MOBILE PHONES AS USEFUL LANGUAGE LEARNING TOOLS

Most youth are passionate about having the most recent mobile phones just to boast among their peers. They use them to make phone calls, take photos, listen to songs, watch videos, or gain access to the internet for entertainment. This paper presents how to change the mobile phone device from a communication device to an educational tool. It demonstrates that a mobile phone could be a useful tool in learning and teaching the English Language. In this paper, the researcher emphasize the potential of mobile phones as a learning tool for students and have incorporated them into the learning environment. The paper discusses the challenges and expected difficulties. Many theories(e.g. Behaviourist learning, Constructivist learning, Situated learning, Sociocultural theory of learning, Informal and lifelong learning) relevant to the use of mobile phones in education are presented and the different tasks and activities relevant to them are explored. The salient features of mobile phones which make them useful for language learning are discussed too. The possible methods that should be used for gaining the best of learning through mobile phones are proposed. Activities are classified in terms of the main theories and areas of learning relevant to learning with mobile technologies. This article concludes with a discussion of how moderate use of mobile phones may bring interest among the learners and transform the learning process as it helps learners to raise their self – esteem and self- confidence. The researcher tries to foresee the future of mobile learning in general and mobile phones in particular in learning English since the English language has become the most requested and widespread means of communication all over the world

Underactuated Attitude Control with Deep Reinforcement Learning

Autonomy is a key challenge for future space exploration endeavors. Deep Reinforcement Learning holds the promises for developing agents able to learn complex behaviors simply by interacting with their environment. This work investigates the use of Reinforcement Learning for satellite attitude control applied to two working conditions: the nominal case, in which all the actuators (a set of 3 reaction wheels) are working properly, and the underactuated case, where an actuator failure is simulated randomly along one of the axes. In particular, a control policy is implemented and evaluated to maneuver a small satellite from a random starting angle to a given pointing target. In the proposed approach, the control policies are implemented as Neural Networks trained with a custom version of the Proximal Policy Optimization algorithm, and they allow the designer to specify the desired control properties by simply shaping the reward function. The agents learn to effectively perform large-angle slew maneuvers with fast convergence and industry-standard pointing accuracy

RAPID ESTIMATIONS OF BIOLOGICAL FINENESS OF COTTON FIBERS USING MICROMAT DATA

Rapid estimations of diameter minus lumen(D-L), and outer perimeter of cotton fibers (P) inmicrons, as a biological fineness of Egyptian cottoncould be calculated with satisfactory levels ofaccuracy from hair weight (H.W) in m/tex andmaturity ratio )MR) obtained from Micromat data(new F/MT instrument), using the following equations:2Circularity x 3.14 x 1.52Hs (Standard finenessDiameter (microns) or Final format (D) (microns) = 1.205 HsorCircularit y × 1.524 x 3.14 x HsPerimeter (microns) or Final format (P) (microns) = 3.7853 HsResults of the current study suggested thatmore attention should be focused on meaning andmeasurements of the three values of biologicalfineness (i.e.) diameter (D), perimeter (P) (microns)and standard fineness (Hs) m/tex. whichcan be derived from the data obtained from Micromatinstrument

DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories

This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate microgravity environments on Earth. Our approach addresses the system and environmental uncertainties in controlling such platforms by training policies capable of precise maneuvers amid dynamic and unpredictable conditions. Leveraging state-of-the-art deep reinforcement learning techniques, our suite achieves robustness, adaptability, and good transferability from simulation to reality. Our Deep Reinforcement Learning (DRL) framework provides advantages such as fast training times, large-scale testing capabilities, rich visualization options, and ROS bindings for integration with real-world robotic systems. Beyond policy development, our suite provides a comprehensive platform for researchers, offering open-access at https://github.com/elharirymatteo/RANS/tree/ICRA24

Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration

Mobility on asteroids by multi-limbed climbing robots is expected to achieve our exploration goals in such challenging environments. We propose a mobility strategy to improve the locomotion safety of climbing robots in such harsh environments that picture extremely low gravity and highly uneven terrain. Our method plans the gait by decoupling the base and limbs' movements and adjusting the main body pose to avoid ground collisions. The proposed approach includes a motion planning that reduces the reactions generated by the robot's movement by optimizing the swinging trajectory and distributing the momentum. Lower motion reactions decrease the pulling forces on the grippers, avoiding the slippage and flotation of the robot. Dynamic simulations and experiments demonstrate that the proposed method could improve the robot's mobility on the surface of asteroids.Comment: Submitted version of paper accepted for presentation at the CLAWAR 2023 (26th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines

Thrombopoietin Receptor Levels in Tumor Cell Lines and Primary Tumors

Thrombopoietin (TPO) receptor agonists represent a new approach for the treatment of thrombocytopenia, which may develop as a consequence of immune thrombocytopenia, chemotherapy treatment, chronic hepatitis C infection, or myelodysplastic syndromes. There are concerns that use of certain growth factors can hasten disease progression in some types of hematologic malignancies and solid tumors. In this study, expression of MPL (TPO-R) mRNA was examined in tumor cell lines, patient tumor samples (renal cell carcinoma, prostatic carcinoma, soft tissue and bony/cartilage sarcoma, colon cancer, and lymphoma), and normal tissues using microarray analysis and qRT-PCR. MPL mRNA is expressed at very low or undetectable levels compared with erythropoietin receptor (EPOR), human epidermal growth factor (ERBB2; HER2), and insulin-like growth factor-1 receptor (IGF1R) in these patient samples. These data suggest TPO-R agonists will likely preferentially stimulate proliferation and differentiation of cells of megakaryocytic lineage, potentially demonstrating their utility for correcting thrombocytopenia in clinical settings

Experimental Verification of Robotic Landing and Locomotion on Asteroids

peer reviewedIn-situ explorations of asteroids and other small celestial bodies are crucial to collect surface samples, which could be the key to understanding the formation of our solar system. Studying the composition of asteroids is also important for future planetary defense and mining resources for in-situ utilization. However, the weak gravitational field poses many challenges for robotic landing and locomotion scenarios on the surface of asteroids. Legged climbing robots are expected to perform well under microgravity, as they can maintain surface attachment, preventing undesired flotation and uncontrolled bouncing. Therefore, we need to consider methods to plan and control the landing and locomotion of climbing robots on asteroids. In this study, we have performed experiments regarding the emulation of two scenarios; 1- Landing, 2- Locomotion. For both landing and locomotion scenarios, separate PD controllers have been utilized

Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration

Mobility on asteroids by multi-limbed climbing robots is expected to achieve our exploration goals in such challenging environments. We propose a mobility strategy to improve the locomotion safety of climbing robots in such harsh environments that picture extremely low gravity and highly uneven terrain. Our method plans the gait by decoupling the base and limbs’ movements and adjusting the main body pose to avoid ground collisions. The proposed approach includes a motion planning that reduces the reactions generated by the robot’s movement by optimizing the swinging trajectory and distributing the momentum. Lower motion reactions decrease the pulling forces on the grippers, avoiding the slippage and flotation of the robot. Dynamic simulations and experiments demonstrate that the proposed method could improve the robot’s mobility on the surface of asteroids

T cell adhesion and cytolysis of pancreatic cancer cells: a role for E-cadherin in immunotherapy?

Pancreatic cancer is an aggressive and potent disease, which is largely resistant to conventional forms of treatment. However, the discovery of antigens associated with pancreatic cancer cells has recently suggested the possibility that immunotherapy might become a specific and effective therapeutic option. T cells within many epithelia, including those of the pancreas, are known to express the αEβ7-integrin adhesion molecule, CD103. The only characterised ligand for CD103 is E-cadherin, an epithelial adhesion molecule which exhibits reduced expression in pancreatic cancer. In our study, CD103 was found to be expressed only by activated T cells following exposure to tumour necrosis factor beta 1, a factor produced by many cancer cells. Significantly, the expression of this integrin was restricted mainly to class I major histocompatibility complex-restricted CD8+ T cells. The human pancreatic cancer cell line Panc-1 was transfected with human E-cadherin in order to generate E-cadherin negative (wild type) and positive (transfected) sub-lines. Using a sensitive flow cytometric adhesion assay it was found that the expression of both CD103 (on T cells) and E-cadherin (on cancer cells) was essential for efficient adhesion of activated T cells to pancreatic cancer cells. This adhesion process was inhibited by the addition of antibodies specific for CD103, thereby demonstrating the importance of the CD103→E-cadherin interaction for T-cell adhesion. Using a 51Cr-release cytotoxicity assay it was found that CD103 expressing T cells lysed E-cadherin expressing Panc-1 target cells following T cell receptor stimulation; addition of antibodies specific for CD103 significantly reduced this lysis. Furthermore, absence of either CD103 from the T cells or E-cadherin expression from the cancer cells resulted in a significant reduction in cancer cell lysis. Therefore, potentially antigenic pancreatic cancer cells could evade a local anti-cancer immune response in vivo as a consequence of their loss of E-cadherin expression; this phenotypic change may also favour metastasis by reducing homotypic adhesion between adjacent cancer cells. We conclude that effective immunotherapy is likely to require upregulation of E-cadherin expression by pancreatic cancer cells or the development of cytotoxic immune cells that are less dependent on this adhesion molecule for efficient effecter function