This work studies optical absorption in the zinc-blende boron-containing quantum dot (QD) structures. Eight structures are studied; two of them are the ternary BInP/GaP and BInP/BP. The emission wavelengths of the structures cover a broad spectrum range from UV to near-infrared. The structure with BP barriers emit at 292 nm. The ternary structure with GaP barrier emit at 720 nm. The structures with GaP barrier have importance in silicon device technology. The absorption peaks are arranged where the smallest energy difference between the transition subbands correspond to a higher absorption peak and are associated with a wide bandgap energy difference between the barrier and QD.

This project was formulated to develop a system, with the purpose of improving the quality of life of deaf people with functional diversity, allowing them to listen and speak through an assisted communication system. As methodology, the research is quantitative of an applied experimental type, considering that the work is carried out to produce new developments, from a necessity of people with hearing, motor and speech diversity. The result is projected as a communication channel, which matches a software development with a device running iOS or Android operating systems. First, Morse code information is processed. Then, it is translated into text or voice commands allowing users to communicate their ideas or learning an alternate form of communication. Moreover, it will be an option to strengthen the inclusive infrastructure to ensure quality in the participatory teaching process, to enable Education Institutions to meet legal requirements. The achievement of the result is expected to reach top 7 levels of technological maturity on the innovation scale. Finally, the relevance of this project focuses on the possibility of offering an option to improve the quality of life of people with functional diversity, through technology.

Timely and precise estimate of the area and distribution of sugarcane crops is crucial for decision-making in sugar mills. It aids in formulating the public policy and in the determination of prices by sugar mills for farmers and enables the supply disposal. Deep learning (DL) is an effective state-of-the-art technology for image processing. DL combined with RS is proven to be effective in crop classification. This study aims to compare 2D CNN and pretrained CNN models on Sentinel-2 and Landsat-8 satellite data at two sugarcane growing locations in Sangli, Maharashtra and four talukas of Karnataka. The potential of 2D and pretrained CNN models, namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201 are evaluated to sugarcane fields and non-sugarcane areas. The results are depicting 2D CNN model performs relatively good with sentinel-2 (Landsat-8) at both locations producing 81.23 and 84.50 (74.18 and 78.68) at Sangli and four talukas, respectively. Whereas deep networks’ highest overall accuracy when applied on Landsat-8 is 95.00 and 92.00 on Sentinel-2. ResNet50 and DenseNet201 shown superior performance over other models with overall accuracies of 95.00 and 92.00, respectively, when used to extract features given to support vector machine (SVM) classifier. Despite significant difference in spatial resolution between Landsat-8 and Sentinel-2, there is not much difference in accuracies in both the dataset at the study locations when applied 2D CNN model and pretrained model. Noticeably, the results indicate that Sentinel-2 have the marginally upper hand in sugarcane classification when features are extracted using CNNs for sugarcane classification in India.

Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. The methodology used is composed by the following phases: acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering.

Pharmacy practice has changed significantly lately. The professionals have the chance to contribute straightforwardly to patient consideration so as to lessen morbimortality identified with medication use, promoting wellbeing and preventing diseases. Healthcare organizations worldwide are under substantial pressure from increasing patient demand. Unfortunately, a cure is not always possible particularly in this era of chronic complications, and the role of physicians has become limited to controlling and palliating symptoms. The increasing population of patients with long-term conditions are associated with high levels of morbidity, healthcare costs and GP workloads. Clinical pharmacy took over an aspect of medical care that had been partially abandoned by physicians. Overburdened by patient loads and the explosion of new drugs, physicians turned to pharmacists more and more for drug information, especially within institutional settings. Once relegated to counting and pouring, pharmacists headed institutional reviews of drug utilization and served as consultants to all types of health-care facilities. In addition, when clinical pharmacists are active members of the care team, they enhance proficiency by: Providing critical input on medicine use and dosing. Working with patients to solve problems with their medications and improve compliance.

With the increasing burden of non-communicable diseases in low-income and middle-income countries (LMICs), biological risk factors, such as hyperglycemia, are a major public health concern in Bangladesh. Optimization of diabetes management by positive lifestyle changes is urgently required for prevention of comorbidities and complications, which in turn will reduce the cost. Diabetes had 2 times more days of inpatient treatment, 1.3 times more outpatient visits, and nearly 10 times more medications than non- diabetes patients, as reported by British Medical Journal. And surprisingly, 80% of people with this so called “Rich Man\'s Disease” live in low- and middle-income countries. According to a recent study of American Medical Association, China and India collectively are home of nearly 110 million diabetic patients. The prevalence of diabetes in this region is projected to increase by 71% by 2035. Bangladesh was ranked as the 8th highest diabetic populous country in the time period of 2010-2011. In Bangladesh, the estimated prevalence of diabetes among adults was 9.7% in 2011 and the number is projected to be 13.7 million by 2045. The cost of diabetes care is considerably high in Bangladesh, and it is primarily driven by the medicine and hospitalization costs. According to Bangladesh Bureau of Statistics, in 2017 the annual average cost per T2DM was $864.7, which is 52% of per capita GDP of Bangladesh and 9.8 times higher than the general health care cost. Medicine is the highest source of direct cost (around 85%) for patients without hospitalization. The private and public financing of diabetes treatment will be severely constrained in near future, representing a health threat for the Bangladeshi population.

Patient satisfaction is a useful measure to provide an indicator of quality in healthcare services. Concern over the quality of healthcare services in Bangladesh has led to loss of faith in healthcare providers, low utilization of public health facilities, and increasing outflow of Bangladeshi patients to hospitals in abroad. The main barriers to accessing health services are inadequate services and poor quality of existing facilities, shortage of medicine supplies, busyness of doctors due to high patient load, long travel distance to facilities, and long waiting times once facilities were reached, very short consultation time, lack of empathy of the health professionals, their generally callous and casual attitude, aggressive pursuit of monetary gains, poor levels of competence and, occasionally, disregard for the suffering that patients endure without being able to voice their concerns—all of these service failures are reported frequently in the print media. Such failures can play a powerful role in shaping patients’ negative attitudes and dissatisfaction with healthcare service providers and healthcare itself