Superovulation treatment is a key instance in Embryo Transfer, the Ovine Industry is an important target in Assisted Reproductive Technologies. In comparison with other ruminants, such as bovines, substantial differences in pathways of response to superovulation treatment in Ovis aries has been reported. We set out to evaluate superovulation protocols in sheep, published by different researchers and technicians, measuring outcome parameters to develop a highly repeatable methodological framework. Eight different protocols were installed in thirty-one post-pubertal embryo donors. Time and replace of intra vaginal pessaries, two sources of follicle-stimulating hormone, two estrous detection methods, and use, or not, GnRH, PGF2 and LH in day of estrus, were rehearsed. Two variables were evaluated, ovarian response and embryo structures collected in-uteri, 5 to 6 days after heat detection.
Reversal of global warming is accepted as an urgent necessity. Atmospheric CO2 concentration is widely used as a proxy and predictive indicator for global warming. Scientific calculation of the cooling efficiency of carbon sequestration options provides a rational basis for optimising climate investment. Based on evapotranspiration data and average rainfall over the Amazon Basin, we determine the cooling power of latent heat transport over the tropical rainforest. A healthy rainforest acts as a heat pump, cooling the Earth’s surface and releasing heat into space. Because of the heat pump, the cooling engendered by evapotranspiration and subsequent cloud-forming is two orders of magnitude greater than the cooling brought about by carbon sequestration in forming biomass. In rainforests situated far inland, the levels of rainfall, evapotranspiration, and latent heat release during cloud formation remain as high, or even higher, than near those near the coast. Whether the coastal forests attract ocean clouds through a biological pump mechanism, or are nourished by on-shore winds, the net result is the amplification of the Amazon's heat pump effect across millions of square kilometres and, therefore, deep into the interior of South-America.
The construction of an Arduino Uno based syringe pump with the ability to measure the patient's body temperature during injection and detect the contrast material bubble has not been done so far. A syringe pump is a device that controls the volume and flow of milliliters to microliters for a certain period of time with very high precision, which gradually pushes the injection piston. This use of syringe pump for drug administration has been done for years. The working principle of this system consists of installing two steel rods to push the syringe piston at a set speed, which works through a coupling system. The advancement of technology has made it possible to use a motor as the driving force of the syringe filled with liquid released through the needle, and the electronic system in this tool is used as a controller. The design using a stepper motor based on Arduino Uno is chosen to adjust the flow rate; the research includes two stages of software and hardware construction. The hardware is implemented with the components needed to build a syringe pump with an Arduino Uno as the brain of the system and a stepper motor as a piston pusher.The flow rate in the syringe pump can be adjusted with the keyboard and displayed on the screen. It is also possible to see the patient's body temperature on the screen, and to detect bubbles in the contrast liquid during injection, we also put a sensor in this device that alerts the operator.
Transfer Learning (TL) utilizes pre-trained models to solve similar problems. The knowledge from the original model is transferred to a new model during training, aiming to leverage previous knowledge in a new task. Natural Computing (NC) algorithms, such as Evolutionary Computation (EC) and Swarm Intelligence (SI), draw inspiration from nature, adapting more easily to new computational problems. This bio-inspired adaptation can enhance the performance of TL techniques, improving generalization and reducing computational costs. We investigate how evolutionary and swarm-intelligence algorithms are applied in TL, their contributions, the addressed problems, and the conducted experiments. We employ a systematic review following the PRISMA protocol, PICOS strategy, and START software to analyze primary studies.
Machine learning is a rapidly growing field with widespread application in various industries, including healthcare. In recent years, significant advancements in machine learning have enhanced our understanding of data analytics and prediction models in analyzing physiological data, such as blood oxygen levels, heart rate, and blood pressure. This paper focuses on physical activity in adolescence during the pandemic period. The study showed that going outside for a short walk can increase blood oxygen levels. Furthermore, the consumption of certain foods can also raise oxygen saturation levels. Maintaining a high blood oxygen level during exercise can improve athletic performance and reduce injury risk. However, during exercise, the blood oxygen level tends to decrease as the heart rate increases to supply more oxygen to the body. When the blood oxygen level drops too low during exercise, it can lead to fatigue, shortness of breath, and even fainting. By tracking heartbeats, blood oxygen levels, and other physiological parameters during exercise with the YAMAY Smart Watch wearable device, individuals can gain insights into their body's response to physical activity and adjust their exercise routine accordingly. After collecting data using wearable devices and mobile apps, machine learning algorithms can be trained to predict changes in physiological parameters of post-activity, providing valuable insights into the effectiveness of different exercises and identifying potential health risks. Our study used supervised machine learning classification algorithms to predict the expected data with the target data. We used K-fold cross-validation techniques to split the dataset into training, validation, and test sets for the supervised machine learning classification algorithms. After testing each of the machine learning models (K-Nearest Neighbor (KNN), Naïve Bayes, and Random Forest), it was found that the Random Forest had the best prediction accuracy of 98.75%. On the other hand, KNN had a poor prediction accuracy, lower than 41.1%. Therefore, the Random Forest model can accurately predict the effect of change on the oxygen saturation level during exercise.
