Search results for: positive and negative models matching

Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: Saad M. Algharib

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The major concern of planners when placing fire stations is finding their optimal locations such that the fire companies can reach fire locations within reasonable response time or distance. Planners are also concerned with the numbers of fire stations that are needed to cover all service areas and the fires, as demands, with standard response time or distance. One of the tools for such analysis is location-allocation models. Location-allocation models enable planners to determine the optimal locations of facilities in an area in order to serve regional demands in the most efficient way. The purpose of this study is to examine the geographic distribution of the existing fire stations in Kuwait City. This study utilized location-allocation models within the Geographic Information System (GIS) environment and a number of statistical functions to assess the current locations of fire stations in Kuwait City. Further, this study investigated how well all service areas are covered and how many and where additional fire stations are needed. Four different location-allocation models were compared to find which models cover more demands than the others, given the same number of fire stations. This study tests many ways to combine variables instead of using one variable at a time when applying these models in order to create a new measurement that influences the optimal locations for locating fire stations. This study also tests how location-allocation models are sensitive to different levels of spatial dependency. The results indicate that there are some districts in Kuwait City that are not covered by the existing fire stations. These uncovered districts are clustered together. This study also identifies where to locate the new fire stations. This study provides users of these models a new variable that can assist them to select the best locations for fire stations. The results include information about how the location-allocation models behave in response to different levels of spatial dependency of demands. The results show that these models perform better with clustered demands. From the additional analysis carried out in this study, it can be concluded that these models applied differently at different spatial patterns.

Keywords: geographic information science, GIS, location-allocation models, geography

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Authors: Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Poonam Rana

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Introduction: Traumatic brain injury (TBI) is defined as the temporary or permanent alteration in brain function and pathology caused by an external mechanical force. It represents the leading cause of mortality and morbidity among children and youth individuals. Various models of TBI in rodents have been developed in the laboratory to mimic the scenario of injury. Blast overpressure injury is common among civilians and military personnel, followed by accidents or explosive devices. In addition to this, the lateral Controlled cortical impact (CCI) model mimics the blunt, penetrating injury. Method: In the present study, we have developed two different mild TBI models using blast and CCI injury. In the blast model, helium gas was used to create an overpressure of 130 kPa (±5) via a shock tube, and CCI injury was induced with an impact depth of 1.5mm to create diffusive and focal injury, respectively. C57BL/6J male mice (10-12 weeks) were divided into three groups: (1) control, (2) Blast treated, (3) CCI treated, and were exposed to different injury models. Serum was collected on Day1 and day7, followed by biphasic extraction using MTBE/Methanol/Water. Prepared samples were separated on Charged Surface Hybrid (CSH) C18 column and acquired on UHPLC-Q-TOF-MS using ESI probe with inhouse optimized parameters and method. MS peak list was generated using Markerview TM. Data were normalized, Pareto-scaled, and log-transformed, followed by multivariate and univariate analysis in metaboanalyst. Result and discussion: Untargeted profiling of lipids generated extensive data features, which were annotated through LIPID MAPS® based on their m/z and were further confirmed based on their fragment pattern by LipidBlast. There is the final annotation of 269 features in the positive and 182 features in the negative mode of ionization. PCA and PLS-DA score plots showed clear segregation of injury groups to controls. Among various lipids in mild blast and CCI, five lipids (Glycerophospholipids {PC 30:2, PE O-33:3, PG 28:3;O3 and PS 36:1 } and fatty acyl { FA 21:3;O2}) were significantly altered in both injury groups at Day 1 and Day 7, and also had VIP score >1. Pathway analysis by Biopan has also shown hampered synthesis of Glycerolipids and Glycerophospholipiods, which coincides with earlier reports. It could be a direct result of alteration in the Acetylcholine signaling pathway in response to TBI. Understanding the role of a specific class of lipid metabolism, regulation and transport could be beneficial to TBI research since it could provide new targets and determine the best therapeutic intervention. This study demonstrates the potential lipid biomarkers which can be used for injury severity diagnosis and identification irrespective of injury type (diffusive or focal).

Keywords: LipidBlast, lipidomic biomarker, LIPID MAPS®, TBI

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Authors: Alshutwi Sitah

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Many countries around the world are struggling to maintain an adequate number of nurses. Inadequate nursing staffing could compromise the quality of patient care. Among many factors that contribute to registered nurses (RN) turnover, the influence of work–family conflict (WFC) has gained little attention. WFC was found to be significantly associated with increased turnover intention (TI) among employees. Furthermore, WFC has been linked to a number of negative consequences, including lower job satisfaction and organizational commitment, sleep insufficiency, insomnia symptoms, obesity, cardiovascular diseases, sleep insufficiency, and high cholesterol. In an effort to find strategies to manage the consequences of WFC, many behavioral, psychological, and career scholars have focused on the role of supervisor support. Family Supportive Supervisor Behaviors (FSSB) has been found to be a promising approach contributing to the reduction of TI in employees’ experiencing WFC. Despite the importance of work–family issues and the influence of FSSB, limited studies have been conducted among the nursing population and none were found that included a sample from Saudi Arabia. Therefore, the main Purpose of this study was to evaluate the influence of FSSB on the relationship among WFC, Stress, and TI in Saudi Arabian registered nurses. Method: A cross-sectional study. Sample: Convenience sampling; 113 Saudi female nurse. Result: Fifty percent of nurses intended to leave their workplace, 68 % of nurses reported having a conflict between work and family, and 44% reported having a high level of stress. A significant positive correlation was found between WFC and TI (r= .43, P < 0.01). A negative correlation was found between FSSB and TI (r= -.53, P < 0.01). Both WFC and stress were associated with TI; however, these associations were buffered (weaken), when nurses had higher FSSB. Conclusion: The FSSB could be seen as a tool to help married, female nurses to demonstrate their professional role without compromising their family responsibilities. Nurses’ turnover is a complex issue that may require multiple prevention strategies; however, enhancing FSSB could be a key resource for maintaining a positive workplace environment and reducing TI.

Keywords: turnover intention, work-family conflict, supervisor support, nursing retention

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Authors: Mojtaba Noghabaei, Khashayar Asadi, Kevin Han

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Simultaneous Localization and Mapping (SLAM) technology has recently attracted the attention of construction companies for real-time performance monitoring. To effectively use SLAM for construction performance monitoring, SLAM results should be registered to a Building Information Models (BIM). Registring SLAM and BIM can provide essential insights for construction managers to identify construction deficiencies in real-time and ultimately reduce rework. Also, registering SLAM to BIM in real-time can boost the accuracy of SLAM since SLAM can use features from both images and 3d models. However, registering SLAM with the BIM in real-time is a challenge. In this study, a novel SLAM platform named Model-Based SLAM (MB-SLAM) is proposed, which not only provides automated registration of SLAM and BIM but also improves the localization accuracy of the SLAM system in real-time. This framework improves the accuracy of SLAM by aligning perspective features such as depth, vanishing points, and vanishing lines from the BIM to the SLAM system. This framework extracts depth features from a monocular camera’s image and improves the localization accuracy of the SLAM system through a real-time iterative process. Initially, SLAM can be used to calculate a rough camera pose for each keyframe. In the next step, each SLAM video sequence keyframe is registered to the BIM in real-time by aligning the keyframe’s perspective with the equivalent BIM view. The alignment method is based on perspective detection that estimates vanishing lines and points by detecting straight edges on images. This process will generate the associated BIM views from the keyframes' views. The calculated poses are later improved during a real-time gradient descent-based iteration method. Two case studies were presented to validate MB-SLAM. The validation process demonstrated promising results and accurately registered SLAM to BIM and significantly improved the SLAM’s localization accuracy. Besides, MB-SLAM achieved real-time performance in both indoor and outdoor environments. The proposed method can fully automate past studies and generate as-built models that are aligned with BIM. The main contribution of this study is a SLAM framework for both research and commercial usage, which aims to monitor construction progress and performance in a unified framework. Through this platform, users can improve the accuracy of the SLAM by providing a rough 3D model of the environment. MB-SLAM further boosts the application to practical usage of the SLAM.

Keywords: perspective alignment, progress monitoring, slam, stereo matching.

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Authors: Pampita Chakraborty, Sukumar Mukherjee

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This study was done to determine the bacteriological profile and antibiotic resistance of the isolates and to find out the potential risk factors for infection with multidrug-resistant organisms. Diabetic foot ulcer is a major medical, social, economic problem and a leading cause of morbidity and mortality, especially in the developing countries like India. 25 percent of all diabetic patients develop a foot ulcer at some point in their lives which is highly susceptible to infections and that spreads rapidly, leading to overwhelming tissue destruction and subsequent amputation. Infection with multidrug resistant organisms (MDRO) may increase the cost of management and may cause additional morbidity and mortality. Proper management of these infections requires appropriate antibiotic selection based on culture and antimicrobial susceptibility testing. Early diagnosis of microbial infections is aimed to institute the appropriate antibacterial therapy initiative to avoid further complications. A total of 200 Type 2 Diabetic Mellitus patients with infection were admitted at GD Hospital and Diabetes Institute, Kolkata. 60 of them who developed ulcer during the year 2013 were included in this study. A detailed clinical history and physical examination were carried out for every subject. Specimens for microbiological studies were obtained from ulcer region. Gram-negative bacilli were tested for extended spectrum Beta-lactamase (ESBL) production by double disc diffusion method. Staphylococcal isolates were tested for susceptibility to oxacillin by screen agar method and disc diffusion. Potential risk factors for MDRO-positive samples were explored. Gram-negative aerobes were most frequently isolated, followed by gram-positive aerobes. Males were predominant in the study and majority of the patients were in the age group of 41-60 years. The presence of neuropathy was observed in 80% cases followed by peripheral vascular disease (73%). Proteus spp. (22) was the most common pathogen isolated, followed by E.coli (17). Staphylococcus aureus was predominant amongst the gram-positive isolates. S.aureus showed a high rate of resistance to antibiotic tested (63.6%). Other gram-positive isolates were found to be highly resistant to erythromycin, tetracycline and ciprofloxacin, 40% each. All isolates were found to be sensitive to Vancomycin and Linezolid. ESBL production was noted in Proteus spp and E.coli. Approximately 70 % of the patients were positive for MDRO. MDRO-infected patients had poor glycemic control (HbA1c 11± 2). Infection with MDROs is common in diabetic foot ulcers and is associated with risk factors like inadequate glycemic control, the presence of neuropathy, osteomyelitis, ulcer size and increased the requirement for surgical treatment. There is a need for continuous surveillance of resistant bacteria to provide the basis for empirical therapy and reduce the risk of complications.

Keywords: diabetic foot ulcer, bacterial infection, multidrug-resistant organism, extended spectrum beta-lactamase

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: M. Card

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Working with anorexia patients can be a challenging experience for mental and health care professionals. The reasons for not wanting to work with this patient population stems from the numerous concerns surrounding the patient’s health – physically and mentally. Many health care professionals reported having strong negative feelings, such as; anger, hopelessness and helplessness when working with anorexia patients. These feelings often impaired their judgement to treatment and affected how they related to the patient. This research focused on psychotherapists who preferred to work with anorexia patients; what countertransference feelings were evoked in them during sessions with patients and most importantly, how they managed the feelings. The research used interpretative phenomenological analysis (IPA) as the theoretical framework and data analysis method. Semi-structured interviews were used with ten experienced psychotherapists to obtain their countertransference experiences with anorexia patients and how they manage it. There were three main themes discovered; (1) the use of supervision, (2) their own personal therapy and finally (3) experience and evolution. The research unearthed that experienced psychotherapists also experienced strong countertransference feelings towards their patients; some positive and some negative. However, these feelings could actually be interpreted as co-transference with their anorexia patients. The psychotherapists were able to own their part in the evocative unconscious nature of a relational therapeutic space, where their personal issues may be entangled in their anorexia patient’s symptomatology.

Keywords: anorexia nervosa, countertransference, co-transference, psychotherapy, relational psychotherapy

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Authors: M. Budakoglu, M. Karaman, A. Abdelnasser, M. Kumral

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The spatial interpolation and spatial correlation of the rare earth elements (REE) of lake surface sediments of Lake Acıgöl and its surrounding lithological units is carried out by using GIS techniques like Inverse Distance Weighted (IDW) and Geographically Weighted Regression (GWR) techniques. IDW technique which makes the spatial interpolation shows that the lithological units like Hayrettin Formation at north of Lake Acigol have high REE contents than lake sediments as well as ∑LREE and ∑HREE contents. However, Eu/Eu* values (based on chondrite-normalized REE pattern) show high value in some lake surface sediments than in lithological units and that refers to negative Eu-anomaly. Also, the spatial interpolation of the V/Cr ratio indicated that Acıgöl lithological units and lake sediments deposited in in oxic and dysoxic conditions. But, the spatial correlation is carried out by GWR technique. This technique shows high spatial correlation coefficient between ∑LREE and ∑HREE which is higher in the lithological units (Hayrettin Formation and Cameli Formation) than in the other lithological units and lake surface sediments. Also, the matching between REEs and Sc and Al refers to REE abundances of Lake Acıgöl sediments weathered from local bedrock around the lake.

Keywords: spatial geochemical modeling, IDW, GWR techniques, REE, lake sediments, Lake Acıgöl, Turkey

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Authors: Yun Yu Stephanie Law

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Sociometer Theory is well tested among young adults in western population, however, limited research is found for other age groups, like adolescent and middle-adulthood in Asia population. Thus, one of the main purposes of this study is to verify the validity of Sociometer Theory in different age groups among Asian. To be specific, we hypothesized that an increase in one’s perceived social rejection is associated to a decrease in his/her state self-esteem among all age groups in Asian population. And we expected that this association can be found among all age groups including adolescent, young adults and middle-adults group in our first study. In this way, we can verify the validity of Sociometer Theory across different age groups as well as its significance in Asian population. Furthermore, those participants who received rejection about ‘mate-role’ would also receive some negative feedbacks regarding their current/future capacity of being a good mate. Results suggested that participants’ state self-esteem sensitivity for mating-capacity rejection is higher when comparing to that of friend-capacity rejection, i.e. greater drop in state self-esteem when receiving mating-capacity feedbacks then receiving friend-capacity feedbacks. These results, however, is just applicable on young adults. Thus, the main purpose of study two would be testing the state self-esteem sensitivity towards social rejection in different domains among three age groups. We hypothesized that group differences would be found for three age groups regarding state self-esteem sensitivity. Research question 1: perceived social rejection is associated to decrease in state self-esteem, is applicable among different age groups in Asia population. Research question 2: there are significant group differences for three age groups regarding state self-esteem sensitivity. Methods: 300 subjects are divided into three age groups, adolescents group, young adult group and middle-adult group, with 100 subjects in each group. Two questionnaires were used in testing this fundamental concept. Subjects were then asked to rate themselves on questionnaire in measuring their current state self-esteem in order to obtain the baseline measurements for later comparison. In order to avoid demand characteristics from subjects, other unrelated tasks like word matching were also given after the first test. Results: A positive correlation between scores in questionnaire 1 and questionnaire 2 among all age groups. Conclusion: State self-esteem decrease to both imagined social rejection (study1) and experienced social rejection (study2). Moreover, level of decrease in state self-esteem vary when receiving different domains of social rejection. Implications: a better understanding of self-esteem development for various age group might bring insights for education systems and policies for teaching approaches and learning methods among different age groups.

Keywords: state self-esteem, social rejection, stage theory, self-feelings

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

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Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

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Authors: Rajeshwari Biradar

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Background: The anecdotal evidences indicate that utilization of HIV services especially in Government facilities is affected by stigma and discrimination among HIV positive female sex workers (FSWs) in Karnataka. To our knowledge, there is no quantitative study on this issue. In this study an attempt is made to examine these aspects among positive FSWs exposed to prevention programs. Methods: This is a cross‐ sectional quantitative survey of HIV positive FSWs in the 3 districts of northern Karnataka using a structured questionnaire. The list of HIV Positive FSWs was organized by stratification, and 607 positive FSWs were selected using a systematic random selection. The data were analyzed using both bivariate and multivariate statistical techniques. Results: Half of the sex workers (52%) are traditional (devadasi, dedicated to the temple), 22% are widowed and the mean age is 33 years. The FSWs practice sex work on an average 13 days a month with 2.3 clients per day and was in sex work for about 13 years. Almost all of them (97%) used condom with the clients they had on the last day of sex work. About 74% were ever registered in the ART center and 47% of them reported being ever on ART, of which 6% dropped out. Multivariate results support the hypothesis that the interventions addressing stigma and discrimination enabled accessing health services in the government facilities (AOR=1.37; p=0.17). Conclusions: Based on the results of the study, programs addressing stigma, discrimination and positive prevention can be implemented in places where government health services are not utilized by HIV positive FSWs. However, the study may be limited by the fact that majority of the FSWs entered into sex work through the traditional devadasi system, which may not be the case in other parts of India.

Keywords: sex work, HIV/AIDS, female sex workers, health

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Authors: Vivek Bhat, Rohini Kelkar, Sanjay Biswas

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Introduction: Cancer patients are at increased risk of bacterial infections. This may due to the disease process itself, the effect of chemotherapeutic drugs or invasive procedures such as catheterization. A wide variety of bacteria including some emerging pathogens are increasingly being reported from these patients. The incidence of multidrug-resistant organisms particularly in the Gram negative group is also increasing, with higher resistance rates seen to cephalosporins, β-lactam/β-lactam inhibitor combinations, and the carbapenems. This study documents the bacteriological spectrum of infections and their resistance patterns in cancer patients. Methods: This study includes all bacterial isolates recovered from infections cancer patients over a period of 18 months. Samples included Blood cultures, Pus/wound swabs, urine, tissue biopsies, body fluids, catheter tips and respiratory specimens such as sputum and bronchoalveolar lavage (BAL). All samples were processed in the microbiology laboratory as per standard laboratory protocols. Organisms were identified to species level and antimicrobial susceptibility testing was performed manually by the disc diffusion technique or in the Vitek-2 (Biomereux, France) instrument. Interpretations were as per Clinical laboratory Standards Institute (CLSI) guidelines. Results: A total of 1150 bacterial isolates were cultured from 884 test samples during the study period. Of these 227 were Gram-positive and 923 were Gram-negative organisms. Staphylococcus aureus (99 isolates) was the commonest Gram-positive isolate followed by Enterococcus (79) and Gr A Streptococcus (30). Among the Gram negatives, E. coli (304), Pseudomonas aeruginosa (201) and Klebsiella pneumoniae (190) were the most common. Of the Staphylococcus aureus isolates 27.2% were methicillin resistant. Only 5.06% enterococci were vancomycin resistant. High rates of resistance to cefotaxime and ciprofloxacin were seen amongst E. coli (84.8% & 83.55%) and Klebsiella pneumoniae (71 & 62.1%) respectively. Resistance to carbapenems (meropenem) was high at 70% in Acinetobacter spp.; however all isolates were sensitive to colistin. Among the aminoglycosides, amikacin retained good efficacy against Escherichia coli (82.9%) and Pseudomonas aeruginosa (78.1%). Occasional isolates of emerging pathogens such as Chryseobacterium indologens, Roseomonas, and Achromobacter xyloxidans were also recovered. Conclusion: The common infections in cancer patients include respiratory, wound, tract infections and sepsis. The commonest isolates include Staphylococcus aureus, Enterococci, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. There is a high level of resistance to the commonly used antibiotics among Gram-negative organisms.

Keywords: bacteria, resistance, infection, cancer

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Authors: Jerry Hu

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Let G = (V, E) with V = {1, 2, ..., k} be a graph, the k positive integers a₁, a₂, ..., ak are G-wise relatively prime if (aᵢ, aⱼ ) = 1 for {i, j} ∈ E. We use an inductive approach to give an asymptotic formula for the number of k-tuples of integers that are G-wise relatively prime. An exact formula is obtained for the probability that k positive integers are G-wise relatively prime. As a corollary, we also provide an exact formula for the probability that k positive integers have exactly r relatively prime pairs.

Keywords: graph, independent set, G-wise relatively prime, probability

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Authors: Faheemah N. Mustafaa, Tamarie Macon, Tabbye Chavous

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A major concern of university leaders worldwide is how to create environments where students from diverse racial/ethnic, national, and cultural backgrounds can thrive. Over the past decade or so in the United States, African American women have done exceedingly well in terms of college enrollment, academic performance, and completion. However, the relative academic successes of African American women in higher education has in some ways overshadowed social challenges many Black women continue to encounter on college campuses in the United States. Within predominantly White institutions (PWIs) in particular, there is consistent evidence that many Black students experience racially hostile climates. However, research studies on racial climates within PWIs have mostly focused on cross-sectional comparisons of minority and majority group experiences, and few studies have examined campus racial climate in relation to short- and longer-term well-being. One longitudinal study reported that African American women’s psychological well-being was positively related to their comfort in cross-racial interactions (a concept closely related to campus climate). Thus, our primary research question was: Do African American women’s perceptions of campus climate (tension and positive association) during their freshman year predict their reports of psychological distress and well-being (self-acceptance) during their sophomore year? Participants were part of a longitudinal survey examining African American college students’ academic identity development, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields. The final subsample included 134 self-identified African American/Black women enrolled in PWIs. Accounting for background characteristics (mother’s education, family income, interracial contact, and prior levels of outcomes), we employed hierarchical regression to examine relationships between campus racial climate during freshman year and psychological adjustment one year later. Both regression models significantly predicted African American women’s psychological outcomes (for distress, F(7,91)= 4.34, p < .001; and for self-acceptance, F(7,90)= 4.92, p < .001). Although none of the controls were significant predictors, perceptions of racial tension on campus were associated with both distress and self-acceptance. More perceptions of tension were related to African American women’s greater psychological distress the following year (B= 0.22, p= .01). Additionally, racial tension predicted later self-acceptance in the expected direction: Higher first-year reports of racial tension were related to less positive attitudes toward the self during the sophomore year (B= -0.16, p= .04). However, perceptions that it was normative for Black and White students to socialize on campus (or positive association scores) were unrelated to psychological distress or self-acceptance. Findings highlight the relevance of examining multiple facets of campus racial climate in relation to psychological adjustment, with possible emphasis on the import of racial tension on African American women’s psychological adjustment. Results suggest that negative dimensions of campus racial climate may have lingering effects on psychological well-being, over and above more positive aspects of climate. Thus, programs targeted toward improving student relations on campus should consider addressing cross-racial tensions.

Keywords: higher education, psychological adjustment, university climate, university students

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Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

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Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

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Authors: Jiaying Gao, Thammananya Sakcharoen, Wilailuk Niyommaneerat

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Haikou is the capital of Hainan, a major tourism province in China with rich ecotourism resources. There is a need to strengthen tourism destination management in Haikou toward sustainable development as a tourism city. The purpose of this study was to investigate the relationship between tourism destination management and sustainable tourism in Haikou. Exploratory factor analysis was used to extract six dimensions of this study. Three dimensions (10 factors) of tourism destination management were analyzed in terms of economic development, social and cultural development, and conservation of ecosystem. Sustainability awareness, tourism development experience, and tourism public infrastructure in three dimensions (12 factors) of sustainable tourism. There were 426 questionnaire respondents, including 225 tourists, 172 residents, 12 tourism agency persons, 10 government persons, 3 self-employed, and 4 others. The Structural equation modeling (SEM) model was finally conducted to test the hypotheses empirically and explore the impact relationship. The study found a significant relationship between tourism destination management and sustainable tourism: social and cultural development had the greatest significant positive impact on the tourism development experience (0.788***). Social and cultural development also showed a significant positive impact and great impetus on tourism public infrastructure (0.561***). A negative effect relationship (-0.096***) emerged between ecosystem conversion and tourism development experience. It showed a positive relationship between economic development and social and cultural development of tourism destination management in promoting sustainable tourism. There are still some gaps for improvement, such as the need for sustainable ecological management to promote local sustainable tourism trends and enhance tourism experience development, which may require a long-term process of mitigation.

Keywords: Haikou (Hainan, China), influence relationship, sustainable tourism, tourism destination management

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Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro

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Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

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Authors: Sabiha Sahin, Kursad Bora Carman, Coskun Yarar

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Introduction: Measuring the diameter of the optic nerve sheath is a noninvasive and easy to use imaging technique to predict intracranial pressure in children and adults. The aim was to measure the diameter of the optic nerve sheath in pediatric head trauma. Methods: The study group consisted of 40 children with healthy and 40 patients with head trauma. Transorbital sonographic measurement of the optic nerve sheath diameter was performed. Conclusion: The mean diameters of the optic nerve sheath of right and left eyes were 0.408 ± 0.064 mm and 0.417 ± 0.065 mm, respectively, in the trauma group. These results were higher in patients than in control group. There was a negative correlation between optic nerve sheath diameters and Glasgow Coma Scales in patients with head trauma (p < 0.05). There was a positive correlation between optic nerve sheath diameters and positive CT findings, systolic blood pressure in patients with head trauma. The clinical status of the patients at admission, blood pH and lactate level were related to the optic nerve sheath diameter. Conclusion: Measuring the diameter of the optic nerve sheath is not an invasive technique and can be easily used to predict increased intracranial pressure and to prevent secondary brain injury.

Keywords: head trauma, intracranial pressure, optic nerve, sonography

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Authors: Nini Johana Marin Rodriguez

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This paper applies DCC, EWMA and OGARCH models to compare the dynamic correlations between exchange rates, oil prices, exchange rates and stock markets to examine the time-varying conditional correlations to the daily oil prices and index returns in relation to the US dollar/local currency for developed (Canada and Mexico) and emerging Latin American markets (Brazil, Chile, Colombia and Peru). Changes in correlation interactions are indicative of structural changes in market linkages with implications to contagion and interdependence. For each pair of stock price-exchange rate and oil price-US dollar/local currency, empirical evidence confirms of a strengthening negative correlation in the last decade. Methodologies suggest only two events have significatively impact in the countries analyzed: global financial crisis and Europe crisis, both events are associated with shifts of correlations to stronger negative level for most of the pairs analyzed. While, the first event has a shifting effect on mainly emerging members, the latter affects developed members. The identification of these relationships provides benefits in risk diversification and inflation targeting.

Keywords: crude oil, dynamic conditional correlation, exchange rates, interdependence, stock prices

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Authors: S. Aisyah Azizan, M. Syafiq Azmi, Yuki Harada, Yoshinobu Maeda, Takaomi Matsutani

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We introduced an all-optical multi-casting characteristics with wavelength conversion based on a novel all-optical triode using negative feedback semiconductor optical amplifier. This study was demonstrated with a transfer speed of 10 Gb/s to a non-return zero 231-1 pseudorandom bit sequence system. This multi-wavelength converter device can simultaneously provide three channels of output signal with the support of non-inverted and inverted conversion. We studied that an all-optical multi-casting and wavelength conversion accomplishing cross gain modulation is effective in a semiconductor optical amplifier which is effective to provide an inverted conversion thus negative feedback. The relationship of received power of back to back signal and output signals with wavelength 1535 nm, 1540 nm, 1545 nm, 1550 nm, and 1555 nm with bit error rate was investigated. It was reported that the output signal wavelengths were successfully converted and modulated with a power penalty of less than 8.7 dB, which the highest is 8.6 dB while the lowest is 4.4 dB. It was proved that all-optical multi-casting and wavelength conversion using an optical triode with a negative feedback by three channels at the same time at a speed of 10 Gb/s is a promising device for the new wavelength conversion technology.

Keywords: cross gain modulation, multicasting, negative feedback optical amplifier, semiconductor optical amplifier

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Authors: Ashenafi Ayele, Shewit Haftu, Tesfalem Araya

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Background: “Post-psychotic depression”, “post schizophrenic depression”, and “secondary depression” have been used to describe the occurrence of depressive symptoms during the chronic phase of schizophrenia. Post-psychotic depression is the most common cause of death due to suicide in schizophrenia patients. Overall lifetime risk for patients with schizophrenia is 50% for suicide attempts and 9-13% lifetime risk for completed suicide and also it is associated with poor prognosis and poor quality of life. Objective: To assess determinant of post psychotic depression in schizophrenia patients ACSH and Mekelle General Hospital, Tigray Ethiopia 2019. Methods: An institutional based unmatched case control study was conducted among 69 cases and 138 controls with the ratio of case to control 1 ratio 2. The sample is calculated using epi-info 3.1 to assess the determinant factors of post-psychotic depression in schizophrenia patients. The cases were schizophrenia patients who have been diagnosed at least for more than one-year stable for two months, and the controls are any patients who are diagnosed as schizophrenia patients. Study subjects were selected using a consecutive sampling technique. The Calgary depression scale for schizophrenia self-administered questionnaire was used. Before the interview, it was assessed the client’s capacity to give intended information using a scale called the University of California, San Diego Brief Assessment of Capacity to Consent (UBACC). Bivariant and multiple Logistic regression analysis was performed to determine between the independent and dependent variables. The significant independent predictor was declared at 95% confidence interval and P-value of less than 0.05. Result: Females were affected by post psychotic depression with the (AOR=2.01, 95%CI: 1.003- 4.012, P= 0.49).Patients who have mild form of positive symptom of schizophrenia affected by post psychotic depression with (AOR =4.05, 95%CI: 1.888- 8.7.8, P=0001).Patients who have minimal form of negative symptom of schizophrenia are affected by post psychotic depression with (AOR =4.23, 95%CI: 1.081-17.092, P=.038). Conclusion: In this study, sex (female) and presence of positive and negative symptoms of schizophrenia were significantly associated. It is recommended that the post psychotic depression should be assessed in every schizophrenia patient to decrease the severity of illness, and to improve patient’s quality of life.

Keywords: determinants, post-psychotic depression, Mekelle city

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Authors: Mohammed Zahid Akter

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In the studies of language change, mental factors including analogy, reanalysis, and frequency have received considerable attention as possible catalysts for language change. In comparison, relatively little is known regarding which functional domains or construction types are more amenable to these mental factors than others. In this regard, this paper will show with data from South Central (Kuki-Chin) Tibeto-Burman languages how language change interacts with certain functional domains or construction types. These construction types include transitivity, person marking, and polarity distinctions. Thus, it will be shown that transitive clauses are more prone to change than intransitive and ditransitive clauses, clauses with 1st person argument marking are more prone to change than clauses with 2nd and 3rd person argument marking, non-copular clauses are more prone to change than copular clauses, affirmative clauses are more prone to change than negative clauses, and standard negatives are more prone to change than negative imperatives. The following schematic structure can summarize these findings: transitive>intransitive, ditransitive; 1st person>2nd person, 3rd person; non-copular>copular; and affirmative>negative; and standard negative>negative imperatives. In the interest of space, here only one of these findings is illustrated: affirmative>negative. In Hyow (South Central, Bangladesh), the innovative and preverbal 1st person subject k(V)- occurs in an affirmative construction, and the archaic and postverbal 1st person subject -ŋ occurs in a negative construction. Similarly, in Purum (South Central, Northeast India), the innovative and preverbal 1st person subject k(V)- occurs in an affirmative construction, and the archaic and postverbal 1st person subject *-ŋ occurs in a negative construction. Like 1st person subject, we also see that in Anal (South Central, Northeast India), the innovative and preverbal 2nd person subject V- occurs in an affirmative construction, and the archaic and postverbal 2nd person subject -t(V) in a negative construction. To conclude, data from South Central Tibeto-Burman languages suggest that language change interacts with functional domains as some construction types are more susceptible to change than others.

Keywords: functional domains, Kuki-Chin, language change, south-central, Tibeto-Burman

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Authors: Sakkaphat T. Ngamake, Arunya Tuicomepee, Panrapee Suttiwan, Rewadee Watakakosol, Sompoch Iamsupasit

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Generally, 'positive' emotion regulation strategies such as cognitive reappraisal have linked to desirable outcomes while 'negative' strategies such as behavioral suppression have linked to undesirable outcomes. These trends have been found in both the elderly and professional practitioners. Hence, this study sought to investigate these trends further by examining the relationship between two dominant emotion regulation strategies in the literature (i.e., cognitive reappraisal and behavioral suppression) and well-being outcomes among the elderly (i.e., successful aging) and their caregivers (i.e., satisfaction with life), using the actor-partner interdependence model. A total of 150 elderly-caregiver dyads participated in the study. The elderly responded to two measures assessing the two emotion regulation strategies and successful aging while their caregivers responded to the same emotion regulation measure and a measure of satisfaction with life. Two criterion variables (i.e., successful aging and satisfaction with life) were specified as latent variables whereas four predictors (i.e., two strategies for the elderly and two strategies for their caregivers) were specified as observed variables in the model. Results have shown that, for the actor effect, the cognitive reappraisal strategy yielded positive relationships with the well-being outcomes for both the elderly and their caregivers. For the partner effect, a positive relationship between caregivers’ cognitive reappraisal strategy and the elderly’s successful aging was observed. The behavioral suppression strategy has not related to any well-being outcomes, within and across individual agents. This study has contributed to the literature by empirically showing that the mental activity of the elderly’s immediate environment such as their family members or close friends could affect their quality of life.

Keywords: emotion regulation, caregiver, older adult, well-being

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Authors: Virpi Oksman

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Drones are new phenomenon in public spaces. Adoption of this kind of new technologies requires public acceptance. Drones and other unmanned aerial systems may have various impacts on people’s living environments, and the public is exposed to possible disadvantages of drones. Public acceptance may be expressed as positive or negative attitude by majority of the citizens towards the new technology or service or as rapid adoption of it in everyday life. In various parts of the globe, in cities and in rural areas, drones as emerging technologies are perceived quite differently. Public acceptance studies of drones have been conducted mostly in highly urbanized environments like in Singapore and in European cities. This paper presents results of a Nordic survey study (N=1000) conducted in Sweden and in Finland. The survey aims at understanding the level of acceptance of different uses of drones in public spaces and the main concerns and benefits related to emerging UAM technologies. The study shows that even though the general attitude towards drones is quite positive, privacy and safety, and noise levels are the main concerns by Nordic citizens. Also, for what purpose and by whom the drones are operated affects the acceptability significantly. The study concludes, that there is need for regulations that safeguard public interests. In addition, considering privacy in design, and quiet environmentally friendly drones support public acceptance of drones.

Keywords: public acceptance, privacy, safety, survey

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Authors: Sutharsini Jesuthasan, N. Umakanth

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Presently the impact of electronic word of mouth on consumer’s purchasing intentions very popular one for a long time period. Even though now this E-WOM got a new evolution through social media. Before this new concept, general people were able to speak with any people on the internet. But likely social media enable people to talk with colleagues, friends and other people on the internet. Meanwhile, this new path way of E-WOM might be more powerful in terms of confusing purchase intention. And negative side of E-WOM very important in this competitive era. So, this study elaborates the negative E-WOM within the context of social media such as face book. And especially this study identifies the influence of negative E-WOM in social media on consumer’s purchase intention. Virtual snowball sampling method was used by researcher to identify the hidden population. Finally, spss 20.0 also used for data analysis purpose. And conclusion and recommendations are given based on the findings. And this research also will support to both parties such as researcher and participants.

Keywords: word of mouth, social media, purchase intention, electronic word of mouth

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Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

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In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

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Authors: Shuai Yang, Bin Li, Sixing Chen

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Engaging and co-creating value with buyers (i.e., the buying organizations) have rapidly become a rising trend for sellers (i.e., the selling organizations) within Business-to-Business (B2B) environments, through which buyers can interact more with sellers and be better informed about products. One important way to achieve this is through engaging with buyers on social media, termed as brand engagement on social media, which provides a platform for sellers to interact with customers. This study addresses the research gap by answering the following questions: (1) Are B2B firms’ brand engagement on social media related to their firm value? (2) To what extent do analyst stock recommendations channel B2B firms’ brand engagement on social media’s possible impact on firm value? To answer the research questions, this study collected data merged from multiple sources. The results show that there is a positive association between seller-initiated engagement and B2B sellers’ firm value. Besides, analyst stock recommendations mediate the positive relationships between seller-initiated engagement and firm value. However, this study reveals buyer-initiated engagement has a counterintuitive and negative relationship with firm value, which shows a dark side of buyer-initiated engagement on social media for B2B sellers.

Keywords: brand engagement, B2B firms, firm value, social media, stock recommendations

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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