શુદ્ધાદ્વૈત તત્વમીમાંસામાં બ્રહ્મનો ખ્યાલ : એક અધ્યયન

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The Different Magnetic Results of Anemi and PPM Measurements on the Buried Remains of a 13th Century Fortress

This study aimed to evaluate the different magnetic results of an electromagnetic induction with proton magnetometer measurements on an archaeological site. The electromagnetic induction allows measuring both the apparent magnetic susceptibility in part per thousand (ppt) and the apparent electrical conductivity in millisiemens (mS/m). A proton magnetometer measures the total magnetic intensity in nanotesla (nT), caused by the induced and remanent magnetisations. An archaeological site where historical documents indicated the presence of a 13th century fortress that built by Lamuri Sultanate was selected as a test area. The measurement were conducted by divided the study area into 10 profiles.Some standard data processing have been applied to the measured data. The result of the first survey with electromagnetic induction showed low magnetic anomalies in the buried remains of Lamuri fortress. The similar value are shown as well by low magnetic field intensity in magnetometer measurement

Pemodelan Poisson Hidden Markov Untuk Prediksi Banyaknya Kecelakaan Di Jalan Tol Jakarta-cikampek

Model Poisson hidden Markov digunakan untuk memodelkan banyaknya kecelakaan yang terjadi di jalan tol Jakarta-Cikampek pada tahun 2013- 2014. Data banyaknya kecelakaan merupakan barisan observasi yang mengalami overdispersi dan bergantung pada penyebab kecelakaan yang diasumsikan tidak diamati secara langsung dan membentuk rantai Markov. Model Poisson hidden Markov dicirikan oleh parameternya. Pendugaan parameter model dilakukan dengan menggunakan metode Maksimum Likelihood yang perhitungannya menggunakan algoritme Expectation Maximization. Nilai dugaan parameter digunakan untuk membangkitkan barisan penduga kecelakaan. Keakuratan model diukur menggunakan Mean Absolute Percentage Error (MAPE). Menggunakan kriteria AIC diperoleh model Poisson hidden Markov 2 state sebagai model terbaik dengan nilai MAPE 34.0786% untuk prediksi satu waktu yang akan datang

Ethnobotanical, phytochemical and pharmacological aspects of daphne mucronata (thymeleaceae)

Daphne mucronata is a shrub well known as a medicinal plant in different regions of Asia. Ethnobotanical, phytochemical and pharmacological studies have revealed strong anti-cancer potential of the plant. Literature reports the evaluation of the initial bioactivity profile and extraction of the plant followed by different chromatographic techniques to obtain fractions. As an outcome,  isolation and identification of coumarins, flavonoids, triterpenoids, lignin  cumarinolignans, glucosides, daphnecin, aquillochin, daphnine and umbelliferone from the plant have been reported. Of these compounds, a diterpene, named gnidilatimonoein, has shown promising anticancer potency in in vitro tests on various cancer cell lines. This review article is an effort to summarize literature published in recent years on the bioactivity of Daphne mucronata

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Pharmacists and telemedicine: an innovative model fulfilling Sustainable Development Goals (SDGs)

The lack of access to safe medicines and quality healthcare services in peri-urban and rural areas is a major challenge driving a health system to innovate new models of care. This commentary will discuss the implementation and impact of the “Guddi baji” tele-pharmacy model, a project piloted by doctHERs, one of Pakistan’s leading telemedicine organizations. This innovative model has described the reintegration of women into the workforce by leveraging technology to improve the level of primary health care services and contributes to safe medication practice in a remote area. Our intervention proposed the deployment of technology-enabled, female frontline health workers known as the Guddi baji (meaning The Good Sister) in a rural village. They serve as an “access point to health care” that is linked to a remotely located health care professional; a licensed doctor or a pharmacist within this model

Electromyography assessment of forearm muscles: Towards the control of exoskeleton hand

Hand plays an important role in a human’s life by offering physical interaction and grasping capabilities. In most stroke cases, the hand is the most vulnerable part of the body that has a high chance of suffering. This has led to the development of a numerous wearable robotic devices such as exoskeleton hands. The exoskeleton hands can provide physical assistance for stroke survivors to regain their abilities in performing basic activities of daily living and to improve their quality of life. The key challenges in developing such a device do not only lie in designing its mechanical but also in designing its controller. In controlling the exoskeleton hand, the principal criterion is to work according to the user’s motion intention. It can be done by utilizing the electromyogram (EMG) signals generated by forearm muscles contributed from the movement and/or grasping abilities of the hand. In this paper, electromyography assessment of forearm muscles towards the control of an exoskeleton hand is presented. The EMG signals are collected non-invasively using multi-channel surface EMG sensors. The contractions of the muscles are detected from several forearm (flexion and extensor) muscles and the data is processed through several pattern recognition steps, before being mapped to various pinching/gripping forces and angular joints. The adaptability and learning process is done through a neural network. The experimental results show separable classes of features and significant range of control inputs that represent the inter-relation between forearm EMG signals, various pinching/gripping forces and angular joints for exoskeleton hand control

Electromyography assessment of forearm muscles: Towards the control of exoskeleton hand

Hand plays an important role in a human’s life by offering physical interaction and grasping capabilities. In most stroke cases, the hand is the most vulnerable part of the body that has a high chance of suffering. This has led to the development of a numerous wearable robotic devices such as exoskeleton hands. The exoskeleton hands can provide physical assistance for stroke survivors to regain their abilities in performing basic activities of daily living and to improve their quality of life. The key challenges in developing such a device do not only lie in designing its mechanical but also in designing its controller. In controlling the exoskeleton hand, the principal criterion is to work according to the user’s motion intention. It can be done by utilizing the electromyogram (EMG) signals generated by forearm muscles contributed from the movement and/or grasping abilities of the hand. In this paper, electromyography assessment of forearm muscles towards the control of an exoskeleton hand is presented. The EMG signals are collected non-invasively using multi-channel surface EMG sensors. The contractions of the muscles are detected from several forearm (flexion and extensor) muscles and the data is processed through several pattern recognition steps, before being mapped to various pinching/gripping forces and angular joints. The adaptability and learning process is done through a neural network. The experimental results show separable classes of features and significant range of control inputs that represent the inter-relation between forearm EMG signals, various pinching/gripping forces and angular joints for exoskeleton hand control

PEMODELAN POISSON HIDDEN MARKOV UNTUK PREDIKSI BANYAKNYA KECELAKAAN DI JALAN TOL JAKARTA-CIKAMPEK

Model Poisson hidden Markov digunakan untuk memodelkan banyaknya kecelakaan yang terjadi di jalan tol Jakarta-Cikampek pada tahun 2013- 2014. Data banyaknya kecelakaan merupakan barisan observasi yang mengalami overdispersi dan bergantung pada penyebab kecelakaan yang diasumsikan tidak diamati secara langsung dan membentuk rantai Markov. Model Poisson hidden Markov dicirikan oleh parameternya. Pendugaan parameter model dilakukan dengan menggunakan metode Maksimum Likelihood yang perhitungannya menggunakan algoritme Expectation Maximization. Nilai dugaan parameter digunakan untuk membangkitkan barisan penduga kecelakaan. Keakuratan model diukur menggunakan Mean Absolute Percentage Error (MAPE). Menggunakan kriteria AIC diperoleh model Poisson hidden Markov 2 state sebagai model terbaik dengan nilai MAPE 34.0786% untuk prediksi satu waktu yang akan datang