Three-body decay of 6^{6}Be

Three-body correlations for the ground-state decay of the lightest two-proton emitter $^{6}$Be are studied both theoretically and experimentally. Theoretical studies are performed in a three-body hyperspherical-harmonics cluster model. In the experimental studies, the ground state of $^{6}$Be was formed following the $\alpha$ decay of a $^{10}$C beam inelastically excited through interactions with Be and C targets. Excellent agreement between theory and experiment is obtained demonstrating the existence of complicated correlation patterns which can elucidate the structure of $^{6}$Be and, possibly, of the A=6 isobar.Comment: 17 pages, 21 figures, 5 table

An Implementation of Polyglot Voice Supervise Home Device Using Raspberry Pi

Most of us tend to enjoy the ease of living by doing the bare minimum. The same applies while operating the devices at home by just a few touches or by using our voice in the preferred language. A smart home is an Internet of Things (IoT) platform that uses the internet to control the devices at our home, and this technology has grown enormously over the past few years encouraging new ideas. And with that thought, this system will be implementing Home Automation with Raspberry Pi and Google Assistant by controlling the appliances like lights, fans, air conditioners, temperature sensors, and more, in any preferred language.  Platforms like If This Then That (IFTTT), Adafruit, Message Queueing Telemetry Transport (MQTT), and Raspberry Pi IO are used to connect the hardware with the software that is a common path for devices that are connected to the Relay module and the Google Assistant. The IFTTT platforms are easily available on our smartphones or a website that makes it easy for us to access different devices at different parts of the house or anywhere. Home automation minimizes the manual switching  ON/OFF of the appliances whilst being controlled by the commands that are given by the users. This project builds an automation system that uses the range of Wifi or Bluetooth, which is easily accessible by the users to connect their devices and control them by voice through Google Assistant. This makes it easy for the users to access their devices wherever they are.  Home automation comes as an advantage for older people and especially the physically disabled. The main objective of this proposed project is to provide a comfortable and a digitalized environment to use the day-to-day appliances with added security

EVALUASI KINERJA GEDUNG FAKULTAS HUKUM UNIVERSITAS SAM RATULANGI AKIBAT BEBAN GEMPA

Level kinerja adalah salah satu faktor utama dalam perencanaan gedung bertingkat yang berfungsi untuk mengetahui batas kekuatan gedung tersebut menerima beban. Batas level kinerja yang digunakan dalam penelitian ini antara lain, Immediate Occupancy (Penggunaan Sedang), Damage Control (Kontrol Kerusakan), Life Safety (Aman untuk Dihuni), Structural Stability (Stabilitas Struktur). Bangunan yang digunakan dalam penelitian ini adalah Gedung Fakultas Hukum Universitas Sam Ratulangi Manado, dengan jumlah lantai 13 dengan atap dan tinggi total bangunan 56 m dengan lebar bentang arah X 21 m dan bentang arah Y 40 m. Adapun penelitian ini juga menggunakan Respon Spectrum area Manado, Sulawesi Utara dengan nilai Ss= 1,036 g dan S1= 0.442 g.Untuk mencari atau menentukan level kinerja biasanya dilakukan dengan cara analisis pushover, atau analisis beban dorong statik untuk mengetahui perilaku keruntuhan bangunan terhadap gempa. Analisis Pushover biasanya dihitung dengan menggunakan bermacam program.Dalam penelitian ini analisis Pushover menggunakan program PERFORM 3D untuk mengetahui beberapa besar gaya maksimum yang dapat ditahan struktur serta besar perpindahan struktur. Analisis pushover (build-in pada program PERFORM 3D dilakukan berdasarkan ATC – 40 (capacity spectrum method) dimana kondisi kerusakan (damage states) dikategorikan dalam berbagai level, dan FEMA – 356 merupakan metode dengan memodifikasi respon elastic linier sistem struktur sehingga diperoleh perpindahan yang disebut sebagai target perpindahan.Dari Hasil penelitian didapat nilai perpindahan maksimum 0,998 m dan gaya geser maksimum 3017866,5 kg untuk arah X dan untuk arah Y nilai perpindahan maksimum 0,728 m dan gaya geser maksimum 2590323,88. Dari hasil evaluasi struktur FEMA 356 level kinerja struktur bangunan tinjauan berada pada batas antara Life Safety (LS), berdasarkan ATC 40 masuk dalam kategori B.  Kata Kunci: pushover, assessment, level kinerja, PERFORM 3

Android application development for identifying maize infested with fall armyworms with Tamil Nadu Agricultural University Integrated proposed pest management (TNAU IPM) capsules

Several pests and diseases wreak havoc on maize crops worldwide. Novel and rapid methods for detecting pests and diseases in real-time will make monitoring them and designing effective management measures easier. In the recent past, maize has been imperilled by fall armyworms (Spodoptera frugiperda), which have caused substantial yield losses in maize. This study aimed to create an Android mobile application via  DCNN (Deep Convolutional Neural Network)-based AI pest detection system for maize producers. Everyone benefits from the deployment of these CNN models on mobile phones, especially farmers and agricultural extension professionals because it makes them more accessible. Automatic diagnosis of plant pest infestations from captured images through computer vision and artificial intelligence research is feasible for technological advancements. Therefore, early detection of maize fall armyworm (FAW) infestation and providing relevant recommendations in maize could result in intensified maize crop yields. . An Android mobile application was created to identify fall armyworm infection in maize and included the recommendations given by Tamil Nadu Agricultural University proposed Integrated Pest Management (TNAU IPM ) capsules in the mobile app on as to how to deal with such a problem. Digital and novel technology was chosen to address these issues in maize. Deep convolutional neural networks (DCNNs) and transfer learning have recently moved into the realm of just-in-time crop pest infestation detection, following their successful use in a variety of fields. The algorithm accurately detects FAW (S. frugiperda) infected areas on maize with 98.47% training accuracy and 93.47% validation accuracy

Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.)

Maize (Zea mays L) is one of the most saleable cereal crops grown worldwide and a dominant staple food in many developing countries. The severe outbreak of fall armyworm in maize causes massive yield loss. Modern technologies, including smartphones, can assist in detecting recognising the fall armyworm infestation in maize. The objective of this study was to develop an automated Artificial Intelligence Powered Expert System (AIPES) for identifying fall armyworm infestation in maize. In addition, it put forward a deep learning-based model that is trained on photographs of healthy and fall armyworm infested leaves, cobs and tassels from a dataset and furnished an application that will be detecting maize fall armyworm infestation using Convolutional Neural Network (CNN) architecture and Mobile Net V 2 framework model. The study developed an Artificial Intelligence (AI) based maize fall armyworm infestation detection system using a DCNN (Deep Convolutional Neural Network) to support maize cultivating farmers. The model executed the objective by accurately identifying the fall armyworm infested maize plant and also classified them vis-c-vis the healthier crop. The deep learning models were trained to detect and recognise fall armyworm infection using more than 11000 images of fall armyworm infested leaves, cobs, and tassels. The created application (AIPES for identifying fall armyworm infestation in maize) using CNN detected and recognised the fall armyworm infestation in maize with a 100 per cent training accuracy rate and 87 per cent validation accuracy. So, the detection of maize fall armyworm and the treatment of fall armyworm-infested maize could lead to a higher maize crop yield.      

Measurement of the 20 and 90 keV resonances in the 18O(p,α)15{}^{18}{\rm O}(p,\alpha){}^{15}N reaction via THM

The $^{18}{\rm O}(p,\alpha)^{15}{\rm N}$ reaction is of primary importance in several astrophysical scenarios, including fluorine nucleosynthesis inside AGB stars as well as oxygen and nitrogen isotopic ratios in meteorite grains. Thus the indirect measurement of the low energy region of the $^{18}{\rm O}(p,\alpha)^{15}{\rm N}$ reaction has been performed to reduce the nuclear uncertainty on theoretical predictions. In particular the strength of the 20 and 90 keV resonances have been deduced and the change in the reaction rate evaluated.Comment: 4 pages, 4 figures, submitted to PR

Dissipative electro-elastic network model of protein electrostatics

We propose a dissipative electro-elastic network model (DENM) to describe the dynamics and statistics of electrostatic fluctuations at active sites of proteins. The model combines the harmonic network of residue beads with overdamped dynamics of the normal modes of the network characterized by two friction coefficients. The electrostatic component is introduced to the model through atomic charges of the protein force field. The overall effect of the electrostatic fluctuations of the network is recorded through the frequency-dependent response functions of the electrostatic potential and electric field at the active site. We also consider the dynamics of displacements of individual residues in the network and the dynamics of distances between pairs of residues. The model is tested against loss spectra of residue displacements and the electrostatic potential and electric field at the heme's iron from all-atom molecular dynamics simulations of three hydrated globular proteins

Fermi surface in BaNi2_2P2_2

We report measurements of the de Haas-van Alphen (dHvA) oscillation and a band structure calculation for the pnictide superconductor BaNi$_2$P$_2$, which is isostructural to BaFe$_2$As$_2$, the mother compound of the iron-pnictide high-$T_c$ superconductor (Ba$_{1-x}$K$_x$)Fe$_2$As$_2$. Six dHvA-frequency branches with frequencies up to $\sim$8 kT were observed, and they are in excellent agreement with results of the band-structure calculation. The determined Fermi surface is large, enclosing about one electron and hole per formula unit, and three-dimensional. This is in contrast to the small two-dimensional Fermi surface expected for the iron-pnictide high-$T_c$ superconductors. The mass enhancement is about two.Comment: To appear in J. Phys. Soc. Jpn., Vol. 78, No.