Impact on Voice after Cervical Spinal Cord Injury

Spinal Cord Injury (SCI) is a major cause of disability and morbidity throughout the world and Asia. The association between CSCI and voice difficulties is clinically well-recognized. So this study was meant to determine the impacts on voice following CSCI. The study aimed to determine the impacts on voice following Cervical Spinal Cord Injury (CSCI). Additionally includes finding out the proportion of voice difficulties among CSCI patients, to identify the number of functional, physical, and emotional impacts on voice after CSCI, and to determine the socio-demographic characteristics of the study population. This study was conducted by using a cross-sectional prospective survey method at the SCI unit of CRP. Participants were selected by using purposive sampling. The result states from the research that CSCI is more common in males than females and nearly half of the person has physical, emotional, and functional impacts on voice after CSCI. Among participants, the maximum participants 22.5% (18) rated their voice problem at a moderate level (VHI=11-20) after CSCI and 11.3% (9) participants faced voice problems at a very severe level, 13.8% (11) participants had severe level voice problem. The association between surgeries happened or not happened and the severity of voice problems among CSCI patients showed statistically non-significant. Patients with cervical spinal cord injury faces several clinical problems in our country, whereas nearly most of them experience mild to moderate voice deficits secondary to poor respiratory support. In Bangladesh, Speech & Language Therapy services for SCI patients are newly introduced in the last few years. So for providing proper comprehensive services to SCI patients the monitoring of communicative function from the acute phase to the community reintegration phase is essential

Sustainable and eco-friendly dyeing of traditional grass cloth with a reactive dye in palm oil medium

Traditional grass cloth has been used in China for a long time for the manufacturing of various household furnishing textiles and ladieswear. However, traditionally the grass cloth is dyed with reactive dyes in an aqueous medium, but the dyeing process is not sustainable because of high energy and water usage and the production of coloured effluent. In this work, the possibility of palm oil/water dual-phase dyeing of traditional grass cloth with a reactive dye, C.I. Reactive Blue 194 (Reactive Blue 194), was explored. The grass cloth soaked in an alkaline solution with 80–140% pick-up was dyed in a palm oil dyebath containing dye powder dispersed in a palm oil medium. The initial study confirmed that the pre-treatment of the fabric with an alkaline solution with 140% pick-up was beneficial for the uniform distribution of the dye in the fibres. The dyeing process parameters (e.g., fixation temperature, solution pH, and fixation time) for the grass cloth dyeing with the Reactive Blue 194 were optimised by using the Taguchi method. The pH of the alkali pre-treatment solution was found to be the most influential factor, as confirmed by the analysis of variance in terms of the percentage of contribution (94.41%), which was statistically significant (P < 0.05). The confirmation tests were carried out under optimal settings, and a higher K/S (24.06) was found compared with the initial condition (21.51). X-ray diffraction analysis indicated that the dyeing process did not affect the crystallinity of the grass cloth fibres. Furthermore, the recovery of palm oil from the spent dyebath was around 99%, and up to five times recycling and reuse of palm oil were studied for the dyeing of grass cloth. The colour strength of the grass cloths dyed in the palm oil recycled up to five times was similar to the cloth dyed in fresh palm oil. The results show that palm oil can be used as a dyeing medium for the sustainable dyeing of grass cloth with effluent reduction, which can be extended to the dyeing of other textile fibres

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient&rsquo;s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme

Community attitudes toward forest conservation programs through collaborative protected area management in Bangladesh

The formulation of conservation policies with options for creating protected areas is significantly influenced by the social factors of the surrounding communities. Therefore, indigenous knowledge, attitudes and perceptions of the local communities need to be explored during the planning and implementation stages of conservation projects. A government-initiated experiment in co-management was conducted in the Rema-Kalenga Wildlife Sanctuary, Bangladesh. This paper analyzes the attitudes toward conservation by members of local communities living in and around the wildlife sanctuary. Training incentives on alternative income-generating (AIG) activities and allotment of agricultural lands were distributed among the Forest User Groups. It is of interest to policy makers and resource managers whether this technique leads to improved attitudes on the part of local people. Although there were different attitudes toward protected areas and conservation, overall, a favorable attitude of the respondents was observed. The opinions of respondents also varied based on factors such as village position, village dependency level on forest resources, ethnicity and gender. Increase in annual income resulting from the augmented skills by trainings on AIG activities and getting agricultural lands leased from the Forest Department contributed significantly to the variation in respondents' conservation attitudes. It is suggested that eliminating inequity and inequality in incentive distribution, discovering and launching training on more need-based livelihood activities, and liberalizing the restriction of resource extraction from the protected area by fixing the harvesting limit would encourage the community to be more cordially and actively involved in the conservation efforts of the sanctuary

Advancements in Urban Environmental Studies : Application of Geospatial Technology and Artificial Intelligence in Urban Studies

Advancements in Urban Environmental Studie