Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence Methods of Project Managers: Perceptions of Team Members and Project Managers

Much and significant research was done for more than 100 years in the area of leadership. However, with the exception of the studies by Hodgetts (1968) and Thamhain and Gemmill (1974), not much has been done in the area of project leadership. This paper presents the findings of four research studies in this area. Among others, the findings show that professionally challenging projects are the single most important factor influencing the behavior of project team members. Team members need to be challenged, they respect leaders who know how to properly exercise their authority, and they need to believe in their leaders, both in terms of the leaders' competence and ethical character.
Design and Simulation of Energy Efficient Carbon Nanotube based Digital Circuits

Scaling of silicon technology has led to its curtailed use for high-performance digital circuits. Subsequently, researchers are investigating other novel materials, structures and devices for enhancing circuit performance. Carbon nanotubes (CNTs) have come out as a possibility for the same due to their excellent carrier mobility and faster carrier transport properties. This paper overviews CNTFETs and some compact models. Using the available models, CNT based digital circuits have been analyzed. The parameters like chirality, diameter and threshold voltage for CNTFET and their influence of these parameters on the device characteristics are investigated. A comparative analysis of CMOS and CNTFET logic circuits is carried out. Voltage and current transfer characteristics of CNTFET inverter are presented. Performance metrics viz. delay, power and power delay product (PDP) are analyzed. It is found that CNTFET based circuits are energy efficient. All the circuits have been simulated on HSPICE circuit simulator tool for 32nm technology node.
The valorization of plum seed oil for the development of topical formulation

The object of this paper was to utilize the plum seed oil for the preparation of the topical formulation for skin care since it can improve elasticity and prevent accelerated skin aging. Soxhlet extraction of plum seed oil was performed using n-hep-tane. The spectrophotometric methods were applied to estimate the photoprotec-tive effect and antioxidant activity of the samples. The warm-warm emulsification process was used for the preparation of topical formulation based on plum seed oil. The antioxidant activity of the plum seed oil was estimated based on the half-maximal inhibitory concentration (9.39 mg/mL). After the incorporation of the oil in the topical formulation, the IC 50 value of 9.33 mg/mL was not significantly changed. The viscosities of the topical formulation and plum seed oil were 1.56×10 6 mPa·s (at the shear rate of 5 s-1) and 60.48 mPa·s (at the shear rate of 50 s-1), respectively. Rheological analysis showed that the plum seed oil and topical formulation were Newtonian and non-Newtonian pseudoplastic fluids, respectively. The formulation adsorbed the light in the UV-Vis range so that it can be used as a w/o emulsion photoprotective cream. The cream with adequate pH value was stable and micro-biologically safe for application to the skin. Due to the use of cheaper ingredients, the formulation is acceptable and suitable for manufacturing.
Embedded Real Time Partial Discharge Pulse Feature Extraction

Partial Discharges(PD) are commonly produced in defects within the insulation systems of high voltage equipment. These discharges are typically nanosecond current pulses in the amplitude range of milli-amperes. A long term exposure of the insulation system to these partial discharges accelerate the aging mechanisms that eventually lead to the final breakdown of the insulation system. Such insulation breakdowns in High Voltage (HV) / Medium Voltage (MV) equipment typically involve arc-flash/fire hazards, posing safety threats. Moreover probable undelivered power and huge financial losses are also associated. Early detection of PD activity can provide warnings about pending insulation/device failures and hence, maintenance or repair activities can be scheduled before breakdown occurs. Moreover, clustering of PD due to different types of sources is of practical importance as it indicates the severity of defect and provides an insight into the time available for repair activities before complete breakdown. State of the art tools for electrical PD monitoring are expensive and cannot be economically deployed over a large network of HV/MV assets. Moreover, they employ classification schemes based on less robust PD features. This thesis marks the completion of the first stage in the process of building an open source, cost-effective, automated embedded online partial discharge detection tool for feature extraction and PD classification based on new, advanced robust features of partial discharges. As an outcome of this thesis, an embedded solution for real time PD detection and feature extraction was developed to facilitate future PD classification.
Behavior of Square Footing Erected on Gypseous Soil Treated by Ceramic Wastes

Gypseous soil, as is well known, includes a specific quantity of gypsum. Gypsum is a water-soluble salt with a solubility of (2.2-2.6) gram/liter in distilled - water. As a result, the presence of gypsum poses a challenge when building structures on this soil, since gypsum dissolves when exposed to water, producing gaps between soil particles and so causing soil collapse. This study examines the behavior of a shallow foundation rested on gypseous soil. Three types of gypseous soil with gypsum contents of (61%, 45.3%, and 27.9%, respectively) are used, and treated with ceramic wastes, which is a waste product of construction. The Soil samples were collected from the site of Tikrit University's campus. The study consists of six test cases, two for each type of soil, one dry and the other wet, where the first test for each case was used as a baseline for comparison, which was the soil test without the addition of ceramic wastes, and the rest was added in three proportions (3%, 6%, and 9%) and three mixing depths for each ratio (B/2, B, 3B/2). The percentage 9% ceramic wastes gives best results of improving, the bearing capacity of the soil improved by 233%, 256 %, and 289% for high gypseous soil, and by 78%, 94%, and 111% for medium gypseous soil, with a percentage of 60%, 87%, and 113% for low gypseous soils. Adding ceramic wastes to wet gypseous soil improves the bearing strength of the soil, lowers settlement to some extent, and reduces the influence of water on it.
Real Time Impedance identification of Li-Polymer Battery with Kalman Filter

In a study of a new and an aged lithium-polymer battery cell, a joint Unscented Kalman Filter coupled with a non-linear equivalent circuit battery model formed the basis for on-line identification of the battery impedance characteristics. In order to evaluate estimated impedance parameters produced by the Kalman filter under real racing conditions, a case study using a dynamic drive-cycle for the Revolve NTNU electric race car were developed. Specifically, the cell model developed describes the non-linear relationship between the open-circuit voltage and the battery State-of-Charge (SoC), the ohmic resistance, the non-linear dependency of the charge-transfer resistance on the faracaic current, and a simplified RC-network to describe the diffusion phenomenon. To verify the consistency of the estimated parameters, an impedance characteristic map was constructed at various levels of SoC and isothermal conditions for the new and aged cells using an industrial grade electrochemical impedance spectroscopy device. Moreover, DC-resistance characterization was conducted to support the analysis. In addition, to avoid introducing errors to the system, the nominal capacity of the respective cells were characterized. Given imposed sampling conditions and load-dynamics, the respective impact of the ohmic resistance and the non-linear charge-transfer dynamics on the overvoltages, was difficult to separate due to the fast electrode kinetics of the cells at temperatures above 25◦C. As a result, the estimated impedance parameters describing the rapid cell dynamics were poorly conditioned on the voltage measurements. As a proof of concept, a simplification was introduced in order to verify the impact of a model-reduction. The results indicated a similar estimation performance with respect to the expected mean of the sum-of-resistances, but with a higher confidence in its inferred value. Thus, for the considered lithium-polymer battery cells operated at cell temperatures above 25◦C, the concatenation of ohmic resistance and charge-transfer dynamics into a single series resistance is likely to be the best candidate to describe the battery State-of-Health (SoH) in terms of their impedance characteristics. The outcome of the simplification is reduced computational effort due to model reduction, and increased confidence in the inferred sum-of-resistance by allowing the series resistance to represent both passive and active electrochemical effects. However, the estimation error for the aged cell with respect to impedance characteristics at low SoC, still persists. This is probably related to limited diffusion, which is not captured with the given Kalman filter decision variables, in addition to assumptions of how impedance parameters evolve in time.
A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II

This paper presents a novel methodology aiming to define and refine a LSTM architecture applied to predict stock market prices. The methodology, dubbed STOCK-PRED: THE LSTM PROPHET OF THE STOCK MARKET, uses iRace and NSGA-II algorithms. The LSTM is built in two steps: (i) initially, iRace determines a robust set of hyperparameters using a compound objective function; afterwards, (ii) one of the best structures is used to define a tiny search space for the NSGA-II populations. In this step NSGA-II optimizes, simultaneously, the Mean Squared Error, in relation to price prediction, and the Accumulated Accuracy Rate for a time horizon of seven days, relative to growth price tendency. The methodology is tested considering stock market tickers from USA and Brazil. Even in a challenging scenario surrounded by possibly turbulent events, the Stock-Pred predicts the prices based on historical data and machine learning techniques. Since the optimization problem is noisy and the objective functions have a high computational cost, we consider a low budget in relation to the number of fitness evaluations. The analysis of the non-dominated solution indicates that the proposed methodology is promising, achieving a MAPE of 1.279%, 1.564% and 2.047% for BVSP, IBM and AAPL stocks respectively.
Investigating and Modeling the Illegal U-Turn Violations at Medians of Limited Access Facilities

Illegal U-turns on freeways and toll roads are risky maneuvers that sometimes result in the turning vehicles causing various types of collisions or disturbances to approaching traffic. These illegal U-turn maneuvers can occur at traversable grass medians and emergency crossovers. Limited literature was found regarding the impact of illegal U-turns on these facilities. Therefore, to understand the roadway and median characteristics that could influence drivers’ propensity to commit illegal U-turns, a sequential modeling methodology was adopted. This methodology combined a Poisson regression model with a Least Absolute Shrinkage and Selection Operator (LASSO) regression procedure to predict the cited violations at traversable median segments. Additionally, a logistic regression model was developed to predict the probability of a cited violation at official use only emergency crossovers. These models included illegal U-turn citations and crashes for the Orlando and Miami metropolitan areas in Florida from 2011 to 2016. The findings indicated that the average distance between access points, median width, speed limit, segment length, and distance to nearest segment were significant in predicting cited violations at traversable medians. Furthermore, the distance to the nearest interchange, distance to the nearest adjacent crossover, and median width were significant in predicting the probability of a cited violation occurring at an emergency crossover. This study helps agencies to predict the locations of illegal U-turn violations and to prioritize roadways for possible treatment to minimize the potential risk of head-on or other collisions due to illegal U-turn events.
Evaluation of AI-based Malware Detection in IoT Network Traffic

: Internet of Things (IoT) devices have become day-to-day technologies. They collect and share a large amount of data, including private data, and are an attractive target of potential attackers. On the other hand, machine learning has been used in several contexts to analyze and classify large volumes of data. Hence, using machine learning to classify network trafﬁc data and identify anomalous trafﬁc and potential attacks promises. In this work, we use deep and traditional machine learning to identify anomalous trafﬁc in the IoT-23 dataset, which contains network trafﬁc from real-world equipment. We apply feature selection and encoding techniques and expand the types of networks evaluated to improve existing results from the literature. We compare the performance of algorithms in binary classiﬁcation, which separates normal from anomalous trafﬁc, and in multiclass classiﬁcation, which aims to identify the type of attack.
Evaluation of Chemical and Rheological Aging Indices to Track Oxidative Aging of Asphalt Mixtures

Oxidative age hardening in asphalt binder leads to embrittlement. Embrittled asphalt is prone to fatigue and thermal cracking. Therefore, the ability to predict asphalt binder oxidative age hardening within a pavement throughout its service life could inform improved pavement material selection, design, and maintenance practices. Studying the evolution of oxidative aging requires the use of key properties to track oxidation levels, termed aging index properties (AIPs) here. The objective of this study is to identify suitable rheological and chemical AIPs to track oxidation levels in asphalt materials. A wide range of laboratory and field aged materials were evaluated in this study. A range of chemical AIPs determined by Fourier transform infrared spectroscopy (FTIR) absorbance peaks and areas were evaluated based on their correlation with laboratory aging duration. Rheological AIPs were evaluated based on the strength of their relationship to the chemical changes induced by oxidation. The rheological AIPs evaluated included the dynamic shear modulus, zero shear viscosity, Glover-Rowe parameter, and crossover modulus. The chemical AIP evaluation that most strongly correlated with laboratory aging duration is the carbonyl plus the sulfoxide absorbance peaks. The results indicate that both the dynamic shear modulus and Glover-Rowe parameter constitute rheological AIPs that relate directly to the chemical changes induced by oxidation.
Design, Synthesis, and Anti-Inflammatory Activity of Some Coumarin Schiff Base Derivatives: In silico and in vitro Study

Introduction Inflammation is a fundamental response of the immune system during tissue damage or pathogen infection to protect and maintain tissue homeostasis. However, inflammation may lead to life-threatening conditions. The most common treatment of inflammation is non-steroidal anti-inflammatory drugs (NSAIDs). Nowadays, the development of safer new NSAIDs is critical as most of the existing NSAIDs have serious adverse effects, such as gastrointestinal (GI) toxicity and cardiotoxicity. In the present study, four compounds as Schiff base derivatives of 7-hydroxy-4-formyl coumarin and 7-methoxy-4-formyl coumarin were designed and synthesized aiming to develop a lead compound that exhibits anti-inflammatory activity and circumvents the side effects of NSAIDs, especially GI toxicity. Materials and Methods Lipinski’s rule of five was applied for each designed molecule to evaluate the drug-likeness properties. Molecular docking studies were performed using the ligands and the cyclooxygenase-2 (COX-2) protein to select the best-scored molecule using AutoDock 4.2. 6. The molecules were then synthesized and characterized. An in vitro anti-inflammatory assay of the compounds against the COX-2 receptor was realized through a protein denaturation assay. Results and Discussion All four synthesized ligands passed Lipinski’s rule of five and exhibited higher binding free energy compared to the positive standard control (ibuprofen), and the Ki values of compounds 5, 7, and 8 were in the nanomolar range. However, only compounds 6 and 7 obtained a higher percentage of inhibition of protein denaturation relative to ibuprofen. Conclusion The present study suggested that compound 7 may be a lead molecule because this ligand not only exhibited the best computational and experimental results but also exhibited the strongest correlation between the concentration and percentage of protein denaturation (R = 0.986 and R2 = 0.972) with the lowest P-value (0.014).
Nitrogen-containing multifunctional copolymers as reagents to sorb rare-earth metals

ABSTRACT N,N-Diallyl-N’-neopentanoylhydrazine was for the first time synthesized by the reaction of allylchloride with neopentanoylhydrazine. There was shown a possibility of involving the said monomer into a radical copolymerization reaction with acrylonitrile. The sorption capacity of acrylonitrile copolymers with N,N-diallyl-N’-acetylhydrazine and N,N-diallyl-N’-neopentanoylhydrazine toward light rare earth metal ions – La(+3), Sm(+3) and Ce(+3) – was investigated. The introduction of a neopentanoyl fragment into multifunctional copolymers was evinced to enhance their activity in the sorption processes of lanthanoids. Among the light rare-earth metals studied, samarium is much better extracted. With increasing concentration of sulfuric acid, the sorption capacity of both copolymers toward samarium ions enlarges and equals 150 mg∙g−1. The rate constants of external and internal diffusion within fixed intervals of time were calculated, with an essential role of both external and internal factors in the diffusion processes shown. The results obtained in exploring the conjoint adsorption of La, Ce, and Sm ions by the copolymer based on N,N-diallyl-N’-neopentanoylhydrazine evince a satisfactory separation as being attainable in acidic and alkaline media; at this point, for Cm/Ce ions the distribution coefficient βCm/Ce equals 4.78 at pH 0.3, and 2.5 at pH 11.4; for La/Ce ions it equals 3.39 at pH 0.3 and 2.2 at pH 11.4.
Local Personal Data Processing with Third Party Code and Bounded Leakage

: Personal Data Management Systems (PDMSs) provide individuals with appropriate tools to collect, manage and share their personal data under control. A founding principle of PDMSs is to move the computation code to the user’s data, not the other way around. This opens up new uses for personal data, wherein the entire personal database of the individuals is operated within their local environment and never exposed outside, but only aggregated computed results are externalized. Yet, whenever arbitrary aggregation function code, provided by a third-party service or application, is evaluated on large datasets, as envisioned for typical PDMS use-cases, can the potential leakage of the user’s personal information, through the legitimate results of that function, be bounded and kept small? This paper aims at providing a positive answer to this question, which is essential to demonstrate the rationale of the PDMS paradigm. We resort to an architecture for PDMSs based on Trusted Execution Environments to evaluate any classical user-deﬁned aggregate PDMS function. We show that an upper bound on leakage exists and we sketch remaining research issues.
Challenges Confronting Postgraduate Supervision at the National University of Lesotho:

Postgraduate supervision can be a challenging form of teaching, with the challenges further compounded by context or institutional-specific factors.The Faculty of Education at the National University of Lesotho has faced numerous challenges in offering postgraduate degrees, with a few qualified supervisors for the large number of students who enroll in these programmes. This study explored the nature of the challenges confronting postgraduate supervision in this context. It was underpinned by the interpretivist paradigm and employed a qualitative approach. Fifteen supervisors participated, of whom nine (three associate professors and six senior lecturers) were individually interviewed. Six junior lecturers took part in a focus group discussion. Guidelines on postgraduate supervision were also subjected to document review. The findings revealed that while some supervisors had positive experiences, others are frustrated. This is due to a lack of training, and lack of clarity on supervisory procedures, as well as the low academic literacy and lack of commitment exhibited by some students. It is recommended that supervisors should be capacitated through training. Furthermore, supervisory guidelines should be reviewed and benchmarked against best practices in the field. Key words: Postgraduate supervision, constructivism, reflective practice, supervisory guidelines, challenges in supervisory practice
Upscaling of traditional fermented foods to build value chains and to promote women entrepreneurship

The IFAD Research Series has been initiated by the Strategy and Knowledge Department in order to bring together cutting-edge thinking and research on smallholder agriculture, rural development and related themes. As a global organization with an exclusive mandate to promote rural smallholder development, IFAD seeks to present diverse viewpoints from across the development arena in order to stimulate knowledge exchange, innovation, and commitment to investing in rural people. Abstract To date, many efforts to eradicate hunger focus on a food systems approach and the improvement of local food processing, of which the optimization of the accompanying value chains is an important aspect. In Africa in particular, the agrifood sector holds great potential for bottom-up entrepreneurship, with its potential to create jobs, generate innovations and serve as a vehicle for economic and social empowerment of women and youth. Many local and traditional foods are nutritious and widely attractive, yet have not reached the broad range of potential consumers due to small-scale processing and a lack of effective value chains. Among the local foods, traditional fermented foods are of special interest. Fermentation is an ancient processing technique that relies on the activity of microbes to transform raw materials into attractive products with increased food safety, commercial value, nutritional value and health features and improved sensory attributes. Small-scale fermentation activities represent an important economic opportunity, particularly for women: entry barriers and start-up costs are low, no possession of particular assets is required, and it can be combined with domestic responsibilities. Despite the obvious benefits, traditional fermentation remains a neglected, small-scale and underexploited practice in many countries, presenting a missed opportunity for the promotion of food and nutrition security and livelihoods. In our contribution, we elaborate on how the existing traditional processing of non-alcoholic fermented foods can be upscaled while enhancing and exploiting functional food properties and strengthening local value chains through fostering women entrepreneurship.