Margin Based Learning Framework with Geometric Margin Minimum Classification Error for Robust Speech Recognition

Statistical learning theorycombines empirical risk and generalization functionin single optimized objective function of margin based learning for optimization. Margin concept incorporating in Hidden Markov Model (HMM)for speech recognition, Margin based learning frame work based on minimum classification error (MCE) training criteria show higher capability over any other conventional DT methods in improvingclassification robustness (generalization capability) of the acoustic model by increasing the functional margin of the acoustic model. This paper introduces Geometric Margin based separation measure in the loss function definition of margin based learning frame work instead of functional margin separation measure to develop a mathematical framework of new optimize objective function of soft margin estimation (SME) for ASR. Derived SME objective function based on Geometric Margin based separation (misclassification) measure would be capable for representing the strength of margin based learning framework in term of classification robustness by minimizing the classification error probability as well asmaximizing the geometric margin

A TAXONOMY-ORIENTED OVERVIEW OF NOISE COMPENSATION TECHNIQUES FOR SPEECH RECOGNITION

ABSTRACT Designing a machine that is capable for understanding human speech and responds properly to speech utterance or spoken language has intrigued speech research community for centuries. Among others, one of the fundamental problems to building speech recognition system is acoustic noise. The performance of speech recognition system significantly degrades in the presence of ambient noise. Background noise not only causes high level mismatch between training and testing conditions due to unseen environment but also decreases the discriminating ability of the acoustic model between speech utterances by increasing the associated uncertainty of speech. This paper presents a brief survey on different approaches to robust speech recognition. The objective of this review paper is to analyze the effect of noise on speech recognition, provide quantitative analysis of well-known noise compensation techniques used in the various approaches to robust speech recognition and present a taxonomy-oriented overview of noise compensation techniques

Burnout and quality of life among healthcare professionals during the COVID-19 pandemic in Saudi Arabia

Background and Objectives. Healthcare professionals (HCPs) have had to deal with large numbers of confirmed or suspected cases of COVID-19 and were at a high risk of burnout and dissatisfaction regarding their work-life integration. This article aims to assess burnout, the work-life balance (WLB), and quality of life (QoL) among healthcare workers and the relationship between these aspects in Saudi Arabia. Methods. An analytical cross-sectional study was conducted among 491 HCPs from five secondary hospitals in Jazan, Saudi Arabia. Three standardized questionnaires were used to gather data, including WLB, burnout, and the WHO Quality of Life-BREF. Results. Healthcare professionals struggled to balance their work and personal lives during COVID-19 and reported many burnout symptoms and a low level of QoL. Two-thirds (68.8%) of HCPs arrived home late from work and (56.6%) skipped a meal. HCPs who worked through a shift without any breaks were found in 57.8%. It was reported that 39.3% of HCPs felt frustrated by technology while being exhausted from their work (60.5%). The correlation coefficients between the WLB and health-related QoL (HRQoL) showed a significant negative correlation for all items, which ranged from (-.099 to -.403, P<0.05). The WLB and burnout scores were successful predictors of low levels of HRQoL (P<0.001 for both explanatory variables). Conclusions. Work-life imbalances, high levels of burnout, and low QoL levels are common among healthcare professionals in Saudi Arabia during COVID-19. Hospital administration should address the WLB and reduce burnout symptoms among HCPs to increase satisfaction and improve the quality of care

Calcium Acetate Versus Calcium Carbonate as Oral Phosphate Binder: Preparation and In Vitro Assessment: Calcium acetate as oral phosphate binder

Calcium acetate is used as an oral phosphate binder to control hyperphosphatemia in patients with chronic renal failure. Compared to calcium carbonate,control of hyperphosphatemia can be achieved at lower calcium administration with calcium acetate which likely reduces the risk of hypercalcemia. In this study,various formulations of calcium acetate tablets were prepared and their disintegration times, dissolution rates and phosphate binding capacities were determined. Dissolution test was carried out using the paddle method according to the United States Pharmacopoeia (USP XXIII). The binding efficiency of the tablets was compared by measuring the amount of insoluble phosphate after mixing with a sodium phosphate solution at pH 6. Calcium acetate tablets had a mean content of 809.6 mg of calcium acetate and a mean weight of 1087 mg. The average breaking load and disintegration times were 66.4±5.5 N and 24.5±2.1 min, respectively. Drug release after 30 and 60 min were 80.45% and 101.42%, respectively. The amount of nondissolved phosphorus following 60 min incubation of calcium acetate and/or calcium carbonate tablets were 372.8 mg (61.2%) and 463.2 mg (76.0%), respectively.Weight variation, friability, disintegration time, and dissolution rate of calciumacetate tablets were in the acceptable pharmacopoeial limits. Ahigh phosphate bindingcapacity of calcium acetate tablets indicated that it can be a suitable alternative tocalcium carbonate in the management of hyperphosphatemia in patients withchronic renal failure

Integrating Remote Sensing and Street View Imagery for Mapping Slums

Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i

Integrating Remote Sensing and Street View Imagery for Mapping Slums

Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i

Relationship between culture of excellence and organisational performance in Iranian manufacturing companies

High-performing organisations are those which practise exemplary Culture of Excellence (CoE). Global competition dictates that only high-performing organisations will survive in the long term. This study attempts to focus on the relationship between CoE constructs and organisational performance (OP) in the context of Iranian manufacturing sector. Data are gathered via survey, of 222 excellence award-winning companies in Iran. The SPSS and smartPLS were used to test the relationship between CoE factors and OP. The result of correlation analysis proved that all 10 CoE constructs were significantly correlated with OP and there exists a significant relationship between CoE and OP. Strong commitment was the most important factor and the bottom rank was high degree of motivation. According to the results, CoE might be supported as a perfect management system to efficiently upkeep organisational competitiveness. Current results might contribute to the improvement of superior OP through creating a reliable and valid measurement tool for CoE. This current research offers interesting insights for researchers and practitioners in the field of quality management

Potential Immunogenic Activity of Computationally Designed mRNA- and Peptide-Based Prophylactic Vaccines against MERS, SARS-CoV, and SARS-CoV-2: A Reverse Vaccinology Approach

The continued emergence of human coronaviruses (hCoVs) in the last few decades has posed an alarming situation and requires advanced cross-protective strategies against these pandemic viruses. Among these, Middle East Respiratory Syndrome coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), and Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) have been highly associated with lethality in humans. Despite the challenges posed by these viruses, it is imperative to develop effective antiviral therapeutics and vaccines for these human-infecting viruses. The proteomic similarity between the receptor-binding domains (RBDs) among the three viral species offers a potential target for advanced cross-protective vaccine designs. In this study, putative immunogenic epitopes including Cytotoxic T Lymphocytes (CTLs), Helper T Lymphocytes (HTLs), and Beta-cells (B-cells) were predicted for each RBD-containing region of the three highly pathogenic hCoVs. This was followed by the structural organization of peptide- and mRNA-based prophylactic vaccine designs. The validated 3D structures of these epitope-based vaccine designs were subjected to molecular docking with human TLR4. Furthermore, the CTL and HTL epitopes were processed for binding with respective human Lymphocytes Antigens (HLAs). In silico cloning designs were obtained for the prophylactic vaccine designs and may be useful in further experimental designs. Additionally, the epitope-based vaccine designs were evaluated for immunogenic activity through immune simulation. Further studies may clarify the safety and efficacy of these prophylactic vaccine designs through experimental testing against these human-pathogenic coronaviruses

Role of mineral trioxide aggregate in dentistry: A bibliometric analysis using Scopus database

Objectives: Mineral trioxide aggregate (MTA) has a long history of providing predictable clinical outcomes in dental applications especially in endodontic procedures. This bibliometric analysis aimed at evaluating the advancements of research in mineral trioxide aggregate and its use in the field of dentistry, together with the detection of most significant authors, organizations, countries, journals, papers, and the exploration of commonly used keywords using a structured approach. Materials and method: The search was conducted using the Elsevier’s Scopus database, gathering publication information related to MTA published from 1993 and 2021 July. Metadata comprising of titles, abstracts, keywords, authors, organizations, and countries were obtained. Bibliometric evaluators with respect to authors, articles published, journals, keywords, and top countries were scrutinized. Data was analyzed using VOS viewer. Results: Between 1993 and 2021, an uptrend in the research performed on MTA was identified. Researchers from United States, Brazil, and Iran actively contributed on MTA, while papers from USA were highly cited. The Journal of Endodontics along with International Endodontic Journal were the top contributing academic journals. Hacettepe University, Turkey and Cardiff University from United Kingdom were the top most contributing organizations. Mahmoud Torabinejad was the most cited author. Most commonly used keywords included Mineral trioxide aggregate, silicate, oxide, root canal filling material. Conclusion: The global rise in the number of publications on mineral trioxide aggregate, tremendous networking and citations have been identified amongst various organizations, authors, and nations through this bibliometric analysis

Improved Photocatalytic and Antioxidant Activity of Olive Fruit Extract-Mediated ZnO Nanoparticles

Photodegradation is an efficient strategy for the removal of organic pollutants from wastewater. Due to their distinct properties and extensive applications, semiconductor nanoparticles have emerged as promising photocatalysts. In this work, olive (Olea Europeae) fruit extract-based zinc oxide nanoparticles (ZnO@OFE NPs) were successfully biosynthesized using a one-pot sustainable method. The prepared ZnO NPs were systematically characterized using UV-Vis, FTIR, SEM, EDX and XRD and their photocatalytic and antioxidant activity was evaluated. SEM demonstrated the formation of spheroidal nanostructures (57 nm) of ZnO@OFE and the EDX analysis confirmed its composition. FTIR suggested the modification/capping of the NPs with functional groups of phytochemicals from the extract. The sharp XRD reflections revealed the crystalline nature of the pure ZnO NPs with the most stable hexagonal wurtzite phase. The photocatalytic activity of the synthesized catalysts was evaluated by measuring the degradation of methylene blue (MB) and methyl orange (MO) dyes under sunlight irradiation. Improved degradation efficiencies of 75% and 87% were achieved within only 180 min with photodegradation rate constant k of 0.008 and 0.013 min−1 for MB and MO, respectively. The mechanism of degradation was proposed. Additionally, ZnO@OFE NPs exhibited potent antioxidant activity against DPPH, hydroxyl, peroxide and superoxide radicals. Hence, ZnO@OFE NPs may have potential as a cost-effective and green photocatalyst for wastewater treatment