Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Association Study Reveals the Genetic Basis of Duck Plumage Colors

Plumage color is an artificially and naturally selected trait in domestic ducks. Black, white, and spotty are the main feather colors in domestic ducks. Previous studies have shown that black plumage color is caused by MC1R, and white plumage color is caused by MITF. We performed a genome-wide association study (GWAS) to identify candidate genes associated with white, black, and spotty plumage in ducks. Two non-synonymous SNPs in MC1R (c.52G>A and c.376G>A) were significantly related to duck black plumage, and three SNPs in MITF (chr13:15411658A>G, chr13:15412570T>C and chr13:15412592C>G) were associated with white plumage. Additionally, we also identified the epistatic interactions between causing loci. Some ducks with white plumage carry the c.52G>A and c.376G>A in MC1R, which also compensated for black and spotty plumage color phenotypes, suggesting that MC1R and MITF have an epistatic effect. The MITF locus was supposed to be an upstream gene to MC1R underlying the white, black, and spotty colors. Although the specific mechanism remains to be further clarified, these findings support the importance of epistasis in plumage color variation in ducks.
Socially Aware Multi-Resource Trading for IoT Applications in Smart Cities using Auction theory

The availability of a large number of devices in the Internet of Things (IoT) allows us to carry out distributed applications in a collaborative fashion. This approach is especially beneficial in smart city scenarios, wherein different types of resources (i.e., for sensing and actuation, in addition to computation and storage) are key to providing effective location-based services. This article addresses sharing heterogeneous resources for IoT applications in smart cities. Specifically, it takes a gametheoretic approach and addresses the problem of allocating resources from different devices as a combinatorial double auction. Features specific to IoT devices, including their affinity (e.g., ownership and co-location), are leveraged to form groups that offer resource bundles with a certain reliability. The proposed solution incurs a low computational complexity and achieves several important economic properties: incentive compatibility, individual rationality, and a balanced budget. Simulations based on a large-scale dataset demonstrate that the combinatorial double auction is effective and can be completed in a short time.
Short Photoperiod-Dependent Enrichment of Akkermansia spec. as the Major Change in the Intestinal Microbiome of Djungarian Hamsters (Phodopus sungorus)

The Djungarian hamster (Phodopus sungorus) is a prominent model organism for seasonal acclimatization, showing drastic whole-body physiological adjustments to an energetically challenging environment, which are considered to also involve the gut microbiome. Fecal samples of hamsters in long photoperiod and again after twelve weeks in short photoperiod were analyzed by 16S-rRNA sequencing to evaluate seasonal changes in the respective gut microbiomes. In both photoperiods, the overall composition was stable in the major superordinate phyla of the microbiota, with distinct and delicate changes of abundance in phyla representing each <1% of all. Elusimicrobia, Tenericutes, and Verrucomicrobia were exclusively present in short photoperiod hamsters. In contrast to Elusimicrobium and Aneroplasma as representatives of Elusimicrobia and Tenericutes, Akkermansia muciniphila is a prominent gut microbiome inhabitant well described as important in the health context of animals and humans, including neurodegenerative diseases and obesity. Since diet was not changed, Akkermansia enrichment appears to be a direct consequence of short photoperiod acclimation. Future research will investigate whether the Djungarian hamster intestinal microbiome is responsible for or responsive to seasonal acclimation, focusing on probiotic supplementation.
Machine Learning Approach to Anomaly Detection Attacks Classification in IoT Devices

Internet of Things (IoT) devices have a dark side could be used against the users or threaten the user by hackers and intruders. In addition, IoT devices have some security issues because the devices are basically connected to the internet and are more likely to get mishandled by hackers using anomaly attacks. In this paper, we proposed the application machine algorithms to detect anomaly attacks in IoT devices. The selected algorithms include are the Support Vector Machine (SVM) and Random Forest (RF). The SVM and RF are powerful supervised learning method that was utilized for both detection and feature selection. A standard anomaly dataset called the NSL-KDD dataset was used for the experimentation in arff format. The results shows an accuracy of approximately 99.9% and 97.9% with RF and SVM respectively, while a false positive rate of 0.1% was achieved in all scenarios for classification of anomaly attacks in IoT devices. This shows that the proposed method RF has higher accuracy than previous literatures, which is very promising. The RF and SVM posted a very encouraging recall and precision as well.
Introduction to the Spring 2023 Issue

In this release of IEEE Journal of Microwaves we complete our MTT 70th Anniversary issue with an additional six papers that did not make our deadline for the original January publication date. We also bring you fifteen regular papers on a wide variety of microwave topics and applications from wireless signaling inside oil and gas pipelines to textile-based wearable transmission lines. Our performance statistics continue to rise, with January 2023 usage data exceeding 35,000, thus doubling our previous record set in January 2021 with our Inaugural issue. We have also now bubbled up in the popularity charts to #3 out of 233 IEEE journals for usage per article published in 2021 and 2022 – an exceedingly proud moment for a journal that has yet to be archived on any database other than IEEE Xplore and will likely not receive an impact factor for another two years!
Cardiac 18F‑FDG uptake and new‑onset rectal cancer

The present study aimed to investigate the factors affecting the cardiac uptake of 18F-fluorodeoxyglucose (18F-FDG) during 18F-FDG positron emission tomography (PET) for new-onset rectal cancer and new-onset colon cancer (ascending, transverse, descending, sigmoid colon cancer) and to examine the association between the cardiac uptake of 18F-FDG and prognosis. The participants were diagnosed with new-onset rectal cancer and new-onset colon cancer (ascending, transverse, descending, sigmoid cancer) at the Iga City General Hospital (Iga, Japan) between January 1, 2013, and March 31, 2018, and underwent an 18F-FDG PET scan for pretreatment staging. The relationship between cardiac maximum standard uptake value (SUVmax), the presence/absence of distant metastasis and prognosis was examined. A total of 26 patients (14 men and 12 women) aged 72.0±10 years with new-onset rectal cancer were selected for the study. No patients had multiple simultaneous cancers. The median cardiac SUVmax was 3.8 and 2.5 in patients with no distant metastasis and distant metastasis, respectively, revealing a statistically significant difference (P<0.01). The median tumor volume on PET-computed tomography (CT) images was 7,815 cm2 and was 66,248 cm2 in patients with no distant metastasis and distant metastasis, respectively, revealing a statistically significant difference (P<0.01). Echocardiography findings revealed no significant difference between patients with and without distant metastasis. The correlation coefficient between cardiac SUVmax and total tumor volume on PET/CT images (primary + lymph + distant metastases) was statistically significant (r=−0.42, P=0.03). Analysis of the association between the occurrence of distance metastasis and cardiac SUVmax as a continuous variable gave a statistically significant result [hazard ratio (HR): 0.30, 95% confidence interval (CI): 0.09-0.98, P=0.045]. Receiver operating characteristic analysis showed a cardiac SUVmax of 2.6 with an area under the curve of 0.86 for determining the presence of distant metastasis (95% CI: 0.70-1.00). The median observation time was 56 months, and nine patients died during observation. Analysis of the association between the overall survival and cardiac SUVmax (cutoff: 2.6) showed 95% CI: 0.01-0.45 and HR: 0.06 (P<0.01); that between the overall survival and total tumor volume on PET images showed 95% CI: 1.00-1.00 and HR: 1.00 (P<0.01); and that between the overall survival and presence of distant metastasis showed 95% CI: 1.72-116.4 and HR: 14.1 (P<0.01). Furthermore, 25 patients (16 men and nine women) aged 71.4±14.2 years with new-onset colon cancer were selected for the study. Analysis of new-onset colon cancer revealed no statistically significance between the cardiac SUVmax and distant metastasis.
Optimal Distributed Cooperative Control Strategy for Frequency Restoration in AC Microgrid under Malicious Attacks

Energy and information flow are deeply integrated in AC microgrid cyber-physical systems. Therefore, the risk of the communication layer, such as malicious attacks, will be transmitted to the physical layer, disrupting the regular operation of microgrids. To this end, this paper proposes an optimal distributed cooperative strategy for the frequency restoration of AC microgrids. The strategy is implemented in a fully distributed form while considering the balance between distributed generators. It is shown that the proposed control strategy can achieve frequency restoration of microgrids event under malicious attacks and hence the security of system is greatly improved. Finally, the effectiveness of the strategy is verified by simulation experiments.
Eight-State Accuracy Prediction of Protein Secondary Structure using Ensembled Model

In order to study the three dimensional structure (3D) and functionalities of proteins, the secondary structure of proteins is imperative. To date, Convolutional Neural Network (CNN) and Long Short Term Memory network (LSTM) are two networks that have been used triumphantly in the study of protein sequences. In this paper, two models for the prediction of eight-state accuracy (Q8 accuracy) for protein secondary structure are proposed, when provided with the amino acid sequence. The first model is the Concatenated Convolutional1D model (CCN1D) wherein the Convolutional 1D networks are concatenated and this model obtains only the short range information between residues. CCN1D does not obtain complete information between the amino acids present within the protein. In order to obtain long range dependency among amino acid residues, Bidirectional LSTM (BLSTM) is used. Thus in order to get accurate results, the second model known as Convolution-BLSTM (C-BLSTM) is proposed. It is a cascaded network of CNN and BLSTM. The evolutionary profiles of proteins are used as the input to both models. The Q8 Accuracy of CCN1D and C-BLSTM is 71.73% at 0.38 Dropout rate and 72.47% at Dropout rate 0.3 respectively for the CullPDB5926 dataset.
Atomic layer deposition of ultrathin metal-oxide films for nano-scale device applications

With continuous dimensional scaling of high-performance transistors, high-k dielectrics are emerging as a very important research area in Si-based FET (field effect transistor) devices. Additionally, due to the uniquely high film quality, easy thickness controllability down to the nm range, and near perfect conformality, the ALD (atomic layer deposition) technique is also emerging as a future deposition technique for various applications. By combining these two promising areas, it is quite possible to open new application areas besides that of Si-based FETs which is under worldwide study. Recently developed Geand GaAs-based transistors with ALD high-k dielectrics have exhibited very promising electrical results, with surface passivation methods showing the possibility to further increase transistor performance. In addition, top-gated molecular devices, such as carbon nanotube and Ge nanowire transistors, were successfully developed with the help of ALD high-k dielectrics. Various other possible applications of ALD high-k dielectrics have also been suggested in this paper.
OWADIS: Rapid Discovery of OWASP10 Vulnerability based on Hybrid IDS

Rapid advancements in internet applications introduce new vulnerabilities and threats that malicious actors are keen to exploit. These activities are becoming more versatile and challenging to address. In addition to implementing firewalls to control the inbound and outbound network traffic, an intrusion detection system (IDS) is commonly employed to monitor the network for malicious activities and policy violations. However, most IDSs are generally designed to monitor network traffic. They are incapable to detect the vulnerabilities embedded in the legitimate packets, especially the vulnerabilities targeting web applications. In this paper, we propose a cloud-based IDS with an emphasis on the detection of OWASP Top 10 Injection vulnerabilities, combined with additional common vulnerabilities such as brute-forcing and session hijacking. Furthermore, DDoS attacks, which are commonly seen, can also be detected with our proposed adaptable HTTP flooding detection engine. We also provide the evaluation to show that our proposed scheme provides fewer false positives than SNORT and gives efficient system throughput based on the leverage of Kafka and Spark streaming.
Supervised Feature Reduction Based on a Mutual Information Measure for Hyperspectral Image Classification

Reducing the dimensionality of high dimensional data without losing its essential information is an important task in information processing. For hyperspectral data, Principal Component Analysis (PCA) is a common dimensionality reduction method which removes the correlation between bands by finding the lower dimensional projections of the original data which have maximum variance. However, since PCA does not take class label information into account, it does not always provide the best set of reduced features for class discrimination. Our proposed approach for dimensionality reduction is based on a measure of Mutual Information (MI). Our new algorithm, called MI-PCA, uses a mutual information measure to find those principal components which are spatially most similar to all the target classes. Our MI-PCA approach selects features which contain the most information about the output class structures and are also uncorrelated. We provide both theoretical and experimental analysis of this method in the paper. Experiments were conducted using hyperspectral data with 191 bands, covering the Washington DC area. The results show that 2 features selected out of 191 using MI-PCA provides 98% and 93% classification accuracy for training and test data respectively, with a Support Vector Machines (SVM) classifier. Whereas, to obtain a similar accuracy, the first 5 principle components are needed for a PCA-only approach. We also compared our approach with measuring the mutual information between the original bands and the class labels. However this approach performed poorly compared to our algorithm as the best features selected were all highly correlated with each other and could only provide discrimination between a small subset of the classes.
Genomic Regions Associated with Milk Composition and Fertility Traits in Spring-Calved Dairy Cows in New Zealand

The objective of this study was to identify genomic regions and genes that are associated with the milk composition and fertility traits of spring-calved dairy cows in New Zealand. Phenotypic data from the 2014–2015 and 2021–2022 calving seasons in two Massey University dairy herds were used. We identified 73 SNPs that were significantly associated with 58 potential candidate genes for milk composition and fertility traits. Four SNPs on chromosome 14 were highly significant for both fat and protein percentages, and the associated genes were DGAT1, SLC52A2, CPSF1, and MROH1. For fertility traits, significant associations were detected for intervals from the start of mating to first service, the start of mating to conception, first service to conception, calving to first service, and 6-wk submission, 6-wk in-calf, conception to first service in the first 3 weeks of the breeding season, and not in calf and 6-wk calving rates. Gene Ontology revealed 10 candidate genes (KCNH5, HS6ST3, GLS, ENSBTAG00000051479, STAT1, STAT4, GPD2, SH3PXD2A, EVA1C, and ARMH3) that were significantly associated with fertility traits. The biological functions of these genes are related to reducing the metabolic stress of cows and increasing insulin secretion during the mating period, early embryonic development, foetal growth, and maternal lipid metabolism during the pregnancy period.
The Role of Envy in the Relation between Social Networking Sites Use and Depression: a Systematic Review

The aim of this systematic review is to examine the role of envy in the relation between Social Networking Sites (SNSs) use and depression. The full text screening identified 9 studies that fell within the inclusion criteria of the current review. Overall, results showed a positive association between SNSs use and depression levels, that was mediated by envy and moderated by several factors such as self-efficacy or friendship type. In the only two studies suitable to infer directionality of the association between the variables considered, depression proved to be a predictor rather than an outcome of SNSs use and envy. To improve the sustainability of SNS platforms, ICT practitioners should address design features that induce social comparison and envy, as these phenomena influence not only well-being but also behaviors relevant to SNSs use (e.g., discontinuance intentions, switching SNSs).
The distance and distribution of galactic supernova remnants from the PMN survey sample

We have used the Parkes-MIT-NRAO (PMN) radio survey, at a common frequency of 4.85 GHz (A==6 cm), to study the distribution of Galactic shell supernova remnants (SNRs). We estimate distances and distribution of the Galactic shell SNRs and find the peak of distances to be between 4 and 6 kpc. Our two-dimensional model of the distribution of Galactic shell SNRs shows a concentration between the Sagittarius and the Scutum arms.
A Note on He's Parameter-Expansion Method of Coupled Van der Pol-Duffing Oscillators

This paper presents the analytical and approximate solutions of the coupled chaotic Van der PolDuffing systems, by using the He's parameter-expansion method (PEM). One iteration is sufficient to obtain a highly accurate solution, which is valid for the whole solution domain. From the obtained results, we can conclude that the suggest method, is of utter simplicity, and can be easily extended to all kinds of non-linear equations.
Using ARIMA and LSTM to Implement Stock Market Analysis

There has been a lot of discussion about the rapid shift that is taking place in the global environment toward the adoption of online shopping on a massive scale as well as the previously unheard-of volatility of the global stock market. Both of these topics have received a lot of attention in recent times. Both of these subjects have generated a significant amount of debate recently. Making accurate predictions about the market has always been a cause of anxiety for investors. Investors have always been interested in finding a practical solution that could perfectly predict the price. This may be accomplished by having an awareness of how the market repeats itself or by analysing patterns that may assist achieve a higher return on investment. Investors have always been interested in discovering a solution that could accurately anticipate the price, regardless of the strategy that has been used. In this investigation, the methodologies of "Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Auto-Regressive Integrated Moving Average" are combined to create a one-of-a-kind hybrid model (ARIMA). This study analyses and predicts movements in the stock market by looking at a broad variety of technologies, including those that are presently in use. The overall objective of this research is to analyse and predict changes in the stock market. An investigation into the development of historical patterns was carried out with the assistance of the data collected from the companies whose stocks are traded on the National Stock Exchange (NSE). This investigation included a time span of six years and made use of the facts pertaining to the stock's previous close, open, high, and low values. We will utilise the historical trends that have occurred in the past so that we may analyse and forecast patterns that will occur in the days that are to come. This will be accomplished via the use of historical trends. Because of its capacity to process large datasets and do trend analysis, Python and R have attracted a lot of attention in recent years.
Registration of 2D monocular endoscopy to 3D CBCT for video-assisted thoracoscopic surgery

Cone-Beam CT (CBCT) is a valuable imaging modality for the intraoperative localization of pulmonary nodules during Video-Assisted Thoracoscopic Surgery (VATS). However, inferring the nodule position from the CBCT to the operative field remains challenging and could greatly benefit from computer-aided guiding. As a first step towards an Augmented Endoscopy guiding system, we propose to register 2D monocular endoscopic views into the 3D CBCT space. Ribs and wound protectors are segmented in both imaging modalities, then registered using an image-to-cloud Iterative Closest Point variant. The method is evaluated qualitatively on clinical VATS video sequences from three patients. The promising results validate this first step towards a seamless monocular VATS navigation.
A Novel Fabric Defect Detection Network in textile fabrics based on DLT

One of the most crucial and difficult computer vision tasks in textile smart manufacturing is automated quality guarantee of textile fabric materials. The algorithms for detecting flaws in fabric are examined in detail in this survey. To begin, this article provides a brief overview of the significance and inevitable arrival of fabric defect detection in the era of artificial intelligence in manufacturing. Second, there are several different types of algorithms that can be used for defect detection; these include statistical, structural, spectral, and model-based algorithms. There is an existing systematic literature review of these procedures. Finally, this research looks at how algorithms can be used to detect flaws in fabrics. Researchers and engineers in the textile industry can use this paper as a resource for learning more about detecting fabric defects. Using the average of four orientations applied to different textural features present in an image, this study proposes a method using Histogram and HF to determine the appropriate CNN with Active contour Feature for the specific type of defect. Multiple machine learning techniques were used to evaluate the constructed models. Findings from this study indicated that 85 percent of the time, participants correctly identified the relevant CNN with Active contour characteristic associated with a given textile fault in silk fabric. An accuracy of 87% is achieved by the best model generated. When we compared the same models made out of different materials, such as jute, diamond pattern fabric, and flower pattern fabric, we found that the accuracy of the diamond pattern fabric was 86% and the accuracy of the jute model was 79%.