Miracle-dz/UniFAR-FTUs huggingface dataset:
SLDDNet: Stagewise Short and Long Distance Dependency Network for Remote Sensing Change Detection

With the rapid development of society, the pace of land change continues to accelerate. Consequently, remote sensing change detection (CD) has become a vital method for monitoring geographical information changes across various domains. However, the increasingly diverse and complex environments and structures where change targets exist pose significant challenges to CD tasks. To address these challenges, we propose a novel approach called stagewise short and long distance dependency network (SLDDNet). SLDDNet uses CNN-Transformer architecture and proposes the Transformer semantic selector to capture long-range feature dependencies and enhance semantic associations from a global perspective. Moreover, the pyramid structure feature stacking is proposed to capture short-range feature dependencies and emphasize local feature information. By integrating these two types of features at each layer, SLDDNet focuses on semantic information and improves its ability to attend to feature details. Furthermore, SLDDNet enhances target position information through axial semantic enhancement and optimizes the network training process using a deep supervision mechanism. Through extensive experiments, SLDDNet outperforms mainstream and state-of-the-art methods on three datasets. Specifically, on the LEVIR-CD dataset, SLDDNet achieves an F1 score of 91.75% and an intersection over union (IoU) of 84.76%. On the WHU-CD dataset, it achieves an F1 score of 92.76% and an IoU of 85.78%. Finally, on the GZ-CD dataset, SLDDNet achieves an F1 score of 86.61% and an IoU of 76.38%.
Human Lung Organoids—A Novel Experimental and Precision Medicine Approach

The global burden of respiratory diseases is very high and still on the rise, prompting the need for accurate models for basic and translational research. Several model systems are currently available ranging from simple airway cell cultures to complex tissue-engineered lungs. In recent years, human lung organoids have been established as highly transferrable three-dimensional in vitro model systems for lung research. For acute infectious and chronic inflammatory diseases as well as lung cancer, human lung organoids have opened possibilities for precise in vitro research and a deeper understanding of mechanisms underlying lung injury and regeneration. Human lung organoids from induced pluripotent stem cells or from adult stem cells of patients’ samples introduce tools for understanding developmental processes and personalized medicine approaches. When further state-of-the-art technologies and protocols come into use, the full potential of human lung organoids can be harnessed. High-throughput assays in drug development, gene therapy, and organoid transplantation are current applications of organoids in translational research. In this review, we emphasize novel approaches in translational and personalized medicine in lung research focusing on the use of human lung organoids.
Enhanced Algorithms for Counting Rectangles in Large Bipartite Graphs using MapReduce

Rectangles for bipartite graphs are like triangles for unipartite graphs as both represent the smallest cycles in such graphs. Rectangle Counting is considered an important task in many bipartite network analysis metrics and is considered the core of computing such metrics, especially in cluster coefficient, bitruss, etc. However, there are few efficient algorithms to deal with this problem, especially in a large bipartite graph. In this work, we use MapReduce to enhance an algorithm to count rectangles in a large bipartite graph. The results show that our proposed MapReduce-based algorithm gives a better execution time than the existing algorithms, especially when it is applied in very large bipartite graphs.
Efficacy of an external cold and vibrating device in reducing discomfort during the administration of an inferior alveolar nerve block in children: A split-mouth randomised crossover study

Background: Local anaesthesia is the backbone of pain management. However, the administration of a local anaesthetic injection itself is considered a painful procedure and triggers fear and anxiety in the patient. Methods: A split-mouth randomised controlled crossover trial was designed to study the efficacy of an “external cold and vibrating” device in reducing discomfort during the administration of an inferior alveolar nerve block (IANB) in children. A total of 40 children who fulfilled the inclusion criteria were evaluated for pain response following administration of IANB with and without the “external cold and vibrating” device. Randomisation was performed to determine whether the subject received the control intervention first or the test intervention first. The intensity of the pain response and discomfort were recorded using the Wong-Baker FACES Pain Rating Scale and the Faces Legs Activity Cry and Consolability scale. Results: There was a statistically significant difference in the pain response between the test group and the control group (p<0.001). Females reported a higher pain response than males, with a statistically significant difference, when the FLACC scores were compared. Conclusions: The “external cold and vibrating” device reduced discomfort during the administration of an IANB in children selected for the study.
Molecular Identification Of Lead-Resistant Bacteria And Assessment Of Their Effects On Vicia Faba Planted In Lead-Contaminated Soil.

: Bioremediation is a crucial tool for managing soil contamination. In this study, we aimed to reduce the side effects of lead (Pb) on Vicia faba seedling using Pb tolerant bacteria strains isolated from polluted places and to study these effects with Pb. Four Pb-tolerant isolates were selected from twenty-two strains. The isolated bacteria were identified using 16S-rRNA gene sequence technique. Identified strains were classified as members of the genus Bacilli . The tolerant isolates were Brevibacillus parabrevis, Brevibacillus reuszeri , Bacillus subtilis and Bacillus amyloliquefaciens . The results indicated to the side effects of Pb on shoot fresh and dry weight, root fresh and dry weight, shoot and root length, proline content, activities of some antioxidant enzymes (catalase, peroxidase and polyphenol oxidase) as well as cytogenetical measurements. Lead inhibited the mitotic index and caused an increase in chromosomal aberrations. The treatment with four bacteria significantly reduced the side effects of Pb on the level of growth and physiological parameters. Also, it improved the mitotic index and reduced the chromosomal aberrations.
Hydra-Zoet for the fresh water systems in the Netherlands - Probabilistic model for the assessment of dike heights

The Hydra set is a set of models to determine the hydraulic boundary conditions for flood defences in the Netherlands. Hydra-zoet is the model to be used for the fresh water enviroment (main rivers and fresh water lakes). This report describes the development and working of this model. part one gives a general discription on the physics of the model and a description of the fresh water systems, part 2 describes the physical, statistical and probabilistic aspects of the model Hydra-Zoet for a sea delta, in this case the delta of the rivers Rhine and Meuse. This delta is also denoted as the tidal-river area or simply the tidal area. Part 3 describes the physical, statistical and probabilistic aspects of the model Hydra-Zoet for a lake delta, in this case the delta of the Vecht and IJssel.
A summary of the Cenozoic stratigraphy and geologic history of the Coalinga region, central California

Cenozoic strata along the southwest side of the San Joaquin Valley provide a nearly complete record of the depositional history and structural evolution of this large sedimentary basin. The diverse sedimentary facies represented are evidence of repeated cycles of transgression and regression that are due to relative changes in sea level. These changes, in turn, can be interpreted in terms of either tectonic activity - that is, uplift or subsidence - or eustatic sea-level change. This chapter briefly describes the stratigraphy and outlines the geologic history of the region surrounding Coalinga and the epicenter of the May 2, 1983, earthquake, concentrating on Cenozoic time, during which tectonic relations most relevant to the existing stress field were established. This paper describes the geologic setting; Paleogene history; and Neogene and Quaternary history.
Research on text event inference techniques for security analysis scenarios

With the development of natural language processing technology, automatic text processing technology oriented to accident safety analysis is gaining more and more attention. By analyzing the passage of an accident text and reasoning out the causes and measures related to that accident, not only can the accident text be processed for automatic analysis, but also the analysis results can be used as safety knowledge to improve the safety awareness of staff. Therefore, we designed a text event reasoning model for safety analysis scenarios. The model is mainly divided into two parts: 1) a natural language inference module (NLI), which uses a homemade RNLI dataset for migration learning (model fine-tuning) on a pre-trained model and stores the inferred sentence relations and relational degrees in a database for matching with user input sentences; 2) an inference library matching (DSM) module, which uses a sentence matching algorithm to match the input accident statements with the The matching result is the correspondence of the input accident text. Based on the results of the two modules, the system is given a comprehensive score, and after several experiments the system is able to achieve an accuracy rate of 79.7% for textual event inference, which verifies the effectiveness of the system.The system not only handles text incidents efficiently, objectively and accurately, the reasoning results can also be used as a safety guide for equipment personnel and technicians, helping the relevant areas to develop appropriate daily work lists and eliminate safety hazards.
Directors' and Officers' Liability Insurance and Corporate Green Innovation

Using the data of A-share listed companies in Shanghai and Shenzhen from 2010 to 2021, we empirically test the impact mechanism of directors' and officers' liability insurance on corporate green innovation. The findings show that the introduction of directors' liability insurance plays a significant role in enhancing corporate green innovation, which still holds after robustness test and endogeneity treatment. It is further found that directors' liability insurance affects corporate green innovation by enhancing the level of corporate risk taking.
Aproximación sobre la climatología de la isla de Margarita y su importancia en los procesos oceánicos

Con la finalidad de identificar patrones climaticos temporales en la Isla de Margarita, Venezuela, fueron utilizadas series de tiempo entre 1966 y 2012 de la precipitacion y evaporacion acumulada y entre 1975 y 2008 de la velocidad promedio del viento. Los analisis de conglomerados realizados indicaron una marcada estacionalidad: de enero a junio sequia caracterizada por bajas precipitaciones (95 mm periodo-1) y de julio a diciembre lluvias moderadas (264 mm periodo-1), intensificandose los dos ultimos meses en respuesta al descenso latitudinal de los frentes frios articos, con ciclos interdecadales tanto de sequias como de lluvias extremas. La evaporacion fue elevada superando a la precipitacion durante todo el ano (276 mm periodo-1 en sequia y 249 mm periodo-1 en lluvia). La temporada de mayor velocidad promedio del viento correspondio a febrero-junio (X= 6,1 m s-1), con celeridades adecuadas para generar surgencia costera, mientras que el resto del ano los vientos son mas debiles (X= 4,6 m s-1). P alabras clave : Meteorologia, precipitacion, vientos, metocean. ABSTRACT In order to identify temporal weather patterns in Margarita Island, Venezuela, time series were analyzed between 1966 and 2012 for the accumulated precipitation and evaporation, and between 1975 and 2008 for the average wind speed. The Cluster analysis performed showed a marked seasonality: January to June form a dry period characterized by low precipitation (95 mm period-1), and from July to December there is a moderate rainy period (264 mm period-1), which intensifies during the last two months in response to a latitudinal decline of Arctic cold fronts, with both interdecadal cycles of droughts and extreme rainfall. Evaporation was high surpassing precipitation throughout the year (276 and 249 mm period-1 in the dry and rainy periods, respectively). The season of highest average wind speed corresponded to February to June (X = 6.1 m s-1), a period with appropriate wind speed to generate upwelling, while during the rest of the year the winds are weaker (X= 4.6 m s-1). K ey words : Meteorology, precipitation, winds, metocean.
What Every CCA Should Know About Drainage

Water table management through the use of artificial subsurface drainage systems is of primary importance in humid areas with poorly or somewhat poorly drained soils to maximize agricultural productivity. Excess precipitation throughout much of Midwestern agricultural watersheds is removed artificially via subsurface drainage systems that intercept and usually divert it to surface waters. Subsurface drainage systems have been installed to allow timely seedbed preparation, planting and harvesting and to protect crops from extended periods of flooded soil conditions. While there are significant production benefits of subsurface drainage, there are also concerns about the environmental impacts of the practice. A specific concern is related to nitrate-nitrogen loss through subsurface drainage systems. Nitrogen, either applied as fertilizer or manure or derived from soil organic matter, can be carried as nitrate with the excess water in quantities that can cause deleterious effects downstream. The movement of nitrogen from agricultural fields via drainage waters is a major factor in nonpoint source pollution of surface waters and, ultimately, of the Gulf of Mexico where it has been implicated as a cause of the Hypoxic Zone (Mitsch et al., 2001; Rabalais, et al., 1996). Understanding some basic underlying principles relative to subsurface drainage, drainage design, drainage management, and the environmental impacts of drainage are important for those involved with agriculture in the drained landscape.
Systematic exploitation of parallel task execution in business processes

Business process re-engineering (or optimization) has been attracting a lot of interest, and it is considered as a core element of business process management (BPM). One of its most effective mechanisms is task re-sequencing with a view to decreasing process duration and costs, whereas duration (aka cycle time) can be reduced using task parallelism as well. In this work, we propose a novel combination of these two mechanisms, which is resource allocation-aware. Starting from a solution where a given resource allocation in business processes can drive optimizations in an underlying BPMN diagram, our proposal considers resource allocation and model modifications in a combined manner, where an initially suboptimal resource allocation can lead to better overall process executions. More specifically, the main contribution is twofold: (i) to present a proposal that leverages a variant of representation of processes as Refined Process Structure Trees (RPSTs) with a view to enabling novel resource allocation-driven task re-ordering and parallelisation in a principled manner, and (ii) to introduce a resource allocation paradigm that assigns tasks to resources taking into account the re-sequencing opportunities that can arise. The results show that we can yield improvements in a very high proportion of our experimental cases, while these improvements can reach 45% decrease in cycle time.
Lightweight Convolutional Neural Network for Fire Classification in Surveillance System

Fire is one of the worst disasters for human life. Fire can happen anywhere and the leading cause can be natural or man. Over the last century, scientists have invented sensor-based methods to minimize damage and provide early warning of fires. However, these applications are only applied in a limited space and distance. For the purpose of fire remote warning and deploying on low-computing devices, this paper proposes a vision-based method using a lightweight convolutional neural network architecture combined with the inception and attention mechanisms. This proposed network includes two main modules: a feature extractor and a classifier. The feature extractor exploits convolution layers, depthwise separable convolution layers, inception module, and attention mechanism to extract high-level feature maps. Next, the classifier applies the global average pooling layer to quickly reduce the feature map dimensions and uses the softmax function to calculate the probability of each class. The experiments performed the training and evaluation on six datasets with an accuracy of over 96%. The fire surveillance system was implemented with simulation videos on GPU, CPU, and Jetson Nano devices, with the highest speeds of 200.95 FPS, 31.08 FPS, and 14.27 FPS, respectively. A set of demonstration videos, source code, and proposed dataset are provided here: https://bit.ly/3Wlpycf.
Enhanced Mask R-CNN blur instance segmentation based on generated adversarial network

Aiming at the detection of blur SEM nanoparticle images, a blur instance segmentation network (BL-Mask R-CNN) based on generative adversarial network deblurring convolution block (Deblur) and Mask R-CNN instance segmentation algorithm is proposed. The network uses the enhanced method of the blind deblurring algorithm (DeblurGAN-v2) of the generative confrontation network to preprocess the image, restore the grainy texture details in the image and generate a clear image that is conducive to instance segmentation and detection. The experimental results show that this method effectively improves the detection accuracy of blurred SEM nanoparticle images. Tested on the blur images of the NFFAEUROPE dataset, the improved BL-Mask R-CNN has compared the detection accuracy of the original Mask R-CNN. The accuracy rate AP has increased from 0.8339 to 0.9613, and achieved good results in the test.
Low-Profile Dual-Band Tripolarized Antenna Using Monopolar Slotted Patch

A dual-band multiport antenna with three orthogonal polarizations is proposed for 2.45 and 5.8 GHz bands. It consists of a monopolar slotted patch with shorting vias for vertically polarized omnidirectional radiations and differential-fed stacked patches for <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula>- and <inline-formula><tex-math notation="LaTeX">$y$</tex-math></inline-formula>-polarized broadside radiations. To avoid undesired effects among different radiation modes, four shorting vias in the monopolar patch are utilized to feed the stacked patches. The final design with 0.07<inline-formula><tex-math notation="LaTeX">$\lambda _{2.45-\text{GHz}}$</tex-math></inline-formula> profile achieves measured 10 dB return loss bandwidths of 50 and 180 MHz at the two target bands, respectively, and an isolation of <inline-formula><tex-math notation="LaTeX">$\ge$</tex-math></inline-formula>40 dB among all the ports, showing significant improvement as compared to the existing designs for the same application in the literature. Its radiation characteristics are validated by the far-field measurements, which illustrate highly symmetrical patterns and low cross polarization in all the operation modes.
Surface defect detection for nylon yarn package based on improved VGG model

At present, there is still seldom research on how to use neural networks for the surface defect detection of nylon yarn packages. The original VGG has some shortcomings, if there are many network layers, it is difficult to train, and if there are few network layers, it is impossible to learn abundant features to meet the requirements of industrial production. In this paper, an improved VGG-based method was proposed for detecting surface defects on nylon yarn packages. A two-path network structure with 16 and 19 convolutional layers is designed to fuse the features learned by the earlier layers and the fused features will be inputted to the classifier to obtain the final output for defect category. To deal with the small sample size problem which affect to train the neural network effectively, we use data augmentation to process the photos of the input and transfer learning to initialize the model parameters. Our experiments demonstrate that the proposed method improves the accuracy by 1.07% over the VGG16-BN.
Low-Voltage Solution-Processed Zinc-Doped CuI Thin Film Transistors with NOR Logic and Artificial Synaptic Function

Low-voltage Zn-doped CuI thin film transistors (TFTs) gated by chitosan dielectric were fabricated at a low temperature. The Zn-doped CuI TFT exhibited a more superior on/off current ratio than CuI TFT due to the substitution or supplementation of copper vacancies by Zn ions. The Zn-doped CuI films were characterized by scanning electron microscope, X-ray diffraction, and X-ray photoelectron spectroscopy. The Zn-doped CuI TFTs exhibited an on/off current ratio of 1.58 × 104, a subthreshold swing of 70 mV/decade, and a field effect mobility of 0.40 cm2V−1s−1, demonstrating good operational stability. Due to the electric-double-layer (EDL) effect and high specific capacitance (17.3 μF/cm2) of chitosan gate dielectric, Zn-doped CuI TFT operates at a voltage below −2 V. The threshold voltage is −0.2 V. In particular, we have prepared Zn-doped CuI TFTs with two in-plane gates and NOR logic operation is implemented on such TFTs. In addition, using the ion relaxation effect and EDL effect of chitosan film, a simple pain neuron simulation is realized on such a p-type TFTs for the first time through the bottom gate to regulate the carrier transport of the channel. This p-type device has promising applications in low-cost electronic devices, complementary electronic circuit, and biosensors.
Differences in daily milk production during early pregnancy alter placental characteristics and neonatal metabolic amino acid levels in dairy cows

We investigated the effects of differences in milk production during early pregnancy on placental characteristics at full term, calf birth weights, and their metabolic status. Thirty-four Holstein cows were categorized into three groups (Low, n = 9; Middle, n = 16; High, n = 9) based on the quartile of average daily 4% fat-corrected milk production during early pregnancy. The High group showed higher milk component production than the other groups (P < 0.05) during early and mid-pregnancy. Although most placental characteristics did not differ significantly among the groups, cows in the High group had larger individual cotyledons and fewer medium-sized cotyledons than those in the Low group (P < 0.05). Plasma amino acid concentrations of calves in the Low and High groups were significantly higher than those of calves in the Middle group, although calf birth weights were similar among the groups. Furthermore, cows in the Low group had longer dry periods than those in the High (P = 0.004) and Middle (P = 0.058) groups. This suggests that cows in the Low group may have provided more amino acids to the fetus because of low lactation and long dry periods. Conversely, cows in the High group required more energy for lactation during early pregnancy, which can reduce nutrient availability to the placenta and fetus; however, increasing individual cotyledonary sizes during late pregnancy may ensure that the same amounts of amino acids as those in cows in the Low group are supplied to the fetus, recovering the birth weights.