Femtosecond Pulsed Laser Deposition of Indium on Si (100)

Deposition of indium on Si(100) substrates is performed under ultrahigh vacuum with an amplified Ti:sapphire laser (130 fs) at wavelength of 800 nm and laser fluence of 0.5 J/cm2. Indium films are grown at room temperature and at higher substrate temperatures with a deposition rate of similar to 0.05 ML/pulse. Reflection high-energy electron diffraction (RHEED) is used during the deposition to study the growth dynamics and the surface structure of the grown films. The morphology of the grown films is examined by ex situ atomic force microscopy (AFM). At room temperature indium is found to form epitaxial two-dimensional layers on the Si(100)-(2x1) surface followed by three-dimensional islands. AFM images show different indium island morphologies such as hexagonal and elongated shapes. At substrate temperatures of 400-420 °C, RHEED intensity oscillations are observed during film growth indicating that the indium film grows in the layer-by-layer mode

Harnessing the power of IoT: a survey of Internet of Things applications in greenhouse agriculture

The Internet of Things (IoT) technology is now widely used in virtually all industries, including agriculture, and is adopting IoT technology. Through their IoT technology, greenhouse agriculture has entered an era of precision farming like never before. This survey is made on the recent progress in greenhouse agriculture with IoT, and the architecture of IoT is illustrated further with its application in greenhouse agriculture. For instance, the chapter investigates various disciplines like Monitoring and Control Systems, Smart Irrigation Systems, Environmental Data Collection and Analysis, and Crop Health Monitoring. It should also be noted that the many advantages IoT brings to greenhouse farming in the way of increased yield and quality of crops, greater efficiency in the use of resources, and reductions in labor and operational costs are also taken into consideration. Not with these benefits, problems like information security and privacy, integration, and interoperability issues still exist. The last part of the discussion will be about the future vision: what changes can we expect in IoT-based greenhouse farming and what new trends are emerging. The survey offers essential lessons about the cost-effectiveness and sustainability of IoT in improving production and productivity in greenhouses.</p

Comparative study of circular and rectangular microstrip patch antennas in Wi-Fi band

The growing ubiquity of Wi-Fi has necessitated the development of small and effective antenna designs, especially microstrip patch antennas. In the Wi-Fi frequency range of 2.4 GHz to 2.4835 GHz, this paper compares the performance of circular and rectangular microstrip patch antennas. The study aims to evaluate their performance regarding radiation characteristics, gain, and bandwidth to help choose antennas for Wi-Fi applications. According to the results, the rectangular patch antenna with a rectangular slot outperforms the circular patch antenna with a rectangular slot regarding return loss, coming in at roughly -37.07 dB. Furthermore, the Voltage Standing Wave Ratio (VSWR)value of the rectangular patch antenna is 1.02, which is higher than that of the circular patch antenna, which is 1.34. The rectangular patch antenna has the most excellent radiation efficiency (64.5%) and offers the broadest bandwidth (about 115 MHz). Engineers and researchers looking to enhance antenna designs for Wi-Fi applications can learn much from this study by considering variables like radiation efficiency, bandwidth, VSWR, and return loss.</p

Comparison between rectangular and circular microstrip patch antenna arrays

In this paper, several designs of microstrip array antennas suitable for wireless communication applications are presented. This paper demonstrates several shapes of microstrip array antennas, such as rectangular and circular patch antenna arrays. Specifically, single, 2 × 1, and 4 × 1 elements of both shapes are designed and simulated by High Frequency Structural Simulator (HFSS). Moreover, this paper presents a comparison between both rectangular and triangular antenna arrays. Since the resonance frequency of these antennas is 2.4 GHz, these antennas are suitable for the ISM band and WLAN).</p

Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.</p

Evaluation of the mixing performance in a planar passive micromixer with T micromixer with square chamber mixing units (SAR)

Microscale mixing methods are crucial in various disciplines, encompassing chemical reactions and biological investigations. The present study used simulation methodologies to investigate the operational efficiency of splitting recombination (SAR) micromixers. The study demonstrates that SAR micromixers offer a notable advantage in enhancing mixing efficiency. The advantage above is a consequence of the effective combination of splitting-recombination and chaotic advection processes within the micromixer architecture. An in-depth analysis of the micromixer's behavior demonstrates that its performance is supported by intricate fluid dynamics, which provide remarkably high mixing efficiency. It is worth noting that the micromixer exhibits its maximum mixing efficiency, which is roughly 99% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it is seen that as the Reynolds number grows, there is a steady decrease in mixing efficiency. At a Reynolds number of 70, the measurement of mixing efficiency yields a value of 75%. However, when the Reynolds number is further increased to a range of 90-100, the efficiency decreases to its lowest value of approximately 60%. The results above highlight the exceptional mixing ability of the SAR micromixer, hence stressing its potential for various applications that demand improved mixing capabilities. The results emphasize the promise of SAR micromixers as a reliable solution for complex mixing processes in many applications, providing valuable insights that may contribute to future developments in microscale mixing technologies.</p

Improving emergency departments: simulation-based optimization of patients waiting time and the number of staff present in a hospital

The emergency department (ED), operating around the clock every day of the year, serves a diverse range of patients with varying medical conditions, making it the vital core of a hospital. Consequently, optimizing and simulating the ED's processes becomes essential to enhance the quality of care provided. This study offers a case analysis employing simulation to assess patient flows in a hospital's emergency department. Our objective is to evaluate the impacts of system enhancements within the ED. This model aims to measure patients' time from their ED entry, determine daily patient numbers, and calculate the overall patient movement time within the department. If the patient's condition is serious, he will be presented immediately to the doctor without waiting. A doctor will be added to the unit if the number of patients exceeds the standard limit.</p

Triple Primary Carcinomas: Prostatic Adenocarcinoma, Bladder Urethral Carcinoma and Papillary Thyroid Carcinoma: A Case Report

Introduction: Patients with multiple tumors represent a segment of the cancer survivor population affected more than once by cancer, the phenomenon is still rare.Case presentation: In this report we present a patient who developed primary bladder urethral carcinoma with synchronous prostatic adenocarcinoma and metachronous papillary thyroid carcinoma where diagnosed within nine month period. There was no clear risk factors could explain this combination except smoking history.Conclusion: The diagnosis of cancer should not exclude the existence of other concomitant malignancies. This combination of multiple primary carcinomas, to our knowledge, has never been reported in the literature

Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis diagnosis

Sarcoidosis is often misdiagnosed and mistreated due to the limitations of radiological presentations. With the recent emergence of COVID-19, doctors face challenges distinguishing between the symptoms of these two diseases. As a result, people are adapting to new practices such as working from home, wearing masks, and using disinfectants. The similarity in symptoms between sarcoidosis and COVID-19 has made it difficult to differentiate between the two conditions, potentially impacting patient outcomes. The diagnostic process for distinguishing between them is time-consuming, labor-intensive, and costly. Researchers and medical practitioners have gained significant attention to computer-aided detection (CAD) systems for sarcoidosis using radiological images to address this issue. This study uses machine learning classifiers, ensembles, and features such as Gray-Level Co-occurrence Matrix (GLCM) and histogram analysis to identify lung sarcoidosis infection from chest X-ray images. The proposed method extracts statistical texture features from X-ray images by calculating a GLCM for each image using various stride combinations. These GLCM features are then used to train the machine learning classifiers and ensembles. The research focuses on multi-class classification, categorizing X-ray images into three classes: sarcoidosis-affected, COVID-19-affected, and regular lungs, as well as binary classification, distinguishing sarcoid-affected cases from others. The proposed method, known for its simplicity and computational efficiency, demonstrates significant accuracy in identifying sarcoidosis and COVID-19 from chest X-ray images.</p