Miracle-dz/UniFAR-FTUs huggingface dataset:
Centralised Control: A Barrier to Shared Decision-making in Egyptian Secondary Schools

This article reports on findings from a larger qualitative research study on shared decision- making (SDM) in Egyptian schools. The main aim of the study was to identify barriers to SDM in Egypt's secondary schools, using qualitative data collected from nine general secondary schools in the Damietta governorate, Egypt. Data were collected through interviews with 85 research participants, including head teachers, deputy heads, heads of department. Board of Trustees members and newly appointed teachers. The data analysis revealed various barriers that interact and hinder the involvement of teachers in the decision-making process at the school level. While cultural factors presented themselves strongly as potential barriers to such involvement, centralised control over schools was perceived by many participants as significantly more influential.
Quantifying Quality Specialization Across Space: Skills, Sorting, and Agglomeration

We quantify the supply-side determinants of quality specialization across space. Specifically, we complement the quality specialization literature in international trade and study how larger cities specialize in higher-quality goods within a country. In our general equilibrium model, firms in larger cities produce goods with higher quality, because agglomeration benefits accrue more to skilled workers who are also more efficient in upgrading quality. Two channels are at work in our model. The first channel is through the treatment effect of agglomeration, such that firms become more productive if they locate in a larger city. The second channel works through sorting, in that more pro-ductive firms receive higher agglomeration benefits and endogenously sort into larger cities. These two effects are further mitigated by the increasing skill premium with respect to city size, though the latter is dominated in the spatial equilibrium. Using firm-level data from China, we structurally estimate the model and find that product quality is on average 23% higher in big cities than that of small cities. We further find that agglomeration forces account for half of the quality difference in big cities while sorting of firms accounts for another half. A counterfactual policy to relax land use regulation in housing production raises the quality of goods produced in big cities by 5.5% and (in-direct) welfare of all residents by 6.2% through reallocation of economic activities across space.
Effect of middle managers’ cultural intelligence on firms’ innovation performance

The purpose of this paper is to examine the effect of the metacognitive, cognitive, motivational and behavioral dimensions of the middle managers’ cultural intelligence (CQ) on firms’ innovation performance in a context of cultural diversity and the mediating role of knowledge sharing in this relationship. The author deepens the analysis by exploring the moderating role of collaborative climate (CC) on the link between CQ and KS. ,A survey was conducted on 186 foreign middle managers working in Tunisian firms. The data analysis was performed via the partial least square method. ,The results revealed that middle managers’ metacognitive CQ has a positive effect on KS, which in turn enhances firms’ innovation performance. In this line, KS partially mediates the relationship between metacogntive CQ and innovation performance. Findings also indicate that CC moderates the link between three dimensions of CQ, namely metacognitive, behavioral and motivational CQs and KS. ,The paper sheds lights on the contribution of middle managers’ CQ and the CC within firms to the KS and innovation performance in a context of cultural diversity. At the best of the author’s knowledge, the links among these variables had not been empirically examined, especially involving samples of middle managers. This study offers important insights for managers by providing them with tools to improve KS and firms’ innovation.
The Difference of Amino Acid Profiling From Two Morphological Purple Sweet Potatoes From Kawi Mountain Cultivars, East Java, Indonesia

Indonesia is one of the main commercial producers of sweet potatoes. Previous study has presented well documented anthocyanin content of Purple Sweet Potatoes (PSP) from local cultivar in Indonesia. However, research about amino acids profiling of local cultivar PSP as high commodities in Kawi Mountain region has not undertaken. This study was explored amino acids profiles in two morphological PSP cultivated in Kawi Mountain region, Ngajum Village, Wonosari Subdistric, Malang Regency, East Java, Indonesia. Fresh storage roots were cleaned with running water and shade dried. The analysis was conducted at PT.Saraswanti Indo Genetech, Bogor, Indonesia. Method of UPLC was used for identification of Ser, Glu, Phe, Ile, Val, Ala, Arg, Gly, Lys, Asp, Leu, Tyr, Pro, Thr, His, Cys and Met; while tryptophan was analyzed using HPLC method. The five largest amino acids in dark-purple skin and flesh PSP were Glu 565.75 mg/kg, Asp 479.74 mg/kg, Arg 413.54 mg/kg, Ala371.87 mg/kg and Leu 336.67 mg/kg, whereas white flesh and purple skin PSP contained Glu 653.67 mg/kg, Asp 447.60 mg/kg, Arg 314.41 mg/kg, Lys 278.77 mg/kg and Leu 256.97 mg/kg. Future research is needed to investigate the health benefit of amino acids content in PSP from Kawi Mountain cultivar.
Evaluating the accrual anomaly in the Chinese stock market with the decomposition method

ABSTRACT We evaluate the explanations for accrual anomaly in the Chinese stock market using the decomposition method. The results show that institutional ownership best explains the accrual anomaly with an explanatory power of about 46%, equity growth (EG) explains 12% of the anomaly, and the residual fraction of around 32% is unexplained by any candidate explanations. Our findings indicate that the naïve investor fixation hypothesis is favored to explain the accrual anomaly in China, and the existing explanations cannot fully explain the anomaly.
Politicized policy access: The effect of politicization on interest group access to advisory councils

Abstract Current scholarship often concludes that technical expertise is one of the most important commodities for interest groups wishing to gain access to political‐administrative venues. Less attention has been given to politicization and the scope of societal support that interest groups bring to bear. Specifically, I hypothesize that the capacity of interest groups to supply broad societal support is decisive for gaining access in highly politicized policy domains. To test this expectation, the article combines a mapping of interest group membership in 616 Belgian advisory councils with survey data from more than 400 organized interests. The empirical analyses demonstrate that interest groups with broad support are more likely to gain access to advisory councils in highly politicized policy domains, but this effect is negatively moderated when interest groups involve their constituencies intensely in advocacy processes.
PLS-PM FOR ECOLOGICAL INTEGRITY MAPPING: CASE STUDY OF THE AVE RIVER BASIN, PORTUGAL

The bonds between pollution sources, contaminant concentrations and ecological damage are quite complex to access, but the proper management of river basins requires the full understanding of those interactions. In the present study, four SEM-PLS models were used to set up environmental cause–effect relationships in a heavily polluted urban catchment: the Ave River Basin. Data were collected within this watershed relative to point source discharges, diffuse emissions, diffuse indicators (e.g. land use), contaminant concentrations and a Portuguese index of macroinvertebrates diversity (IPtIN). The same dataset was used in the four SEM-PLS models, but each run was based on a different combination of latent variables. In all cases, it was possible to recognize that nitrogen discharges from livestock production, industrial and urban treatment plants are the dominant threats to water quality. The model results were robust, characterized by high, adjusted R Squared values – from 0.744 to 0.931. The models were also used to draw water quality maps. Now, the purpose was to test their prediction capability. Since more than 80% of the water courses were correctly classified for IPtIN, the predictions were considered feasible.
Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral–proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.
Three Essays on the Economic Aspects of New Information Technologies

In the previous decade, we have observed a rapid growth in data volumes and in the processing power. New types of data and the algorithms to analyze them are known under the umbrella term “Big Data" technologies. They are adapted in various industries and are likely to become ubiquitous. This thesis comprises three empirical studies pertaining to Big Data technologies. Our results contribute to the analysis of their economic role and indicate promising avenues for future research in this area. In the first two chapters of the thesis, we explore a particular use case of Big Data: usage-based insurance. Policyholders with such contracts install a drive recorder that continuously monitors the vehicle’s motion. We have aggregated and combined these driving logs, generally referred to as “telematics data", with traditional contract and claim data. Our goal is twofold: a) to study accident risk factors b) to establish whether there are informational problems in the automobile insurance market. Telematics-based variables contain statistically significant predictors of subsequent accident involvement. This result prompt us to conclude that telematics data can be used to refine risk classification and inform the design of incentive contracts. Furthermore, the combined results with chosen insurance contracts indicate multiple and counteracting effects of private information. In a second part of the thesis, we analyze the willingness to share personal data when this data is used for subsequent price discrimination. We have designed a laboratory experiment during which participants could sell a bundle of personal data. Participants were categorized based on the content of their personal data and received category-dependent payoffs in a subsequent stage. The experimental variations modified the category-dependent payoff structure. We find no effect of subsequent financial discrimination on the general willingness to sell personal data. A significant change in the data reservation price is only observed under strong negative discrimination. We observe important gender differences in the reservation price for private information and the role of underlying privacy concerns.
Parental Adherence to Infant Sleep Safety Recommendations

Objective: Key infant sleep safety messages to prevent sudden infant death syndrome (SIDS) recommend putting infants to bed in the supine position and not sharing a bed with infants in order to prevent them from suffocating. Though these messages have been promoted by health care providers and health educators, adhering to them regularly remains a challenge for parents. Methods: Using a web-based survey on a national sample, factors associated with increased probability of adherence were examined to assess how parents negotiate infant safety recommendations. Results: Findings revealed that parents had knowledge of these recommendations but did not always follow them; adherence depended on how effective parents believed the recommendations are at preventing death and how the messages were presented. Conclusion: Health care providers and educators of infant safety should continue to focus on changing the public’s beliefs about the risks of the prone position and bed sharing, with an understanding of how and why the information about infant sleep safety is negotiated in the day to day lives of parents.
Response surface methodology: critical parameters on the production of mangrove wood biochar yield

The infiltration of oxygen is unfavorable since it burned away the biomass. Other parameters were also contributed to the production of biochar. The experiments on mangrove wood were conducted using a fixed bed pyrolysis reactor with the temperature range of 300°C to 600°C, oxygen range from 0% to 10% and holding time from 2 hours to 4.4 hours. Response surface methodology (RSM) was implanted for process optimization. There were a significant effect of oxygen percentages and holding time on the production of biochar yield within the range of study. Results show that the optimum biochar yield of 15.2% when the pyrolysis temperature, oxygen percentage, and holding time were 402.5°C, 2.29% and 2 hours respectively. Confirmation experiment showed that 15.1% of biochar yield was obtained with the same pyrolysis parameters which validated the previous optimization.
Micro‐computed tomography imaging and probabilistic modelling of rock fracture by freeze–thaw

A major problem in studies of rock fracture by frost is the paucity of direct observations in space and time of the initiation and growth of microcracks and their transition to macrocracks. Such observations are essential to understand the location, timing and controls of rock fracture by freeze–thaw. The aim of the present work is to image and elucidate the early stages of rock fracture by applying imaging and statistical methods to a frost‐weathering experiment using intact specimens of a limestone (chalk) and sandstone. First, micro‐computed tomography (μ‐CT) is used to visualize rock fracture in three dimensions over the course of 20 freeze–thaw cycles and to estimate transverse strain using a pixel‐based approach. Second, probabilistic correlation functions are applied to quantify the progressive expansion of the fracture phase and associated damage to rock specimens. The method of μ‐CT is demonstrated for visualizing the growth and coalescence of microcracks and their transition to macrocracks. Fracture proceeded faster and to a greater extent in chalk relative to sandstone, and the macrocracks in chalk were mostly concentric and vertical. Both fracture development and positive transverse strain (dilation) accelerated after cycle 15, suggesting that a threshold has been exceeded, after which macrocracks were evident. Of three probabilistic correlation functions applied to the μ‐CT results, the modified lineal‐path function – which measures the continuous connectivity of the fracture phase in a specific direction – reveals that damage was more extensive in the chalk than the sandstone. It also allows a novel approach to define and quantify three zones of microcracking during freeze–thaw cycling of anisotropic rock: (1) the zone of inherent flaws; (2) the zone of active microcracking; and (3) the zone of weak influence during microcracking. The broader significance of this work is that it provides a new approach to investigate mechanistically how frost action damages rock. © 2019 John Wiley & Sons, Ltd.
A Deep Reinforcement Learning Approach to Stock Trading

This study investigates the viability and potential of using state of the art Deep Reinforcement Learning for stock trading. We specifically use a Deep Deterministic Policy Gradient (DDPG). The model trades stocks in four indices: DJIA (USA), TSX (Canada), JSE (South Africa) and SENSEX (India). We find that DDPG agents using past log return (R) and trading volume (TV) as predictors yield the best performance. The models outperform a buy-and-hold benchmark for all markets in terms of mean return. Adding Google search volume (G) as a predictor does not improve performance in developed markets (USA and Canada), but is valuable in emerging markets (South Africa and India). The algorithm is tested also after implementing transaction cost, where agents are restricted to only trade once every month or quarter. Several agents outperform the benchmark in terms of mean return. Results are compared to a simple linear regression. In terms of mean return, the DDPG agent always outperforms the equivalent linear regressions.
Curare la malattia da HIV: ritorno al paziente?

HIV disease has dramatically changed in the last two decades from a progressive, lethal disease to a chronic manageable condition. These changes are due to the availability of potent antiretroviral combination therapy, which also have the potential to contribute significantly to the control of the epidemic. Among persons living with HIV, incidence of immunosuppression-related opportunistic illnesses has clearly decreased, while an increase was observed in the prevalence of age-related noncommunicable comorbidities, including cardiovascular, metabolic, renal, bone and hepatic disease, due to chronic inflammatory state and to an overall aging of the population of persons with HIV. It has been predicted that by 2030 more than 80% of older persons with HIV will have at least one comorbidity, compared to 19% of non HIV-infected persons, and that one fourth of these persons will have three or more comorbidities. Among persons with HIV, the prevalence of frailty is increasing. Choice of therapeutic approach to HIV disease should take into account, in addition to the ability of drug combination to suppress viral replication, the potential for long term adherence to treatment, the lack of long term toxicity, the possibility to fully restore immune function and prevent immune activation, thus reducing the risk of chronic inflammation related disease. In addition the overall impact of treatment on patients' well-being must be considered, and patients related outcomes should be used to measure this impact.
CREDIT RISK AND BANK PROFITABILITY OF COMMERCIAL BANKS IN GHANA

This study attempted to explore the connection between credit risk management and the profitability of the Ghana Stock Exchange (GSE) listed commercial banks. The study specifically sought to examine the relationship between credit risk and the profitability of the firms as measured by ROA. The study adopted these variables to measure credit risk (non-performing loan ratio, cost per loan asset, capital reserved 0.1ratio and asset growth ratio) and return on asset (ROA) as a profitability estimator. Following some diagnostic and specification studies to address the fundamental assumptions of the Classical Linear Regression Model (CLRM). The study uncovered that NPLR had a significantly negative effect on the firms’ profitability as measured by ROA [β=-0.1671, (p=0.1360)>0.05]. Also, the cost per loan asset (CPLA) had a positive influence on the firms’ profitability as measured by ROA [β= 0.0249, (p=0.8252)>0.05]. For the other variables of credit risk measurements, capital reserved ratio, and assets growth ratio. The capital reserve ratio had a significant positive association with ROA [β=0.2867, (p=0.0095) <0.05. Similarly, asset growth ratio had a statistically significant negative connection with firm’s profitability measured by ROA [β=-0.3835, (p=0.0004) <0.05. Based on the results, the study suggested that since credit risk had a negative association with the firm’s profitability, the companies should adopt efficient credit risk management methods to assist guide their profitability. This point is raised because, as demonstrated by the results of the study, an increase in credit risk led to a decrease in the profitability of the companies.
Locating People of Interest in Social Networks

By representing relationships between social entities as a network, researchers can analyze them using a variety of powerful techniques. One key problem in social network analysis literature is identifying certain individuals (key players, most influential nodes) in a network. We consider the same problem in this dissertation, with the constraint that the individuals we are interested in identifying (People of Interest) are not necessarily the most important nodes in terms of the network structure. We propose an algorithm to find POIs, algorithms to collect data to find POIs, a framework to model POI behavior and an algorithm to predict POIs with guaranteed error rates. First, we propose a multi-objective optimization algorithm to find individuals who are expected to become stars in the future (rising stars), considering dynamic network data and multiple data types. Our algorithm outperforms the state of the art algorithm to find rising stars in academic data. Second, we propose two algorithms to collect data in a network crawling setting to locate POIs in dark networks. We consider potential errors that adversarial POIs can introduce to data collection process to hinder the analysis. We test and present our results on several real-world networks, and show that the proposed algorithms achieve up to a 340% improvement over the next best strategy. Next, We introduce the Adversarial Social Network Analysis game framework to model adversarial behavior of POIs towards a data collector in social networks. We run behavior experiments in Amazon Mechanical Turk and demonstrate the validity of the framework to study adversarial behavior by showing, 1) Participants understand their role, 2) Participants understand their objective in a game and, 3) Participants act as members of the adversarial group. Last, we show that node classification algorithms can be used to predict POIs in social networks. We then demonstrate how to utilize conformal prediction framework [103] to obtain guaranteed error bounds in POI prediction. Experimental results show that the Conformal Prediction framework can provide up to a 30% improvement in node classification algorithm accuracy while maintaining guaranteed error bounds on predictions. LOCATING PEOPLE OF INTEREST IN SOCIAL NETWORKS