Development of doubled haploid maize lines by using in vivo haploid technique

The doubled haploid technology is now an integral component of modern maizebreeding programs. In this study, the maternal haploid induction (gynogenesis)method was used to derive Doubled-Haploid (DH) lines from elite maize germplasmadapted to Turkey. Temperate haploid inducers (RWS, RWK-76, RWS x RWK-76 andWS14) were used as pollinators, and a set of 30 single-crossses (in FAO 650-700maturity groups) were used as source materials. Putative haploid seeds were selectedbased on expression of R1-nj anthocyanin color marker. Highest haploid induction rate(20.42%) was recorded by using RWK-76 as inducer line, and the lowest haploidinduction rate (17.75%) was obtained through WS14. Putative haploid seeds weregerminated and seedlings were treated with 0.06% colchicine + 0.5%dimethylsulfoxide solution. Following transfer of seedlings into the field, 2178 D0plants were obtained out of a total of 3012 treated haploids. Live plants were from89% of 2178 seedlings which are planted to the field. Fertile plants were formed 57%of live plants. Inbreeding was succeeded in 31.23% of fertile plants and only 7.8% ofinbreeding plants were able to produce seeds. Consequently, 27 doubled haploid lineswere developed

Multilabel Text Classification Menggunakan SVM dan Doc2Vec Classification Pada Dokumen Berita Bahasa Indonesia

Seiring dengan berkembangnya informasi yang ada di sekitar dengan pesat, maka jenis informasi yang ada pun menjadi sangat bervariasi dan sangat banyak jumlahnya, dan akan semakin terus bertambah. Dengan kondisi tersebut, kita akan mengalami kesulitan untuk mengenali jenis dari informasi tersebut satu persatu. Oleh karena itu dengan adanya proses klasifikasi teks dan dokumen sangatlah membantu untuk memilah dan mengenali informasi-informasi apa saja yang ada, baik informasi yang lama maupun informasi yang baru dan belum pernah ditemui sebelumnya. Bertujuan untuk dapat mengidentifikasi dan mengklasifikasikan dokumen-dokumen berita dalam bahasa Indonesia ke dalam beberapa kategori sekaligus, maka dibuatlah sebuah penelitian berupa sistem untuk menangani klasifikasi dokumen teks dalam bahasa Indonesia. Sistem tersebut akan memproses berita-berita yang diberikan, dan kemudian akan memberikan 2 kategori yang paling mendekati terhadap isi dari berita tersebut. Sistem dibuat dengan menggunakan Python, memanfaatkan Doc2Vec untuk mengambil fitur dataset, dan SVM untuk melakukan klasifikasi terhadap banyak kelas. Dataset yang digunakan adalah kumpulan dokumen berupa berita-berita yang diperoleh dari CNN Indonesia tahun 2016-2017, dan terbagi dalam 5 kategori berita utama, yaitu: Politik, Ekonomi, Teknologi, Olahraga, dan Hiburan. Dikarenakan sedikitnya literatur untuk klasifikasi text dalam bahasa Indonesia, maka pada penelitian ini hanya menargetkan akurasi sebesar 70% saja. Namun dari hasil ujicoba, akurasi yang diperoleh melebihi 90%. Hasil prediksi untuk kelas dokumen pun memiliki tingkat keberhasilan yang tinggi. Dengan penggunaan dataset dan penanganan preprocessing yang tepat untuk dokumen bahasa Indonesia, maka hasil yang dicapai bisa lebih bagus dan akurat

3D Multimodal Brain Tumor Segmentation and Grading Scheme based on Machine, Deep, and Transfer Learning Approaches

Glioma is one of the most common tumors of the brain. The detection and grading of glioma at an early stage is very critical for increasing the survival rate of the patients. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems are essential and important tools that provide more accurate and systematic results to speed up the decision-making process of clinicians. In this paper, we introduce a method consisting of the variations of the machine, deep, and transfer learning approaches for the effective brain tumor (i.e., glioma) segmentation and grading on the multimodal brain tumor segmentation (BRATS) 2020 dataset. We apply popular and efficient 3D U-Net architecture for the brain tumor segmentation phase. We also utilize 23 different combinations of deep feature sets and machine learning/fine-tuned deep learning CNN models based on Xception, IncResNetv2, and EfficientNet by using 4 different feature sets and 6 learning models for the tumor grading phase. The experimental results demonstrate that the proposed method achieves 99.5% accuracy rate for slice-based tumor grading on BraTS 2020 dataset. Moreover, our method is found to have competitive performance with similar recent works

DETECTION OF PERSONAL PROTECTIVE EQUIPMENT IN EXTREME CONSTRUCTION CONDITIONS USING ANN

The number of deaths in the construction industry is greater than in other industries through a number of countermeasures.  Although workers may intentionally or unintentionally neglect to wear such safety measures, Personal protective equipment (PPE) was continuously being developed to prevent this types of accidents. Performing a safety check manually might be difficult since there can be a lot of coworkers at a site. It is essential to identify worker noncompliance with PPE in an automated and real-time manner. Detection of Personal Protective Equipment in Extreme Construction Conditions Using ANN is the topic of this paper. The web-based collection of 2,509 images from video recordings of many construction sites are utilized as the model's training data set. This Artificial Neural Networks (ANN) model is utilised in the study, which makes use of transfer learning and a basic variation of the YOLOv5 deep learning network. A dataset called CHVG to identify the workers PPE. Described model achieves the parameters as Accuracy as 97%, Recall 97% and Precision 96%. Overall, the analysis shows that computer vision-based techniques for automating safety-related compliance processes on construction sites are both feasible and useful

Location Dependent Channel Characteristics for Implantable Devices

This paper presents an impact on an in-vivo channel with respect to the position of ex-vivo antenna placement and its location. The paper also shows how the location of the antenna is impacting the channel. Three different parts are considered for the simulations using measured data for 500 MHz bandwidth. The results in the paper present the high location dependent characteristics of the in-vivo channel in the context of changing the position of the ex-vivo antenna. These findings can help in the system design for the future of the implantable devices design to be placed inside the human body

The Effect of Customers’ Attitudes Towards Chatbots on their Experience and Behavioural Intention in Turkey

Chatbots are a recent technology that brands and companies adopt to provide 24/7 customer service. However, some customers have several concerns regarding technology, and therefore, prefer talking to humans rather than chatbots. Brands must improve their chatbots based on customer experience because customers satisfied with chatbots are more likely to use them to contact brands/companies. Therefore, this article investigated the effect of perceived ease of use, usefulness, enjoyment, and risk factors on customer experience and behavioral intention regarding chatbots. The study also looked into the impact of customer experience on behavioral intention. The sample consisted of 211 chatbot users of Turkish recruited using non-probability convenience sampling. Data were analyzed using the Statistical Package for Social Sciences (SPSS) and SmartPLS3. The results showed that perceived ease of use and usefulness affected behavioral intention, but perceived risk had no impact on customer experience and behavioral intention regarding chatbots. Perceived enjoyment affected only customer experience. Lastly, customer experience affected behavioral intention

From Sophisticated Analysis to Colorimetric Determination: Smartphone Spectrometers and Colorimetry

Smartphone-based spectrometer and colorimetry have been gaining relevance due to the widespread advances of devices with increasing computational power, their relatively low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing for “lab-on-a-chip” systems. They find applications in interdisciplinary fields, including but not limited to medical science, water monitoring, agriculture, and chemical and biological sensing. However, spectrometer and colorimetry designs are challenging tasks in real-life scenarios as several distinctive issues influence the quantitative evaluation process, such as ambient light conditions and device independence. Several approaches have been proposed to overcome the aforementioned challenges and to enhance the performance of smartphone-based colorimetric analysis. This chapter aims at providing researchers with a state-of-the-art overview of smartphone-based spectrometer and colorimetry, which includes hardware designs with 3D printers and sensors and software designs with image processing algorithms and smartphone applications. In addition, assay preparation to mimic the real-life testing environments and performance metrics for quantitative evaluation of proposed designs are presented with the list of new and future trends in this field

Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision

De-Noising Signals using Wavelet Transform in Internet of Underwater Things

Internet of Underwater Things (IoUT) is an emerging field within Internet of Things (IoT) towards smart cities. IoUT has applications in monitoring underwater structures as well as marine life. This paper presents preliminary work where sensor nodes were built on Arduino Uno platform with temperature and pressure sensors with wireless capability. The sensors nodes were then tested in the Flumes of the COAST laboratory to determine the maximum depth achievable in fresh water before the signal is lost as radio frequencies are susceptible to interference under water. Further, the received signals were de-noised using Wavelet Transform, Daubechies thresholding techniques at level 5. Preliminary results suggest that at a depth of 30 cm, signal was lost, de-noising of the signal was achieved with very small errors (a mean squared error of 0.106 and 0.000446 and Peak-Sign-to-Noise Ratios of 70.18 dB and 58.83 dB for the pressure and temperature signals, respectively. Results from this study will lay the foundation to further investigations in wireless sensor networks in IoUT integrating the de-noising techniques