Miracle-dz/UniFAR-FTUs huggingface dataset:
Road Segmentation based on Deep Learning with Post-Processing Probability Layer

In order to solve the problem of road image semantics segmentation, a novel semantic segmentation method based on deep learning is proposed in this paper, and it is verified that this method can optimize the segmentation effectively comaring with traditional Full Convolution Neural Network (FCN) model. Firstly, the traditional Full Convolution Neural Network model is constructed. And then according to the principle of the post-processing probability layer method proposed in this paper, the label of all road image training sets is used to compute and transform it to form a two-dimensional array which can represent the classification probability of each pixel, and it is combined with the Full Convolution Neural Network model to be used for road image semantics segmentation. Secondly, the tensorflow neural network framework is used to simulate the above two models. Finally, the experimental results show that the CNN model with the proposed post-processing probabilistic layer is able to get better results in road semantics segmentation in KITTI data sets. The pixel accuracy is improved from 88.8% to 91.3%, and the mean pixel accuracy is increased from 82.9% to 85.7%. The mean intersection over union increased from 72.5% to 77.9%.
Comprehensiveness of Quality of Life Instruments in Capturing Concerns Related to Chemotherapy-Induced Neutropenia

Neutropenia is a serious hematologic consequence of cancer chemotherapy that can lead to further complications such as febrile neutropenia (FN). FN is potentially life threatening and often requires hospitalization. Few studies have evaluated the impact of neutropenia on quality of life (QoL). This study quantified QoL using two nonneutropenia-specific instruments, the EQ-5D questionnaire, a generic tool used to measure health-related QoL, and the Functional Assessment of Cancer Therapy - General (FACT-G) questionnaire, and a neutropenia-specific instrument, the Functional Assessment of Cancer Therapy - Neutropenia (FACT-N) questionnaire. The FACT-G is a 27-item questionnaire that examines QoL in patients with cancer using four subscales. A neutropenia-specific subscale (NSS) has been developed for use with the FACT-G; this combined questionnaire is the FACT-N. Data were collected from patients, who provided informed consent, and who were admitted to Sunnybrook Health Sciences Centre, Toronto, Canada, for the treatment of chemotherapy-induced FN. Linear regression models were fitted to examine the relationship of scores from the neutropenia-specific instrument with those obtained from the other instruments. Two models were fitted using the NSS as the response variable. Predictors for the regression models were the FACT-G scores for each of the subscales (physical, emotional, social and functional wellbeing) and the five domains of the EQ-5D (mobility, self-care, usual activity, pain/discomfort and anxiety/depression) along with the visual analog scale (VAS) component of this tool. The physical and emotional wellbeing subscales of the FACT-G had a strong relationship to the NSS (p 0.5). For the EQ-5D, the pain/discomfort domain had the strongest relationship to the NSS (p=0.18); the remaining domains, with or without the VAS, all demonstrated a weaker relationship (p > 0.5). Model fit was assessed by the adjusted R 2 statistic; it was 0.54 when FACT-G subscales were used as the predictors compared to −0.04 for the EQ-5D domains indicating that the FACT-G was a better predictor of neutropenia-related concerns. Neutropenia concerns appear to be more closely related to cancer specific QoL compared to general quality of life as demonstrated by the stronger relationship of the NSS to the FACT-G than to the EQ-5D. This may be due to the comprehensiveness of the FACT-G questionnaire where a possible score anywhere from 0 to 24 or 28 can be obtained in each of the subscales, compared to three-point descriptive system for each of the domains of the EQ-5D.
Estimating publication bias in meta-analyses of peer-reviewed studies: A meta-meta-analysis across disciplines and journal tiers

Selective publication and reporting in individual papers compromise the scientific record, but are meta-analyses as compromised as their constituent studies? We systematically sampled 63 meta-analyses (each comprising at least 40 studies) in PLOS One, top medical journals, top psychology journals, and Metalab, an online, open-data database of developmental psychology meta-analyses. We empirically estimated publication bias in each. Across all meta-analyses, “statistically significant” results in the expected direction were only 1.17 times more likely to be published than “nonsignificant” results or those in the unexpected direction (95%CI: [0.94, 1.47]), with a confidence interval substantially overlapping the null. Comparable estimates were 0.83 for meta-analyses in PLOS One, 1.02 for top medical journals, 1.54 for top psychology journals, and 4.70 for Metalab. The severity of publication bias did differ across individual meta-analyses; in a small minority (10%; 95% CI: [2%, 21%]), publication bias appeared to favor "significant" results in the expected direction by more than 3-fold. We estimated that for 89% of meta-analyses, the amount of publication bias that would be required to attenuate the point estimate to the null exceeded the amount of publication estimated to be actually present in the vast majority of meta-analyses from the relevant scientific discipline (exceeding the 95th percentile of publication bias). Study-level measures (“statistical significance” with a point estimate in the expected direction and point estimate size) did not indicate more publication bias in higher-tier versus lower-tier journals, nor in the earliest studies published on a topic versus later studies. Overall, the mere act of performing a meta-analysis with a large number of studies (at least 40) and that includes non-headline results may largely mitigate publication bias in meta-analyses, suggesting optimism about the validity of meta-analytic results.
Recent Strategies for Hydrogen Peroxide Production by Metal-Free Carbon Nitride Photocatalysts

Hydrogen peroxide (H2O2) is a chemical which has gained wide importance in several industrial and research fields. Its mass production is mostly performed by the anthraquinone (AQ) oxidation reaction, leading to high energy consumption and significant generation of wastes. Other methods of synthesis found in the literature include the direct synthesis from oxygen and hydrogen. However, this H2O2 production process is prone to explosion hazard or undesirable by‑product generation. With the growing demand of H2O2, the development of cleaner and economically viable processes has been under intense investigation. Heterogeneous photocatalysis for H2O2 production has appeared as a promising alternative since it requires only an optical semiconductor, water, oxygen, and ideally solar light irradiation. Moreover, employing a metal-free semiconductor minimizes possible toxicity consequences and reinforces the sustainability of the process. The most studied metal‑free catalyst employed for H2O2 production is polymeric carbon nitride (CN). Several chemical and physical modifications over CN have been investigated together with the assessment of different sacrificial agents and light sources. This review shows the recent developments on CN materials design for enhancing the synthesis of H2O2, along with the proposed mechanisms of H2O2 production. Finally, the direct in situ generation of H2O2, when dealing with the photocatalytic synthesis of added-value organic compounds and water treatment, is discussed.
DOES VALUE CO-CREATION IMPACTS CUSTOMER LOYALTY AND REPURCHASE INTENTION ?

The study conducted is focused towards analyzing the association between value co-creation and its association towards customer satisfaction and ultimately customer loyalty driving towards customer’s repurchase intention. The investigation is carried out among the young mobile phone users, the respondents being from the Private Universities of Karachi. The research utilizes path analysis to test the hypotheses and achieve the objectives of the research conducted. In total, 172 completed and usable questionnaires were received from respondents comprising of young university going mobile phone users. Convenience sampling was used selection of the sample. The results achieve via path analysis revealed that Customer Perceived Vale and Customer Trust positively impacts Co-creation of Value which results towards Customer Satisfaction and Customer Loyalty and ultimately customers repurchase intention. This research contributes signifi cantly towards developing strategies for increasing satisfaction and loyalty pattern of the customer within the telecommunication industry of the country. This conducted research can further be replicated within other service sectors and also other age groups residing in the same service sector. The research conducted delivers an inimitable perspective of Value Co-creation in Pakistan Telecommunication sector which has been not much covered in the given geographic area. The model can be further used in understanding the consumer behavior in other service sector and also it can be applied on other age groups.
Corporate governance as a key aspect in the failure of worker cooperatives

The article analyses governance difficulties at Fagor Electrodomésticos, for decades the world’s largest industrial cooperative, and sheds light on how the cooperative model and governance might have contributed to the firm’s bankruptcy. The case study examines how the cooperative model influenced the speed and quality of decision making. The roles of the main cooperative governing bodies (the General Assembly, Governing Council and Social Council) are evaluated and their limitations to effectively supervise and work with management to make difficult strategic decisions. Several governance improvement measures are proposed in order to help other large cooperatives combine democratic control and economically sound governance.
SINGLE LAYER PERCEPTRONS NEURAL NETWORKS FOR ONLINE EIGENSTRUCTURE ASSIGNMENT IN MIMO SYSTEMS

: To improve the stability performance and the response form of MIMO systems, the control of the modes is relevant to impose the design speciﬁcations. A bioinspired method for online eigenstructure assignment for the design of multivariable control systems is presented in this paper. The method is based on artiﬁcial neural networks for the execution of the control law and for training the controller gains by state feedback. Aiming at online tuning of state space controllers, the gain matrix that satisﬁes all the design speciﬁcations, a proposal is presented to compute the gain matrices that best meet a given operating range of MIMO dynamic systems. The proposal is evaluated in a mathematical model that represents a fourth order RLC circuit with two input voltages and two controllable voltage levels.
GENDER DIFFERENCES IN NEGATIVE RELATIVE ASPECTS: THE ROLE OF PERSONALITY AND COMMUNICATION STYLE

The research aimed at the exploration of gender differences in the quality of close relationships with a mother, a father, a sibling, a partner, and a friend concerning the negative exchange in the form of conflicts and antagonism, as well as the role of personality traits and communication styles in the development of individual differences. The sample consisted of 400 participants (69% females), age from 19 to 51. Data were collected using the short version of personality questionnaire Big Five Plus Two (VP+2-70), questionnaire about close relationship quality Network of Relationship Inventory (NRI), and the scale for estimating communication skills, Communicator Style Measure (CSM). The results confirmed gender specificities concerning a degree of the negative exchange in the relationship types, and their correlations with personality traits and communication style. Females had stronger negative exchange with partners and fathers, while males had it with siblings. Extraverted females had more conflicts with their mothers. Higher Openness and Positive Valence of males was followed by greater conflicts with partner and lower Antagonism with friends, whereas the higher Consciousness was followed by greater negative exchange with siblings. Daughters with more assertive communication and sons with more expressive communication had stronger degree of confrontations with parents. On the other hand, males with more assertive communication, and females with more expressive communication had stronger conflicts with partners. Stronger negative exchange of females with fathers and partners was probably due to their greater orientation to deal with relationship problems and emancipation needs. The result that more assertive women had stronger degree of conflict with parents maybe the results from the fostering of the honest communication in the family of origin. Confronting more assertive women with their parents may stem from fostering honest and directive communication in the family of origin. Given that the society pressures males to be independent, the result that more expressive males had stronger negative exchange with their family of origin had been expected. These findings showed that expressiveness indicate immaturity. The willingness of more assertive men to engage in a negative exchange with a partner indicates their involvement in the relationship.
Adversarial Imitation Attack

Deep learning models are known to be vulnerable to adversarial examples. A practical adversarial attack should require as little as possible knowledge of attacked models. Current substitute attacks need pre-trained models to generate adversarial examples and their attack success rates heavily rely on the transferability of adversarial examples. Current score-based and decision-based attacks require lots of queries for the attacked models. In this study, we propose a novel adversarial imitation attack. First, it produces a replica of the attacked model by a two-player game like the generative adversarial networks (GANs). The objective of the generative model is to generate examples that lead the imitation model returning different outputs with the attacked model. The objective of the imitation model is to output the same labels with the attacked model under the same inputs. Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model. Compared with the current substitute attacks, imitation attacks can use less training data to produce a replica of the attacked model and improve the transferability of adversarial examples. Experiments demonstrate that our imitation attack requires less training data than the black-box substitute attacks, but achieves an attack success rate close to the white-box attack on unseen data with no query.
Psychological and Psychosocial Deficits Among Pediatric Survivors of Hematological Neoplasms and Their Risk Factors

Pediatric cancer patients are surviving cancer and living long, relatively healthy lives due to advances in pediatric cancer therapeutics. Even with modern medical research and technology, pediatric cancer is still the leading cause of death for children and adolescents in the United States. Based on statistics from the American Cancer Society (ACS), hematological cancers, such as leukemia and lymphoma, are the leading type of non-solid malignancies in children (ACS, 2016). For decades pediatric cancer survival rates have steadily increased with an overall cure rate of over 75%. While it is great that the survivor rate has and is steadily increasing year after year, the intense curative therapy that is given to produce the high rates of survival can cause long-term effects on the body including psychological and psychosocial deficits. This integrative literature review (ILR) was aimed at describing the prevalence of late effect psychological and psychosocial deficits and their risk factors in pediatric survivors of hematological cancers.
Four QTL underlie resistance to a microsporidian parasite that may drive genome evolution in its Daphnia host

Despite its pivotal role in evolutionary and ecological processes the genetic architecture underlying host-parasite interactions remains understudied. Here we use a quantitative trait loci approach to identify regions in the Daphnia magna genome that provide resistance against its microsporidium parasite Ordospora colligata. The probability that Daphnia became infected was affected by a single locus and an interaction between two additional loci. A fourth locus influenced the number of spores that grew within the host. Comparing our findings to previously published genetic work on Daphnia magna revealed that two of these loci may be the same as detected for another microsporidium parasite, suggesting a general immune response to this group of pathogens. More importantly, this comparison revealed that two regions previously identified to be under selection coincided with parasite resistance loci, highlighting the pivotal role parasites may play in shaping the host genome.
Enterprise Risk Management, Top Management Demographics, Macro Environment and Organizational Performance: Evidence from Kenyan State-Owned Corporations

This journal is a conceptualization of the relationship between Enterprise risk management, Top management demographics, Macro environment and performance. Some researchers have argued that Enterprise risk management influence performance positively whereas others have advanced that the influence is negative whereas others have contended that this strategic management practice is fairly recent, adopted to varying extent and its influence on performance is yet to be fully realized. There is also no agreement on the moderating influence of Top management demographics on the relationship between Enterprise risk management and organizational performance. The moderating influence of Macro environment on the relationship between Enterprise risk management and performance is also not conclusive. It was for this reason that there was need to explore the existence of these relationships. The context of the study was Kenyan State-owned corporations.
Air Pollution and Health Status in Sub-Sahara Africa (SSA)

Air pollution is projected to be higher in low-income countries most of which are in sub-Sahara Africa (SSA) than other parts of the world; yet not many studies provide evidence relating air pollution with health condition in the region. This paper contributes to empirical literature evidence in this regard by examining the effect of air pollution measured using Carbon dioxide emission (CO 2 ) on life expectancy and infant mortality rates in the SSA region. The Fixed and Random effects model were fitted to a panel of 44 countries from the period 1960 to 2017. The results suggest that poor air quality contributes to existing low health status in SSA inducing a fall in life expectancy and rise in infant mortality rates. The evidence showed that a 1% increase in CO 2 emission leads to a fall in life expectancy at birth by approximately 1.5 years and increase in infant death by about 0.1 %. Findings indicate that existing poor health outcome in SSA are connected to poor air quality. In the bid to achieve the Sustainable Development Goal (SDG) on health, there is need for governments in the region to focus on reducing air pollution, particularly in achieving significant fall in infant deaths and improvements in life span. Keywords: Air pollution, Health Status, Fixed and Random effects model, SSA DOI : 10.7176/JESD/10-22-03 Publication date: November 30 th 2019
A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.
Joint Scheduling and Incentive Mechanism for Spatio-Temporal Vehicular Crowd Sensing

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers’ mobile devices equipped with on-board sensors for various urban sensing tasks. Because of the importance of ensuring satisfactory spatio-temporal sensing coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize the task completion rate under drivers’ traveling constraints. Different from prior work, we propose Hector, a joint trajectory scheduling and incentive mechanism for spatio-temporal UVCS systems, which concentrates on capturing the interactive effects between scheduling and incentive mechanisms. Technically, we first reduce the dimensions of the original scheduling problem by mapping it into an augmented set cover problem with spatio-temporal constraints. Then, based on reverse combinatorial auctions, we design Hector, whose incentive mechanism with the presence of uncertain future trajectory information makes scheduling and compensation decisions in real-time. Specifically, Hector is <italic>truthful</italic>, <italic>individual rational</italic> and <italic>computationally efficient</italic>. Furthermore, the social cost yielded by Hector is close-to-optimal, and the approximation ratio is <inline-formula><tex-math notation="LaTeX">$H_m$</tex-math><alternatives><mml:math><mml:msub><mml:mi>H</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-2960328.gif"/></alternatives></inline-formula>. The advantageous properties of Hector are verified by both rigorous theoretical analysis and extensive simulations based on the real world datasets in the Chinese city Shenzhen which consists of 726,000 taxi trajectories.
Tools and a web server for data analysis and presentation in microbial ecology

The methods for data presentation are important in bioinformatics as data processing algorithms. The article describes the software package for the extensive analysis of tables with estimates of bacterial abundance levels in environmental samples. The package was designed to be executed in a distributed hardware environment, with powerful packages in Python in the backend and interactive front-end forms. Most of microbial ecology-specific functionality is implemented by the scikit-bio Python package, together with the other Python packages intended for big data analysis. Interactive visualisation tools are implemented by the D3.js software library, therefore, the software project is named D3b. The package is a suite of tools for the analysis of microbial ecology data implemented as a web-service and as a desktop application. It supports a substantial part of the graphical and analytical descriptions of microbial communities used in scientific publications. Source codes are available at github (sferanchuk/d3b_charts) and the on-line version of the system is accessible at d3b-charts.bri-shur.com.