Review of an Accurate System Utilizing GPS Technology

GPS provides precise position and control data anywhere on the planet and in any weather condition. GPS was originally intended for military usage, but in the 1980s, the US Department of Defense made it available for civilian use. The scientific applications of GPS in the military, community, and commercial sectors are expanding on a regular basis. Agriculture, construction, mining, measurement, package delivery, and logistical supply chain management all benefit from GPS technology. Precision GPS time synchronization is critical for big networks, navigation, finance systems, financial markets, and power grids. Wireless services are impossible to imagine without them. In this paper, we will go over the key aspects of GPS technology as well as a discussion of the systems that use it

Python-based software for solving clever PDEs

Several recent investigations have identified a technique for approximating solutions to partialdifferential equations (PDEs). However, there was little room for adaptive frameworks that facilitate theexploration of new concepts. We can compensate by using the PyDEns library in Python. Using thePyDEns module and the open-source Batch Flow framework, you can solve a variety of partialdifferential equations. There are partial differential equations, such as those that describe heat andwaves, and other equations, such as this one. In this article, we explain how to solve differentialequations using neural networks using a new method introduced by Python PyDEns. Partial differentialequations can be solved with this software tool

Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration

The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions

DYNAMIC BEHAVIOR OF PILE GROUP MODEL IN TWO – LAYER SANDY SOIL TO LATERAL EARTHQUAKE EXCITATION

The behavior of pile foundations under earthquake loading is an important factor affecting the performance of such structures. Observations from past earthquakes have shown that piles in stiff soils generally perform well, while those installed in soft or liquefiable soils are more subjected to problems arising from ground amplification or excessive soil movements. A series of laboratory tests were conducted to measure the response of pile foundation when subjected to dynamic loads. A special vibration box was manufactured. The accompanied measurements include vertical and horizontal displacement and settlement of pile cap, acceleration in three dimensions in both soil and cap and earth pressures. The model pile used has an outer diameter of 18 mm and inner diameter of 15 mm. A group of (2x2) piles was tested. It was concluded that for soil bed in dry state, the acceleration amplitudes increase with frequency for both soil relative densities (loose and medium). The maximum acceleration in the foundation is lower than in soil bed for all operating shaking frequencies and soil states. The decreasing of the maximum acceleration recorded in the foundation as compared to that in the soil bed is between 10-100 % for loose and medium state of soil, and the decrease in loose state is more than in medium state. This means that there is damping effect or attenuation of vibration waves

DON\u27T BLAME THE VACCINE

Background: The mRNA COVID vaccine is a rare cause of myocarditis in young patients. We describe a case of cardiogenic shock with extensive workup ruling out COVID vaccine induced myocarditis. Case: 42-year-old female who drinks 5 Monster energy drinks and 3-4 cups of coffee daily presented to the hospital with palpitations two weeks following her mRNA COVID vaccine. EKG showed atrial tachycardia with heart rates of 160 beats per minute. Adenosine and Lopressor were administered resulting in hemodynamic instability requiring norepinephrine. An echocardiogram showed dilated cardiomyopathy with ejection fraction of 15%. Right heart catheterization was performed, and the cardiac index was 1.22 L/min/m², systemic vascular resistance was 1918 dynes*sec*cm-5 and wedge pressure was 31 mm Hg. The patient was started on nitroprusside, furosemide, and milrinone drips and she began to improve. The patient was adamant the vaccine is what triggered her heart failure and extensive testing was performed to rule out COVID vaccine induced myocarditis. Workup showed normal coronary arteries and no evidence of infiltrative disease or myocarditis on cardiac MRI. The etiology was from tachycardia induced cardiomyopathy triggered by excessive stimulants and the patient had successful atrial tachycardia ablation of the right superior pulmonary vein. She was discharged on medical therapy for heart failure and advised to stop drinking energy drinks. Decision-making: Once the patient did not respond to the rate controlling agents an echocardiogram showed reduced ejection fraction. Right heart catheterization confirmed cardiogenic shock and nitroprusside and milrinone were started to help reduce afterload and improve contractility. Workup to exclude COVID induced myocarditis lead to the diagnosis of tachycardia induced cardiomyopathy and atrial tachycardia ablation was performed. Conclusion: We report a case of cardiogenic shock with workup diagnosing tachycardia induced cardiomyopathy induced from a combination of excessive monster energy drinks and coffee. She was treated successfully with afterload reduction, inotrope support, and atrial tachycardia ablation

Implementation and evaluation of the VA DPP clinical demonstration: protocol for a multi-site non-randomized hybrid effectiveness-implementation type III trial.

BackgroundThe Diabetes Prevention Program (DPP) study showed that lifestyle intervention resulted in a 58% reduction in incidence of type 2 diabetes among individuals with prediabetes. Additional large randomized controlled trials have confirmed these results, and long-term follow-up has shown sustained benefit 10-20 years after the interventions ended. Diabetes is a common and costly disease, especially among Veterans, and despite strong evidence supporting the feasibility of type 2 diabetes prevention, the DPP has not been widely implemented. The first aim of this study will evaluate implementation of the Veterans Affairs (VA) DPP in three VA medical centers. The second aim will assess weight and hemoglobin A1c (A1c) outcomes, and the third aim will determine the cost-effectiveness and budget impact of implementation of the VA DPP from a health system perspective.Methods/designThis partnered multi-site non-randomized systematic assignment study will use a highly pragmatic hybrid effectiveness-implementation type III mixed methods study design. The implementation and administration of the VA DPP will be funded by clinical operations while the evaluation of the VA DPP will be funded by research grants. Seven hundred twenty eligible Veterans will be systematically assigned to the VA DPP clinical demonstration or the usual care VA MOVE!® weight management program. A multi-phase formative evaluation of the VA DPP implementation will be conducted. A theoretical program change model will be used to guide the implementation process and assess applicability and feasibility of the DPP for VA. The Consolidated Framework for Implementation Research (CFIR) will be used to guide qualitative data collection, analysis, and interpretation of barriers and facilitators to implementation. The RE-AIM framework will be used to assess Reach, Effectiveness, Adoption, Implementation, and Maintenance of the VA DPP. Twelve-month weight and A1c change will be evaluated for the VA DPP compared to the VA MOVE!ProgramMediation analyses will be conducted to identify whether program design differences impact outcomes.DiscussionFindings from this pragmatic evaluation will be highly applicable to practitioners who are tasked with implementing the DPP in clinical settings. In addition, findings will determine the effectiveness and cost-effectiveness of the VA DPP in the Veteran population

A Comparative Study of Perceived Stress among Female Medical and Non-Medical University Students in Dammam, Saudi Arabia

Objectives: The objective of this study was to investigate any differences between female undergraduate medical and non-medical students for: 1) prevalence and causes of perceived academic stress, and 2) changes in physical, mental, psychological and emotional health as well as life-style since starting college studies. Methods: A cross-sectional study was conducted at Dammam University, Dammam, Saudi Arabia, in January 2008. All 319 pre-clinical female medical students were included in the study and 297 non-medical students from the College of Applied Studies and Community Services (CASCS) were selected by stratified random sampling. The study instrument was a questionnaire on the “Influence of Studying on Students’ Health”. Results: More medical students (48.6%) reported being frequently stressed due to studies than CASCS students (38.7%, P <0.01). Unsuitable teaching methods, an unsatisfactory study environment, and fear of failure in examinations were more frequently mentioned by medical than non-medical students (P <0.05). While underlying social problems were significantly more common in medical students, economic problems were more prevalent among CASCS students (P <0.05, P <0.05). More medical than non-medical students reported a worse status of physical and mental health, anxiety and depression and negative life-style changes since initiation of the college programme. Conclusion: Medical students were at higher risk of physical and mental health problems than non-medical students due to academic stress. Since a substantial proportion of CASCS students also experienced academic stress, we recommend that a student support committee be established for both colleges to provide counselling and guidance in healthy ways to cope with stress