Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Pursuit of Innovation and Efficiency in Complex Engineering Projects—A Study of the Antecedents and Impacts of Ambidexterity in Project Teams

Projects, as the common unit of organizing complex tasks, often feature unique challenges that require both radical innovation and incremental improvements to processes and solutions in order to deliver the project within constraints. Both exploration and exploitation are likely to be needed for complex engineering projects to succeed. This study, through an in-depth case study of a complex infrastructure project facing unique challenges and tight budget and schedule, investigates the antecedents of ambidexterity and the effect of ambidexterity on project performance. The main contributions of this research include finding: (1) ambidexterity at the project team level to be a significant contributor to project performance; (2) the effects of temporal separation and project context on project performance are mediated by the project team's degree of ambidexterity; and (3) contrary to the prediction from organizational literature, structural separation has not contributed to the case project team's ambidexterity. The authors believe that among the three antecedents for ambidexterity at the project level as predicted by organizational literature, structural separation is rendered ineffective by the natural cycling between exploration and exploitation in the project environment and by the need to integrate the two under time pressure and resource constraints. The development of an effective project context can facilitate the temporal cycling between exploration and exploitation by creating awareness that overcomes the inertia in transitioning between the two by setting performance expectations and appropriate social context. Implications for theory development and practice are discussed.
The Impact of Research Collaboration Intention on Technology Transfer in Virtual Academic Communities: A Conditional Process Model

This paper aims to explore the impact of research collaboration intention on technology transfer in virtual academic community, as well as the mediating role of collaboration behavior and the moderating role of perceived ease of use and intellectual property protection between them. Based on the Theory of planned behavior (TPB) and Technology acceptance model (TAM), this paper constructed a conditional process model demonstrating the relationship between research collaboration intention, research collaboration behavior and technology transfer. A self-administered questionnaire was employed on researchers of muchong.com, jg.com. cn et al. to collect data; subsequently, 558 usable responses were analyzed using SPSS and Amos. The result showed that (1) Research collaboration intention has a significant positive impact on technology transfer; (2) research collaboration behavior partly mediates the relationship between research collaboration intention and technology transfer; (3) perceived ease of use positively moderates the relationship between research collaboration intention and research collaboration behavior; (4) perception ease of use and intellectual property protection have a moderated mediation between research collaboration intention and technology transfer. This model plays a positive role in promoting the research collaboration behavior and technology transfer of Chinese VACs researchers.
Computer aided architectural design based on BIM

At present, the design requirements and construction management requirements of modern buildings are higher, which not only meet people's needs for the environment, but also meet the practical functional indicators of buildings. With the help of BIM technology, the incongruity of traditional architectural design mode can be broken, and then the visibility of architectural design and the simulation of construction management can be realized. This paper expounds the advantages of BIM computer aided architectural design technology, analyzes the specific role of this technology in architectural engineering, constructs an integrated framework of architectural structural design model based on BIM, and preliminarily realizes the integration of architectural and structural model information through the developed model transformation platform, which provides reference for developing integrated architectural engineering software system based on BIM technology and improving the level of computer aided architectural design.
Intelligent Approaches in the Software Development Process: a Systematic Literature Mapping

Accessibility in computing, when well applied, allows for the inclusion of users and the breaking of barriers in the interaction process. However, approaches to support accessibility validation are commonly unknown or ignored. The objective of this work is to understand accessibility issues considered in the software development process. This research has carried out an initial literature systematic mapping. The result indicates that there are gaps in the use of smart approaches to assess accessibility during the software development process.
Application of Artificial Neural Network in Automated English Speech Scoring

Nowadays, automated speech scoring has been promoted quite much. Artificial Neural Networks are widely used in machine learning and scoring. In this paper, different deep learning models are adopted to solve problems in speech scoring process. RNN is good at Speech Noise Reduction, while CNN can be applied both to extracting sequence relations in speech and to recognizing English accent features. Deep Neural Network help train scoring models.
A New Islanding Detection Method using Phase-Locked Loop for Inverter-Interfaced Distributed Generators

This paper proposes a new islanding detection method for inverter-interfaced distributed generators (DG). To detect islanding conditions, this paper calculates the phase angle variation of the system voltage by using the phase-locked loop (PLL) in the inverter controllers. Because almost all inverter systems are equipped with the PLL, the implementation of this method is fairly simple and economical for inverter-interfaced DGs. The detection time can also be shortened by reducing communication delay between the relays and the DGs. The proposed method is based on the fact that islanding conditions result in the frequency and voltage variation of the islanded area. The variation depends on the amount of power mismatch. To improve the accuracy of the detection algorithm, this paper injects small low-frequency reactive power mismatch to the output power of DG.
Detection of Anomalous Values within TIA Project Data History for Industrial Control Systems

Attacks on industrial control systems (ICS) have been intensively studied during the last decade. Malicious alternations of ICS can appear in several different ways, e.g. in changed network traffic patterns or in modified data stored on ICS components. While several heuristics and machine learning methods have been proposed to analyze different types of ICS data regarding anomalies, no work is known that uses the data of Totally Integrated Automation (TIA) Portal for anomaly detection. TIA Portal is a popular software system for organizing the ICS, with which configuration and programming data can be viewed, changed and deleted. By saving the single project datasets historically, old versions of the current system configurations can be restored. In this initial work, we propose heuristics that detect anomalies in the TIA Portal data. In particular do we analyze the historical modifications within TIA Portal data by investigating long-term backups. Our approach covers both, changes to the data caused by infiltrated attacks as well as malicious changes made by employees who have direct access to the machines. We therefore started to examine real TIA Portal project data of an automotive manufacturer’s production line, covering a period of about three years of historical data, for various features that may indicate anomalies.
A procedure for the application of product modelling

A procedure for the application of product modelling, resulting from a recently concluded PhD project, is presented. The procedure focuses on the various design of products, where the basic product structure is well defined. The procedure is based on four theory elements: reference models for product modelling, work-study analysis, the task concept from systems theory, and the object-oriented paradigm. The procedure has been tested in a Danish company, and it has been shown via a project of moderate size (in this case using 700 engineering hours, including time for experience collection) that it is possible to get started and use product modelling in industrial companies. Furthermore, it has been demonstrated that the application of product models can save considerable engineering resources (1500-3000 hours per year). Another conclusion is that the product modelling activities are an important means for supporting and integrating engineering activities, as the product modelling activities provide engineers from design and methods engineering departments with valuable insight into the knowledge and procedures used for specifying products and as well as into manufacturing methods.
Controlling bullwhip and inventory variability with the golden smoothing rule

A major cause of supply chain deficiencies is the bullwhip effect. Supply chain managers experience this variance amplification in both inventory levels and replenishment orders. In this paper, we analyse a major cause of the bullwhip effect, namely the classical Order-Up-To (OUT) policy and suggest a remedy (a smoothing replenishment rule). In general, dampening variability in orders may have a negative impact on customer service due to increase in inventory variance. We quantify the variance of the Net Stock (NS) levels and compute the required safety stock as a function of the smoothing required. The contribution of this paper is the analysis of the tradeoff between bullwhip and inventory variance resulting in a 'golden smoothing rule'. The golden smoothing rule refers to the fact that the values of two proportional feedback controllers are set equal to the Golden Ratio. [Received 5 September 2006; Revised 22 January 2007; Accepted 13 March 2007]
A prognostic algorithm for machine performance assessment and its application

This paper explores a method to assess assets performance and predict the remaining useful life, which would lead to proactive maintenance processes to minimize downtime of machinery and production in various industries, thus increasing efficiency of operations and manufacturing. At first, a performance model is established by taking advantage of logistic regression analysis with maximum-likelihood technique. Two kinds of application situations, with or without enough historical data, are discussed in detail. Then, real-time performance is evaluated by inputting features of online data to the logistic model. Finally, the remaining life is estimated using an ARMA model based on machine performance history; degradation predictions are also upgraded dynamically. The results such as current machine running condition and the remaining useful life, are output to the maintenance decision module to determine a window of appropriate maintenance before the machine fails. An application of the method on an elevator door motion system is demonstrated.
A study on the application of music influence and genre through principal component analysis in big data system

The evolution of music is the development of human society, and music is closely related to human life. To explore the expression of musical influence, we created a directed network using the influence relationship between artists in Python. The out-degree of the nodes was then defined as the musical influence. The top ten most influential artists were selected, with The Beatles at the top of the list. Then, we extracted a subset of size 200 from influence_data by weight and plotted the subnetwork. To measure the similarity between genres, the data were downscaled by principal component analysis. We update the dataset based on five principal components that contribute a total of 91%. Ten artists were randomly selected from each genre, and cosine similarity was calculated based on pairs of components. After calculating the average, a similarity matrix between genres was constructed. The heat map shows that not all genres have higher intra-genre similarity than inter-genre similarity, but the influence of intra-genre is more obvious. Then, the box plots were used to identify the differences between genres.
Research on the Scheme Construction of Hotel Intelligent System Based on Smart City Concept

Social demand and the development of computer science and technology and informatization promote the demand of intelligent system engineering construction in hotels. Intelligent management includes hotel office intelligence, ordering intelligence, guest room intelligence, service intelligence, etc. This whole set of intelligent management system can adapt to modern development. Room temperature, humidity, lighting, etc. can be intelligently controlled, which is convenient for improving user experience. In recent years, the construction of "smart city" in China has developed rapidly. Many cities have implemented or formulated the construction scheme of "smart city", and the upgrading of urban informatization is developing rapidly on a large scale. How to use modern technology to realize modern management, integrate hotel management, and build the hotel into an intelligent comprehensive place is the key to the success of modern hotel management. Based on the concept of smart city, this paper analyzes the concept of intelligence, the background and the content of hotel intelligent construction, and studies the construction of hotel intelligent system.
Estimation for Seasonal Factors of Similarity-Based Traffic for Urban Roads in Florida

The Florida Department of Transportation (FDOT) estimates seasonal factors (SFs) by using data obtained from approximately 300 telemetry traffic monitoring sites, which are used in the calculation of annual average daily traffic (AADT) at portable traffic monitoring sites (PTMSs). Both the estimation and the application of seasonal factors are based on the consideration of similarities in the traffic characteristics of roads and on engineering judgment. FDOT desires a more objective and data-driven method to improve the accuracy of SF estimation for PTMSs. A previous study investigated traffic and land use data in Southeast and North Florida and demonstrated the possibility of identifying the link between land use variables and seasonal factors. This paper describes a statewide investigation to identify potentially influential variables that contribute to seasonal fluctuations in traffic volumes in urban areas in Florida. A method is also developed for assigning seasonal factors to PTMSs. The test results show that the errors of the estimated seasonal factors are on average 5%. This method is promising for application in both urban and rural areas to improve accuracy in AADT estimation.
A Plug-in Hybrid Electric Vehicle Loss Model to Compare Well- to-Wheel Energy Use from Multiple Sources

Hybrid electric vehicles (HEV) come in many sizes and degrees of hybridization. Mild hybrid systems, where a simple idle stop strategy is employed, eliminate fuel use for idling. Multiple motor hybrid systems with complex electrically continuously variable transmissions in passenger cars, SUVs and light duty trucks have large increases in fuel economy. The plug-in hybrid electric vehicle (PHEV) takes the electrification of the automobile one step further than the HEV by increasing the battery energy capacity. The additional capacity of the battery is used to propel the vehicle without using onboard fuel energy. Commercial software of great complexity and limited availability is often used with sophisticated models to simulate powertrain operation. A simple method of evaluating technologies, component sizes, and alternative fuels is the goal of the model presented here. The objective of this paper is to define a PHEV model for use in the EcoCAR competition series. E85, gaseous hydrogen, and grid electricity are considered. The powertrain architecture selected is a series plug-in hybrid electric vehicle (SPHEV). The energy for charge sustaining operation is converted from fuel in an auxiliary power unit (APU). Compressed hydrogen gas is converted to electricity via the use of a fuel cell system and boost converter. For E85, the APU is an engine coupled to a generator. The results of modeling the vehicle allow for the comparison of the new architecture to the stock vehicle. In combination with the GREET model developed by Argonne National Lab, the multiple energy sources are compared for well to wheel energy use, petroleum energy use, and greenhouse gas emissions.
Challenges on Real-World Skin Lesion Classiﬁcation: Comparing Fine-tuning Strategies for Domain Adaptation using Deep Learning

—Skin lesion diagnosis is a challenging task even for experimented dermatologists. By using a computer-assisted diagnostic tool, misdiagnosed skin lesions are likely to decrease. Deep neural networks have emerged in recent years due to the increased computational power and their generalization capacity for new data. The major drawback of training a network is that it requires large amounts of data, often difﬁcult to obtain. In this work, we introduce a real-world dataset of single lesions cases composed by clinical images, particularly challenging due to image variations (scale, size, point of view, acquisition device) and data imbalance. To tackle these challenges, we propose a domain adaptation approach by pre-training on large, general, datasets, such as ImageNet and ﬁne-tuning on public datasets of clinical dermatological images. This approach is also compared with one where the target dataset is enriched with samples of public datasets. The overall performance obtained for this real-world dataset was not ideal, with F1-scores below 45%. However, interesting conclusions could be drawn on how ﬁne-tuning generally yields better aggregated results (marginal increase of F1-score), although some speciﬁc categories beneﬁt from increased training samples in a merged dataset. These results pave the way for new strategies towards the real-world application of skin lesion classiﬁcation models, moving forward from more controlled settings, where results are typically impressive, however not yet translatable into practice.
Channel Measurement and Characteristics Analysis on 3.5GHz Outdoor Environment

— If the channel characteristics of an outdoor cell are known, it makes it possible to use the optimal frequency and to optimize the system design. This paper presents the correlations between channel parameter Path Loss (PL), root mean square D elay Spread (DS), and K-factor established based on channel measurements. The channel measurements were performed in a 3.5 GHz Non-Line of Sight (NLOS) environment. We measured the channel characteristics in Dunsan area in Korea using a channel sounder and 1×1 antennas. The correlations between channel parameters show that the wireless channel characteristics can be determined and effective communication system design can be produced for use in similar environments.
ADDIS ABABA UNIVERSITY COLLEGE OF NATURAL SCIENCES Amharic Open Information Extraction

Open Information Extraction is the process of discovering domain-independent relations by providing ways to extract unrestricted relational information from natural language text. It has recently received increased attention and applied extensively to various downstream applications, such as text summarization, question answering, and informational retrieval. Although a lot of Open Information Extraction systems have been developed for various natural language text, no research has been conducted yet for the development of Amharic Open Information Extraction (AOIE). As litrature has shown, the rule-based approach operating on deep parsed sentences yields the most promising results for Open Information Extraction systems. However, to the best of our knowledge, there is no fully implemented deep syntactic parser available for Amharic language. Therefore, in this thesis, we propose the development of a rule-based AOIE system that utilizes shallow parsed sentences. The proposed system has six components: Preprocessing, Morphological Analysis, Phrasal Chunking, Sentence Simplification, Relation Extraction, and Post-processing. In the Preprocessing, each word in the input text is labeled with an appropriate POS tag, and then well-formed and informative sentences are filtered out for further processing based on POS tags of words. The Morphological Analysis component produces morphological information about each word of input sentences. The phrasal chunking component divides the input sentence into non-overlapping phrases based on POS and morphological tags of words. The Sentence Simplification component segments the sentence into a number of self-contained simple sentences that are easier to process. In the Relation Extraction, relation instances are extracted from those simplified sentences and finally the post-processing components prints extracted relations in N-ary format. The proposed method and algorithms were implemented in prototype software and evaluated with a dataset from different domains. In the evaluation, we showed that the system achieved an overall precision of 0.88.
JM-JABF210178 1141..1156

Background Infectious mononucleosis (IM) is a disease caused by the Epstein-Barr virus (EBV). IM is most common among young adults, especially those aged 15 to 24 years, with the incidence rate ranging from 6 to 8 cases per 1000 person-years. The most common symptoms and signs among IM patients include sore throat, lymph node enlargement, fever, tonsillar enlargement, pharyngeal inflammation, fatigue, and rashes. However, the diagnostic accuracy of the symptoms and signs of IM is inconsistent among studies. A recent systematic review of the diagnostic accuracy for individual symptoms and signs found that the absence of sore throat (sensitivity, 0.81; negative likelihood ratio [LR-], 0.51-0.62), headache (sensitivity, 0.66; LR-, 0.63-0.73), or any This article was externally peer reviewed. Submitted 23 May 2021; revised 5 August 2021; accepted 9 August 2021. From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA (XC, MHE, LH). Funding: None. Conflict of interest: The authors have no relevant financial information or potential conflicts to disclose. Corresponding author: Xinyan Cai PhD, MSPH, 108 Miller Hall, University of Georgia Health Sciences Campus, Athens, GA 30602, Phone: 803-261-1074 (E-mail: Xinyan. Cai@uga.edu).
Four-way Classification of Tabla Strokes with Models Adapted from Automatic Drum Transcription

Motivated by musicological applications of the four-way categorization of tabla strokes, we consider automatic clas-siﬁcation methods that are potentially robust to instrument differences. We present a new, diverse tabla dataset suitably annotated for the task. The acoustic correspondence between the tabla stroke categories and the common popular Western drum types motivates us to adapt models and methods from automatic drum transcription. We start by exploring the use of transfer learning on a state-of-the-art pre-trained multiclass CNN drums model. This is compared with 1-way models trained separately for each tabla stroke class. We ﬁnd that the 1-way models provide the best mean f-score while the drums pre-trained and tabla-adapted 3-way models generalize better for the most scarce target class. To improve model robustness further, we investigate both drums and tabla-speciﬁc data augmentation strategies.