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The integration of artificial intelligence (AI) through virtual assistants and chatbots is transforming oncology care by providing continuous, personalized, and accessible support. This study aims to evaluate the role of AI-powered tools in improving patient engagement, symptom management, emotional support, and treatment adherence in oncology. A qualitative methodology was employed, which included an extensive review of peer-reviewed literature from 2015 to 2023 and the development of a conceptual framework for oncology-specific chatbot systems. This framework incorporates natural language processing, machine learning, and personalized response algorithms. Key findings from this study indicate that these AI tools enhance access to healthcare information, empower patients, and reduce the burden on healthcare systems, particularly in remote or underserved regions. However, challenges remain concerning data privacy, accuracy, and the need for human intervention in complex cases. The study underscores the importance of maintaining a balance between innovative AI applications and human-centered care, advocating the integration of AI-based technologies as complementary tools in oncology.

The World Health Organization has underscored swimming as a significant exercise for attaining health, and it is also recognized as a competitive sport in many nations. Individuals across all age groups choose swimming as a means to enhance their fitness levels. Maintaining the cleanliness of the swimming pool is imperative to prevent the spread of waterborne diseases. Despite regular weekly or monthly maintenance, cleanliness is often compromised due to service provider limitations. In the contemporary landscape, artificial intelligence technologies are progressively assuming roles where human providers fall short. This article proposes the integration of ethical robots to augment cleaning services both within and around swimming pools. These waterproof robots are designed to navigate within the pool environment, efficiently collecting debris and waste into their attached receptacles. The deployment of such ethical robotics marks a significant advancement in swimming pool maintenance, promising enhanced efficiency and hygiene standards. This work presents the design and implementation of an autonomous swimming pool cleaning robot, integrating multiple functional units: Power, sensor, wireless communication, motor, and water quality monitoring. After the power is on, the robot starts calibrating its sensors and establishes a connection with a remote human-machine interface to transmit the initial operational status. By utilizing advanced image processing algorithms, specifically color moments, the robot identifies and classifies floating debris while continuously monitoring water quality parameters. When debris is detected, the robot calculates its trajectory based on X and Y coordinates, adjusting its movement accordingly to collect the debris with a salvage net. It incorporates ultrasonic sensors for obstacle detection, employing a threshold-based avoidance algorithm to navigate around obstacles effectively. The cleaning process is repeated until the pool is cleared, after which the robot returns to its charging station, powering down non-essential systems in preparation for the next cycle. This study highlights the efficiency and effectiveness of robotic automation in pool maintenance, demonstrating significant advancements in the integration of robotics, sensor technology, and real-time data communication. The findings contribute valuable insights into future developments in robotic cleaning systems and their applications in various environments.

The immature brain may be particularly vulnerable to injury during critical periods of development. To address the biologic basis for this vulnerability, mice were subjected to traumatic brain injury at postnatal day 21, a time point that approximates that of the toddler-aged child. After motor and cognitive testing at either 2 weeks (juveniles) or 3 months (adults) after injury, animals were euthanized and the brains prepared for quantitative histologic assessment. Brain-injured mice exhibited hyperactivity and age-dependent anxiolysis. Cortical lesion volume and subcortical neuronal loss were greater in brain-injured adults than in juveniles. Importantly, cognitive decline was delayed in onset and coincided with loss of neurons in the hippocampus. Our findings demonstrate that trauma to the developing brain results in a prolonged period of pathogenesis in both cortical and subcortical structures. Behavioral changes are a likely consequence of regional-specific neuronal degeneration.

We have fabricated reduced area InGaAs/InP DHBTs for high speed circuit applications. To produce the small dimensions required, a process involving both wet chemical and ECR plasma etching was developed. Optical emission spectroscopy was used for end-point detection during plasma etching. With this improved process, an ft of 170 and fmax of 200 GHz were achieved for 1.2 × 3 µm2 emitter size devices with a 500 Å base.

An InP double hetero-junction bipolar transistor (DHBT) distributed power amplifier MMIC, with 35 dB gain, 42 GHz bandwidth and 15 dBm output power is reported. This represents, the highest power and largest gain reported over this bandwidth from a single chip HBT amplifier. A lumped preamplifier, with a novel distributed output is used to obtain high gain and wide bandwidth at these power levels.

Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images.  The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.

An optical receiver for 40 Gbit/s communication systems with 8,274 V/W conversion gain and 950 mVp-p limiting differential output is reported. A back-to-back sensitivity of -9.4 dBm at a bit error rate of 10...12 was observed for a 231-1 pseudorandom binary sequence. The receiver incorporates an InP based pin diode, a transimpedance amplifier and a limiting amplifier. This is the largest conversion gain with high sensitivity reported for 40 Gbit/s receivers without optical amplification.