Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception of National Park Soundscape and Its Effects on Visual Aesthetics

Soundscape perception is a very weak link in the national park landscape evaluation system in China. A thorough understanding of soundscapes and their effects on visual aesthetics is important for the management of national park landscapes. In this study, features of soundscapes (e.g., loudness, frequency, preference, and auditory satisfaction) were investigated based on 394 valid questionnaires of residents in the Qianjiangyuan National Park Pilot Area. The effects of soundscape on visual aesthetics were analyzed using the PLS-SEM. The results demonstrated that: (1) Peddling voice and insect sound were the loudest components in the soundscape, running water and birdsong were the most commonly heard and most preferred, religious sound was the quietest and least frequently heard, and horn was the least preferred. Residents in the Pilot Area were generally satisfied with the auditory environment. (2) Both sound frequency and preference have significant effects on auditory satisfaction, but preference (path coefficient = 0.426) has a larger effect than does frequency (path coefficient = 0.228). (3) Loudness has negligible effects on visual aesthetics, but other soundscape characteristics did influence visual aesthetics. Soundscape preference had the most significant effect (path coefficient = 0.305), followed by auditory satisfaction (path coefficient = 0.174), and sound frequency (path coefficient = 0.165). Among them, effects of perception frequency are the indirect utilities.
Impact of Shunt FACTS Devices on Security Constrained Unit Commitment

Shunt FACTS Devices (SFD) would play an important role in maintaining security and reduce total generation cost in the economical operation of power systems. The application of this device to the AC model of securityconstrained unit commitment (SCUC) for the day ahead scheduling is presented in this paper. The proposed AC model of SCUC with SFD would include active and reactive power flow constraints which increase the network controllability at normal operation and contingency. A general SFD model is introduced for the reactive power management in SCUC which is based on the reactive power injection model (RPIM). The case studies demonstrate the effectiveness of the SFD application to SCUC with AC network constraints. Meanwhile simulation results demonstrate the combined use of these devices to SCUC have a significant impact on maintaining network security, preventing load shedding, lower total generation cost and increase using the maximum capacity of the existing transmission network.
Gasoline Direct Injection Engine Emissions of OC and EC: Laboratory Comparisons with Port Fuel Injection Engine

To better understand carbonaceous aerosol emissions from gasoline vehicles, a gasoline direct injection (GDI) vehicle with and without a gasoline particle filter (GPF) installed and a port fuel injection (PFI) vehicle were tested on a chassis dynamometer using standard emission drive cycles. Carbonaceous particles emitted from the vehicles were collected on quartz filters and analyzed using three different thermal optical protocols to assess the sensitivity of organic carbon (OC) and elemental carbon (EC) emission estimates to the methods, showing OC obtained by the IMPROVE and EC by the NIOSH protocol was the lowest. Compared to the PFI vehicle, the GDI vehicle had higher EC and OC emissions under cold-start cycles by 1415% and 46%, respectively. However, the OC emission from the PFI vehicle was higher than GDI during an aggressive driving cycle by 146%. By considering OC collected on a quartz filter behind a Teflon filter, the emissions from PFI vehicle were found to be more volatile than the GDI vehicle. This is consistent with the OC forming characteristics for GDI and PFI engines, which are pyrolyzed particles from incomplete combustion and incomplete volatilization of fuel droplets, respectively. Generally, the particle phase OC emissions from gasoline engines are more volatile than other sources (e.g., biomass burning), supported by the very low level of pyrolyzed organic carbon (POC) and small differences among protocols in the current study. Once the GDI vehicle was equipped with a GPF, the removal efficiency of EC was > 98%, but OC emissions could increase as a result of regeneration, suggesting that the effect of a GPF on total carbon emitted to the atmosphere needs further evaluation, especially considering the formation of secondary organic aerosol.
Imaging of descending necrotizing mediastinitis: A report of 11 cases and review of the literature

Introduction: Descending necrotizing mediastinitis is a rare and fatal condition that results from the spread of cervicofacial infections to the mediastinum. Early and appropriate management is needed to reduce mortality and morbidity rates. Imaging, especially CT, plays a central role in the diagnosis, treatment, and monitoring of patients. This study is aimed at assessing the role of computed tomography in diagnosing and planning the management of DNM. Material and methods: This is an 8-year retrospective study on 11 patients who fulfilled the ESTRERA criteria. All the patients underwent thoracic radiography along with an enhanced cervicothoracic CT scan. Results: Median age was 34 years with a sex-ratio of 1.75. 4 patients had diabetes. The primary oropharyngeal infection was an odontogenic abscess in most cases. The reported symptoms were mainly thoracic pain and fever. Thoracic radiography and cervicothoracic CT scan confirmed the diagnosis and showed different findings. 7 patients were diagnosed with type I DNM, 1 patient with type IIA and 3 patients with type IIB. Conclusion: DNM is a medico-surgical emergency where imaging, in particular CT, plays an essential role in establishing the diagnosis and guiding the surgical approach to ensure optimal patient care.
Adobe InDesign CC Classroom in a Book

The fastest, easiest, most comprehensive way to learn Adobe InDesign CC Classroom in a Book, the best-selling series of hands-on software training workbooks, offers what no other book or training program doesan official training series from Adobe Systems Incorporated, developed with the supportof Adobe product experts. Adobe InDesign CC Classroom in a Book contains 16 lessons that cover the basics, providing countless tips and techniques to help you become more productive with the program. You can follow the book from start to finish or choose only those lessons that interest you. Purchasing this book gives you access to the downloadable lesson files you need to work through the projects in the book, and to electronic book updates covering new features that Adobe releases for Creative Cloud customers. For access, go to www.peachpit. com/redeem and redeem the unique code provided inside this book. The Classroom in a Book series is by far the best training material on the market. Everything you need to master the software is included: clear explanations of each lesson, step-bystep instructions, and the project files for the students. Barbara Binder,Adobe Certified InstructorRocky Mountain Training
Exponential stability of sampled-data control systems with enhanced average sampling interval

The stabilisation of the sampled-data control systems is addressed to tolerate large sampling intervals that tend to destroy the system's stability. A new sampling sequence is constructed from two sequences with two different sampling time intervals, where the short one corresponds to a stable system, while the long one produces an unstable system. The decay rate of the state trajectory of the former is utilised to overcome the growth rate of the latter, such that the resulting system with mixed sampling intervals is globally exponentially stable. A sufficient condition for stabilisation is given. It is applicable to the system with a single sampling interval under the missing data scenario. The examples are given to illustrate the proposed results.
Three Recent and Lesser-Known Glacier-Related Flood Mechanisms in High Mountain Environments

Glacial lake outburst floods, and specifically those triggered by avalanche-induced seiche waves, have been studied in considerable detail during the past several decades. Less attention has been given to other cryospheric flood phenomena, which include floods sourced primarily from englacial conduits, permafrost-linked rockfall and avalanches, and earthquake-triggered glacial lake floods. The article reviews examples of each phenomenon, based on field sampling and laboratory analyses, that have occurred in the Nepal Himalaya during the past decade, drawing parallels with similar events in other countries throughout the high mountain world. In most cases, the frequency of these events appears to be increasing globally, as is their potential to inflict significant damage downstream. We argue that each type of glacier flood requires more detailed study to develop the most effective prevention, mitigation, and adaptation approaches possible. Such studies will most likely be strengthened if they include a reconnaissance of the event as soon after its occurrence as possible, along with the participation, insights, and experience of local people, in addition to the use of increasingly powerful remote sensing technologies. How scientists can more quickly and effectively share the results of their research with decision-makers, and how decision-makers and governments can deliver more timely mitigation programs, are areas that also require further strengthening.
A collision avoidance algorithm in Simultaneous Localization and Mapping problem for mobile platforms

A collision avoidance algorithm applicable in simultaneous localization and mapping (SLAM) has been developed with a prospect of an on-line application for mobile platforms to search and map the operation area and avoid contact with obstacles. The algorithm, which was implemented in MATLAB R ○ software, is based on a linear discrete-time state transition model for determination of the platform position and orientation, and a ‘force’ points method for collision avoidance and deﬁnition of the next-step of platform motion. The proposed approach may be incorporated into real-time applications with limited on-board computational resources.
Normal Approximation for Bayesian Mixed Effects Binomial Regression Models

Bayesian inference for generalized linear mixed models implemented with Markov chain Monte Carlo (MCMC) sampling methods have been widely used. In this paper, we propose to substitute a large sample normal approximation for the intractable full conditional distribution of the latent eﬀects (of size k ) in order to simplify the computation. In addition, we develop a second approximation involving what we term a suﬃcient reduction (SR). We show that the full conditional distributions for the model parameters only depend on a small, say r (cid:2) k , dimensional function of the latent eﬀects, and also that this reduction is asymptotically normal under mild conditions. Thus we substitute the sampling of an r dimensional multivariate normal for sampling the k dimensional full conditional for the latent eﬀects. Applications to oncology physician data, to cow abortion data and simulation studies conﬁrm the reasonable performance of the proposed approximation method in terms of estimation accuracy and computational speed.
ML BASED STOCK AND SUPPLY REGULATION SYSTEM

This project can be used by shopkeepers who prefer to get benefited from new technology that will assist them in maximizing their profit via forecasting systems, stock monitoring, and demand forecasting with an add on of recommendation system. The purpose of this document is to describe the features, and interfaces to use the ML-Based Stock and Supply Regulation System and the requirements needed to develop it. It describes how the shopkeeper can take advantage of this product to empower the middleman, increase his/her profits, understand the pattern of supply and demand and increase customer relations using the ML-Based Stock and Supply Regulation System.
Deep learning-based object detection algorithm for mask surface defects

In order to solve the problem of product defects in the mask production process. In this paper, an algorithm for target detection of mask surface defects based on YOLOv5 is proposed. The industrial-like camera and industrial-like light source are used to collect the data set, and the data set is filtered and manually labeled. The extracted features of mask defects, broken ear straps, and solder joints are trained and tested using the YOLOv5 algorithm. The experiments show that the YOLOv5 algorithm can effectively identify various defects on the mask surface, improve mask production quality and reduce mask production cost.
The Nature of the Relationship between Project Complexity and Project Delay

In the context of a growing social complexification, projects have evolved in the pastdecades from simple endeavours to complex and uncertain undertakings. Consequently,project complexity has emerged as an important research direction, and recently severalproject complexity frameworks have been suggested. However, little research has beendone in this area and there has been no study on the relationship of project complexity,in its holistic sense, and the risk of delay. Therefore, the study investigates the intricaterelationship between project complexity and project delay. The research is conducted inthe context of Enterprise Resource Planning system (ERP) implementation projects,which are inherently complex and often record delays. The study has a qualitative nature and adopts an inductive approach. Nine ERPimplementationprojects have been studied in order to answer the research question.Several sources of evidence (semi-structured interviews and questionnaires) have beenutilized to ensure the credibility of the research findings through triangulation. The study contributes to the research field by verifying and augmenting the existingframeworks on reasons for project delay, complexity categories and their interplay. Itwas identified that complexity in a holistic sense represents a necessary condition forproject delay. Moreover, the study showed that although ERP projects are oftenconsidered to be technically complex, their complexity stems mainly from ‘subjective’(or perceived) and ‘uncertainty’ complexity dimensions. Finally, the conceptual modelof Eden et al. (2005) was modified to reflect the findings of the study.
Linear feedback control for form-drag reduction on bluff bodies with a blunt trailing edge

The work described in this thesis is a computational investigation applying linear feedback control to reduce form-drag on bluff bodies with a blunt trailing edge. For such bodies, a large portion of the aerodynamic drag is associated with an unsteady separated region or wake downstream of the body. The development of tractable feedback strategies to control unsteady wakes promises strong benefits, both in terms of industrial applications and for furthering our understanding of the flow mechanisms at play. For this purpose, large-eddy simulations are carried out where a linear feedback controller targets an increase in the mean pressure force on the rear (base) of the body. The flows over two distinct geometries are examined: a backward-facing step and a bluff body with a rounded leading edge, often referred to as a D-shaped body. The control is effected by zero-net-mass-flux slot jets, responding to sensors located on the body base. Open-loop characterization provides information on the effects of actuation and some physical insight into the relation between the base pressure and wake dynamics. System identification is used to obtain a low-order model of the flow’s response to actuation that can be used for control. The control strategy is based on the premise that reducing the fluctuations in the nearwake will cause an increase in the mean base pressure, hence a reduction in form-drag. The controllers are designed with classical frequency-domain methods, using a sensitivity transfer function to attenuate the size of the pressure force fluctuations. The influence of parameters such as the Reynolds number and the location and type of actuators is studied. For all cases, low-order linear feedback controllers successfully reduce the pressure force fluctuations and achieve sensible drag reductions. They do so with higher efficiency than the open-loop forcing considered. Uncertainties in the model and flow conditions can be to some extent mitigated by the robustness of the controller. The results support the conjecture linking the fluctuating and mean base pressure, although it is observed that further work is needed before such an approach can be used for optimization.
Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.
Interoperability: Present trends & future roles

The variety of standards available for interoperability in the building systems market should provide our industry with a firm foundation for delivering benefits and value to end users. Unfortunately, after a decade of hard work, these standards are only beginning to deliver as promised. The problem in many cases is that risk, cost and perceptions collide as systems are specified, bid and installed.
Decision analysis related to solar farm investments based on analysis hierarchical process and fuzzy AHP for sustainable energy production

Renewable solar energy is one of the most preferable clean energy sources with no environmental impacts. The Marrakech Safi region (Moroccan context), with its arid and semi‐arid climate, can ensure sustainable energy production‐oriented for a green economy. This paper aims to perform a decision analysis using the analysis hierarchical process (AHP) and Fuzzy AHP to identify the suitable areas for potential solar farms. The adopted approaches used 15 factors to produce suitability maps. AHP and Fuzzy AHP were used to minimize the subjectivity related to the factor notation, and to improve the result of the models. The factors' weights were reevaluated using the sensitivity analysis to assess their influence by comparing the theoretical and the effective weights. The final map was validated by overlaying other solar projects implemented in the region. Results show that the suitable areas to host the solar farms cover more than 35% (1300 Ha) of the entire region's surface using AHP and Fuzzy AHP. According to the results, there was no significant difference in Decision‐Making between the two methods in determining the five highest priorities of attributes selection for solar power plants. Moreover, the similarity analysis performed demonstrated that 32% of unequal areas between AHP and Fuzzy AHP can be explained by the factors' weights and the difference of the used approaches. The climatic potential and the geographic location of the study area offer a large suitable area for the installation of solar farms. These findings constitute decision‐making support for solar farm investments in a green economy.
Evaluation of Language Runtimes in Open-source Serverless Platforms

: Serverless computing is revolutionising cloud application development as it offers the ability to create modular, highly-scalable, fault-tolerant applications, with minimal operational management. In order to contribute to its widespread adoption of serverless platforms, the design and performance of language runtimes that are available in Function-as-a-Service (FaaS) serverless platforms is key. This paper aims to investigate the performance impact of language runtimes in open-source serverless platforms, deployable on local clusters. A suite of experiments is developed and deployed on two selected platforms: OpenWhisk and Fission. The results show a clear distinction between compiled and dynamic languages in cold starts but a pretty close overall performance in warm starts. Comparisons with similar evaluations for commercial platforms reveal that warm start performance is competitive for certain languages, while cold starts are lagging behind by a wide margin. Overall, the evaluation yielded usable results in regards to preferable choice of language runtime for each platform.
Modeling Social Networks using Data Mining Approaches-Review

Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has beeniscovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology.