Search results for: linear predictive coding (LPC)

Authors: Esther Burkitt, Dawn Watling

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Background: Four types of patterns of simultaneous mixed emotions have been identified in middle childhood, adolescence and adulthood. The present study applied an analogue emotion scale which permits measuring of intensity of opposite valence emotions over time rather than bipolar ratings and used an exhaustive coding scheme to investigate whether children in early childhood experience previously identified and additional types of mixed emotional experiences. Methods: To explore the presence of simultaneous mixed emotion experiences in early childhood, 112 children (59 girls) aged 5 years 1 month - 7 years 2 months (X=6 years 1 month; SD = 10 months) were recruited across the UK. They were allocated on the basis of alternation by gender on class lists to one of two conditions hearing vignettes describing mixed emotion events in an age and gender matched protagonist or themselves (other, n = 57 and self, n = 55). Findings: New types of flexuous, vertical and other experiences were identified alongside sequential, prevalent, highly parallel and inverse types of experiences identified in older populations. Conclusions: The analogue emotion scale uncovered a broader range of simultaneous mixed emotional experiences than previously identified. The value of exploring the utility of the findings in emotion assessments is discussed along with suggestions to explore impacts of educational and cultural influences on children’s mixed emotional experiences.

Keywords: childhood, emotion, graphing, self

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Tzu-I Chiang

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A central question for finite-nonfinite distinction in Mandarin Chinese is how does Mandarin encode temporal information without the grammatical contrast between past and present tense. Moreover, how do L2 learners of Mandarin whose native language is English and whose L1 system has tense morphology, acquire the temporal encoding system in L2 Mandarin? The current study reports preliminary findings on the relationship between topic prominence and the temporal encoding in L1 and L2 Chinese. Oral narratives data from 30 natives and learners of Mandarin Chinese were collected via a film-retell task. In terms of coding, predicates collected from the narratives were transcribed and then coded based on four major verb types: n-degree Statives (quality-STA), point-scale Statives (status-STA), n-atom EVENT (ACT), and point EVENT (resultative-ACT). How native speakers and non-native speakers started retelling the story was calculated. Results of the study show that native speakers of Chinese tend to express Topic Time (TT) syntactically at the topic position; whereas L2 learners of Chinese across levels rely mainly on the default time encoded in the event types. Moreover, as the proficiency level of the learner increases, learners’ appropriate use of the event predicates increased, which supports the argument that L2 development of temporal encoding is affected by lexical aspect.

Keywords: topic prominence, temporal encoding, lexical aspect, L2 acquisition

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Authors: Hannah Dean

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The growth ideology, and its association with progress, is an important construct in the narrative of modernity. This ideology is embedded in neoclassical economic growth theory which conceptualises growth as linear and predictable, and the entrepreneur as a rational economic manager. This conceptualisation has been critiqued for reinforcing the managerial discourse in entrepreneurship studies. Despite these critiques, both the neoclassical growth theory and its adjacent managerial discourse dominate entrepreneurship studies notably the literature on female entrepreneurs. The latter is the focus of this paper. Given this emphasis on growth, female entrepreneurs are portrayed as problematic because their growth lags behind their male counterparts. This image which ignores the complexity and diversity of female entrepreneurs’ experience persists in the literature due to the lack of studies that analyse the process and contextual factors surrounding female entrepreneurs’ experience. This study aims to address the subordination of female entrepreneurs by questioning the hegemonic logic of economic growth and the managerial discourse as a true representation for the entrepreneurial experience. This objective is achieved by drawing on Schumpeter’s theorising and narrative inquiry. This exploratory study undertakes in depth interviews to gain insights into female entrepreneurs’ experience and the impact of the economic growth model and the managerial discourse on their performance. The narratives challenge a number of assumptions about female entrepreneurs. The participants occupied senior positions in the corporate world before setting up their businesses. This is at odds with much writing which assumes that women underperform because they leave their career without gaining managerial experience to achieve work-life balance. In line with Schumpeter, who distinguishes the entrepreneur from the manager, the participants’ main function was innovation. They did not believe that the managerial paradigm governing their corporate careers was applicable to their entrepreneurial experience. Formal planning and managerial rationality can hinder their decision making process. The narratives point to the gap between the two worlds which makes stepping into entrepreneurship a scary move. Schumpeter argues that the entrepreneurial process is evolutionary and that failure is an integral part of it. The participants’ entrepreneurial process was in fact irregular. The performance of new combinations was not always predictable. They therefore relied on their initiative. The inhibition to deploy these traits had an adverse effect on business growth. The narratives also indicate that over-reliance on growth threaten the business survival as it faces competing pressures. The study offers theoretical and empirical contributions to (female) entrepreneurship studies by presenting Schumpeter’s theorising as an alternative theoretical framework to the neoclassical economic growth theory. The study also reduces entrepreneurs’ vulnerability by making them aware of the negative influence that the linear growth model and the managerial discourse hold upon their performance. The study has implications for policy makers as it generates new knowledge that incorporates the current social and economic changes in the context of entrepreneurs that can no longer be sustained by the linear growth models especially in the current economic climate.

Keywords: economic growth, female entrepreneurs, managerial discourse, Schumpeter

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Authors: A. Alssbaiheen, S. Love

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Mobile government (m-government) is one of the promising technologies for developing the governance of developing countries. While developing countries often have less advanced internet infrastructure compared to the developed world, mobile phone penetration is very high in the Gulf Cooperation Council (GCC) countries and mobile internet use offers a means to transcend traditional logistical barriers to accessing government services. The study explores the challenges and opportunities of the mobile government in Saudi Arabia. Semi-structured interviews were conducted with a diverse cohort of Saudi mobile users. A total of 77 semi-structured interviews were collected and subsequently analysed using open, axial, and selective coding. The participants’ responses revealed that many opportunities exist for the development of m-government in Saudi Arabia, including high popular awareness of government initiatives in e-government, and willingness to use such services, largely due to the time-saving and convenience aspects it offers compared with traditional bureaucratic services. However, numerous barriers were identified, including the low quality and speed of the internet, service customization, and concerns about privacy data security. It was also felt that in addition to infrastructure challenges, the traditional bureaucratic attitude of government department would itself hinder the effective deployment and utilization of m-government services.

Keywords: awareness, barriers, challenges, government services, mobile government, m-government, opportunities

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Authors: Marchant Van Der Schyff

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South Africa remains one of the most dangerous places for women – lesbians in particular – to live freely and safely, where a culture of patriarchy and a lack of socio-economic opportunity are ubiquitous throughout its communities. While the Internet has given a wider platform to provide insights to issues plaguing lesbians, very little information exists regarding the elements used in the construction of these online reports. This is not only due to the lack of language required to contextualise lesbian issues, but also persistent institutional and societal homophobia. This article describes the technical frames used in the online news reporting of four case studies of ‘queercide’. Through using a thematic coding sheet, data was collected from 70 online articles purposively selected based on priori population characteristics. The study found technical elements, such as the length of online reports, credible sources used, ‘code driven’-, and ‘user driven’ elements which were identified in the coded online articles. From the conclusions some clear trends emerged enabling the construction of a Venn-type diagram which present insights to how the murder of lesbians (referred to as ‘queercide’ in the article) is being reported on by online news media compared to the contemporary theoretical discussions on how these cases should be reported on.

Keywords: journalism, lesbian murder, queercide, technical frames, reporting, online

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Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Sung Hee Ahn, Ju Rang Han

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Background: With increasing importance in healthcare organization on patient satisfaction and nurses’ job satisfaction, many studies have been conducted. But no research has been administered how nurses’ satisfaction with healthcare organization influence patient satisfaction and loyalty. Purpose: This study aims to conceptualize nurses‘ satisfaction, patient satisfaction with and patient loyalty to hospitals using a hypothetical linear structural equation model, and to identify the significance of path coefficients and goodness of fit index of the structural equation model as well. Method: A total of 2,079 nurses and 6,776 patients recruited from 5 university hospitals in South Korea participated in this study. The data on nurses, including ward nurses and outpatient nurses, were collected from June 24th to July 12th, at the 204 departments of the 5 hospitals through an on-line survey. The data on the patients, including both inpatients and outpatients, were collected from September 30th to October 24th, 2013 at the 5 hospitals using a structured questionnaire. The variable of nurses’ satisfaction was measured using a scale evaluating internal client satisfaction, which is used in SSM Health Care System in the US. Patient satisfaction with the hospital and nurses and patient loyalty were measured by assessing the patient’s intention to revisit and to recommending the hospital to others using a visual analogue scale. The data were analyzed using SPSS version 21.0 and AMOS version 21.0. Result: The hypothetical model was fairly good in terms of goodness of fit (χ2= 64.897 (df=24, p <. 001), GFI=. 906, AGFI=.823, CFI=.921, NFI=.951, NNFI=.952. RMSEA=.114). The significance of path coefficients includes followings 1)The nurses’ satisfaction has significant influence on the patient satisfaction with nurses. 2)The patient satisfaction with nurses has significant influence on the patient satisfaction with the hospital. 3)The patient satisfaction with the hospital has significant influence on the patients’ revisit intention. 4)The patient satisfaction with the hospital has significant influence on the patients’ intention to the recommendations of the hospital. Conclusion: These results provide several practical implications to hospital administrators, who should incorporate ways of improving nurses' and patients' satisfaction with the hospital into their health care marketing strategies.

Keywords: linear structural equation model, loyalty, nurse, patient satisfaction

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Authors: Arturo Galván, Jatziri Y. Moreno-Martínez, Israel Enrique Herrera Díaz, José Ramón Gasca Tirado

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In recent years, one of the methods most used for the construction of tunnels in soft soil is the shield-driven tunneling. The advantage of this construction technique is that it allows excavating the tunnel while at the same time a primary lining is placed, which consists of precast segments. There are joints between segments, also called longitudinal joints, and joints between rings (called as circumferential joints). This is the reason because of this type of constructions cannot be considered as a continuous structure. The effect of these joints influences in the rigidity of the segmental lining and therefore in its structural response. A parametric study was performed to take into account the effect of different parameters in the structural response of typical segmental tunnels built in soft soil by using non-linear numerical models based on Finite Element Method by means of the software package ANSYS v. 11.0. In the first part of this study, two types of numerical models were performed. In the first one, the segments were modeled by using beam elements based on Timoshenko beam theory whilst the segment joints were modeled by using inelastic rotational springs considering the constitutive moment-rotation relation proposed by Gladwell. In this way, the mechanical behavior of longitudinal joints was simulated. On the other hand for simulating the mechanical behavior of circumferential joints elastic springs were considered. As well as, the stability given by the soil was modeled by means of elastic-linear springs. In the second type of models, the segments were modeled by means of three-dimensional solid elements and the joints with contact elements. In these models, the zone of the joints is modeled as a discontinuous (increasing the computational effort) therefore a discrete model is obtained. With these contact elements the mechanical behavior of joints is simulated considering that when the joint is closed, there is transmission of compressive and shear stresses but not of tensile stresses and when the joint is opened, there is no transmission of stresses. This type of models can detect changes in the geometry because of the relative movement of the elements that form the joints. A comparison between the numerical results with two types of models was carried out. In this way, the hypothesis considered in the simplified models were validated. In addition, the numerical models were calibrated with (Lab-based) experimental results obtained from the literature of a typical tunnel built in Europe. In the second part of this work, a parametric study was performed by using the simplified models due to less used computational effort compared to complex models. In the parametric study, the effect of material properties, the geometry of the tunnel, the arrangement of the longitudinal joints and the coupling of the rings were studied. Finally, it was concluded that the mechanical behavior of segment and ring joints and the arrangement of the segment joints affect the global behavior of the lining. As well as, the effect of the coupling between rings modifies the structural capacity of the lining.

Keywords: numerical models, parametric study, segmental tunnels, structural response

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Authors: Robert E. Kenney, Efrain Antunez, Samuel Nodal, Ameer Malik, Richard B. Aguilar

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Physician burnout has gained much attention during the COVID pandemic. After-hours workload, HCC coding, HEDIS metrics, and clinical documentation negatively impact career satisfaction. These and other influences have increased the rate of physicians leaving the workforce. In addition, roughly 1% of the entire physician workforce will be retiring earlier than expected based on pre-pandemic trends. The two Medical Specialties with the highest rates of burnout are Family Medicine and Primary Care. With a predicted shortage of primary care physicians looming, the need to address physician burnout is crucial. Commonly reported issues leading to clinician burnout are clerical documentation requirements, increased time working on Electronic Health Records (EHR) after hours, and a decrease in work-life balance. Clinicians experiencing burnout with physical and emotional exhaustion are at an increased likelihood of providing lower quality and less efficient patient care. This may include a lack of suitable clinical documentation, medication reconciliation, clinical assessment, and treatment plans. While the annual baseline turnover rates of physicians hover around 6-7%, the COVID pandemic profoundly disrupted the delivery of healthcare. A report found that 43% of physicians switched jobs during the initial two years of the COVID pandemic (2020 and 2021), tripling the expected average annual rate to 21.5 %/yr. During this same time, an average of 4% and 1.5% of physicians retired or left the workforce for a non-clinical career, respectively. The report notes that 35.2% made career changes for a better work-life balance and another 35% reported the reason as being unhappy with their administration’s response to the pandemic. A physician-led primary care-focused health organization, Cano Health (CH), based out of Florida, sought to preemptively address this problem by implementing several supportive measures. Working with >120 clinics and >280 PCPs from Miami to Tampa and Orlando, managing nearly 120,000 Medicare Advantage lives, CH implemented a number of changes to assist with the clinician’s workload. Supportive services such as after hour and home visits by APRNs, in-clinic care managers, and patient educators were implemented. In 2021, assistive Artificial Intelligence Software (AIS) was integrated into the EHR platform. This AIS converts free text within PDF files into a usable (copy-paste) format facilitating documentation. The software also systematically and chronologically organizes clinical data, including labs, medical records, consultations, diagnostic images, medications, etc., into an easy-to-use organ system or chronic disease state format. This reduced the excess time and documentation burden required to meet payor and CMS guidelines. A clinician Documentation Support team was employed to improve the billing/coding performance. The effects of these newly designed workflow interventions were measured via analysis of clinician turnover from CH’s hiring and termination reporting software. CH’s annualized average clinician turnover rate in 2020 and 2021 were 17.7% and 12.6%, respectively. This represents a 30% relative reduction in turnover rate compared to the reported national average of 21.5%. Retirement rates during both years were 0.1%, demonstrating a relative reduction of >95% compared to the national average (4%). This model successfully promoted the retention of clinicians in a Value-Based Care setting.

Keywords: clinician burnout, COVID-19, value-based care, burnout, clinician retirement

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Authors: Samuel Onyekachi Chidi

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Nigeria today faces more internal threats stemming from ethnic and religious conflicts than external sources. This article seeks to examine the ethno-religious conflicts in Nigeria from 2015 to 2021 and their impact on national security. The research was guided by six objectives. The theoretical framework adopted for this study is Structural Conflict Theory, which provides an adequate explanation, a predictive rationale for the frequent occurrence of ethno-religious conflicts and a tendency to provide the necessary insight for their resolution. The results of the study revealed that there is a strong relationship between ethnicity, religion, conflict and national security and that the ethno-religious conflicts experienced in Nigeria have gross implications for national security. The study recommends that the secularity of the Nigerian state be restored and preserved and that the state of origin be removed and replaced by the state of residence in all our national documents, as this will reduce ethnic identity, which is in opposition to nationalism. Religious leaders, traditional rulers, the media and other stakeholders should support the government in its fight to reduce ethno-religious conflict by sensitizing its youth, preaching unity and peaceful coexistence, and discouraging the use of violence as a means of settling disputes between groups and individuals.

Keywords: ethnicity, religion, conflict, national security

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Authors: Yang Xiong, Hua Cheng

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In this paper, for the basic problem of wireless sensor network topology control and deployment, the Roesser model in rectangular grid sensor networks is presented. In addition, a general constructive realization procedure will be proposed. The procedure enables a distributed implementation of linear systems on a sensor network. A non-trivial example is illustrated.

Keywords: grid sensor networks, Roesser model, state-space realization, multidimensional systems

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Authors: Mengyu Lu, Yongsheng Guo

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This study investigates how employee incentives affect employee performance in financing from customers. This study followed a grounded theory approach where data were collected through 29 interviews. Main themes and categories were identified through the coding processes. This study found that casual conditions, including financial barriers, informal finance, business location, customer base and customer relationship, influenced the adoption of customer finance in the case of SMEs. The SMEs build and maintain long-term relationships with customers through personal communications. The SMEs engage and motivate employees in customer communications and business financing strategy through financial incentives programs, including bonuses, salary rises, rewards and non-financial incentives, including training opportunities, extra holiday leave, and flexible working hours. Employee performance was measured through financing contribution and job contribution. As a consequence, customers will be well served by employees and get a better customer experience. SMEs can get benefits such as employee engagement, employee satisfaction and sustainable financing sources. This study gets in sight of employee incentives in improving employee performance in customer finance and makes implications to human capital theories. Suggestions are provided to the decision-makers in businesses as incentive programs improve employee performance that, eventually contributes to overall business performance.

Keywords: SMEs, financing from customers, employee incentives, performance-based measurement

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

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Authors: A. Alyami, S. Christmas, K. Neeltje, G. Pollakis, B. Paxton, Z. Al-Bayati

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The Human leukocyte antigen-G (HLA-G) molecule plays an important role in immunomodulation. To date, 16 untranslated regions (UTR) HLA-G haplotypes have been previously defined by sequenced SNPs in the coding region. From these, UTR-1, UTR-2, UTR-3, UTR-4, UTR-5, UTR-6 and UTR-7 are the most frequent 3’UTR haplotypes at the global level. UTR-1 is associated with higher levels of soluble HLA-G and HLA-G expression, whereas UTR-5 and UTR-7 are linked with low levels of soluble HLA-G and HLA-G expression. Human immunodeficiency virus type 1 (HIV-1) infection results in the progressive loss of immune function in infected individuals. The virus escape mechanism typically includes T lymphocytes and NK cell recognition and lyses by classical HLA-A and B down-regulation, which has been associated with non-classical HLA-G molecule up-regulation, respectively. We evaluated the haplotypes of the HLA-G 3′ untranslated region frequencies observed in three HIV-1 groups from the Netherlands and their susceptibility to develop infection. The three groups are made up of mainly men who have sex with men (MSM), injection drug users (IDU) and a high-risk-seronegative (HRSN) group. DNA samples were amplified with published primers prior sequencing. According to our results, the low expresser frequencies show higher in HRSN compared to other groups. This is indicating that 3’UTR polymorphisms may be identified as potential prognostic biomarkers to determine susceptibility to HIV.

Keywords: Human leukocyte antigen-G (HLA-G) , men who have sex with men (MSM), injection drug users (IDU), high-risk-seronegative (HRSN) group, high-untranslated region (UTR)

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Authors: Giang Tran Thi Huong, Hieu Dong Van, Tung Dao Duy, Saadanov Iskender, Isakeev Mairambek, Tsutomu Omatsu, Yukie Katayama, Tetsuya Mizutani, Yuki Ozeki, Yohei Takeda, Haruko Ogawa, Kunitoshi Imai

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Newcastle disease virus (NDV) is a contagious viral disease of the poultry industry and other birds throughout the world. At present, very little is known about molecular epidemiological data regarding the causes of ND outbreak in commercial poultry farms in Kyrgyzstan. In the current study, the NDV isolated from the one out of three samples from the unvaccinated flock was confirmed as NDV. Phylogenetic analysis indicated that this NDV strain is clustered in the Class II subgenotype VIId, and closely related to the Chinese NDV isolate. Phylogenetic analyses revealed that the isolated NDV strain has an origin different from the 4 NDV strains previously identified in Kyrgyzstan. According to the mean death time (MDT: 61.1 h) and a multibasic amino acid (aa) sequence at the F0 proteolytic cleavage site (¹¹²R-R-Q-K-R-F¹¹⁷), the NDV isolate was determined as mesogenic strain. Several mutations in the neutralizing epitopes (notably, ³⁴⁷E→K) and the global head were observed in the hemagglutinin-neuraminidase (HN) protein of the current isolate. The present study represents the molecular characterization of the coding gene region of NDV in Kyrgyzstan. Additionally, further study will be investigated on the antigenic characterization using monoclonal antibody.

Keywords: Kyrgyzstan, Newcastle disease, genotype, genome characterization

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Authors: Dimitris E. Kontaxis, George Litainas, Dimitris P. Ptochos

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Real-time monitoring of the supply chain conditions and procedures is a critical element for the optimal coordination and safety of the deliveries, as well as for the minimization of the delivery time and cost. Real-time monitoring requires IoT data streams, which are related to the conditions of the products and the means of transport (e.g., location, temperature/humidity conditions, kinematic state, ambient light conditions, etc.). These streams are generated by battery-based IoT tracking devices, equipped with appropriate sensors, and are transmitted to a cloud-based back-end system. Proper handling and processing of the IoT data streams, using predictive and artificial intelligence algorithms, can provide significant and useful results, which can be exploited by the supply chain stakeholders in order to enhance their financial benefits, as well as the efficiency, security, transparency, coordination, and sustainability of the supply chain procedures. The technology, the features, and the characteristics of a complete, proprietary system, including hardware, firmware, and software tools -developed in the context of a co-funded R&D programme- are addressed and presented in this paper.

Keywords: IoT embedded electronics, real-time monitoring, tracking device, sensor platform

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Authors: P. Singh, R. M. Banik

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Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold.

Keywords: Bacillus cereus, L-glutaminase, assay parameters, artificial neural network

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Authors: Abdulaziz Afandi

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Ayesha Hasan

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This study offers unique perspectives and analyses of peace and conflict journalism through interpretative repertoire, media frames, and critical discourse analyses. Two major English publications in Pakistan, representing both long and short-form journalism, are investigated to uncover how the Afghan refugee repatriation from Pakistan in 2016-17 has been framed in Pakistani English media. Peace journalism focuses on concepts such as peace initiatives and peace building, finding common ground, and preventing further conflict. This study applies Jake Lynch’s Coding Criteria to guide the critical discourse analysis and Lee and Maslog’s Peace Journalism Quotient to examine the extent of peace journalism in each text. This study finds that peace journalism is missing in Pakistani English press, but represented, to an extent, in long-form print and online coverage. Two new alternative frames are also proposed. This study gives an in-depth understanding of if and how journalists in Pakistan are covering conflicts and framing stories that can be identified as peace journalism. This study represents significant contributions to the remarkably limited scholarship on peace and conflict journalism in Pakistan and extends Shabbir Hussain’s work on critical pragmatic perspectives on peace journalism in Pakistan.

Keywords: Afghan refugee repatriation, Critical discourse analysis, Media framing , Peace and conflict journalism

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Authors: Kazuyoshi Mori

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Examples of parameterization of stabilizing controllers that require only one of right-/left-coprime factorizations are presented. One parameterization method requires one side coprime factorization. The other requires no coprime factorization. The methods are based on the factorization approach so that a number of models can be applied the method we use in this paper.

Keywords: parametrization, coprime factorization, factorization approach, linear systems

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

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Authors: Indri Mahadiraka Rumamby, R. R. Dwinanti Rika Marthanty, Jessica Sjah

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Smoothed particle hydrodynamics (SPH) was originally created to simulate nonaxisymmetric phenomena in astrophysics. However, this method still has several shortcomings, namely the high computational cost required to model values with high resolution and problems with boundary conditions. The difficulty of modeling boundary conditions occurs because the SPH method is influenced by particle deficiency due to the integral of the kernel function being truncated by boundary conditions. This research aims to answer if SPH modeling with a focus on boundary layer interactions and continuous flow can produce quantifiably accurate values with low computational cost. This research will combine algorithms and coding in the main program of meandering river, continuous flow algorithm, and solid-fluid algorithm with the aim of obtaining quantitatively accurate results on solid-fluid interactions with the continuous flow on a meandering channel using the SPH method. This study uses the Fortran programming language for modeling the SPH (Smoothed Particle Hydrodynamics) numerical method; the model is conducted in the form of a U-shaped meandering open channel in 3D, where the channel walls are soil particles and uses a continuous flow with a limited number of particles.

Keywords: smoothed particle hydrodynamics, computational fluid dynamics, numerical simulation, fluid mechanics

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Authors: Bin Liu

Abstract:

MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Ntokozo Dennis Ndwandwe

Abstract:

The aim of the study was to explore the barriers facing the implementation of peace education as a strategy to combat violence in selected secondary schools in the Western Cape Province of South Africa. The problem that motivated this enquiry was the absence of stable peace and the increase of incidents of violence in schools. A qualitative approach was followed when conducting the study, and small samples of three case studies of secondary schools were used. Method used in collecting data consisted of semi-structured interviews; focus group interviews and observation. The participants consisted of the program manager for Quaker for Peace Centre (QPC), three principals, nine teachers, and fifteen learners. Data were analysed by transcribing, organising, marking by hand and coding that produced labels that allowed key points to be highlighted. Findings revealed that the effective implementation of peace education was being constrained by factors such as financial constraints, inadequate time allocated, lack of parental involvement, over work-loaded teachers, negative attitude and other societal influences. It is recommended that teachers should receive an ongoing training for peace education. Therefore, the government should prioritise and provide funds for peace education. In addition, parental involvement should be improved in order to enhance the implementation of peace education in selected secondary schools.

Keywords: barriers, implementation, conflict, peace, peace education, conflict resolution, violence

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Authors: Ojo Oo, Akinde S. B., Kiilani A. O., Jayeola Jo, Jogbodo T. M., Ajani Ka, Olaniyan So, Adeagbo Oy, Bolarinwa Ra, Durosomo Ha, Sule W. F.

Abstract:

Lockdown imposed to control SARS-CoV-2 transmission hampered malaria control services in Nigeria. Considering COVID-19 vaccination, we assessed Plasmodium falciparum (Pf) antigen and SARS-CoV-2 immunoglobulin-G (IgG) positivity among adults in Osogbo, Osun State, Nigeria. Consenting attendees of four Healthcare Centres were consecutively enrolled for blood sampling; relevant socio-demographic/behavioral/clinical/environmental data were collected with a questionnaire. Samples were tested, using commercial rapid test kits, for Pf antigen and SARS-CoV-2 IgG and results were analyzed using logistic regression. Participants' mean age was 40.99 years (n=200), and they were predominantly females (84.5%), traders/businessmen/women (86.0%), with self-reported receipt of COVID-19 vaccine from 123 (61.5%). Pf antigen positivity was 17.5% (95% CI: 12.23–22.77%) with age (p=0.004), marital status (p=0.004), report of stagnant water around the workplace (p=0.041) and bush around homes (p=0.008) being associated. SARS-CoV-2 IgG positivity was 56.5% (95% CI: 49.63–63.37%) with age (p=0.012) and receipt of COVID-19 vaccination (p=0.001) being associated. Although the vaccinated had a 22.8 times higher likelihood of IgG positivity, no factor was predictive of COVID-19 vaccine receipt. We report 17.5% Pf antigen positivity with four predictors, and 56.5% SARS-CoV-2 IgG positivity with two predictors.

Keywords: COVID-19, vaccine, IgG, Plasmodium falciparum, SARS-CoV-2

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Authors: S. Jursová, P. Pustějovská, S. Brožová, J. Bilík

Abstract:

The paper deals with possibilities of interpretation of iron ore reducibility tests. It presents a mathematical model developed at Centre ENET, VŠB–Technical University of Ostrava, Czech Republic for an evaluation of metallurgical material of blast furnace feedstock such as iron ore, sinter or pellets. According to the data from the test, the model predicts its usage in blast furnace technology and its effects on production parameters of shaft aggregate. At the beginning, the paper sums up the general concept and experience in mathematical modelling of iron ore reduction. It presents basic equation for the calculation and the main parts of the developed model. In the experimental part, there is an example of usage of the mathematical model. The paper describes the usage of data for some predictive calculation. There are presented material, method of carried test of iron ore reducibility. Then there are graphically interpreted effects of used material on carbon consumption, rate of direct reduction and the whole reduction process.

Keywords: blast furnace technology, iron ore reduction, mathematical model, prediction of iron ore reduction

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Authors: Khatesiree Sripoothon, Usanee Sengpanich, Rattana Sittioum

Abstract:

The objective of this study is to examine the factors influencing consumer purchasing decisions about healthy food. This model consists of two latent variables: Consumer Perception relating to NCDs and Consumer Perceived Product Value. The study was conducted in the northern provinces of Thailand, which are popular with tourists and have received support from the government for health tourism. A survey was used as the data collection method, and the questionnaire was applied to 385 tourists. An accidental sampling method was used to identify the sample. The statistics of frequency, percentage, mean, and structural equation model were used to analyze the data obtained. Additionally, all factors had a significant positive influence on healthy food purchasing decisions (p<0.01) and were predictive of healthy food purchasing decisions at 46.20 (R2=0.462). Also, these findings seem to underline a supposition that consumer perceptions of NCDs and perceived product value are key variables that strengthens the competitive effects of a healthy-friendly business entrepreneur. Moreover, reduce the country's public health costs for treating patients with the disease of NCDs in Thailand.

Keywords: healthy food, perceived product value, perception of non-communicable diseases, purchasing decisions

Procedia PDF Downloads 145