Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Based Postoperative Recovery and Nursing of Total Hip Arthroplasty

Objective To develop a deep learning-assisted recovery and nursing system after total hip arthroplasty and to conduct clinical trials in order to verify its accuracy. Methods In our study, based on manual labeling, the human hip X-ray image library was established, and the deep neural network based on Mask R-CNN was built. The labeled medical images were used to train the model, providing reference for nursing decision after hip replacement. A total of 80 patients with hip injury from 2016 to 2019 were selected for the study. In our paper, the patients were divided into experimental group and control group. The pertinence and effectiveness of the model for postoperative care were evaluated by comparing the hip pain (VAS index), recovery (Harris score), self-care ability (Barthel index), and postoperative complication rate between the two groups. Results The pain and complications in the experimental group were significantly lower than those in the control group, the difference being statistically significant (P < 0.05); the recovery of hip joint and self-care ability were higher than those in the control group, the difference being statistically significant (P < 0.05); the other differences were not statistically significant (P > 0.05). Conclusion The application of deep learning method in the rapid nursing after total hip replacement can significantly improve the nursing ability. Compared with the traditional method, it has stronger pertinence, faster postoperative recovery, lower incidence of complications, and greatly improves the postoperative quality of life of patients with hip injury.
Dynamic optimization based reactive power planning for improving short-term voltage performance

xiii CHAPTER 1. OVERVIEW . 1 1.
The Effects of Schedule-Driven Project Management in Multi-Project Environments

Project Management is the most basic and key component in a project. Multi-project environments enable us to develop projects at a quicker pace than the normal traditional project managements. Lack of project management creates variations in the timeline of individual projects, creating delays for the immediate consequent sub-project if present any, thus increasing the importance of studying and understanding project management in multi-project environments. Customers who request projects would be particularly about the deadline for the successful submission of the projects. Hence schedule-driven project management should be applied to the multi-project environments which comparably increases the complexity of the project management than the other multi-project environments. This paper deals with the constraints in schedule driven project management with variables like resources, workforce, project improvisations, etc., and how the similarity would affect the multi-project environments.
A 'Quality of Use' Model in Blended Learning

Blended learning is considered as a new research area. Previous scholars suggested more studies required to explore and confirm the quality dimensions for this area. Hence, the aim of this study is to develop a predictive model based on ‘quality of use’ concept in blended learning using flipped classroom. The model will be incorporated with contemporary variables, namely student engagement and student interaction. Data were obtained using survey method. A total of 299 undergraduate students participated in the study. Data were analyzing using SmartPLS. The finding revealed that the enhancement of ‘quality of use’ model by integrating student engagement and student interaction attained the validity and reliability of a predictive model for blended learning using flipped classroom. However, this model is only applicable for the context on this study. The replication of similar study need to be conducted in order to confirm the variables used in similar research context. © 2016 Published by Future Academy www.FutureAcademy. org.uk
Deposit‐Derived Block‐and‐Ash Flows: The Hazard Posed by Perched Temporary Tephra Accumulations on Volcanoes; 2018 Fuego Disaster, Guatemala

The impact of hazardous pyroclastic density currents (PDCs) increases with runout distance, which is strongly influenced by the mass flux. This article shows that the mass flux of a PDC may derive not only from vent discharge during the eruption, but also from partly hot, temporary stores (accumulations) of aerated pyroclastic material perched high on the volcano. The unforeseen PDC at Fuego volcano (Guatemala) on 3 June 2018 happened c.1. 5 hr after the eruption climax. It overran the village of San Miguel Los Lotes causing an estimated 400+ fatalities. Analysis of the facies architecture of the deposit combined with video footage shows that a pulsatory block‐and‐ash flow flowed down the Las Lajas valley and rapidly waxed, the runout briefly increasing to 12.2 km as it filled and then spilled out of river channels, entered a second valley where it devastated the village and became increasingly erosive, prior to waning. Paleomagnetic analysis shows that the PDC contained only 6% very hot (>590°C) clasts, 39% moderately hot (∼200°C–500°C) clasts, and 51% cool (<200°C) clasts. This reveals that the block‐and‐ash flow mostly derived from collapse of loose and partly hot pyroclastic deposits, stored high on the volcano, gradually accumulated during the last 2–3 years. Progressive collapse of unstable deposits supplied the block‐and‐ash flow, causing a bulk‐up process, waxing flow, channel overspill and unexpected runout. The study demonstrates that deposit‐derived pyroclastic currents from perched temporary tephra stores pose a particular hazard that is easy to overlook and requires a new, different approach to hazard assessment and monitoring.
Surface urban heat islands in 932 urban region agglomerations in China during the morning and before midnight: spatial-temporal changes, drivers, and simulation

Abstract Research was insufficient on the surface urban heat islands (SUHIs) in China during the morning and before midnight. Therefore, we studied the spatio-temporal variations and ten determinants of SUHI intensities (SUHIIs) at ∼10:30 am and ∼10:30 pm of 932 urban region agglomerations in the five ecological regions in China and further simulate these SUHIIs using the machine learning algorithm. The main findings were as follows. (1) The daytime SUHIIs in all regions were largest in the summer, and when the humid regions had higher SUHIIs than other regions. The maximum SUHIIs were usually significantly positively correlated with the minimum ones. (2) The SUHIIs were significantly partially correlated with the enhanced vegetation index, nighttime light intensity, PM2.5, albedo, population density, precipitation, urban area size and landscape shape index in many or a few cases. (3) The root-mean-square errors of simulated daytime and nighttime SUHIIs were mostly less than 1.22 and 2.00 °C, respectively.
Design of Magnetron Cooling Method Based on Heat Exchanger System for Microwave Heating Application

Microwave heating is a novel thermal recovery technique developed for heavy oil reservoirs. Particulary in deep reservoirs, it allows more effective thermal recovery to counter heat loss. The magnetron as a microwave generator works based on cavity resonant vibrations and it needs an appropriate cooling system to avoid damage to its cavity elements. In this paper, the design of a cooling system based on a heat exchanger as well as the investigation of the proper material and coolant to fit the microwave characteristics are proposed.
A two-stage approach for structural damage detection using a damage localization indicator vector and cluster particle swarm optimization

A two-stage approach based on the finite element model is proposed for structural damage detection. In the first stage, the damage identification problem is transformed into an -minimization issue related to the natural-frequencies-based sensitivity matrix, which can be quickly solved using the alternating directional multiplication method. Based on this, a damage localization indicator vector (DLIV) is proposed for dimensionality reduction. In the second stage, a particle swarm optimization (PSO) algorithm with four-cluster topology, namely Cluster PSO, is employed to identify the location and severity of the actual damage after dimensionality reduction. Therefore, the proposed two-stage approach can be denoted as the DLIV-based Cluster PSO. In the numerical simulations, the performance of the proposed DLIV-based Cluster PSO is evaluated for single and multiple damage detection of a cantilever beam. The simulation results demonstrate that the proposed DLIV-based Cluster PSO approach provides faster convergence, less computational time and better noise tolerance.
Identification of Hub Genes Associated with Nonspecific Orbital Inflammation by Weighted Gene Coexpression Network Analysis

Background Nonspecific orbital inflammation is a common ophthalmopathy with a high prevalence among adult females. Yet, its molecular mechanisms behind are poorly understood. Regulation of gene expression probably plays an important role in this disease. Thus, we utilized gene coexpression networks to identify key modules and hub genes involved in nonspecific orbital inflammation. Methods Data of gene expression in nonspecific orbital inflammation samples (n = 61) and healthy samples (n = 28) were obtained from the public Gene Expression Omnibus database. Afterward, differentially expressed genes were performed. Then, weighted correlation network analysis was done to define the key modules. Next, functional enrichment analysis was conducted by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway in key modules. Finally, a protein-protein interaction network and Cytohubba plugin were used to screen hub genes. Results Differential expression of 716 genes was identified, among which 169 genes were upregulated and 547 genes were downregulated in the nonspecific orbital inflammation group. In weighted correlation network analysis, we clarified 2 key modules (MEturquoise and MEblue) that are likely to play key roles in nonspecific orbital inflammation. Functional enrichment analysis indicated that these genes are predominately involved in immune response and matrix homeostasis. In addition, among 2 key modules, there are 20 hub genes identified. Conclusion With this new approach, we identified several genes that could be critical to pathologies of nonspecific orbital inflammation. These findings may contribute to further therapeutic target development.
Effect of professional events on competences for encouraging reading literacy

Purpose: We want to determine the impact of participation in a professional event on the competences of library professionals in the field of reading literacy. We are interested to find whether professionals are more motivated to work after attending a professional event, feel more confident in working in the field of reading literacy, more connected to others and see more sense in their work, and whether participation in the event affects their work activities after the event itself. Methodology: We used a case study with a quantitative survey method to collect data. First, we analysed selected scientific and professional literature to define the concepts of motivation, self-confidence, belonging and meaning, and responsibility of an individual‘s professional development. We linked our case study to the predefined theoretical starting points. The design of the questionnaire structure was linked to the impact assessment. Results: Understanding the concepts of motivation, self-confidence, belonging and meaning is crucial in strengthening the competences of professional staff. In the first part we compare the results over a three-year period, and in the second part we use the results to determine how participation in a professional event affects the individual. Comparative results of all three events show that obtaining the necessary information positively affects self-confidence at work. The research has shown that participation in a professional event has a positive effect on motivation, belonging, meaningful work and understanding of professional and personal role. Research limitations are linked to the size of the study, as it is based on one case study, but includes a large enough sample that can indicate the starting point for a more extensive study to determine the impact of professional events on motivation, confidence, belonging and acceptance meaningful in the professional field. Originality/value: Studies of the impact of participation in professional trainings on the competences of professionals are rare, so our research is an additional incentive to argue for regular professional training and monitor the development of individual key elements (i.e. motivation, confidence, meaning, belonging) of professional competencies. The research highlights the importance of an individual‘s commitment to promoting reading literacy and building reading culture.
Siloed in breaking silos: A case study of interdisciplinary curriculum (mis)alignment

Higher Education Institutes globally are rapidly developing inter/ transdisciplinary education initiatives at varying scales. However, several operational challenges persist in interdisciplinary teaching as there are few incentives to share resources or engage in discourses for mutual deconstruction of knowledge frameworks. Nonetheless, collaboration and communication are vital to bridge the varying epistemic frameworks when different disciplines are brought together. Especially because the way individuals understand concepts contain traces of disciplinary-specificities; without clarity through a common platform, the messiness is reflected in the curriculum design, development and delivery of interdisciplinary courses. Reflecting upon a case study of an interdisciplinary graduate programme that was experiencing curriculum (mis)alignment
Breaking a Path by Creating a New One: How Organizational Change Boosts Children’s Cancer Care

The theory of organizational path dependence helps us to understand why organizational change is sometimes extremely difficult, if not impossible. Although recent conceptualizations of path dependence allow for strategic agency, the theory is still underspecified regarding how to break an organizational path. In an attempt to develop the theory in this regard, we make use of a case in the sphere of global health with an undisputed life-saving impact. More precisely, we trace a significant improvement in the field of pediatric cancer care back to path-breaking organizational change: the successful introduction of multicenter cooperation within a healthcare system that is arguably locked into a centralization path. Drawing on longitudinal research conducted within Russian children’s hospitals, we conclude that path-breaking change is an intricate process based upon distinct activities, and supported by mechanisms, directed towards the creation of a new organizational path.
Pedestrian detection and tracking for mobility on demand

This paper presents a pedestrian detection and tracking system to be used aboard mobility on demand systems. Mobility on demand is a transportation paradigm in which a fleet of vehicles is shared among a community, with rides provided upon request. The proposed system is capable of robustly gathering pedestrian paths in space using 2D LiDAR and monocular cameras mounted onboard a moving vehicle. These gathered pedestrian paths can later be used to infer network traffic to learn to anticipate the location of ride requests throughout a day. This allows mobility on demand systems to more efficiently utilize resources, saving money and time while providing a more favorable experience for customers. The onboard LiDAR is used to cluster and track objects through space using the Dynamic Means algorithm. Pedestrian detection is performed on images from the mounted cameras by extracting a combination of histogram of oriented gradients and LUV color channel features which are then classified by a set of learned decision trees. Temporal information is leveraged to achieve higher detection quality by accruing classification votes. Both a standard fusion technique and a novel extrinsic calibration error-resistant fusion method are tested to fuse camera and LiDAR information for pedestrian path collection. The novel error-resistant fusion system is shown to outperform standard fusion techniques under both normal conditions and when synthetic extrinsic calibration noise is added. System robustness and quality is demonstrated by experiments carried out in real world environments, including the target environment, a university campus.
An Intelligent Model for Success Prediction of Initial Coin Offerings

Assessment of Initial Coin Offerings (ICOs) is crucial for investment decisions in the ICO market. Since most ICOs succeed in raising funds, failed ICOs must be discriminated against through intelligent classification methods. In this context, this research proposes an intelligent decision model for predicting ICOs’ success that merges both the Information Gain Directed Feature Selection (IGDFS) technique as a features rank procedure to select the discriminative features representing the intial pool of features for Genetic Algorithm (GA) to find the ICO’s optimal feature set and Fuzzy Support Vector Machine for Class Imbalance Learning (FSVM-CIL) to tackle the problem of imbalanced classification. Two benchmark datasets were used to examine the proposed hybrid model referred to as IGDFS-FSVM. The experimental results reveal that the proposed model that employs an intelligent technique for ICO’s feature selection outperforms state-of-the-art classifiers without features selection. In this regard, we conclude that the proposed hybrid model is a practical approach to support investment decisions in the ICO market.
Environmental Design in Education and Training

Increasingly, individuals are using more blended, hybrid, and online deliver formats in education and training. Although research exists about how the physical and social environment impact learning and training in traditional face-to-face settings, we have limited knowledge about how the environment affects learners when they are interacting with technology in their learning situations. In particular, concerns arise about levels of engagement, whether learning is enhanced, the impact or helpfulness of robotics, and how the social dynamics change. These five panelists bring expertise in education at the undergraduate and graduate levels, training within industry and the military, and the use of various teaching and training methods. The panelists will present their perspectives to several questions relative to how the environment can (or cannot) accommodate enhanced learning in education and training when technology is involved. Ample time will remain for audience participation.
Measurement of Acoustic Cavitation

Methods of measuring cavitation activity and thres- holds are described. Their value as practical measures for cavitation- dependent processes are discussed and compared, as far as on the basis of accuracy, repeatability, convenience, etc. It is shown that measurements of the acoustic output from cavitation in certain frequency bands show promise of giving maximum informa- tion about both threshold and activity. Techniques are suggested for practical metering systems. Some liquid processing applications of ultrasonics are very complex physically, and in these cases a simple cavitation activity measure may not be a reliable measure of performance. Tests are described for use in these cases which simulate, under controlled conditions, the process under investi- gation.
Seawater intake monitoring using low cost sensor network

Water is the new oil of the 21st century due to increased consumption and demand. High-quality water with free of contamination is vital for human beings and many industries such as oil and gas, petrochemicals, pharmaceuticals, and food. To meet the massive amount of freshwater production, United Arab Emirates (UAE) relies on the energy-intensive Multi-Stage Flash (MSF) and Multi-Effect Distillation (MED) technologies to provide fresh water for various applications. These energy-intensive processes consume a significant share of UAE oil and gas. In general, thermal desalination is an energy-intensive process. UAE is shifting to use Reverse Osmosis (RO) desalination for the freshwater production. To reduce the energy consumptionand the pretreatment stages onsite monitoring of the seawater intake has to be intensively recorded. This study discusses a design of a seawater test station, which has a sensor network to measure the quality parameters of seawater, including the water’s pH.
Modeling of Impedance Effects for the APS-MBA Upgrade

Understanding the sources of impedance is critical to accelerator design, and only becomes more important as vacuum chambers become smaller and closer to the electron beam. The multibend achromat upgrade at the Advanced Photon Source (APS) requires small, 22-mm diameter vacuum chambers and even smaller (6 mm) gaps for the insertion devices, so that both rf heating and wakefield-driven transverse instabilities become important concerns. We discuss modeling the primary sources of geometric impedance using the electromagnetic finite difference codes GdfidL and ECHO, and how these codes are influencing vacuum and accelerator component design.
IRDFPR-CMDNN: An energy efficient and reliable routing protocol for improved data transmission in MANET

Mobile Ad hoc Networks (MANET) are structure less, autonomous wireless networks with mobile nodes that dynamically establish data transmission connections. Due to dynamic topological change, MANET routes are unbalanced and break repeatedly. Hence, providing efficient and reliable data delivery with effective utilization of network resources is a challenging issue to be considered in MANET. This paper proposes an instant-runoff Ranked Decision Forests Probit Regression-based Connectionist Multilayer Deep Neural Network (IRDFPR-CMDNN) for efficient data transmission and higher data delivery with a minimum end-to-end delay. This IRDFPR-CMDNN method performs route identification, data delivery, and route maintenance with more than three layers. Then the mobile nodes are sent to the input layer of the Connectionist Multilayer Deep Neural Network. In hidden layer 1, the Instant-runoff Ranked Decision Forests algorithm is applied for classifying the mobile nodes depending on the residual energy and load capacity. With selected mobile nodes, the Probit Regression is applied for finding the nearest neighboring nodes in the second hidden layer based on the link quality and received signal strength for route path establishment. Then multiple paths for routing are established from source to destination node and start to perform the data transmission. If link failure occurs during the data transmission, another alternative route with better link quality is selected for routing. In this way, energy-efficient data transmission is performed from source to destination with a higher data delivery rate and minimal time consumption. Experimental evaluation is carried out on energy consumption, packet delivery ratio, packet drop rate, throughput, and end-to-end delay with varying numbers of mobile nodes and data packets. Simulation results show that the IRDFPR-CMDNN technique effectively enhances data delivery, throughput and minimizes energy consumption, packet loss rate, delay with respect to conventional methods.