Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the effect of Brand Personality on Brand Loyalty: Mediating Role of Customer Satisfaction

Purpose - This study conceptualizes the measuring effect of brand personality along with its five dimensions on brand loyalty with the help of customer satisfaction played the role of mediation among the users of Samsung cell-phones. Design/MethodologyApproach - The study was in quantitative nature. Data was collected from 418 respondents from the different cities of Sindh like Karachi, Hyderabad, Mirpurkhas, and Sukkur through a survey questionnaire, and data was analyzed through smart PLS Software by applying the Structural Equation Modeling (SEM) technique. Findings - The findings executed that the keeping in view all other things constant one unit change in brand personality cause a change of 0.919 in customer satisfaction, one unit change in brand personality cause a change of 0.879 in brand loyalty and one unit change in customer satisfaction will cause a change of 0.095 in brand loyalty. As the results suggest that the brand personality has a positive strong relation with brand loyalty directly, here customer satisfaction has played the role of partial mediator between brand personality and brand loyalty at very weak relations with brand loyalty. Thus increasing error is at the minimum position because all the values are lying on the positive axis. Practical Implications - This study strongly assists marketers to improve customer loyalty strategies. This study has figured out the clear picture for marketers that if the customers perceive highly based on brand personality so it would be nice for marketers to strengthen the brand personality attributes for brand loyalty. Origionality/Value - This study is not only emphasized the brand personality but also put an in-depth eye on customer satisfaction. In this study, customer satisfaction played a partial mediating role because of the positive and strong relationship found between brand personality and brand loyalty.
First-Principles Study of Molecular Adsorption on Lead Iodide Perovskite Surface: A Case Study of Halogen Bond Passivation for Solar Cell Application

Organic molecules have recently been used to modify the surface/interface structures of lead halide perovskite solar cells to enhance device performance. Yet, the detailed interfacial structures and adsorption mechanism of the molecular modified perovskite surface remain elusive. This study presents a nanoscopic structural view on how organic molecules interact with the perovskite surface. We focus on the halogen bond passivated lead iodide perovskite surface, based on first-principles calculations. Our calculations show that organic molecules can interact with the perovskite surface via halogen bonds, which modifies the interfacial structures of the perovskite surface. We also constructed a detailed potential energy surface of the perovskite surface by moving the adsorbed molecule along different axes of the unit cell in order to comprehensively understand perovskite surface structures. This study demonstrates the effectiveness of modifying the perovskite surface structure via a molecular adsorption appr.
Lightweight and real-time object detection model on edge devices with model quantization.

Environment perception is of vital importance in autonomous driving as it serves as autonomous car’s “eye” to perceive the surrounding environment. We propose a solution to achieve real-time environment perception on resource constrained edge devices like mobile phones, even though they have limited computation resources. We used float-to-int quantization based on TensorFlow Lite to quantilize the SSD model. TensorFlow Lite achieves accuracy lossless quantization with quantization-aware training by inserting fake quant operator during training, which imitates the loss introduced by quantization in feedforward training. Our experiment shows that superiority of our proposed method, which is highly speed, light weight and relatively high accuracy.
Optimal Current Modeling and Identification for Fast and Efficient Torque Ripple Minimization of PMSM Using Theoretical and Experimental Models

For a permanent magnet synchronous machine (PMSM), torque ripple is a critical issue, especially under low speeds. Torque ripple minimization (TRM) control aims to find the optimal currents to be injected to cancel the existing torque ripple. In this article, we propose an optimal current modeling technique for the TRM control. In the proposed technique, torque harmonics are modeled by using a set of polynomials in which their degree parameters are determined by the theoretical torque model and their coefficients are determined by the experimental torque model. The optimal current model is derived from the identified polynomials to minimize the torque harmonics and machine loss in which there is no need of machine parameters. The proposed approach is both computation and memory efficient as the optimal currents are calculated from equations, which eliminates the need for the time-consuming optimization procedure and memory-consuming lookup table. The proposed approach is fast and efficient for the TRM control, which is critical to practical applications with fast-changing loads. Extensive experiments and comparisons are conducted on a laboratory PMSM to validate the proposed modeling technique under various operating conditions.
Graphical-model based high dimensional generalized linear models

: We consider the problem of both prediction and model selection in high dimensional generalized linear models. Predictive performance can be improved by leveraging structure information among predictors. In this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph structure among the predictors. The graphic information among predictors is incorporated node-by-node using a decomposed representation and the sparsity is encouraged both within and between the decomposed components. We propose an eﬃcient iterative proximal algo-rithm to solve the optimization problem. Statistical convergence rates and selection consistency for the doubly sparse regularized estimator are established in the ultra-high dimensional setting. Speciﬁcally, we allow the dimensionality grows exponentially with the sample size. We compare the estimator with existing methods through numerical analysis on both simulation study and a microbiome data analysis.
A Blind Spectral Unmixing in Wavelet Domain

In this article, a wavelet-based energy minimization framework is developed for joint estimation of endmembers and abundances without assuming pure pixels while considering noisy scenario. Spectrally dense and overlapped hyperspectral data are represented using biorthogonal wavelet bases that yield a compact linear mixing model in the wavelet domain. It acts as the data term and helps to reduce solution space of the unmixed components. Three prior terms are incorporated to better handle the ill-posedness, i.e., the logarithm of determinant volume regularizer enforces minimum endmember simplex, smoothness (spatial) prior to individual abundance maps, and spectral constraint through learning dictionary of abundances. Alternating nonnegative least-squares is employed to optimize the regularized and constrained nonnegative matrix factorization functional in the wavelet domain. We conduct theoretical analysis, discuss convergence and algorithmic details. Experiments are conducted on synthetic and three real benchmark hyperspectral data AVIRIS Cuprite, HYDICE Urban, and AVIRIS Jasper Ridge. The efficacy of the proposed algorithm is evaluated by comparing results with state of the art.
E-assessment-beyond the traditional assessment in digital environment

An assessment is an integral part of the learning process, which determines whether and to what extent the learning objectives have been achieved. It indicates whether the used tools, technologies and pedagogical approaches have produced positive results. Today technologies are implemented in all stages and activities in the learning process. They allow teachers to diverse, enrich and develop traditional assessment methods. E-assessment guarantees rapid, instant, automated results and feedback and the implementation of new approaches for assessing learners’ knowledge. Learning Management Systems offer a variety of tools for e-assessment and provide the opportunities to integrate assessment exercises into other learning activities. The aim of the current work is to present some of the innovative assessment tools in Learning Management Systems illustrated by LMS Moodle. They allow the implementation of new assessment approaches that differ from traditional ones. They prove the thesis that e-assessment is not equivalent to traditional assessment carried out in a digital environment and provides new attractive opportunities and techniques for evaluating various aspects and levels of knowledge, skills and competencies.
Acne: Advocating for holistic support

As health practitioners, having a holistic approach to patients is critical. Patients are a complex interplay of physical, emotional, and social well-being. Limiting treatments to only some of these domains will inevitably yield suboptimal health and reduced quality of life. Thus, it is imperative that we advocate for acne-prone individuals so that they can receive the well-rounded care they deserve.
Computed tomography-based flap brachytherapy for non-melanoma skin cancers of the face

Purpose Non-melanoma skin cancers of the face are at high-risk for local recurrence and metastatic spread. While surgical interventions such as Mohs microsurgery are considered the standard of care, this modality has the potential for high rates of toxicity in sensitive areas of the face. Catheter flap high-dose-rate (HDR) brachytherapy has shown promising results, with high rates of local control and acceptable cosmetic outcomes. Material and methods Patients with non-melanoma skin cancers (NMSC) located on the face were treated with 40 Gy in 8 fractions, given twice weekly via catheter flap HDR brachytherapy. Clinical target volume (CTV) included the visible tumor plus a margin of 5 mm in all directions, with no additional planning target volume (PTV) margin. Results Fifty patients with 53 lesions on the face were included, with a median follow-up of 15 months. All were considered high-risk based on NCCN guidelines. Median tumor size and thickness were 18 mm and 5 mm, respectively. Median PTV volume and D90 were 1.7 cc and 92%, respectively. Estimated rate of local control at twelve months was 92%. Three patients (5%) experienced acute grade 2 toxicity. Two patients (4%) continued to suffer from chronic grade 1 skin toxicity at 12 months post-radiotherapy (RT), with an additional two patients (4%) experiencing chronic grade 2 skin toxicity. Forty-nine lesions (92%) were found to have a good or excellent cosmetic outcome with complete tumor remission. Conclusions CT-based flap applicator brachytherapy is a valid treatment option for patients with NMSC of the face. This modality offers high rates of local control with acceptable cosmetic outcomes and low rates of toxicity.
Long Term Evaluation of Biodegradation and Biocompatibility In-Vivo the Mg-0.5Ca-xZr Alloys in Rats

Biodegradable alloys in Mg have the advantages of traditional metallic materials and those of biodegradable polymers with superior strength, lower density and ideal rigidity for fixing bone fractures. The biocompatibility and biodegradability of the five concentrations of Mg-0.5Ca-xZr alloys used were assessed using clinical and laboratory examinations that followed over time: tissue reaction, histological and imaging (RX, CT and SEM) evolution at 1, 2, 4 and 8 weeks after implant. The main purpose of this study was to investigate in vivo the long-term effect of Mg-0.5Ca-xZr alloys in rats. The results confirmed that Mg-0.5Ca-xZr alloys are biocompatible and biodegradable and are recommended to be used as possible materials for new orthopedics devices.
The impact of hybridization on upper first molar shape in robust capuchins (Sapajus nigritus x S. libidinosus)

To better understand the impact of hybridization on development and morphology, I analyze an understudied phenotype in hybrid morphology research: tooth shape. I apply a 2D geometric morphometric approach to compare variation in first upper molar cusp tip positions and crown outline shape among 31 crested capuchins (Sapajus nigritus), 37 bearded capuchins (S. libidinosus), and 44 hybrids (S. nigritus x S. libidinosus). A principal components analysis shows that group membership accounts for a significantly greater proportion of variance along the first major axis of M1 shape variation than does allometry. While most hybrids have S. nigritus-like M1s, several possess a transgressive M1 shape not observed in either parental species. Procrustes distances are greater in hybrids compared to the parental capuchins, and two-block partial least squares analyses show that hybrids exhibit weaker integration between cusp tip positions and crown outline shape. These results demonstrate that hybridization generates novel M1 shapes and support the hypothesis that destabilized development results in elevated phenotypic variance in hybrids. Further studies of dental shape in hybrid primates will generate important data for on-going efforts to detect potential hybrids in the hominin fossil record and to understand the evolutionary outcomes of anthropogenic hybridization.
ProSAS: Proactive Security Auditing System for Clouds

The multi-tenancy in a cloud along with its dynamic and self-service nature could cause severe security concerns, such as isolation breaches among cloud tenants. To mitigate such concerns and ensure the accountability and transparency of the cloud providers towards their tenants, verifying cloud states against a list of security policies, a.k. a. security auditing, is a promising solution. However, the existing security auditing solutions for clouds suffer from several limitations. First, the traditional auditing approach, which is retroactive in nature, can only detect violations after the fact and hence, often becomes ineffective while dealing with the dynamic nature of a cloud. Second, the existing runtime approaches can cause significant delay in the response time while dealing with the sheer size of a cloud. Finally, the current proactive approaches typically rely on prior knowledge about future changes in a cloud and also require significant manual efforts, and thus become less practical for a dynamic environment like cloud. To address those limitations, we present a novel proactive security auditing system, namely, ProSAS, which can prevent violations to security policies at runtime with a practical response time, and yet does not require prior knowledge about future changes. More specifically, ProSAS first establishes its models (e.g., dependency relationships between cloud events, and critical events) through learning from historical data (e.g., logs); it then predicts future critical events which would likely follow a received event by leveraging the dependency relationships; afterwards, it proactively verifies the impacts of those future events, and prevents those events which can cause violations of security policies. ProSAS is integrated into OpenStack, a popular cloud management platform, and we provide a concrete guideline to port ProSAS to other popular cloud platforms, such as Google Cloud Platform, and Amazon EC2. Our experiment results using both real and synthetic data demonstrate the improvement of efficiency (i.e., reducing response time to 1,450 nanoseconds at best and 8.5 milliseconds on average for a large-scale cloud with 10,000 tenants) and level of automation (i.e., learning more than 20 new critical events spanning 100 days) in proactive security auditing by ProSAS.
Effectiveness of entrepreneurial universities: experiences and challenges in digital era (a systemic approach)

2021. ABSTRACT The modern world actively discusses the challenges that will lead to the Fourth Industrial Revolution. As the specialists assume, part of the jobs will be replaced by artificial intelligence. In addition, creation of completely new jobs is expected. As for the jobs that might be maintained in future, skills upgrading will be particularly important. In such a turbulent environment of economic development, universities, and in particular entrepreneurial universities, play a significant role. We think so as these universities have special structures that promote form a university-industry-government triple helix, reskilling and commercialization of new ideas, etc. Due to the above-mentioned factors, they will neutralize the expected fluctuations in the best way. Later we will discuss the systemic links between the mutual influence of entrepreneurial universities and economy. We consider that continuous connection with such universities will enable business to adapt to the inevitable changes with minimal
Effect of empowerment program on basic nursing skills of novice nurses in Iran: A randomized control trial study

Abdolbaset Rigimotlagh1 , Mohammadreza Firouzkouhi2 , Ali Mansouri3 , and Abdolghani Abdollahimohammad2, , 1 Khash Institute of Health Higher Education, Zahedan University of Medical Sciences, Zahedan, Iran 2 Medical-Surgical Nursing Department, Nursing and Midwifery Faculty, Zabol University of Medical Sciences, Zabol, Iran 3 Community Health Department, Nursing and Midwifery Faculty, Zabol University of Medical Sciences, Zabol, Iran
Ambient air pollution and emergency department visits among children and adults in Casablanca, Morocco

This study presents the relationships between ambient air pollutants and morbidity and emergency department visits among children and adults performed in Great Casablanca, the most populated and economic region in Morocco. This research was analyzed using conditional Poisson model for the period 2011–2013. In the period of study, the daily average concentrations of SO2, NO2, O3 and PM10 in Casablanca were 209.4 µg/m3, 61 µg/m3, 113.2 µg/m3 and 75.1 µg/m3, respectively. In children less than 5 years old, risk of asthma could be increased until 12% per 10 µg/m3 increase in NO2, PM10, SO2 and O3. In children over 5 years and adults, an increase of 10 µg/m3 air pollutant can cause an increase until 3% and 4% in respiratory consultations and acute respiratory infection, respectively. Similarly, impact on emergency department visits due to respiratory and cardiac illness was established. Our results suggest a not negligible impact on morbidity of outdoor air pollution by NO2, SO2, O3, and PM10.
Phytochemical Screening and Spectroscopic Determination of Total Phenolic andFlavonoid Contents of Eclipta Alba Linn

This research aims to detect the Phytochemical Screening and in-vitro total phenolic and flavonoids contents of chloroform and methanolic extracts of Eclipta Alba. All the main phytoconstituents were present in methanolic extract as compared to chloroform extract. The total phenolic content and total flavonoid content were determined by Folin–Ciocalteu and aluminum chloride methods respectively. The total phenolic contents of methanolic extract (269.5±0.5mg/g) and chloroform (182±0.5 mg/g), were expressed as Gallic acid equivalents The flavonoid contents were found to be (139.5±0.5mg/g) and (85.666±0.2886mg/g) in methanol and chloroform extracts respectively using rutin equivalents. The results are in concurrence with the phytochemical properties present in the plant.
Impact of Phase Angle Jump on DFIG Under LVRT Conditions: Challenges and Recommendations

The increased penetration of wind power has made its low voltage ride through (LVRT) capability more crucial than ever. Currently, the LVRT performance of wind generators is verified using experimental voltage sags with different magnitudes and durations. The actual fault, however, also causes the phase angle jump (PAJ) of the grid voltage. In this paper, the impact of PAJ on the LVRT capability of doubly fed inductor generator (DFIG) under three-phase fault is analyzed. To achieve this, detailed analytical calculations and experimental verifications are conducted. The results demonstrate that the PAJ imposes more challenges on the LVRT by intensifying the rotor overcurrent through the natural stator flux. As a result, DFIGs that have passed the existing LVRT test may still fail to comply with the grid code requirements in practice. To tackle this issue, an improved test procedure is recommended. The new procedure considers the impedance angle of the target system as an additional test condition, thus can reveal the LVRT performance of DFIG under the potential PAJ.
Approach of Different Properties of Alkylammonium Surfactants using Artificial Intelligence and Response Surface Methodology

Abstract Response surface methodology (RSM) and artificial neural networks (ANNs) architectures to predict the density, speed of sound, kinematic viscosity, and surface tension of aqueous solutions were developed. All models implemented using the root mean square error (RMSE) for training and validation phase were evaluated. The ANN models implemented show good values of R2 (upper than 0.974) and low errors in terms of average percentage deviation (APD) (lower than 2.92 %). Nevertheless, RSM models present low APD values for density and speed of sound prediction (lower than 0.31 %) and higher APD values around 5.18 % for kinematic viscosity and 14.73 % for surface tension. The results show that the different individual artificial neural networks implemented are a useful tool to predict the density, speed of sound, kinematic viscosity, and surface tension with reasonably accuracy.
Temperature Influence on the Calcium Phosphate Coatings by Chemical Method

The increasing interest in the use of brushite and monetite as resorbable calcium phosphate cements or graft materials is related to the fact of these phases being metastable under physiological environment, with higher solubility than hydroxyapatite phase. In this study, X-ray diffraction (XRD) and scanning electron microscopy with field emission gun (FEG-SEM) analyses were performed in order to assess the temperature influence on the production of calcium phosphate coatings by a chemical deposition method. Titanium substrates were successfully coated with brushite and monetite by a chemical deposition method and a brushite-monetite transformation was assessed with the increasing temperature. Brushite deposition was kinetically favored at low temperatures, whereas monetite was the major phase at higher temperatures.