Immune thrombocytopenic purpura in a 5-month-old female with rotavirus infection

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65028/1/22368_ftp.pd

SME ACCESS TO FINANCE IN EMERGING ECONOMIES: A COMPARATIVE STUDY OF PAKISTAN AND MALAYSIA

It has been widely accepted that SMEs make a significant contribution to the socio-economic and political infrastructure in both developed and developing countries. SMEs make up more than 96% of all businesses in emerging economies that provide two out of three private sector jobs. Yet, the importance of SMEs notwithstanding as confronted with several hindrances which impede their development. In emerging economies access to finance has been found as one of the major constraints to SMEs growth. In Malaysia and Pakistan SMEs contributing towards economic growth, however huge finance gap for SME sector exist in both countries. By summarizing current data this paper highlights the share of SME sector towards economic growth, financing gap and supply/demand side challenges towards access to finance in Malaysia and Pakistan. The comparison of both countries revealed that challenges are more severe in lower-middle income country (Pakistan) because of inappropriate policies and their implementation. However, the upper-middle income country (Malaysia) consistent growth can minimize the finance gap for SME sector quickly. JEL Code: M

21ST CENTURY EMERGING LEADERSHIP COMPETENCIES IN MALAYSIAN HIGHER LEARNING INSTITUTIONS

Purpose: This paper explores the 21st-century emerging leadership competencies in a Malaysian higher learning institution. As the forces of change are transforming the leadership landscape, new leadership capabilities are required for the 21st-century evolving globalized environment. Hence, research is needed to determine the key emerging leadership competencies in the higher learning institutions. Findings: The significance of the results were the relevance of the leadership competency concept in the context of higher learning institution, future leaders need competencies for effective leadership, and the core competencies of academic leaders are necessary. Additionally, besides the discussion on the emerging leadership competencies of visioning and strategic thinking, leadership agility, adaptability and change, relationship and collaboration, the new findings from the field data were corporate leadership and cross-cultural competence. Research limitations/implications: This qualitative case study focused on one higher learning institution. At the same time the research also provided the in-depth context-rich information. Practical implications: The knowledge and adoption of the emerging leadership competencies concept would enhance the development of progressive leadership. Originality/value: There is limited study on the emerging leadership competencies in the higher learning institutions. Hence, there is value in this research. The findings were original contributions to knowledge. Also, this study showed the link between the expected attributes of institutional leadership to the dimensions of transformational leadership and the key emerging leadership competencies. JEL Classification: L29

Liquid phase synthesis of aromatic poly(azomethine)s, their physicochemical properties, and measurement of ex situ electrical conductivity of pelletized powdered samples

Aromatic bis-aldehydes have been used as building blocks in the synthesis of polyazomethines (a class of conjugated Schiff bases) and their physicochemical properties have been studied. Six dialdehydes have been synthesized, 3a-3f, via etherification reaction between aromatic diols (2a-2f) and 4-fluorobenzaldehyde (1) (see Scheme ), and then polymerized with 1,4-phenylenediamine (4a) and 4,4′-oxydianiline (4b) (see Scheme ). The chemical structures of the bis-aldehydes were elucidated by FTIR, 1H NMR and 13C NMR spectroscopic studies, elemental analysis and single crystal whereas the polymers were studied by FTIR and NMR spectroscopy. Their physicochemical properties were examined by their inherent viscosity, organosolubility, differential scanning calorimetry, X-ray powder diffraction, thermogravimetric analysis, solvatochromism, and photoluminescence. We report the electrical conductivity of each polymer measured by the four probe method. The results indicate that the electrical conductivity of polymers lies in range 0.019–0.051 mScm−1 which is reasonably higher than any reported value

Liquid phase synthesis of aromatic poly(azomethine)s, their physicochemical properties, and measurement of <i>ex situ</i> electrical conductivity of pelletized powdered samples

<p>Aromatic <i>bis</i>-aldehydes have been used as building blocks in the synthesis of polyazomethines (a class of conjugated Schiff bases) and their physicochemical properties have been studied. Six dialdehydes have been synthesized, <b>3a-3f</b>, via etherification reaction between aromatic diols (<b>2a-2f</b>) and 4-fluorobenzaldehyde (<b>1</b>) (see Scheme <a href="#F0013" target="_blank">1</a>), and then polymerized with 1,4-phenylenediamine (<b>4a</b>) and 4,4′-oxydianiline (<b>4b</b>) (see Scheme <a href="#F0014" target="_blank">2</a>). The chemical structures of the <i>bis</i>-aldehydes were elucidated by FTIR, ¹H NMR and ¹³C NMR spectroscopic studies, elemental analysis and single crystal whereas the polymers were studied by FTIR and NMR spectroscopy. Their physicochemical properties were examined by their inherent viscosity, organosolubility, differential scanning calorimetry, X-ray powder diffraction, thermogravimetric analysis, solvatochromism, and photoluminescence. We report the electrical conductivity of each polymer measured by the four probe method. The results indicate that the electrical conductivity of polymers lies in range 0.019–0.051 mScm⁻¹ which is reasonably higher than any reported value.</p

Deep Learning-Based Feature Engineering to Detect Anterior and Inferior Myocardial Infarction Using UWB Radar Data

Cardiovascular disease is the main cause of death worldwide. The World Health Organization (WHO) reports that 17.9 million individuals die yearly due to complications from heart disease and other heart-related ailments. ECG monitoring and early detection are critical to decreasing myocardial infarction (MI) mortality. Thus, a non-invasive method to accurately classify different types of MI would be extremely beneficial. Our proposed study aims to detect and classify Anterior and Inferior MI infarction with advanced deep and machine learning techniques. A newly created UWB radar signal-based image dataset is used to conduct our study experiments. A novel Convolutional spatial Feature Engineering (CSFE) technique is proposed to extract the spatial features from the image dataset. The spatial features consist of both spatial and temporal information which allows machine learning models to leverage both the spatial and temporal relationships present in the data. Study results show that using the proposed CSFE technique, the advanced machine learning techniques achieved high-performance accuracy scores. The K-Neighbors Classifier (KNC) outperformed with a high-performance accuracy score of 98&#x0025; for detecting Anterior and Inferior patients. The applied methods are fully hyperparametric tuned, and performance is validated using the k-fold cross-validation method

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human&rsquo;s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors&rsquo; knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition