Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping innovation in the global photovoltaic industry: a bibliometric approach to cluster identification and analysis

The photovoltaic industry acts as a key force in the transition toward a sustainable energy production model. Only a niche market a few years ago, photovoltaic panels and installations are now becoming a mainstream electricity provider. As a highly dynamic, multi-technology and globally distributed industry it is a challenge to identify and quantify its appearance in regional clusters. This challenge is even greater for its critically important innovation activities, including reaching higher efficiency of the cells, and increasing (other) functional and design qualities, like flexibility and color, as well as developing cheaper production methods. Yet because of the industry? s important role in securing a sustainable energy supply and in contributing to the regional economies in which it is established, a deeper understanding of the its global presence and activities is of significant scientific importance and policy relevance. This research follows the approach of 'paper trail' of the industry's innovation process as revealed in patents and scientific publications. By using these documents as sources from which to extract indicators for innovative activity, inputs, outputs and collaboration networks, a detailed picture of the photovoltaic industry innovation and its constituent regional clusters is constructed. This allows not only for the identification and analysis of major regional changes and global shifts of the industry and variation in cluster types, but also enables the estimation of models to identify at least some of the critical factors in photovoltaic clusters' innovation growth pattern, in so far as they can be revealed by bibliometric indicators. In order to select the relevant documents, we first determine which technologies are involved in the photovoltaic industry, like concerning the materials of the cells and shape of the panels, and derive this from expert opinion. We use the USPTO patent database and the Scopus database to retrieve relevant data published between 2005 and 2014. This data is used to carry out a multiple regression model estimation. Providing an understanding of the global location changes and growth and underlying factors in photovoltaic cluster's innovation development is new. The analysis exemplifies the currently increasing scientific attention to the role of cities and regions in transitions of socio-technical systems towards higher levels of sustainability, while referring to local seedbed conditions, including knowledge spillovers, and to networking power of technology actors like multinational companies and universities. The results offer insights to policy makers who aim to avoid barriers to innovation arising from global shifts.
Master Thesis

The global listed equity real estate markets are growing and are becoming a more important part of the financial markets. Nevertheless, many investors and academics cannot properly sort an investment within a mixed-asset portfolio yet. Therefore, this thesis examines the characteristics of listed equity real estate to evaluate its contribution as a distinct asset class to a mixed-asset portfolio of an US investor. The study discusses the return enhancement, diversification, and inflation hedging capabilities of passive investment strategies on a national and global level. The empirical tests led to the following conclusions. First, risk and performance metrics indicate high risk compared to other asset classes. Nevertheless, absolute return enhancement capabilities for less risk-averse investors are identified. Second, the detailed correlation analysis reveals promising global diversification possibilities but increasing correlations during times of turmoil. Third, spanning and out-of-sample tests are contradicting and weaken the idea of economical and statistical advantages of adding listed equity real estate. Fourth, indications for long-term inflation hedging and time diversification capabilities are found. Next to these findings, the study contributes to both financial research and practice and identifies avenues for future research.
Root Cause Analysis for the Overstock of Goods in Arab Marine Ports

In global supply chain, Marine ports are critical nodes where value adding and logistics-related activities take place. The integration of ports on supply chain management creates high level of uncertainty for downstream activities due to the increasing of processes complexities and range. One of the common problems in Marine Ports in Middle East is the accumulation of goods which lead to delay in the downstream activities of the supply chain. Therefore, the main objectives of this study is first to demonstrate the causes of overstock or accumulation of goods problem at Marine ports in practically in Alexandria and Jeddah seaports using cause and effect diagram (fishbone diagram). Second, is to identify root causes of this problem especially for hazardous material and automotive products using Pareto analysis. Finally, recommend a solution for this problem based on the root cause analysis in order to minimize the probability of problem occurrence and improve the performance of the supply chain. Case study research strategy was used to identify and examine the causes of this problem in real–life context and determine their frequency. In particular, two case studies were conducted Alexandria and Jeddah ports. Structured interviews conducted with the executives of ports. The study findings shows that the main two root causes of overstock or the accumulation of goods in the Arab marine ports are the complicated customs and documentary procedures and delay in conducting inspection procedure in particular for the hazardous goods and automotive goods.
An Empirical Analysis of Uncertainty Estimation in Genomics Applications

The usability of machine learning solutions in critical real-world applications relies on the availability of an uncertainty measure that reﬂects the conﬁdence in the model predictions. In this work, we present an empirical analysis of uncertainty estimation approaches in Deep Learning models. We contrast Bayesian Neural Networks (BNN) against Monte Carlo-dropout (MC-dropout) methods to evaluate their performance and uncertainty scores in two classiﬁcation tasks with different dataset characteristics.
Chronic treatment with coenzyme Q10 mitigates the behavioral dysfunction of global cerebral ischemia/reperfusion injury in rats

Objective(s): The Ischemia/reperfusion (I/R) phenomenon has a critical role in brain injuries induced by some kinds of stroke. The current study investigates the effects of Coenzyme Q10 (Q10) on global cerebral I/R in rats. Materials and Methods: Fifty male Wistar rats were used in this study. The global cerebral I/R was induced by obstructing both common carotid arteries for 20 min and the animals were treated with Q10 (200 mg/kg; PO.) for 6 weeks. Depressive and anxiety-like behaviors were assessed using the elevated plus-maze and forced swimming test, respectively. Working and spatial learning and memory were assessed by the Y-maze continuous alternation task and Morris water maze. The brain tissues were evaluated for brain edema, brain-derived neurotrophic factor (BDNF) levels, and superoxide dismutase (SOD) activities. Results: Our results indicated that global cerebral I/R increased anxiety and depression-like behavior as well as reduced cognitive performance. Moreover, the levels of BDNF and activities of SOD are reduced in stroke animals. Chronic post-stroke treatment with Q10 decreased brain edema. Furthermore, Q10 administration reduced anxiety and depressive-like behavior as well as cognitive impairments in stroke animals. Q10 also increased the SOD activities and BDNF levels in the brain tissues of stroke animals. Conclusion: Finally, we can conclude that using Q10 supplementation may be beneficial against the global cerebral I/R complications.
Committee-Based Blockchains as Games between Opportunistic Players and Adversaries

We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are “slashed. ” Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
A Deep Learning-based Method for Tooth Segmentation on Panoramic Dental X-ray Images

— Segmentation of the tooth region is essential in the dental field to aid clinical diagnosis and make an appropriate surgical plan. However, this process is very tedious, challenging, and time-consuming. To address this problem, we propose a self-attention U-Net (SANet) for the fully automated tooth segmentation on panoramic dental X-ray images. Experimental results show that the SANet achieves higher performance than the baseline segmentation method.
Ta-W-O nanostructured photoanodes for enhanced solar fuel production: experimental and density functional theory investigation

APA Citation Nashed, R. (2014). Ta-W-O nanostructured photoanodes for enhanced solar fuel production: experimental and density functional theory investigation [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/1234 MLA Citation Nashed, Ramy. Ta-W-O nanostructured photoanodes for enhanced solar fuel production: experimental and density functional theory investigation. 2014. American University in Cairo, Master's Thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/1234
Some Clinical and Inflammatory Aspects of Trichomonas vaginalis Infection among Women with Pelvic Inflammatory Diseases

Infection with Trichomonas vaginalis and its relation to some diseases have lately had much attention. The objective of this study was to assess the infection rate of T. vaginalis and its relation to pelvic inflammatory diseases, infertility, and vaginosis. The study also assessed some demographic, clinical, and immunological parameters in women infected with T. vaginalis. The study included 160 non-pregnant married women who attended some private clinics and public hospitals in Baghdad from October 2020 to February 2021. All participants had symptoms of vaginal discharge only or vaginal discharge with lower abdominal pain. The participants were divided into an infertility group (n = 61) and fertility group (n = 99). The participants were also divided into Pelvic Inflammatory Disease (PID) group (n=41) and non-PID group (n=119). All participants underwent vaginal examination. Vaginal swabs were taken from all participants for T. vaginalis, bacteria, yeast, clue cell, pH, and vaginal leukocyte examinations. Sera were also taken to measure both IL-1α and IL-8 using sandwich ELISA technique to compare them with apparently healthy control subjects who had no vaginal discharge. The study revealed that the total infection rate of T. vaginalis was 14.37%. The results showed no significant relations between T. vaginalis positive status and each of age, infertility, vaginosis, contraceptive use, and high BMI. While a significant relation (P=0.03) was noticed between T. vaginalis positive status and PID. Women with PID exhibited a higher infection rate with T. vaginalis (24.39%) versus non PID women (10.92%). High clue cells number was not significantly related to T. vaginalis positive status, only if both PID and vaginosis were considered in the statistical analysis. In addition, high leukocytes count was not related to T. vaginalis positive status. While abnormal vaginal pH was significantly (P=0.01) related to T. vaginalis positive status. Finally, the levels of both IL-1α and IL-8 were noticed to be declined in women who had T. vaginalis infection, although they were not declined significantly. This study gives an insight about some clinical aspects of T. vaginalis infection among PID women. T. vaginalis is highly predictable in women with PID. Consequently, attention and more investigation would be needed on this topic in Iraq.
THE PERFORMANCE OF VARIOUS SUPERVISED MACHINE LEARNING CLASSIFICATION ALGORITHMS IN SENTIMENT ANALYSIS OF ONLINE CUSTOMER FEEDBACK IN RESTAURANT SECTOR OF HOSPITALITY INDUSTRY

: In the world of internet, connecting different businesses to businesses and businesses to customers offering a multitude of choices leading to a dilemma in the minds of the customers. The customers largely depend on the available online reviews for making purchase decisions as well it helps the business to make decisions. Sentiment Analysis has a wide range of commercial applications. One of the main applications of sentiment analysis is how to extract emotions from a customer review, and how to classify the customer reviews into positive or negative reviews, & find fake reviews. Sentiment Analysis and text classification methods are applied to two different datasets of restaurant reviews namely dataset 1 and dataset 2. The results show SVM algorithm outperforms not only in text classification but also can be deployed in detecting fake reviews. More specifically, A comparison is made five supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbours (KNN-IBK), KStar (K*) and Decision Tree (DT-J48) for sentiment classification of reviews
GET IN WHERE YOU FIT IN: HOW REALISTIC JOB PREVIEWS IMPACT SALESPERSON CONGRUENCE AND SATISFACTION

Few investments are as consequential to the integrity of a marketing organization as the cultivation of a mission-driven sales force. Salespeople interact with customers on behalf of the organization and their exchanges with customers are implicative of organizational consent. A troubling study by the Gartner Research and Advisory firm reports that 69% of employees do not internalize the core values of their organizations and a staggering 90% admit their behaviors often contradict organizational values (George, 2018). The report estimates that companies investing in training programs aimed at aligning employees with organizational values can expect a rise in annual revenues of up to 9%. Arguably, the return on investment may be even higher for salespeople, given their central role in driving revenue and profitability. At any rate, the onboarding process is a pivotal point of entry for any training program as it filtrates prospective trainees based on various criteria, including fit with organizational values and mission.
MODELLING CONCEPT FOR ORGANIZATIONAL POLICY USING GOAL ONTOLOGY

In this paper we propose a modelling concept for the organizational goals as a tool to evaluate organization data conformance toward the organization goal. We suggest that this model is important in assisting the organization to utilize the organization data and information from the vast amount of data for decision making which will be in line with the organization’s goals. We develop our model based on organizational goal elements such as the main goal, sub-goals, actions and tasks. A formal ontology is developed to specific role between the organization goal elements. We develop a metric model in order to interpret organization data. We apply a case study to evaluate our model development, metric development and relationship development. Overall, the main contribution of this paper is to propose a conceptual model that seeks to support the evaluation of organization data based on organization goal elements in the achievement of the organization’s goals.
THE CAUSAL LINK BETWEEN SAVINGS AND ECONOMIC GROWTH IN BOSNIA AND HERZEGOVINA THE CAUSAL LINK BETWEEN SAVINGS AND ECONOMIC GROWTH IN BOSNIA AND HERZEGOVINA

The causal link between savings and economic growth has been extensively discussed in the economic growth and development literature, but the question of the direction of this link has not yet been clearly defined. The aim of this paper is to determine the direction and intensity of savings causality (components of private savings) and economic growth in Bosnia and Herzegovina. Granger’s causality test, the TodaYamamoto procedure, was applied to test for causality between savings and economic growth. The results of Granger’s causality test indicated that there is no causal link between components of private savings with economic growth in Bosnia and Herzegovina. It was found that among the variables there is cointegration, but not causality, which means that the variables have a common stochastic trend. This fully corresponds to the characteristics of the Bosnia and Herzegovina’s economy.
Dual-polarized elliptic-H slot-coupled patch antenna for 5G applications

: A dual-polarized, slot-coupled dielectric patch antenna design is presented in sub-6 GHz for 5G base stations. Proposed antenna is implemented using two dielectric patch layers (main patch and parasitic patch) above a feeding line layer. Excitation is realized by crossed elliptic-H slots in order to create orthogonal ± 45 ◦ polarizations. Through the use of patches at proper heights together with elliptic-H slots, significant improvement in impedance bandwidth, matching level, isolation and front-to-back ratio is acquired. Prototype antenna has an impedance bandwidth of 18.5% (3.23–3.85 GHz) for | S 11 | , | S 22 | < − 15 dB, covering the 5G spectra of 3.30–3.80 GHz. The isolation between ports ( | S 21 | ) is obtained as < − 25 dB in desired operating band. Antenna exhibits stable, broadside radiation patterns with half power beam-widths of 59 ◦ –64 ◦ and 69 ◦ –72 ◦ in E/H-planes, respectively. Gain of the antenna varies within the range of 7.9–8.4 dBi. Proposed antenna is also low profile (9.8 mm). It needs no reflector or cavity-backed structure and meets all the requirements of 5G base stations as an antenna element. Design details, numerical studies, and experimental results are presented.
GAZE FOLLOWING IN INFANTS

Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language development. However, how gaze following emerges is a topic of great debate. Representational theories assume that in order to follow adults’ gaze, infants must have a rich sensitivity to adults’ communicative intention from birth. In contrast, learning-based theories hold that infants may learn to gaze follow based on low-level social reinforcement, without the need to understand others’ mental states. Nagai, Asada and Hosoda [Advanced Robotics, 20, 10 (2016)] successfully taught a robot to gaze follow through social reinforcement and found that the robot learned in stages: first in the horizontal plane, and later in the vertical plane – a prediction that does not follow from representational theories. In the current study, we tested this prediction in an eye-tracking paradigm. Six-month-olds did not follow gaze in either the horizontal or vertical plane, whereas 12-month-olds and 18-montholds only followed gaze in the horizontal plane. These results confirm the core prediction of the robot model, suggesting that children may also learn to gaze follow through social reinforcement coupled with a structured learning environment.
Kinematics Biomechanical Analysis and Three Dimensional Reconstruction Diagnostic Technique of Carpal Joint in One-Humped Camel (Camelus dromedarius)

The database of biomechanical variables gained from healthy camels was needed to investigate the main characters of normal gait in one-humped camel and changes of velocity and displacement as well as the anatomical changes of the carpal joint during walking. Nine healthy camels of three different sizes were selected for gait analysis by using Motion track program and four carpal joints cadavers obtained from four healthy dromedary camels of both sex were used for three dimension reconstruction computed tomography (CT). Gait analysis of camels carpus during walking yielded some data as step length, stride length, stride time, stride frequency, angular change and displacement of the carpal joint. Our results revealed that different sizes of camels had a significant effect on gait parameter as step length, stride length, and stride frequency. Meanwhile, small camels increased their velocity during walking by significant decreased the stride time. Large sized camels showed a significant higher degree of extension of the carpal joint than small camel, especially right carpal joint. Additionally, hyperextension of camels carpus occasionally occurred. On the other hand, horizontal and vertical displacement of the carpal joint during walking was a significant difference of medium sized camel than small and large camel. The present findings will be a helpful method in lameness diagnosis to recognize between the normal and diseased gait and surviving the inhabitant environment.
Effectiveness of the Big Math for Little Kids Program on the Early Mathematics Skills of Children with Risk Group

The aim of this study is to determine the effect of Big Math for Little Kids (BMLK) Program on the early mathematics skills of children with lower socioeconomic level. The participants of the study consist of between 60-72 months of aged children with lower socioeconomic level recruited to kindergartens of Ministry of National Education from Turkey in Diyarbakır in the 2018-2019 academic year. The sample of the study consists of 40 children with above mentioned characteristics. Of these, 20 were assigned to the experimental group and 20 to the control group. Two schools were selected for experimental and control group, and the study was conducted after selecting ten children in a class of each school. In the study pretest-posttest with control group experimental model was used. The data of the study were collected through the Tests of Early Language Development Test (TELD-3) to determine children with adequate language skills of their own age group, and Test of Early Mathematical Ability (TEMA-3) to assess early mathematical development of children. As a result, BMLK program was determined to be effective in the mathematics development of children with lower socioeconomic level.
Cointegration Rank Estimation for High-Dimensional Time Series with Breaks

An intuitive and simple-to-use procedure for estimating the cointegration rank of a high-dimensional time series system with possible breaks is proposed in this paper. Based on a similar idea to principal component analysis, the cointegration rank can be estimated by the number of the eigenvalues of a certain non-negative definite matrix. There are several advantages to the new method: (a) the dimension of the cointegrated time series is allowed to vary with the sample size; (b) it is model-free; and (c) it is simple to use and robust against possible breaks in trend. The cointegration rank can be estimated without the need for a priori testing and estimating of the break points. Asymptotic properties of the proposed methods are investigated when the dimension of the time series increases with the sample size, which offers a new alternative to deal with high-dimensional time series. Illustrations of simulations are also reported.