Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Branding Strategies and Effects in the U.S Market

The traditional branding methods of emphasizing the material effects of the products have been proved unsuccessful in todays U.S marketing environment. Consumers are exposed to an increased amount of information on digital media platforms. Emotional branding is a marketing technique that can generate demands in this ever-changing market environment. Emotional branding appeals to consumers emotions and therefore stimulates their purchasing behaviors. There are various strategies within the concept of emotional branding. The three frequently discussed emotional branding strategies are forming brand communities, attaching prosocial aspects to the products, and providing customized services. Those strategies are employed by successful brands in the United States. This paper will analyze those strategies and their applications in detail. Psychological theories are helpful in explaining the effect of emotional branding. Maslows model of human needs evaluates the factors that meet consumer needs and expectations during the branding process. Each emotional branding strategy mentioned in this paper can target a need from the top levels of Maslows model, including social, esteem, and self-actualization needs. In this paper, case studies in the market in the United States are used to address the application of both branding strategies and Maslows theory. With the consideration of consumer expectations, emotional branding strategies are effective in forming bonds between brands and consumers and increasing brand loyalties.
A Comprehensive Survey of RGB-Based and Skeleton-Based Human Action Recognition

With the advancement of computer vision, human action recognition (HAR) has shown its broad research worth and application prospects in a wide range of fields such as intelligent security, automatic driving and human-machine interaction. Based on the type of data captured by cameras and sensors, e.g., RGB, depth, skeleton, and infrared data, HAR methods can be classified into RGB-based and skeleton-based. RGB data is easy and inexpensive to obtain, but RGB-based methods need to cope with a large amount of irrelevant background information and are easily affected by factors such as lighting and shooting angle. The skeleton-based methods eliminate the impact of background variables and require little computational work due to their skeleton-focused features, but they lack the context data necessary for HAR. This paper gives a thorough survey of these two approaches, covering deep learning methods, handcrafted feature extraction methods, common datasets, challenges, and future research directions. The skeleton-based action recognition methods Section specifically presents the most well-liked 2D and 3D pose estimation algorithms. This survey aims to give researchers new to the area or engaged in a long-term study a selection of datasets and algorithms, as well as an overview of the present issues and expected future directions in the field.
Molecular Simulation Study on the Wettability of a Surface Texturized with Hierarchical Pillars

By using molecular dynamics simulation, we investigate the wettability of a surface texturized with a periodic array of hierarchical pillars. By varying the height and spacing of the minor pillars on top of major pillars, we investigate the wetting transition from the Cassie–Baxter (CB) to Wenzel (WZ) states. We uncover the molecular structures and free energies of the transition and meta-stable states existing between the CB and WZ states. The relatively tall and dense minor pillars greatly enhance the hydrophobicity of a pillared surface, in that, the CB-to-WZ transition requires an increased activation energy and the contact angle of a water droplet on such a surface is significantly larger.
Subgrouping in a ‘dialect continuum’: A Bayesian phylogenetic analysis of the Mixtecan language family

Subgrouping language varieties within dialect continua poses challenges for the application of the comparative method of historical linguistics, and similar claims have been made for the use of Bayesian phylogenetic methods. In this article, we present the first Bayesian phylogenetic analysis of the Mixtecan language family of southern Mexico and show that the method produces valuable results and new insights with respect to subgrouping beyond what the comparative method and dialect geography have provided. Our findings reveal potential new subgroups that should be further investigated. We show that some unexpected groupings raise important questions for phylogenetics and historical linguistics about the effects of different methods of primary data gathering and organization that should be considered when interpreting subgrouping results.
A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.
Visualizing multiple results from Nonlinear CUB models with R grid Viewports

Nonlinear CUB models have been recently introduced in the literature to model rating or ordinal data. They extend the standard CUB(Combination of Uniform and Binomial), which is a mixture modelcombining a discrete Uniform and a Shifted Binomial random variables. Unlike CUB, Nonlinear CUB models account for the unequal spacing among response categories. Nonlinear CUB can be effectively used in a variety of fields, for example whenever questionnaires with questions having ordered response categories are used to measure human perceptions and attitudes. This paper proposes a new graphical representation, which works with R grid Viewports in order to summarize multiple results from Nonlinear CUB models in a unique plot. A case study on the perceived risk in fraud management ispresented.
Higher-Order Nonlinear Vibration Analysis of Timoshenko Beams by the Spline-Based Differential Quadrature Method

Higher-order nonlinear vibrations of Timoshenko beams with immovable ends are studied. The nonlinear effects of axial deformation, bending curvature and transverse shear strains are considered. The nonlinear governing differential equations are solved using a spline-based differential quadrature method (SDQM), which is constructed based on quartic B-splines. Ratios of the nonlinear to the linear frequencies are extracted and their variations with the ratio of amplitude to radius of gyration are examined. In contrast to the well-recognized finding for the nonlinear fundamental frequency of beams, some higher-order nonlinear frequencies decrease with the increase of ratio of amplitude to radius of gyration.
Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System

Smart wearable patch systems that combine biosensing and therapeutic components have emerged as promising approaches for personalized healthcare and therapeutic platforms that enable self-administered, noninvasive, user-friendly, and long-acting smart drug delivery. Sensing components can continuously monitor physiological and biochemical parameters, and the monitoring signals can be transferred to various stimuli using actuators. In therapeutic components, stimuli-responsive carrier-based drug delivery systems (DDSs) provide on-demand drug delivery in a closed-loop manner. This review provides an overview of the recent advances in smart wearable patch systems, focusing on sensing components, stimuli, and therapeutic components. Additionally, this review highlights the potential of fully integrated smart wearable patch systems for personalized medicine. Furthermore, challenges associated with the clinical applications of this system and future perspectives are discussed, including issues related to drug loading and reloading, biocompatibility, accuracy of sensing and drug delivery, and largescale fabrication.
Entrepreneurial Competence Development Program: Implementing Efficiency through Knowledge Sharing

The goal of this study was to investigate the impact of introducing methods used in other countries on student success. The results suggested that the introduction of specific practices to improve entrepreneurial competence would be more effective forgoing such special training methods. This was validated by the results of a test survey conducted by an expert panel. According to the panel, the quality of knowledge among students in the groups in which new methods were introduced was as follows: 7.5 points on average out of 10 for the first group; 7.9 points on average out of 10 for the second group; 7.8 points on average out of 10 for the third group; and 6.4 points on average out of 10 for the fourth group. These data suggest that new practices improve students’ knowledge and skills. The students themselves also assessed the improvement in their quality of knowledge. More specifically, the students in the second group gave a very high average score of 4.4 out of a possible 5 for the effectiveness of the European methods. The same parameter was rated 3.4 out of 5 points by the fourth group.
Dendrimers: Synthesis, Encapsulation Applications and Specific Interaction with the Stratum Corneum—A Review

Dendrimers are increasingly being studied in the context of encapsulation. Many potential applications of dendrimers are based on their properties. They are used in drug delivery systems, cosmetics, food and chemistry. This review is first devoted to different synthesis approaches for dendrimers and to their ability to encapsulate active molecules. Their applications in different fields, as well as their cytotoxicity, are then detailed. To conclude this review, the main works on the interaction of dendrimers with the stratum corneum (SC) are also presented.
Bethe graphs attached to the vertices of a connected graph - a spectral approach

A weighted Bethe graph B is obtained from a weighted generalized Bethe tree by identifying each set of children with the vertices of a graph belonging to a family F of graphs. The operation of identifying the root vertex of each of r weighted Bethe graphs to the vertices of a connected graph of order r is introduced as the -concatenation of a family of r weighted Bethe graphs. It is shown that the Laplacian eigenvalues (when F has arbitrary graphs) as well as the signless Laplacian and adjacency eigenvalues (when the graphs in F are all regular) of the -concatenation of a family of weighted Bethe graphs can be computed (in a unified way) using the stable and low computational cost methods available for the determination of the eigenvalues of symmetric tridiagonal matrices. Unlike the previous results already obtained on this topic, the more general context of families of distinct weighted Bethe graphs is herein considered.
Switching Text-Based Image Encoders for Captioning Images With Text

Visual understanding, such as image caption generation, has received extensive attention. Describing images with textual information is one way to help people achieve barrier-free visibility. This study focuses on the text-based image captioning (TextCaps) task. The TextCaps task is more complex than the traditional image captioning task because it depends on optical character recognition (OCR) and the textual information that appears in the image. It also requires consideration of the relationship between recognized objects and OCR’s linguistic part in the image. In this study, we propose maximizing the use of multiple modalities in an image to improve TextCaps performance. We enrich the image and OCR linguistic features using pre-trained Contrastive Language-Image Pre-training (CLIP) models. We then introduce using two additional attention models in a transformer architecture to strengthen the representation of the image modality. The experimental results demonstrate that our proposed method, which introduces a multimodal transformer with four image-related modalities, outperforms existing methods for the TextCaps dataset.
Self Tuning Mechanism using Input Scaling Factors of PI like Fuzzy Controller for Improved Process Performance

Design of fuzzy controller includes mainly selection of rule base, membership functions election and scaling factors. In some applications fixed value of scaling factors generates oscillations in process response. In this paper, an algorithm for tuning of scaling factors of fuzzy PI (proportional-integral) controller based on rule base is proposed. The input scaling factors are tuned by updating factors whose values are obtained using rule base with the error and change in error as inputs. The rules are designed for tuning the scaling factors based on the performance measures such as peak overshoot (OV), Rise time (RT) and Integral of square error (ISE). The performance comparison of conventional FLC with self tuned fuzzy controller has been done in terms of several performance measures such as peak overshoot, settling time, rise time and integral square error (ISE). In addition to the responses due to step set-point change, a disturbance is also added in some systems. Simulation results show the effectiveness and robustness of the proposed self tuning mechanism. A simulation analysis of a wide range of processes with and without delay time is carried out and comparison of results shows improvement due to self tuning of fuzzy controller.
Breeding Hope during the Crisis: A Study on Unemployed Laborers’ Willingness to Receive Educational Compensation under the Chinese “Zero-COVID” Policy

Under the Chinese “Zero-COVID” policy, many laborers were forced out of work. Participating in educational compensation can effectively help unemployed laborers achieve re-employment. This paper selected Jining, Shandong Province, as the research area, analyzed the data using a questionnaire survey and key interviews, and observed factors that affect and change willingness to receive educational compensation (WTEC) and the willingness to accept the training duration (WTTD) of unemployed laborers. The study found that 77.78% of unemployed laborers are willing to receive educational compensation, and the WTTD is 12.05 days. Among them, eight factors affect WTEC, such as the years of education, the duration of unemployment, whether there are dependents in the family, and the family’s size. Nine factors such as gender, age, education, unemployed duration, dependents, and other supportable incomes affect WTTD. Based on this, by taking measures from the government and laborers, WTEC and WTTD can be improved, and then the rate of re-employment can be enhanced, and finally, the employment problem can be ameliorated.
Preliminary Reports on the Experiences of Mothers in the Early Introduction of Complementary Feeding to Their Infants: a Phenomenological Study

Objective − The objective of the study was to explore the experiences of mothers with the early complementary feeding to infants in the IV Koto Sub-district, Agam District, West Sumatera Province of Indonesia.Materials and Methods − Qualitative methods, with a phenomenological approach were used in this study. This study was carried out from September to November 2022 in District IV Koto, Agam Regency. Ten mothers of infant aged 6-23 months who introduced complementary foods to their infants before six months of age were included in this study using a purposive sampling technique. Mothers who were not able to communicate well were excluded from this study. Semi-structured individual interviews were conducted to collect the data. Data analysis used conventional content analysis.Results − The experience of mothers in early complementary feeding of their infants were explored using five themes. These findings were that mothers perceived the benefits of early complementary feeding, infant cues, the need to work outside the home, insufficient breastmilk supply, receiving advice regarding early complementary feeding from grandmothers, and cultural beliefs.Conclusion − Mothers perceived the benefit of early complementary feeding for their infants and themselves. Therefore, health professionals should provide assistance and counseling to mothers breastfeeding their babies so they can continue with exclusive breastfeeding and introduce complementary foods at the right time.
Comparison of four different nutritional risk screening tools in hospitalized children

ABSTRACT Objective: Early detection of malnutrition risk in hospitalized children can improve health outcomes and quality of life; however, the number of studies where the pediatric screening tool is appropriate for Turkish children is limited. Therefore, this article aims to determine the prevalence of malnutrition risk in pediatric patients evaluated with Screening Tool for Risk on Nutritional Status and Growth, Screening Tool for the Assessment of Malnutrition in Pediatrics, Pediatric Yorkhill Malnutrition Score, and Simple Pediatric Nutrition Screening Tool with original and adjusted cutoffs and to evaluate which pediatric screening tool is appropriate for Turkish children. Methods: In this cross-sectional study, four published nutritional risk screening tools (Screening Tool for Risk on Nutritional Status and Growth, Screening Tool for the Assessment of Malnutrition in Pediatrics, Pediatric Yorkhill Malnutrition Score, Pediatric Nutrition Screening Tool) were applied to pediatric inpatients (n=604) aged 1 month to 17 years, admitted to a pediatric ward for at least 24 hours. Results: Pediatric Nutrition Screening Tool with adjusted cutoffs had the greatest recognition rate (94.2%) of acute malnutrition. Having a high nutritional risk by Pediatric Yorkhill Malnutrition Score was associated with an increased risk of acute (OR: 6.57 for Screening Tool for Risk on Nutritional Status and Growth, 5.84 for Screening Tool for the Assessment of Malnutrition in Pediatrics, and 20.35 for Pediatric Yorkhill Malnutrition Score) and chronic malnutrition (OR: 1.27 for Screening Tool for Risk on Nutritional Status and Growth, 3.28 for Screening Tool for the Assessment of Malnutrition in Pediatrics, and 1.72 for Pediatric Yorkhill Malnutrition Score). Classifying the at-risk category by the Pediatric Nutrition Screening Tool was related to raised odds of malnutrition (OR: 2.64 for original and 5.24 for adjusted cutoffs). This positive association was also observed for acute (OR: 4.07 for original cutoffs, and 28.01 for adjusted cutoffs) and chronic malnutrition (OR: 1.14 for original cutoffs, and 1.67 for adjusted cutoffs). Conclusion: Pediatric Nutrition Screening Tool with adjusted cutoffs and Pediatric Yorkhill Malnutrition Score have higher diagnostic accuracy than other screening tools in assessing the nutritional status of hospitalized Turkish children and detecting children, particularly with acute malnutrition.
Efficient circuit simulation via adaptive moment matching and matrix exponential techniques

of thesis entitled Efficient Circuit Simulation Via Adaptive Moment Matching and Matrix Exponential Techniques Submitted by Wenhui ZHAO for the degree of Master of Philosophy at the University of Hong Kong in December 2013 This dissertation presents two efficient circuit simulation techniques for very large scale integrated (VLSI) circuits. Model order reduction (MOR) plays a significant role in VLSI circuit simulation as nowadays the system model may contain millions of equations or variables. MOR is needed to reduce the order of the original system to allow the simulation to be performed with an acceptable amount of time, reasonable storage and reliable accuracy. Multi-point moment matching is one of the state-of-the-art methods for MOR. However, the moment order and expansion points are usually selected in a heuristic way, which cannot guarantee the global accuracy of the reduced-order model (ROM). Therefore, it is important to utilize an adaptive algorithm in exercising multi-point moment matching. In this regard, we propose a novel automatic adaptive multi-point moment matching algorithm for MOR of linear descriptor systems. The algorithm implements both adaptive frequency expansion point selection and automatic moment order control guided by a transfer function-based error metric. Without a priori information of the system response, the proposed algorithm leads to a much higher global accuracy compared with standard multipoint moment matching without adaptation. The moments are computed via a generalized Sylvester equation which is subsequently solved by a newly proposed generalized alternating direction implicit (GADI) method. Another technique for circuit simulation proposed in this thesis is the matrix exponential (MEXP) method. MEXP method has been demonstrated to be a competitive candidate for transient simulation of VLSI circuits. Nevertheless, the performance of MEXP based on ordinary Krylov subspace is unsatisfactory for stiff circuits, because the underlying Arnoldi process tends to oversample the high magnitude part of the system spectrum while undersampling the low magnitude part that is important to the final accuracy. In this thesis, we explore the use of extended Krylov subspace to generate more accurate and efficient approximation for MEXP. We also develop a formulation, called generalized extended Krylov subspace, that allows unequal positive and negative dimensions in the subspace for better performance, and propose an adaptive scheme based on the generalized extended Krylov subspace to select the ratio between the positive and negative dimensions. (An abstract of 347 words) Efficient Circuit Simulation Via Adaptive Moment Matching and Matrix Exponential Techniques by Wenhui ZHAO ( 趙文慧 ) A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Philosophy at The University of Hong Kong. December 2013