Miracle-dz/UniFAR-FTUs huggingface dataset:
The Study on the Morphology and Compression Properties of Microcellular TPU/Nanoclay Tissue Scaffolds for Potential Tissue Engineering Applications

Thermoplastic polyurethane (TPU) materials have shown promise in tissue engineering applications due to their mechanical properties and biocompatibility. However, the addition of nanoclays to TPU can further enhance its properties. In this study, the effects of nanoclays on the microstructure, mechanical behavior, cytocompatibility, and proliferation of TPU/nanoclay (TPUNC) composite scaffolds were comprehensively investigated. The dispersion morphology of nanoclays within the TPU matrix was examined using transmission electron microscopy (TEM). It was found that the nanoclays exhibited a well-dispersed and intercalated structure, which contributed to the improved mechanical properties of the TPUNC scaffolds. Mechanical testing revealed that the addition of nanoclays significantly enhanced the compressive strength and elastic resilience of the TPUNC scaffolds. Cell viability and proliferation assays were conducted using MG63 cells cultured on the TPUNC scaffolds. The incorporation of nanoclays did not adversely affect cell viability, as evidenced by the comparable cell numbers between nanoclay-filled and unfilled TPU scaffolds. The presence of nanoclays within the TPUNC scaffolds did not disrupt cell adhesion or proliferation. The incorporation of nanoclays improved the dispersion morphology, enhanced mechanical performance, and maintained excellent biocompatibility. These findings suggest that TPUNC composites have great potential for tissue engineering applications, providing a versatile and promising scaffold material for regenerative medicine.
North Atlantic multidecadal variability in coupled climate models - Mechanisms and responses

Global atmosphere-ocean general circulation models have been used to investigate mechanisms controlling the North Atlantic low-frequency variability, with the focus on the Atlantic Meridional Overturning Circulation, the Subpolar Gyre dynamics and the North Atlantic basin scale sea surface temperatures, called the Atlantic Multidecadal Oscillation. The understanding of the dynamics of, and mechanisms behind the variability in these components of the climate system are of great importance for both climate reconstruction and predictability. We have shown that the air-sea interaction plays an important role in the ocean dynamics and ocean circulation in the North Atlantic.
ECU Testing for Safe Power Supply in Car with Vector Tools and Hardware-in-Loop

Delivering the product to the consumer will depend heavily on testing, validation, and verification. Customers may reject a product outright if it has flaws, fails to meet standards, or is otherwise unsatisfactory. Before installing the ECU with embedded software in a real car, automation testing is the best technique to test it. A hardware-in-loop (HiL) setup is an envirounment where testing of the ECU is carried out in a lab, with the use of vector tools like vector CANOe and vTest studio, This study focuses on testing of the ECU for over current and over temperature situations in order to check the position of the control unit switch to activate the safe features of ECU for the automotive safety integrity level loads (ASIL) to protect the car passengers.
Automatic Segmentation Methods for Lung Cancer Using Deformable Models and Deep Learning

In every country, cancer is now the main cause of death and the biggest barrier to an improvement in life expectancy. For both men and women, lung cancer is the leading cause of cancer related mortality. One of the main lung cancer treatment options is radiation therapy. It is crucial to spare the tissues close to the tumor while administering the recommended dose to the tumor targets. To overcome this, an accurate segmentation of lung tumor is necessary. For oncologists, manual segmentation is a tedious and time-consuming task. Hence, the development of automatic segmentation and classification is essential. At present clinical practices frequently uses conventional segmentation methods. Because of significant developments in computer vision, artificial intelligence is receiving more and more attention lately when it comes to automatically segmenting medical images. In this paper, deep learning based automatic segmentation and classification techniques are proposed to detect cancer. The 3D-convolutional neural network (3D-CNN) is applied to separate cancer part which is then integrated with Markov model based active contour deformable model to improve the average Dice similarity coefficient of lung in detecting the cancer.
Variable Selection of Lasso and Large Model

In order to clarify the variable selection of Lasso, Lasso is compared with two other variable selection methods AIC and forward stagewise. First, the variable selection of Lasso was compared with that of AIC, and it was discovered that Lasso has a wider application range than AIC. The data simulation shows the variable selection of Lasso under orthonormal design is consistent with AIC, Lasso under orthonormal design can be solved by using the stepwise selection algorithm. The removed variables of Lasso appear again under nonorthonormal design, the variable selection of Lasso under nonorthonormal design isn’t consistent with AIC. We continue to compare the variable selection of Lasso and forward stagewise. Based on the analysis of these studies, it is pointed out that the variable selection of Lasso is complex. An infinite number of parameters enable the design matrix to achieve orthonormalization, so that the solution of Lasso can be found with the stepwise selection algorithm, which may be the reason for the success of the large model represented by ChatGPT.
Investigation of the Pozzolanic Activity Improvement of Yellow Phosphorus Slag with Thermal Activation

Yellow phosphorus slag (YPS) is a byproduct from the production of yellow phosphorus. It has potential pozzolanic activity and can be used as a supplementary cementitious material. However, the early strength of cement mortar decreases significantly with increasing YPS dosage, which restricts the utilization of YPS in cement and concrete. This study aimed to increase the pozzolanic activity of YPS ash by thermal activation. The strength method, alkali dissolution method and polymerization degree method were used to evaluate the effect of thermal activation at different temperatures on the pozzolanic activity of YPS ash. The results showed that YPS ash calcined at 800 °C helps to enhance the early strength because the fluorine in cuspidine (Ca4Si2O7F2) is insoluble, reducing the retarding effect on the mortar. The higher late strength of YPS ash calcined at 100 °C was due to the low polymerization degree of [SiO4]. The pozzolanic activity of YPS ash is positively correlated with the dissolution concentration of (Si + Al) and the compressive strength and negatively associated with the polymerization degree. This paper shows a possibility for the large-scale utilization of YPS.
City Distribution, a Key Element of the Urban Economy: Guidelines for Practitioners

City distribution plays a key role in supporting urban lifestyles, helping to serve and retain industrial and trading activities, and contributing to the competitiveness of regional industry. Despite these positive effects, it also generates negative (economic, environmental and social) impacts on cities worldwide. Relatively little attention has been paid to these issues by researchers and policymakers until recently. The analyses found in City Distribution and Urban Freight Transport aim to improve knowledge in this important area by recognizing and evaluating the problems, with a focus on urban freight transport systems.
Technical Characteristics and Accuracy Capabilities of Delta Differential One-Way Ranging (DeltaDOR) as a Spacecraft Navigation Tool

The technique of determining spacecraft angular position by Delta Differential One-Way Ranging (DeltaDOR) is described. The dependence of measurement accuracy on parameters which define the spacecraft signal spectrum is shown. An illustration of DeltaDOR measurement accuracy, using the Mars Observer spacecraft, is presented. Guidelines for specifying spacecraft tone spectra are presented based on typical accuracy and ambiguity resolution needs, on frequency allocation for deep space tracking, on the efficient use of spacecraft signal power, and on the efficient use of ground tracking resources.
Forest Wildlife Ecology and Habitat Management

Concepts and Theory Common Knowledge Axioms Wildlife in a Forest Environment of Forest Wildlife Plants in a Forest Water in a Forest Forest Diversity Vegetation Forests as Ecological Systems Forest Succession Healthy Forest Conditions Survive and Reproduce Hazards Diseases Predators Humans Biology Resources Integrating Forestry and Wildlife Management Toward a Common Perspective Forest Vegetation Forest Management Integrating Factors Wildlife Management Restore and Manage Resources and Diversity Management Strategies Ecosystems Wildlife Habitat Relationships Ecological Factors Habitat Models Habitat Evaluation and Monitoring Integrated Management The Menominee Paradigm Planning Inventory Sampling FAAWN: A Data Model for Forest Attributes and Wildlife Needs Database Management Systems The Data Model Design
Synthetic Speech Spoofing Detection Based on Online Hard Example Mining

The online hard example mining (OHEM) algorithm has been successfully applied for object detection in images. In this paper, we propose an innovative application of the OHEM algorithm for training synthetic speech spoofing detection models, which addresses the problem of imbalance between simple and hard samples in the dataset. Our experimental results show that the introduction of the OHEM algorithm significantly decreases the equal error rate (EER) for four deep neural network models, namely ResNet18, ResNet50, SE-Res2Net, and Raw-Res2Net. The relative decreases are 42%, 28%, 25%, and 22%, respectively. Raw-Res2Net is a new network architecture proposed in this paper, which uses raw audio as the input. The study finds that this model performs significantly better than the other three experimental models in identifying some spoofing attack algorithms. Moreover, compared to the two baseline systems of the ASVspoof 2019 competition, the EERs of the Raw-Res2Net model are relatively reduced by 63% and 68%, respectively. Finally, by combining the scores of multiple synthetic speech spoofing detection systems based on the OHEM algorithm, the Raw-Res2Net-OHEM model complements the results of other models well. Without using any data augmentation techniques, the final fused system achieves an EER and a Minimum Tandem Detection Cost Function (min-DCF) of 0.77% and 0.022, respectively, on the ASVspoof 2019 LA dataset, outperforming the results reported in all published literature on fused systems.
The role of microRNAs in neurobiology and pathophysiology of the hippocampus

MicroRNAs (miRNAs) are short non-coding and well-conserved RNAs that are linked to many aspects of development and disorders. MicroRNAs control the expression of genes related to different biological processes and play a prominent role in the harmonious expression of many genes. During neural development of the central nervous system, miRNAs are regulated in time and space. In the mature brain, the dynamic expression of miRNAs continues, highlighting their functional importance in neurons. The hippocampus, as one of the crucial brain structures, is a key component of major functional connections in brain. Gene expression abnormalities in the hippocampus lead to disturbance in neurogenesis, neural maturation and synaptic formation. These disturbances are at the root of several neurological disorders and behavioral deficits, including Alzheimer’s disease, epilepsy and schizophrenia. There is strong evidence that abnormalities in miRNAs are contributed in neurodegenerative mechanisms in the hippocampus through imbalanced activity of ion channels, neuronal excitability, synaptic plasticity and neuronal apoptosis. Some miRNAs affect oxidative stress, inflammation, neural differentiation, migration and neurogenesis in the hippocampus. Furthermore, major signaling cascades in neurodegeneration, such as NF-Kβ signaling, PI3/Akt signaling and Notch pathway, are closely modulated by miRNAs. These observations, suggest that microRNAs are significant regulators in the complicated network of gene regulation in the hippocampus. In the current review, we focus on the miRNA functional role in the progression of normal development and neurogenesis of the hippocampus. We also consider how miRNAs in the hippocampus are crucial for gene expression mechanisms in pathophysiological pathways.
Improvement of wheat cultivation technology with Increased yield and product quality

The article is devoted to into the study of the optimisation of the process of using liquid nitrogen mineral fertilisers based on a carbamide-ammonia mixture, both in the serial form of CAM-32 and in the innovative CAM+S (with the addition of the sulfur mesoelement (S)) as a top dressing for winter wheat in spring during tillering. The application process was carried out superficially on crops with large-drop nozzles with a serial rod sprayer “Tuman-2” and an injec-tion-innovative multi-injector unit “Tuman-2M”. The article presents the improvement of the technology of cultivation of winter wheat of the "Basis" variety with the use of liquid mineral nitrogen and nitrogen-sulfur-containing fertilisers. The results obtained will improve the technology of wheat cultivation and improve the quality of the crop. The results of the research are an in-crease in yield compared with the use of granular solid mineral fertilisers: ammonium nitrate with the same nitrogen equivalent from 16.7 to 2.64 t/ha or from 34.5 to 58.1% and an improvement in grain quality to the level of class I compared with class III wheat quality when it is cultivated us-ing innovative technologies described in this work.
Multispecies crop mixtures increase insect biodiversity in an intercropping experiment

Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species‐rich taxon providing important ecosystem services. To counteract the negative consequences of agricultural intensification, creating a less hostile agricultural ‘matrix’ through growing crop mixtures can reduce harm for arthropods without yield losses. While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and agrochemical input used to study arthropods are lacking. Here, we experimentally manipulated crop diversity (1–3 species, fallows), crop species (wheat, faba bean, linseed and oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. We tested whether arthropod responses were affected by crop diversity, mixtures and management. Additionally, we measured crop biomass. Crop biomass increased with crop diversity under high‐input management, while under low management intensity, biomass was highest in two‐species mixtures. Increasing crop diversity positively affected arthropod abundance and diversity, under both low‐ and high‐input management. Crop mixtures containing faba bean, linseed or oilseed rape had particularly high arthropod diversity. Mass‐flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could increase flower visits by insects up to 1.5 million per hectare, thus likely also supporting pollination and pest‐control ecosystem services. Flower visitor network complexity increased in mixtures containing linseed and faba bean and under low‐input management. Intercropping can counteract insect declines in farmland by creating beneficial matrix habitat without compromising crop yield.
Breast Cancer Detection Using Deep Learning: An Investigation Using the DDSM Dataset and a Customized AlexNet and Support Vector Machine

The most lethal and devastating form of cancer, breast cancer, is often first detected when a lump appears in the breast. The cause can be attributed to a typical proliferation of cells in the mammary glands. Early breast cancer detection improves survival. Breast cancer screening and early detection are commonly carried out using imaging techniques such as mammography and ultrasound. Convolutional neural networks (CNNs) can identify breast cancer on mammograms. Layers of artificial neurons detect patterns and properties in images to help identify abnormalities more accurately. CNNs may be trained on large datasets to improve accuracy and handle more complex visual information than traditional methods. We introduced a unique approach termed BreastNet-SVM with the objective of automating the identification and categorization of breast cancer in mammograms. This study uses a nine-layer model with two fully connected layers to retrieve data features. Furthermore, we utilized support vector machines (SVM) for classification purposes. To conduct this experiment, we used a well-known benchmark dataset Digital Database for Screening Mammography (DDSM). It is shown that the suggested model has a 99.16% accuracy rate, a 97.13% sensitivity rate, and a 99.30% specificity rate. The top approaches for detecting breast cancer were compared to the recommended BreastNet-SVM model. In terms of accuracy, the proposed BreastNet-SVM model fared better in experimental results on a DDSM dataset.
Establishing Functional Retina in a Dish: Progress and Promises of Induced Pluripotent Stem Cell-Based Retinal Neuron Differentiation

The human eye plays a critical role in vision perception, but various retinal degenerative diseases such as retinitis pigmentosa (RP), glaucoma, and age-related macular degeneration (AMD) can lead to vision loss or blindness. Although progress has been made in understanding retinal development and in clinical research, current treatments remain inadequate for curing or reversing these degenerative conditions. Animal models have limited relevance to humans, and obtaining human eye tissue samples is challenging due to ethical and legal considerations. Consequently, researchers have turned to stem cell-based approaches, specifically induced pluripotent stem cells (iPSCs), to generate distinct retinal cell populations and develop cell replacement therapies. iPSCs offer a novel platform for studying the key stages of human retinogenesis and disease-specific mechanisms. Stem cell technology has facilitated the production of diverse retinal cell types, including retinal ganglion cells (RGCs) and photoreceptors, and the development of retinal organoids has emerged as a valuable in vitro tool for investigating retinal neuron differentiation and modeling retinal diseases. This review focuses on the protocols, culture conditions, and techniques employed in differentiating retinal neurons from iPSCs. Furthermore, it emphasizes the significance of molecular and functional validation of the differentiated cells.
Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction

This letter proposes a novel data-driven probabilistic physics-informed graph convolutional network (GCN) for active distribution system voltage prediction with PVs and EVs. It leverages both measurements and network topology to accurately and efficiently predict node voltages without the need for an accurate distribution system power flow model. The dropout-enabled Bayesian inference is developed to achieve uncertainty quantification of the voltage prediction. Thanks to the network model embedding, it also has robustness against topology changes, a key difference with existing machine learning-based approaches. Comparison results with other state-of-the-art machine learning methods on a realistic 759-node distribution system demonstrate that the proposed method can achieve better accuracy and robustness under different scenarios.
A Sensitivity of the White–Noise Distribution on the Performance of the Bootstrap Method in Uncovering the True Sampling Distribution of Autoregressive Processes

Bootstrap is a resampling method of estimating parameters or sampling distributions based on observed data. In order to apply the bootstrap approach when evaluating the parameters of time-series models, we need to consider the lack of independence between the observations. This study addresses the sensitivity of the white-noise distribution to the performance of the bootstrap method in uncovering the true sampling distribution of parameter estimates of autoregressive models. In order to study the performance, we use three white–noise (normal, exponential, and uniform) distributions for three (first and higher-order) models.
PAC Code Construction for Spin-Torque Transfer Magnetic Random Access Memory

In this paper, we investigate, for the first time, the design and optimization of the polarization-adjusted convolutional (PAC) code for the spin-torque transfer magnetic random access memory (STT-MRAM) channel. A crucial problem for the application of PAC codes to the STT-MRAM channel is to optimize the index set of the non-frozen bits of the PAC codes. Hence, a rate-profile optimization method based on the genetic algorithm assisted bit-swapping is proposed. Simulation results show that the PAC code with the optimized rate-profile outperforms both the polar code and the PAC code with existing generic rate-profiles.
Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis

Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem

A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer's order usually contains several items and needs to be packed as a whole before shipping. In particular, 5% of tens of millions of packages are using plastic wrapping as outer packaging every day, which brings pressure on the plastic surface minimization to save traditional logistics costs. Because of the huge practical significance, we focus on the issue of packing cuboid-shaped items orthogonally into a least-surface-area bin. The existing heuristic methods for classic 3D bin packing don't work well for this particular NP-hard problem and designing a good problem-specific heuristic is non-trivial. In this paper, rather than designing heuristics, we propose a novel multi-task framework based on Selected Learning to learn a heuristic-like policy that generates the sequence and orientations of items to be packed simultaneously. Through comprehensive experiments on a large scale real-world transaction order dataset and online AB tests, we show: 1) our selected learning method trades off the imbalance and correlation among the tasks and significantly outperforms the single task Pointer Network and the multi-task network without selected learning; 2) our method obtains an average 5.47% cost reduction than the well-designed greedy algorithm which is previously used in our online production system.