grains per plant, resulting in a significantly higher yield. Increases in the relative water content and proline content were also observed in the transgenic seedlings under drought stress, with a decrease in the malondialdehyde content, all of which are representative of drought tolerance. Taken together, these findings suggest that OsEF1A plays a positive regulatory role in rice nutritional development under drought stress. These findings will help support future studies aimed at improving yield and stress tolerance in rice at the molecular level, paving the way for a new green revolution.
Bayesian Analysis of an Excitonic Absorption Spectrum in a Cu2O Thin Film Sandwiched by Paired MgO Plates

We analyzed the absorption spectrum of a Cu2O thin film sandwiched by paired MgO plates with the Metropolis algorithm of Bayesian estimation to estimate the energy shift of a yellow excitonic band gap. In the absorption spectrum, discrete excitonic resonant transitions with homogeneous and inhomogeneous widths are superimposed upon the tail parts of excitonic continuum and band-to-band absorption bands. On the basis of the Metropolis samplings with 2.5 million steps after sufficient burn-in steps, the spectral parameters of the excitonic resonant transitions can be estimated satisfactorily with a high degree of accuracy. In particular, we demonstrated that the yellow excitonic band gap in the Cu2O thin film shifts to the lower energy side than that of Cu2O bulk crystals. This result is consistent with the stress relaxation model for such Cu2O thin films, which was reported in Eur. Phys. J. B 86, 194 (2013).
Flow of liquids through paper

The flow of liquids through paper is challenging to model due to the complexity and disordered layout of the fibre matrix. The expanding use and capability of microfluidic paper-based analytical devices ( $\unicode[STIX]{x1D707}$ PADs) and their requirement for precision has increased the need to accurately predict the flow of liquids through paper. Many studies have developed models and revealed some of the physical mechanisms responsible for the flow behaviour, but we still lack a complete understanding, particularly in relation to how the fluid fills the various voids with a wide range of shapes and sizes in the fibre matrix of paper. In the featured article, Chang et al. (J. Fluid Mech., vol. 845, 2018, 36–50) used a combined experimental and theoretical approach to uncover the importance of the liquid filling the intra-fibre pores, showed that this results in deviation from the flow behaviour predicted by the Lucas–Washburn equation and developed a model which accounts for this effect.
Exact Tandem Repeats using Suffix Array and Longest Common Prefix

Tandem Repeats (TRs) are the repeats which occur in a chromosome, while a pattern of 1 or more nucleotides (A, C, G, T) is repeated more than one time and the repetitions are continuous. Repeating patterns also referred to as motifs or base patterns, can be of various lengths. There is a complete of 25 linkage groups or chromosomes in the Zebrafish genome. The proposed algorithm reported exact tandem repeats with a motif length of at least three 3 with the help of an algorithm that uses a suffix array and longest common prefix. An overall of 119,909 repeats, with a base pattern of at least 3 nucleotides were detected. A massive number of repeats with a base period of 18, 24, 27 30 had been detected. The algorithm produces better results as compared to the existing results in terms of a number of repeats.
Exploring Potential Applications of Ising Machines for Power System Operations

Recent advancements in Ising machines, both via quantum and quantum-inspired annealing, have shown promising results in solving difficult optimization problems. Owing to the potential of these dedicated machines, there is increasing interest in applying Ising machines to power system operations. However, despite Ising machines exhibiting computational advantages over conventional computational resources, modeling capability of Ising machines limits their potential applications. Hence, this study explores these potential applications in two steps. First, using the known characteristics of Ising machines and theoretical derivation, this study constructed an Ising machine applicability table for optimization problems. Then, using the constructed table, this study assessed the suitability of well-known optimization problems in power system operations to identify potential applications. Furthermore, to understand the performance of applying Ising machines, this study solved a phasor measurement unit placement problem using a quantum-inspired Ising machine. The results show the potential of solving large-scale problems that are unsolvable by conventional methods. The Ising machine usage guideline for power system operations summarizes the findings of this study.
Research Trends of Physics Identity Development in University: How About Indonesia?

The concept of physics identity plays an essential role for physics teacher professionalism. That is why physics identity development has become a trend of research in West country. Through this study, we aimed to analyze research related to physics identity development in various international journals. We reviewed 17 emphirical studies published in 2013-2023 in reputable journals in context of undergraduate physics. The findings of the study were discussed based on in what ways student’s physics identity development, and what factor that influences the development. We indentify gaps in the existing knowledge base, particularly in Indonesia context, and we purpose further study directions. This study provides an overview of how physics identity development in university and give information where is Indonesia position.
Dimension Expansion Method and Implementation of Broadband Planar 2-D 3 × 3 Nolen Matrix

Traditional two-dimensional (2-D) beamforming network (BFN) topology is constructed by cross-connecting two stacks of one-dimension (1-D) BFNs, which results in a nonplanar topology, limited bandwidth, and bulky size. To overcome these limitations, a dimension expansion method is proposed in this article. By using the dimension expansion method, the traditional 2-D BFN topology can be planarized effectively. To validate the effectiveness of the dimension expansion method, a broadband planar 2-D beamforming topology constructed by six $3\times3$ sub-Nolen matrices and a novel $9\times9$ crossed phase shift network (CPSN) is designed. Vertically installed planar (VIP) couplers and broadband differential phase shifters are used to construct the broadband sub-Nolen matrices. A modified CPSN consisting of nine subcrossovers is designed to skillfully circumvent the intersection of different paths between the two expanded BFN groups. For verification, a broadband planar 2-D Nolen matrix operating at 1.8 GHz is designed, fabricated, and measured. Measured results match well with simulation. A bandwidth of 50.79% based on a stable generated array factor is realized, which further highlights the effectiveness of the dimension expansion method and its broadband characteristics.
ALGORITHM FOR AUTOMATIC RECOGNITION OF CARDIAC ARRHYTHMIAS

Problem statement: Cardiovascular diseases currently remain one of the leading causes of death. It is important to monitor the state of the cardiovascular system in early stages of pathology in order to diagnose these diseases in a timely manner. A special place is occupied by various arrhythmias among diseases of the cardiovascular system. The most common ones of various arrhythmias are extrasystoles. Ventricular extrasystoles are considered the most life-threatening among extrasystoles. In order to diagnose ventricular extrasystoles at an early stage of their development, it is necessary to process and analyze large amounts of electrocardiographic data. In this regard, the development and software implementation of algorithms for automatic recognition of ventricular arrhythmias based on electrocardiographic data through modern computer technologies is an urgent task. Work objective is developing an algorithm for automatic recognition of ventricular arrhythmias and its software implementation. Results: An algorithm for automatic recognition of ventricular extrasystoles, which is characterized by simplicity of implementation and minimal requirements for computing resources, has been developed. At the same time, high values of sensitivity and specificity are maintained for ECG signals with single ventricular extrasystoles. The algorithm is implemented in the LabVIEW software environment and tested using ECG files taken from international databases on cardiac arrhythmias, as well as using stimulated ECG signal models. Practical significance: The developed algorithm can be used in automatic processing and analysis of long-term ECG recordings and recognition of ventricular arrhythmias.
Describing the Body Shape of Rainbow Sardine (Dussumieria Acuta) Using Landmark-Based Geometric Morphometric Analysis

This study utilized geometric morphometric analysis of the body shape between sexes of Dussumieria acuta . Canonical Variate Analysis (CVA) and Discriminant Function Analysis (DFA) of the relative warp scores show significant differences in the body shape between males and females. Females exhibit body shapes that are geared to support larger number of eggs as shown by the distension of their belly region and a broader body compared to the males which possess a slender body shape. Variations observed within sexes can be attributed to selection pressures present in the environment. It is concluded that landmark-based geometric morphometric analysis is a useful tool in describing variations within and between sexes of D. acuta.
Research on water extraction method from remote sensing images of lakes in cold and arid regions based on deep learning

Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock module inside the Resnet34 network to obtain the SEResnet34 network. Finally, the Dual Attention Network (DANet) is added outside the SEResnet34 network to enable the model to adaptively enhance the perception of water body parts. In the Decode section, upsampling is performed by bilinear interpolation and fused with the feature extraction layer layer by layer to finally obtain the segmented image for water body extraction. (3) SER34AUnet model training: Firstly, the established dataset is divided into training and test sets in the ratio of 8:2. Next, the FCN_Resnet50, DeeplabV3_Resnet50, Unet semantic segmentation network model and the improved model in this paper are trained and tested. Finally, the visual and numerical analyses of the two lake water segmentation results and the three evaluation indexes of the test set, respectively, demonstrate the good applicability of the method proposed in this paper for lake water extraction in cold and arid regions.
Optimization and Characterization of Novel ALCAM-Targeting Antibody Fragments for Transepithelial Delivery

Activated leukocyte cell adhesion molecule (ALCAM) is a cell adhesion molecule that supports T cell activation, leukocyte migration, and (lymph)angiogenesis and has been shown to contribute to the pathology of various immune-mediated disorders, including asthma and corneal graft rejection. In contrast to monoclonal antibodies (mAbs) targeting ALCAM’s T cell expressed binding partner CD6, no ALCAM-targeting mAbs have thus far entered clinical development. This is likely linked with the broad expression of ALCAM on many different cell types, which increases the risk of eliciting unwanted treatment-induced side effects upon systemic mAb application. Targeting ALCAM in surface-exposed tissues, such as the lungs or the cornea, by a topical application could circumvent this issue. Here, we report the development of various stability- and affinity-improved anti-ALCAM mAb fragments with cross-species reactivity towards mouse, rat, monkey, and human ALCAM. Fragments generated in either mono- or bivalent formats potently blocked ALCAM–CD6 interactions in a competition ELISA, but only bivalent fragments efficiently inhibited ALCAM–ALCAM interactions in a leukocyte transmigration assay. The different fragments displayed a clear size-dependence in their ability to penetrate the human corneal epithelium. Furthermore, intranasal delivery of anti-ALCAM fragments reduced leukocyte infiltration in a mouse model of asthma, confirming ALCAM as a target for topical application in the lungs.