Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Cutting-Edge Strategies for Soil Carbon Enhancement and Emission Reduction

The Cli mate p roof s oils by steering soil and residue micro biomes (ClipsMicro) project is based on the central premise that soil microbes play a vital role in carbon sequestration and greenhouse gas emissions. The project aims to enhance stable soil carbon formation and reduce GHG emissions by effectively managing the soil microbiome. To achieve this, an integrated approach will be developed, involving improved soil amendments and strategic cover crop usage. Specifically, the project will focus on enhancing compost properties through biological and mineral amendments and integrating BNI (Biological Nitrification Inhibition) cover crops into cropping strategies. As part of the ClipsMicro project, this review explores the current state-of-the-art knowledge on the use of mineral additions in the composting process to increase organic matter sequestration and the application of biological nitrification inhibition in field conditions.
Gene expression A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments : systematically incorporating validated biological knowledge

Motivation: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into ‘active regions’ (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling arraydata itself.Hereweproposeasupervisedframework fordoing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. Methodology: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). Results: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that theHMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments. Supplementary information: The supplementary data are available at http://tiling.gersteinlab. org/hmm/ Contact: mark.gerstein@yale.edu
CycleGAN-based image translation from MRI to CT scans

In recent years, the application of deep learning techniques to medical image analysis has shown promising results in improving diagnosis and treatment of diseases. One such technique is CycleGAN, a variant of Generative Adversarial Networks (GANs) that enables unpaired image-to-image translation. This paper presents a CycleGAN-based approach for transforming CT and MRI scans, which can provide doctors with more diagnostic information and assist in the prediction and diagnosis of tumors. Our experiments are based on brain scan images collected from the Kaggle dataset, with no paired information available. The generator and discriminator models of the CycleGAN are trained with the Adam optimizer and a cycle consistency loss weight (λ) of 10. The total training time is about 12 days, and the model is tested for 75 epochs with a fixed learning rate of 0.0002. The results demonstrate the effectiveness of the proposed method, achieving high-quality image translation from MRI to CT scans. The advantages of CycleGAN in medical image analysis include its ability to handle unpaired data, perform cross-domain image translation, ensure cycle consistency, and generate diverse outputs. Future work can further explore the use of CycleGAN for other medical image analysis tasks and investigate how to optimize the model performance.
Combining feature pyramid and CycleGAN for image generation

Image generation has always been a hot topic in computer vision community, which aims to learn the data distribution from a give image dataset and then generates new images obeying this distribution. Thanks to the rapid development of convolutional neural networks, breakthroughs have been made in the accuracy and speed of image generation. Currently, image generation is mainly based on the framework for generating countermeasures networks. However, limited by the quality of features, the generated images still have problems such as edge blurring, which restricts large-scale practical applications. In this paper, we propose an image generation algorithm that combines feature pyramid network (FPN) and CycleGAN. Specifically, the FPN containing two upsampling and feature fusion operations is added after the residual blocks and before the decoder of the CycleGAN generator, which can help the generator produce more detailed images and learn from features at different scales. Qualitative and quantitative experimental results show that the CycleGAN model with FPN outperforms the original CycleGAN model in terms of image generation.
The feeding behavior and food preferences of siamang (Symphalangus syndactylus Raffles, 1821) at Taman Safari Indonesia Bogor

Siamang (Symphalangus syndactylus Raffles, 1821) is a diurnal primate that belongs to the endangered category, according to the IUCN. Siamangs are classified as arboreal and frugivorous animals that selectively choose food. Siamang’s food preferences tend to be determined by the fruit availability in its habitat. One of the efforts to protect siamangs is ex-situ conservation. Feeding in ex-situ conservation should be adjusted to provide in their natural habitat. This study aims to identify siamang’s feeding behavior and food preferences in one of the ex-situ conservation facilities, namely Taman Safari Indonesia Bogor. The objects of observation consisted of two adult males, one adult female, and one sub-adult female. The methods used were instantaneous sampling, focal animal sampling, and ad-libitum sampling—statistical analysis using One-Way ANOVA and Two-Way ANOVA with Tukey’s test. The frequency and duration of siamang feeding behavior differed significantly between age classes, while no difference was based on sex. Food preferences among individuals are influenced by gender. Males prefer fruits such as apples and salak over vegetables, while females prefer soft-stemmed vegetables, namely water spinach and spinach.
Use of epiphyte by bird communities in IPB Dramaga campus

Epiphytes have a direct or indirect influence on terrestrial ecosystems. The presence of epiphytes can increase habitat complexity vertically and benefit animals, especially birds. The objectives of this study were to identify the species of epiphytes, their host trees, and the birds and to analyze how composition of epiphytes and host trees affects the diversity and abundance of birds. The research was conducted in seven sites within the IPB Dramaga Campus from December 2022 to February 2023. We collected biotic data (the number and type of epiphytes, host trees, and birds) and abiotic data (temperature and humidity) at each site. We used Paleontological Statisctic (PAST) to calculate the diversity index and R software to examine the correlation between birds and epiphytes. We found a significant positive correlation between the species richness of epiphytes, host trees, and birds. The sites with the highest diversity and abundance of trees, epiphytes, and birds were Conservation Park and Campus Forest Park. Sites with more diverse and abundant epiphytes had lower temperatures on average. We observed four bird species that used epiphytes: Pycnonotus aurigaster, Aegithina tiphia, Sitta frontalis, and Dicaeum trochileum. These species foraged for invertebrates on or near the Asplenium sp. epiphytes.
Identification of forest insects based on machine learning

Forest is one of the core subjects of terrestrial ecosystems, and forest insects form an important part of forest ecosystems. In recent years, the monitoring of forest insects has become particularly important as forest species diversity continues to evolve. In that case, this paper proposed a methodology to identify forest insects based on machine learning. This paper focused on three questions: for the data set, owing to the lack of existing datasets of forest insects, a delta-coder technique was used to learn the features of a few-shot dataset and generate more data (enhanced dataset) for machine learning program to learn; for the neural network, the author pre-processed the neural network by training an improved ResNet-18 network on insect section of ImageNet, then, the pre-trained ResNet-18 network was used as prototypical network and was trained by the enhanced dataset; for the effectiveness estimation of the identification model, SVM, ResNet, and VGGNet were used to do the same identifying process and compare the accuracy of this paper’s work and other three algorithms. By modifying the algorithm over and over again, a machine learning-based forest insect identification model was derived with an accuracy of 97.89%.
Expansion of the CT-scans image set based on the pretrained DCGAN for improving the performance of the CNN

Diagnosis of COVID-19 based on CT scans images using artificial intelligence can massively reduce medical manpower, time, and resources and reduce misdiagnosis rate. However, there are few relevant image sets publicly available at present, which is not conducive to the training of models such as classification. Transfer learning has been extensively considered in image classification, but less utilized in image generation. This work attempts to expand this dataset with discriminators using Deep Convolutional Generative Adversarial Network (DCGAN) models of Imagenet pre-training models and verify the significance of pre-training models for generating image veracity. A DCGAN model using the Imagenet pre-training model (i.e. resnet18) and a DCGAN network without the pre-train model generated 100 positive and 100 negative images to extend the original dataset, respectively. These images were classified using a Convolutional Neural Network (CNN) binary classifier, and the accuracy improved from 82.67% to 85.33% after adding the pre-training model. Furthermore, the comparison shown by the Gradcam visualization shows that the discriminator with the pre-training model can better capture the key details of the images. This experiment shows that even though Imagenet and lung images are totally uncorrelated, there are still some features that can be transferred. It also demonstrates that Imagenet pre-training models can improve the quality of GAN-generated images, extending the application of migration transfer learning.
Flight activity, associative learning, and memory in Asian honey bee Apis cerana

Honey bees have an important role as pollinators and are able to learn the flowering patches during foraging. These experiments aimed to investigate the associative learning and memory abilities of Apis cerana to find sugar and to observe the flight activities of the bees. The bees were trained using sugar solution at several distances using red and green papers as the landmarks that were placed beneath the sugar. Associative learning and long-term memory tests were carried out on day four and eleven after the training, respectively. On day four, we changed the angle of the sugar solution position to learn the length of time bees can find the shifting sugar. Our results confirmed that A. cerana could performed the training, as shown by the increasing number of A. cerana to the sugar from day one to day three during the training due to the recruitment process of the recruiter bee. A. cerana succeeded in associating each color and angle of landmarks and find the sugar in 15 minutes given in the day four. However, A. cerana failed the long-term memory test. Temperature and humidity showed negative and positive correlations, respectively, with the increasing number of flight activities of the bees.
SCAM-GAN: Generating brain MR images from CT scan data based on CycleGAN combined with attention module

The diagnosis of epilepsy often depends heavily on Magnetic Resonance Imaging (MRI). Unfortunately, the utilization of MRI is constrained, due to its expensive price and lengthy operating times. More significantly, certain people with claustrophobia or cardiac pacemakers are not candidates for MRI owing to the risk of harm. Computed tomography (CT) images, in comparison, are considerably faster, cheaper, and free from the same restrictions. As opposed to conventional medical imaging synthetic techniques, which rely on abundant unpaired data or a minimum number of paired data, the proposed method in this study utilizes unpaired training data to predict an MR picture from a CT scan. This method overcomes tight registration problem in paired training and reduces the challenge of context mismatch in unpaired training. It is possible to convert 2D brain MR pictures from CT scans using the Spatial & Channel Attention Mechanism-Generative Adversarial Network (SCAM-GAN) that includes cycle-consistent and adversarial losses. The superiority of the proposed strategy was established by quantitative comparisons versus separate training approaches which are paired and unpaired.
Colorization of gray scale images based on convolutional block attention and Pix2Pix network

Image colorization is a key research direction in the field of computer vision, which aims to predict the color information of grayscale images and convert them into color images. Traditional image colorization methods need to provide a large number of color points to mark each object in the image. As a result of the rapid development of convolutional neural networks, image colorization methods have also improved, with deep learning methods benefiting the most. However, due to the lack of feature quality, there are still many challenges in practical applications, such as boundary blurring. In this paper, we propose a gray image colorization method based on the Convolutional Block Attention Module (CBAM) and Pix2Pix network. Specifically, we add the CBAM module to the U-net generator network, which helps adaptively capture sensitive features, thereby enhancing the quality of features and image coloring. The effectiveness of the proposed model is verified by experiments.
Characterization of medicinal Ganoderma of GKR7 isolate based on cultural, morphological, and molecular characters

Ganoderma has commonly known as traditional medicinal mushroom. The GKR7 fruiting body of Ganoderma has been tested for antimicrobial activity against EPEC-antibiotic resistant strains and has shown great inhibition. However, the pure culture and fruiting body of the GKR7 have not been characterized. This study aimed to characterize the GKR7 based on its cultural features, fruiting body morphology, and molecular analysis. Cultural features and fruiting body morphology were determined by describing macroscopic and microscopic characteristics while molecular analysis was performed using internal transcribed spacer (ITS5/ITS4) region sequencing. The culture showed white then yellow or brown colonies. Hyphal features showed septate, hyaline, dichotomous branches, and having clamp-connection. Oxidation test with gallic/tannic acid formed dark discoloured areas under the colony mat indicating as white rot fungi. The fruiting body which cultivated in Albasia (Albizia falcataria) wood sawdust had hard corky to woody basidioma, dark brown stipe, shell-like pileus, upper surface of pileus white then reddish brown and lower surface yellowish white with irregular circular pores, and hyaline-brown double-walled truncate basidiospores. Phylogenetic analysis indicated that the GKR7 clustered in the same species clade as Ganoderma sichuanense. Based on its characteristics, the GKR7 was identified as G. sichuanense which was first reported in Indonesia.
End to end multifunctional autoencoder-based classification and denoising network

Brain tumours are one of the common diseases in human beings. Currently, brain Nuclear Magnetic Resonance (MRI) is the main means of detecting brain diseases. There are many works related to the classification and noise reduction of brain MRI images, but there are few works that combine these two parts simultaneously, i.e., noise reduction processing and classification of brain images at the same time. In this work, these two parts of work will be combined together, extracting the features of brain MRI images through an encoder composed of convolutional neural networks, using the features to classify the images, and then using the features to reduce the noise of the images to produce a low-noise image. According to the model, this work also proposes a new loss function. The new loss function is composed by adjusting the weights of the loss functions related to the classification and denoising tasks. This paper tries three different ways of weight assignment, and the experimental results show that the dynamic parameter assignment approach achieves the best results for image classification, but none of the three approaches achieves acceptable results for noise reduction.
SewerOD: A visual sewer disease detection dataset for machine learning

The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://github.com/SewerOD.
Vegetative response of shallot (Allium cepa Aggregatum group) bulbs on vernalization and gibberellin treatment in different environments

This research is an initial study of the effects of vernalization and Gibberellins in two different environments. The research was conducted in a split-split plot design with three factors and three times repetition. The main plot was the environment (environment 1, environment 2), the split plot was vernalization temperature (3±2 and 9±2°C), and split-split plot was Gibberellin concentration (0, 50, 100, 150 ppm). The result showed that the vernalizations and Gibberellin have significantly affected plant height (p<0.01). The environment significantly affected plant height, number of tillers, and number of leaves. There was an interaction between Gibberellin and environment treatment on plant height and an interaction between vernalization, Gibberellin, and environment on the number of leaves. The environment with temperature: 29±2.18°C, RH: 45±9.14%, light intensity: 9272±7669 μmol m-2 S-1 showed plant height and the number of tillers higher than other environments, contrary to the number of leaves. According to the principal component analysis, this environment is more suitable for all vernalization and Gibberellin treatments than other environments. The results of this study indicate that the environment more influences vegetative growth than the other two treatments.
Using VoteNet to detect beverage bottles

When using point cloud to detect indoor objects, the collected point cloud often has a lot of noise, such as points that are too far away, background points that have nothing to do with the objects to be detected, which will have a bad impact on the detection. In order to solve these problems, a method is proposed to preprocess the collected original point cloud before training and prediction. First, the points that are too far away should be eliminated. Second, the background such as the table should be eliminated as much as possible. Then, the coordinate conversion is required for the point cloud. Finally, VoteNet is used to train these processed point clouds to obtain the model, which is then used for prediction. The experimental results show that the model is good when using these processed point cloud data for training and prediction. In addition, this paper proposes to obtain point cloud by scanning objects, and then process the obtained point cloud to expand the dataset. The significance of this study is using the collected point cloud data for final training and prediction and expanding the dataset without using 3D cameras.
YOLO sparse training and model pruning for street view house numbers recognition

This paper proposes a YOLO (You Only Look Once) sparse training and model pruning technique for recognizing house numbers in street view images. YOLO is a popular object detection algorithm that has achieved state-of-the-art performance in various computer vision tasks. However, its large model size and computational complexity limit its deployment on resource-constrained devices such as smartphones and embedded systems. To address this issue, we use a sparse training technique that trains YOLO with L1 norm regularization to encourage the network to learn sparse representations. This results in a significant reduction in the number of parameters and computation without sacrificing accuracy. Furthermore, we apply a model pruning technique to the sparse-trained model to reduce the model size and computation. We evaluate our proposed method on the SVHN (Street View House Numbers) dataset. We show that it performs comparably to the original YOLO model while reducing the model size by 5% and the computation time by 7%. Overall, our proposed YOLO sparse training and model pruning technique provides an effective solution for deploying YOLO-based object detection models on resource-constrained devices.
Analyzing the impact of sustainable economic development from the policy text network: Based on the practice of China’s bay area policy

In order to break through the surface analysis of the content structure of policy texts, an in-depth discussion of the linkage between regional policy makers and objectives is helpful to analyze the formation mechanism of policy effects. Through social network analysis and multi-index analysis, this study takes the QianwanNew Area of Ningbo and the Guangdong-Hong Kong-Macao Greater Bay Area as representatives to explore the policy framework for the sustainable development of manufacturing industry in the two bay areas respectively. Through the construction of government department cooperation network, policy keyword co-occurrence network, department keyword correlation network, and the analysis of network density, network centrality, structural holes, and cohesive subgroups, it is found that the impact results show great differences, which is related to the network structure of manufacturing policy text.