Miracle-dz/UniFAR-FTUs huggingface dataset:
Lake Powell Management Alternatives and Values: CVM Estimates of Recreation Benefits

Abstract This paper presents data analyses based on information gathered from a recreation survey distributed during the spring of 1997 at Lake Powell. Recreation-linked management issues are the foci of the survey and this discussion. Survey responses to contingent valuation method (CVM) queries included in the questionnaire quantify visitor recreation values. The CVM estimates of the benefits provided by potential resource improvements are compared with the costs of the improvements in a benefit-cost analysis. The CVM questions covered three resources management issues including water quality improvement, sport fish harvest enhancement, and archeological site protection and restoration. The estimated benefits are remarkably high relative to the costs and range from $6 to $60 million per year. The dichotomous choice format was used in each of three resource CVM question scenarios. There were two levels of enhancement for each resource. There are, therefore, several consistency requirements—some of them unique to the dichotomous choice format—that the data and benefit estimates must satisfy. These consistency tests are presented in detail in the ensuing analysis.
Detection error analysis of active power filter

The harmonic current detection of APF(Active Power Filter) decides its compensation performance. The d q detection method based on park transformation is researched,and its current detection error is analyzed deeply,including influences of the frequency and phase errors of PLL(PhaseLocked Loop) and the lowpass filter on current detection error. The causes are discussed under different voltage environments. Conclusions,such as the DC voltage adjustment influences the detection precision when voltage is unbalanced,are drawn and verified with simulation.
Flow over thick airfoils in ground effect - an investigation on the influence of camber

Results from an experimental investigation of aerodynamic ground effect on three airfoils, NACA 0015, 4415 and 6415, carried out in a low speed wind tunnel are presented. The pressure distribution on the airfoil surface was obtained from pressure tappings. Mean velocity measurements were performed over the surface of the airfoil and velocity contours were plotted. Measurements of mean velocity and turbulence intensities were performed in the wake region at two locations. Experiments were carried out by varying the angle of attack from 00 to 100 and ground clearance from the minimum possible value to one chord length. It was found that high values of pressure coefficient are obtained on the lower surface when the airfoil is close to the ground. This region of high pressure extended almost over the entire chord length for higher angles of attack. The flow was found to accelerate over the airfoils with the highest acceleration observed for NACA 4415 and lowest for NACA 6415. For NACA 4415 model, a very high mean velocity is observed near the suction peak location. For this airfoil, the flow was found to separate from the surface for angles of attack 100 and above, resulting in considerably lower velocities over the surface and a thick and highly turbulent wake region.
Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.
fatalityCMR: capture-recapture software to correct raw counts of wildlife fatalities using trial experiments for carcass detection probability and persistence time

This report documents a user interface for the analysis of carcass searches and trial experiments data in the context of the environmental assessment of wind farms. The interface was developed in R. The interface has been tested for accuracy using multiple datasets and the software offers the possibility to generate simulated data. If users find or suspect errors, please contact the U.S. Geological Survey (USGS). Every effort has been made by the USGS or the U.S. Government to ensure the spreadsheet interface is error free; however, errors may exist in the spreadsheet interface. The distribution of the spreadsheets does not constitute any warranty by the USGS, and no responsibility is assumed by the USGS in connection therewith.
Research on Particle Impact Noise Auto Detecting System for Aerospace Relay

Due to traditional particle impact noise detection(PIND)system for remainder detection with low precision,high leave out rate and wrong judgment rate,a remainder auto detecting method based on wavelet analysis was preseated.A PIND system for space relay with self-owned intellectual property rights was designed.The experiment result indicates that the detecting system runs stably with friendly human interface,the performance is excellent and the main indexes excelled the same type product in the world.The system is also suitable for remainder detecting for other military electronic components.
Research protocols for designing studies/pilot trials to evaluate and to improve effectiveness of wild boar management in relation to African swine fever virus

The ecological plasticity of wild boar and their growing populations can generate conflicts with human activities and can be a threat to livestock and public health. Particularly, the emergence of African Swine Fever in Europe is of major importance. However, there are gaps in knowledge about wild boar ecology, population monitoring, management and population control that prevent the design and application of the best science-based ASF control policies, and/or adaptive evaluation of the actions taken. The effectiveness of wildlife policies is known to be directly proportional to their acceptance by stakeholders. However, it is unknown how the acceptance of these policies and different management scenarios vary among stakeholder groups, in different socio-economic and cultural contexts. Acceptance by stakeholders in different contexts determines the success of management strategies. Finally, factors that influence wild boar abundance and disease spread are not bound by national borders. Thus, there is need to coordinate national and international decision-making. In this context, this report presents research protocols to address a number of knowledge gaps previously identified by EFSA, and aims to improve the strategy to control ASF in the short-term. Twelve research objectives grouped into six categories address aspects of: (i) wild boar ecology, i.e. studies on basic aspects of wild boar population dynamics and assessment of the factors that determine the presence of wild boar near outdoor pig farms; (ii) wild boar monitoring, i.e. implementation of practical methods to estimate wild boar density and strategies to promote their application; (iii) wild boar management and population control, i.e. effect of feed availability, role and efficacy of recreational hunting and professional culling, efficacy of wild boar trapping and different fencing methods and the use of trained dogs in ASF affected areas; (iv) social acceptance by the stakeholders; (v) assessment and management of risk factors (biosecurity awareness and implementation among backyard pig farmers, evaluation of passive surveillance and carcass removal); and (vi) national and international decision-taking. We propose protocols for each specific research objective, their study design, implementation methodology, required time frames and budget limitations. We comparatively summarize the protocols and discuss them in terms of solving overlaps and interactions among protocols that address different research objectives, which eventually can be combined to optimize the use of resources and budgets and to reduce the required time needed to achieve objectives. © European Food Safety Authority, 2021
Graded elastic meta-waveguides for rainbow reflection, trapping and mode conversion

Precise control of elastic waves is a challenge for many applications in the field of mechanical vibrations, ultrasonic inspection, and energy harvesting. Graded arrays of resonators on elastic substrates recently revealed superior performances for broadband wave trapping and mode conversion. In this study we present elastic waveguides able to govern waves at different scales exploiting rainbow reflection, trapping and mode conversion. We investigate whether these mechanisms, and the associated control, can be used for energy harvesting or signal conversion devices.
DESIGN, EVALUATION AND COMPARITIVE STUDIES OF ORAL THIN FILMS AND EDIBLE GEL LOADED WITH ANTI HISTAMINE DRUG FOR GERIATRICS AND PEDIATRICS

The Aim of present study is to Design, Evaluate & Compare studies between fast dissolving Oral thin films (OTF) & Edible gel (EG) loaded with Antihistamine drug for Geriatrics and Pediatrics. The Anti-Histamine Drug called Desloratadine is a tricyclic antihistamine, which has a selective and peripheral H1-antagonist action used to treat allergies. Twelve Formulations (F1–F12) of OTF were prepared by casting method using different concentrations of polymers i.e, Sodium alginate (1, 1.5, 2w/v %) and Xanthum (0.4, 0.7w/v %), as the conc. of polymer increased tensile strength and folding endurance were improved with more dissolution time. Ten Formulation (EG1-EG10)of EG were prepared by varying concentrations of Gellan Gum (0.1-0.5%), 0.3% and above concentration of polymer showed good physical and Rheological properties. Optimized Formulation among OTF was F4 with 1.5% Sodium alginate showing good folding endurance, tensile strength, disintegration time and 98.9% drug release with no casting problem and appearance. Optimized formulation among EG was EG8 with no syneresis showing 99% drug release with good consistency and viscosity increasing the mouth feel. Comparative study parameter of F4 and EG8 clearly revealed the instability of OTF and stability of EG at room temperature and also due to its high patient compliance based on appearance and ease of administration, EG was preferred as better dosage form for geriatrics and paediatrics.
Journal of Economic Development, Environment and People

This study aims to know influence the mediation of organization learning on strategic change, knowledge management and transformational leadership and performance of Indonesian Islamic Banks. Design / methodology / approach This study uses quantitative research with a purposive sampling technique with a population of 14 Indonesian islamic banks. The targeted unit of analysis is the Islamic banks manager. There were 239 questionnaires returned and 11 of them did not meet the requirements, so only 228 were used in this study. This study uses SmartPLS 3 to test the hypothesis. This research shows that strategic change, knowledge management and transformational leadership affect organization learning. This study also shows that strategic change, transformational leadership knowledge management and organization learning have an effect on firm performance. Apart from that, the mediating role of the learning organizations also provides consistent results. Research limitation / implications Islamic banks managers must be able to identify the importance of change for the company, because of the big risks. Supporting knowledge and leadership skills in companies makes Islamic banking better competitive. These three variables become better if the company uses a learning organization because the company will change more quickly and achieve better performance with previous experience. Originality / value Similar research is usually carried out in manufacturing and service companies and is carried out in developed countries (Europe, America and Africa). The focus of this research is the development of strategic change supported by knowledge management and transformational leadership on performance mediated by learning organizations. This research was also conducted for the first time in Asia with the object of Islamic banks in Jakarta Indonesia.
BEYOND THE HUMAN UNSTABLE MEDIA ART CONSTRUCTING NEW SOCIAL REALITIES

In times of current social and ecological crises, there is a need for urgent action. Many argue that the major cause of climate change is that our dominant paradigms are deeply rooted in traditional Western human-centred ideology and anthropocentric attitudes towards nature. The need to envision a completely new ontology, which redefines toxic humannonhuman relationship, places artists in a unique position. Although the role of arts in (re)shaping social realities is recognized, it often remains unclear how exactly it is done. Drawing widely on posthuman theory, this study argues for the potential of art to social change, illustrating specific ways through which art engages with the critique of anthropocentrism and creates new social imagination, which includes both human and nonhuman perspectives and realities. This research is situated in the frames of so-called “new sociology of arts”. Focusing specifically on the works of art, this study looks for their relation to broader sociocultural contexts of the ongoing posthuman turn. Drawing on “Manifesto for the Unstable Media” (1987) published by V2_ Lab for Unstable Media, this research analyses the selection of artistic projects that fall under the category of “Unstable Media”. The unique characteristics of unstable media art, in particular its technological nature, participatory and interactive aspects, and instability are seen as an intrinsic factor to social change. Through utilizing a qualitative content analysis method, this research analyses the selection of artworks from the digital V2_Archive. The findings identified three major categories that form the basis of this study and are presented through the more extensive analysis of twentythree the most illustrative examples of artworks. Results of the analysis show that unstable media art is particularly effective in taking a more inclusive approach and recognizing the realities of nonhuman biological agents, positioning the human among the myriad of conscious and sentient nonhuman forms of life. Another inference drawn from this study is that unstable media art becomes a platform to rethink human relationship with technological others. Finally, unstable media art takes a non-anthropocentric view on the environment, constructing an environmentally aware society, suggesting sustainable solutions and imagining non-hierarchical interspecies relations.
Clustering Based Sentiment Analysis Using Randomized Clustering Cuckoo Search Algorithm

Summary Nowadays online social networks have become one of the most important platforms, where people all over the world express their opinions, feelings, and their own experience. They do so either by texting or using images, emoji, and videos. Sentiment analysis of social media data is very important in making decisions in different areas. For example, corporates need to know what people feel regarding their products, governments need to understand public opinion towards certain decisions. In this paper, we designed an architecture that can be used to analyze social media text data sentiments based on their clustering. The suggested architecture composed from three main components namely: data cleaning, similarity finding, and randomized clustering Cuckoo search (RCCS). A formula that combine the similarity degree is suggested to improve the accuracy. As well, we utilized the power of the Cuckoo Search with the Levy flight algorithm to cluster the text data. Our architecture is used to detect the optimal or near-optimal number of clusters that best describe a text dataset. To test our model, we used the Niek Sanders tweets dataset. The proposed model achieved better performance comparing it with the other six algorithms. The six algorithms involved in our comparisons are K-Means, Latent Dirichlet Allocation (LDA), Scalable Multi-stage Clustering (SMSC), and Grouping Like-minded people using Interests Centers GLIC algorithm with its three different variations. According to our experiments, we claim that our model is efficient and very helpful in the sentiment analysis of social media text data . K ey words:
Multigrid Distributed Deep CNNs for Structural Topology Optimization

Structural topology optimization with traditional approaches is compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component’s performance during the optimization process. This computation cost scales up when performed on 3D high-resolution geometries. Researchers have developed deep learning (DL) based approaches, but these methods were demonstrated mainly us-ing low-resolution 3D geometries (with a typical resolution of 32 × 32 × 32 ). We propose a DL-based method trained with a convolutional neural network (CNN) on high-resolution 3D geometries 128 × 128 × 128 . With the initial strain energy (objective function of structural topology optimization) and target volume fraction (% material to be preserved after optimization) as the only inputs to the CNN, we predict the final optimized topology while maintaining the volume fraction constraint. To train the CNN at a high resolution is again a computational challenge. Therefore, we propose multi-resolution CNN, where we train the network at a lower resolution and then transfer the learned network to continue training at a higher resolution. Further, we significantly speed up the training time by 4 . 77 × using distributed deep learning framework on GPU clusters (PSC Bridges-2).
Exploit Generation from Software Failures

— We normally monitor and observe failures to measure the reliability and quality of a system. On the contrary, the failures are manipulated in the debugging process for fixing the faults or by attackers for unauthorized access of the system. We review several issues to determine if the failures (especially the software crash) are reachable and controllable by an attacker. This kind of efforts is called exploitation and can be a measurement of the trustworthiness of a failed system
The Intergenerational Incidence and Social Welfare of Renewable Energy Support Policies vs. Carbon Pricing

This paper examines the lifetime and intergenerational economic incidence of renewable energy (RE) subsidies and carbon pricing for climate change mitigation, employing a calibrated dynamic general-equilibrium model with overlapping generations for the U.S. economy. We explore the political economy implications of the different regulatory approaches based on majority voting of generations alive at the time the policy is introduced. We emphasize issues for policy design focusing on the financing of RE subsidies and policy interactions with distortionary income taxation. Notwithstanding the supremacy of carbon pricing on grounds of aggregate efficiency, we find that smart designs for RE support policies, which link the financing of the support for RE technologies to the carbon intensity of fossil-based energy technologies, constitute a politically viable option.
A Hybrid Intrusion Detection Model For VANET Using SDN And Growing Hierarchical Self-Organizing Maps

VANET is the most promising, fast-growing and modern technology. VANETs enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). The VANET's security is one of the most crucial concerns from a prediction and prevention standpoint. Machine Learning based Intrusion Detection System (IDS) can be used to sense the abnormalities, but it suffers from low detection rates, high false alarm rates, and, further, it requires massive amounts of data for prediction accuracy. This paper recommended a framework for the 5G-based VANET system that combines Software-Defined Networking (SDN) with Growing Hierarchical Self-organizing Maps (GHSOM). The suggested system will be a new mix of SDN and a Growing Hierarchical Self-organizing Maps (GHSOM)-based network solution to improve security in both dimensions, detecting and blocking assaults. The suggested method investigates the effect of GHSOM settings on the detection ratio of various types of attacks on a VANET. Experiment findings demonstrate that our technology can sense malicious network traffic efficiently, prevent and mitigate DDoS assaults, and improve VANET’s security and recovery speed from assaulting traffic. Furthermore, the proposed system has a high accuracy rate. The experimental results illustrate the proposed model's usefulness and efficiency in terms of detection accuracy and other metrics investigated.
The Rise of High Involvement Management in Britain

We discuss the nature and genesis of high involvement management (HIM) in Britain. Although an increasing proportion of British workplaces have adopted HIM practices over the last quarter century only a minority of managements have a strong high involvement orientation. HIM is associated with Total Quality Management and other lean production methods but is no more likely to be adopted in a context of enriched jobs than where jobs are more routinized. HIM usage is linked with the characteristics of the organization itself, such as whether the organization is family-owned, and the size and the composition of the workforce. External factors are much less important. There is little evidence that HIM is driving trade unionism out, as suggested by the union substitution hypothesis. Although there is recent evidence that it is associated with higher workplace productivity there is no evidence to suggest that HIM improves worker well-being. If anything, it is associated with higher levels of anxiety. Work enrichment, on the other hand, is correlated with positive outcomes for workers and employees alike.