A High-Capacity Optical Metro Access Network: Efficiently Recovering Fiber Failures with Robust Switching and Centralized Optical Line Terminal.

This study proposes and presents a new central office (CO) for the optical metro access network (OMAN) with an affordable and distinctive switching system. The CO's foundation is built upon a novel optical multicarrier (OMC) generation technique. This technique provides numerous frequency carriers that are characterized by a high tone-to-noise ratio (TNR) of 40 dB and minimal amplitude excursions. The purpose is to accommodate multiple users at the optical network unit side in the optical metropolitan area network (OMAN). The OMC generation is achieved through a cascaded configuration involving a single phase and two Mach Zehnder modulators without incorporating optical or electrical amplifiers or filters. The proposed OMC is installed in the CO of the OMAN to support the 1.2 Tbps downlink and 600 Gbps uplink transmission, with practical bit error rate (BER) ranges from 10-3 to 10-13 for the downlink and 10-6 to 10-14 for the uplink transmission. Furthermore, in the OMAN's context, optical fiber failure is a main issue. Therefore, we have proposed a possible solution for ensuring uninterrupted communication without any disturbance in various scenarios of main optical fiber failures. This demonstrates how this novel CO can rapidly recover transmission failures through robust switching a and centralized OLT. The proposed system is intended to provide users with a reliable and affordable service while maintaining high-quality transmission rates.

Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach.

In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Prevalence and Risk Factors of Pressure Ulcers in Bedbound Diabetic vs. Non-diabetic Patients in a Lower-Middle-Income Country.

Introduction Pressure ulcers, also known as bedsores, are a significant concern for bedridden individuals, presenting both physical and socioeconomic challenges. Factors such as prolonged immobility, chronic medical conditions, and poor nutrition contribute to their development. Despite extensive research in some regions, studies comparing diabetic and non-diabetic populations remain limited, particularly in low-income settings. This study aimed to investigate the risk factors and frequency of pressure ulcers among bedridden patients, addressing this gap in understanding and guiding targeted interventions. Materials and methods A cross-sectional study was conducted across four government hospitals in Peshawar, Pakistan. A total of 388 bedridden patients with pressure ulcers were included, and data were collected through a questionnaire. The questionnaire covered demographics, comorbidities, duration of bedbound status, BMI, and caregivers' awareness of pressure ulcer care. Data analysis was performed using SPSS version 22.0 (Armonk, NY: IBM Corp.), with qualitative data presented as frequencies and percentages and quantitative data as mean and standard deviation. Chi-square tests were utilized for significance, with p<0.05 considered significant. Results Of the 388 patients analyzed, 230 (59.3%) were diabetic, highlighting the prevalence of diabetes among pressure ulcer cases. The majority of diabetic patients with ulcers were over 41 years old, and 293 (75.5%) had comorbidities. Surgical intervention was the primary cause of ulcers in 213 (54.8%) cases, followed by stroke in 77 (19.8%) cases. Notably, 252 (65%) of caregivers exhibited inadequate knowledge regarding ulcer care. Stage II ulcers were prevalent in both diabetic and non-diabetic cohorts. Conclusions Pressure ulcers are poorly controlled complications observed in bedridden individuals, highlighting a critical need for comprehensive preventive measures and caregiver education to alleviate the burden of pressure ulcers, especially in diabetic patients. Factors such as prolonged immobility, surgical interventions, and insufficient caregiver knowledge contribute to the development of pressure ulcers. Understanding these complexities is essential for implementing effective care approaches and mitigating the impact of pressure ulcers.

Pharyngeal Pouch Co-existing with Thyroid Mass: A Rare Presentation.

A 60-year male presented with a 4-year history of a mass in the lower third of the neck, which had gradually increased in size. On initial examination, it was considered a thyroid mass. On detailed examination, another mass was found in the upper neck which was doughy in consistency and reducible in size. With available investigations, he was diagnosed as a thyroid mass associated with pharyngeal pouch. On exploration of the neck, the whole mass revealed to be a pharyngeal pouch. We have discussed the patho-physiology of the pharyngeal pouch and reviewed the literature in detail.