Hard Issues Research Articles

The influence of values on hard issue attitudes

ABSTRACT In light of extensive findings that Americans lack ideological structure to their attitudes, political scientists have turned to values as a source of attitudinal constraint in the mass public. However, most of this work has exclusively examined “easy” issues – those that are highly visible and familiar to voters. In this study, I conduct an original survey to investigate the extent to which values predict attitudes toward three “hard” issues: nuclear energy expansion, needle exchange programs, and laws that allow health care providers to opt out of providing medication that could terminate a pregnancy. Overall, I find that while values structure the attitudes of most individuals, those who are poorly informed about politics fail to mimic the patterns associated with the well-informed. These findings suggest that although values can be a source of attitudinal constraint in the general public, their influence is conditional upon political sophistication.

An Improved Variable Kernel Width for Maximum Correntropy Criterion Algorithm

The maximum correntropy criterion (MCC) algorithm has attracted much attention due to its capability of combating impulsive noise. However, its performance depends on choice of the kernel width, which is a hard issue. Several variable kernel width schemes based on various error functions have been proposed to address this problem. Nevertheless, these methods may not provide an optimal kernel width because they do not contain any knowledge of the background noise that actually has influence on the optimization of the kernel width. This brief proposes an improved variable kernel width MCC algorithm, which is derived by minimizing the squared deviation at each iteration. We also design a reset mechanism for the proposed algorithm to improve its tracking capability when the estimated vector encounters a sudden change. Simulations for system identification and echo cancellation scenarios show that the proposed scheme outperforms other variable kernel width algorithms.

Mining Urban Traffic Condition from Crowd-Sourced Data

Traffic congestion is an inherent and hard issue to be tackled in huge urban areas, particularly in developing countries where transportation infrastructures have not been grown well to fulfill speedy developing request demands. This paper proposes novel solutions to these issues by devising mobile crowd-sourcing based approaches to traffic estimation. A framework for effective collecting, integrating and analyzing traffic-related data shared by mobile crowds has been devised. Besides, essential issues on predicting traffic conditions at streets where real-time data is missed are also resolved by applying data mining techniques to historical data. A prototype system has been developed to validate the proposed solutions. The experimental results show the feasibility and the effectiveness of the proposed methods revealing that they are ready to be applied in the practice.

Automatic gray images colorization based on lab color space

<p>The colorization aim to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concatenate with both a, and b channels before converting to RGB image. The results were promised compared with other methods.</p>

A Study on Zigbee Based Human Body Monitoring System

This paper introduce us a real time body monitoring system. In now a day’s security and health care becomes an hard issue. To take off and to monitor a patient continuously is very hard and huge task for doctors. This system plays an important role in monitor patients physical and mental health conditions. ZigBee can measure physical parameters like temperature, heart rate, blood pressure, patient’s movement with the help of some devices either inserted in body or externally attached to human body. It helps to get correct or accurate readings from human body and passes it to ZigBee. Next step is to process the data. The data will be sent to hospital or doctor if in case of any emergency by ZigBee wireless module. The hospital and monitoring system receive all the information about patients and save this information into database from which they can judge the condition of patients by studying the monitoring records and patient’s health. This data can be deployed in graphical form or in numerical form in dataset. According to the information collected from monitoring system the patient doctors having advantage to understand the patient condition more gracefully and also start the treatment according to continues information gathers.

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored.

Diversity of News Sources in Climate Change Reporting in Pakistani Press

This study examines the diversity of news sources in climate change reporting in the Pakistani press. A longitudinal quantitative content analysis of climate change reporting of five significant climate change-related events such as Earth Summit ((1992), Rio de Janeiro, Kyoto Protocol (1997), Ratification of Kyoto Protocol Globally (2005), COP15 (2009) Copenhagen and COP21 (2015) Paris is conducted. The samples were selected through systematic random sampling technique by choosing every alternate day of newspapers’ publications. A total of 1498 diverse news sources were found in 324 climate change-related stories during the five events mentioned above. The findings of this study revealed that elite sources dominated the marginalized sources collectively in coverage of the three newspapers during the five events. However, the climate change-related event/incidents represented highly in the reporting of Pakistani press. Further, a thin proportion of mentions of media-related sources such as News agencies and Journalists also found. Thus, this study recommends that similar studies should be conducted on news content of other soft and hard issues in Pakistani press to examine the diversity of sources.

Online learning behavior analysis based on machine learning

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

THE PEDAGOGICAL IMPORTANCE OF THE JOURNAL “THE EASTERN WOMAN”

In 1923, with the aim of furthering the promotion of political, social and cultural-educational work among women in Azerbaijan, the monthly, literary-art, socio-political journal “The East Woman” has begun to be published. “The East Woman” journal had to struggled on the most difficult and hardest issues in its early days, it is possible to concentrate this fight mainly around three tasks. The first of these is to save working Turkish women from Sharia, religion and superstition, old traditions, to get them out of the narrow family life and to fight decisively with all appearing forces for the sake of the ability to assimilate their true freedoms; secondly, propagate and encourage them to attract Azerbaijani women captured for centuries under the charism regime, deprived of their rights and freedoms, to new public living quarters, factories and plants, cultural and educational front, the work of council structure; the third was to struggle to create the necessary conditions for women to work properly on the new front. “The East Woman” journal commented on the history, the origin and the content of the work of Maternity and Child Protection in Azerbaijan providing articles of various content relating to the protection of mothers and infants from the first issue. In some articles an advice was given to mothers, an information on maternal and child health was conveyed and fight measures were shown. It was spoken about how to cherish, to feed, to educate children and other issues in the period from birth to the age of kindergarten . In other group articles news about work, measures done in connection with the above mentioned issues was reflected. Thus, research shows that “The East Woman” journal provides extensive information on the creation of maternity and child protection and its responsibilities in Azerbaijan. The journal has conducted great pedagogical propaganda by publishing information, news, materials giving scientific knowledge about nurseries, kindergartens, child care offices, maternity hospitals, children's hospitals, etc. in its pages.

SAR Target Small Sample Recognition Based on CNN Cascaded Features and AdaBoost Rotation Forest

Automatic target recognition (ATR) has made great progress with the development of deep learning. However, the target feature in synthetic aperture radar (SAR) image is not consistent with human vision, and the SAR training samples are always limited. These hard issues pose new challenges to the SAR ATR based on convolutional neural network (CNN). In this letter, we propose an improved CNN model to solve the limited sample issue via the feature augmentation and ensemble learning strategies. Normally, the high-level features that are more comprehensive and discriminative than the middle-level and low-level features are always employed for category discrimination. In order to make up the insufficient training features in the limited sample case, the cascaded features from optimally selected convolutional layers are concatenated to provide more comprehensive representation for the recognition. To take full advantage of these cascaded features, the ensemble learning-based classifier, namely, the AdaBoost rotation forest (RoF), is introduced to replace the original softmax layer to realize a more accurate limited sample recognition. Through the AdaBoost RoF method, not only are these features further enhanced by the rotation matrix but also a strong classifier is constructed by several weak classifiers with different adjusted weights. The experimental results on MSTAR data set show that the cascaded features and ensemble weak classifiers can fully exploit effective information in limited samples. Compared with the existing CNN method, the proposed method can improve the recognition accuracy by about 20% under the condition of ten training samples per class.

Large-Area Soft e-Skin: The Challenges Beyond Sensor Designs

Sensory feedback from touch is critical for many tasks carried out by robots and humans, such as grasping objects or identifying materials. Electronic skin (e-skin) is a crucial technology for these purposes. Artificial tactile skin that can play the roles of human skin remains a distant possibility because of hard issues in resilience, manufacturing, mechanics, sensorics, electronics, energetics, information processing, and transport. Taken together, these issues make it difficult to bestow robots, or prosthetic devices, with effective tactile skins. Nonetheless, progress over the past few years in relation with the above issues has been encouraging, and we have achieved close to providing some of the abilities of biological skin with the advent of deformable sensors and flexible electronics. The naive imitation of skin morphology and sensing an impoverished set of mechanical and thermal quantities are not sufficient. There is a need to find more efficient ways to extract tactile information from mechanical contact than those previously available. Renewed interest in neuromorphic tactile skin is expected to bring some fresh ideas in this field. This article reviews these new developments, particularly related to the handling of tactile data, energy autonomy, and large-area manufacturing. The challenges in relation with these advances for tactile sensing and haptics in robotics and prosthetics are discussed along with potential solutions.

Courageous conversations in supervision

INTRODUCTION: Courageous conversations, commonly identified as conversations which are associated with some form of emotion, are features of many social workers’ daily routine. In supervision, such conversations are typically required to address issues of supervisee professional competence, ethical issues or the supervision relationship and/or process. These conversations, which are challenging, are at times avoided and, at other times, may be poorly handled.APPROACH: Following identification of the obstacles which may impede addressing challenging issues in professional practice, this article focuses the supervisor’s role in courageous conversations. The importance of building a supervision environment which can support robust conversations is highlighted. Here the contracting process, where the expectations of supervision are negotiated and the power inherent in the supervision relationship can be identified, is considered foundational. The skills and attributes needed by the supervisor to manage these difficult encounters are explored and three kinds of interventions are identified as helpful: relational, reflective, and confrontational. A framework for a courageous conversation is provided which highlights the need for clarity about the motivation, purpose and desired goals. Finally, a structure for the proposed conversations is presented.IMPLICATIONS: With an understanding of the dynamics and of the skills required, supervisors can better prepare themselves for courageous conversations. When supervision relationships are based on negotiation and shared understanding about power, difference and expectations, hard issues can be raised and honestly confronted and at the same time the integrity of all involved can be maintained.

Outside counsel can help with the hard issues

Board & Administrator for Administrators OnlyVolume 36, Issue 2 p. 3-3 Articles Outside counsel can help with the hard issues First published: 19 September 2019 https://doi.org/10.1002/ban.30974Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume36, Issue2October 2019Pages 3-3 RelatedInformation

A Heuristic Algorithm to Find Power Efficient Pre Configured Cycles (PEP-cycles) and Resolve NP Hard Issues

Now a days ICT has powerful impact on economy and environmental conditions of the country. The principle of green networking gives idea to design a energy aware network. These networks focus on the energy reduction and consumption of unnecessary energy to save the environment. It is predicted that the global IP traffic will be approx 63.9 Exabyte’s per month in coming years .These increased bandwidth requires more bandwidth and it consumes large amount of energy and causes global warming too. To reduce this global warming effect various power saving schemes are opted by the service providers that contributes to save the environment. In this scenario we develop a heuristic to choose the power efficient pre configured cycles (PEP- cycles) from the group of all cycles to minimize the effect of NP hard problem. The developed algorithm is used to achieve fast speed protection of optical network. The main aim of the algorithm is to reduce the complexity of the optimization and to resolve the complexity problem.

Defensive Investment in Municipal Water Hardness Reduction

AbstractHousehold willingness to pay (WTP) for reductions in water hardness was estimated using a defensive investment framework. Using total dissolved solids data observed in municipal water supplies in combination with product‐by‐store level point‐of‐sale scanner data on consumable water softening product sales, we reveal preference estimates of WTP for water hardness reduction and quality control. Using instrumental variable regressions, we show that household marginal WTP increases as the observed total dissolved solids in municipal water increases. Estimations show that on average a household is willing to pay approximately $12 annually to reduce municipal water hardness, which is around 4% of the average annual water utility bill in the United States. Aggregated county WTP estimates vary geographically. Households have a nonnegligible WTP for water hardness reduction, which has important policy implications for optimal water hardness management by municipal water authorities and those policies aimed to target salinity management within surface and subsurface water supplies. Particularly, this paper informs municipal water managers of the demand side of water hardness issues and that desalination can be a solution to solve water hardness issues in some areas but not everywhere.

A structured index describing the ease of disassembly for handcrafted product

AbstractBoth economic and environmental aspects significantly influence the design process since the early phases of preliminary design. The total Life Cycle Assessment (LCA) and the End of Life (EoL) of products have to be defined in the early design phases too but, for industrial products that are not feasible to automatic production, they are hard issues. However, the EoL of products can be assessed by evaluating the disassembly of joints assembling the product, even when the production process is subject to an important contribute of workmanship. In this paper, a useful method is proposed to analyze the disassembly plant of products, in order to optimize the design process in the early preliminary phases. The method quantitatively evaluates a Disassembly Index that describes the attitude of a product to be disassembled. A case study describes the disassembly attitude of structural subassemblies of a sailboat. In order to test the applicability of the model described to both manual and automated disass...

A Survey on the Variable-Order Fractional Differential Equations: Mathematical Foundations, Models, Numerical Methods and Its Applications

Variable-order fractional differential equations (FDEs), where the fractional order varies with time (t), space (x) or others variables, successfully investigates the time or/and space dependent dynamic systems. The objective of this study is to provide a survey of the most relevant recent literatures and findings in the areas of primary definitions, numerical methods, models and its applications. More so, the powerful mathematical tool, variable-order fractional derivative models have been remarkably acknowledged as an alternative and precise approach which can effectively deal with real world phenomenon. Subsequently, this paper offers an overview on the researches of variable-order FDEs. In addition, it also helps the readers for the selection of an appropriate models and numerical method to solve some specific situations and faced problems. This work plays a vital role in studying of variable-order fractional calculus, which is contribute to unravel some uncanny features of systems change and handle some hard issues in real-world applications.

Nanostructured copper-carbon nanotubes composites for aircraft applications

PurposeThe reliable performance of critical components working under extreme conditions is paramount to the safe operation of aircraft, and material selection is critical. Copper alloys are an obvious choice for such applications whenever a combination of transport, mechanical and tribological properties is required. However, low strength and hardness issues require development of new copper alloys and composites to improve service life and reliability. This study aims to investigate the effect of carbon nanotubes as reinforcement phase in copper-matrix composites.Design/methodology/approachThe development of novel copper-based composites refined to the nanoscale was envisaged through mechanical milling of mixtures containing copper and carbon nanotubes (2 Wt.%). Milling took place in a planetary ball mill for times varying between 1 h and 16 h at 400 rpm. A ball-to-powder ratio of 20:1 and alumina vial and copper spheres were used under dry conditions or with addition of isopropyl alcohol. Scanning electron microscopy/energy dispersive spectroscopy, size distribution, Raman spectroscopy and X-ray diffraction were used to study the produced powders.FindingsAttained results show that mechanical milling of the studied system produces nanostructured powders containing second-phase carbon nanotubes homogeneously distributed in the metallic matrix, together with severe copper grain refinement. This should correspond to increased residual microstresses, envisaging significant improvement of mechanical properties of the produced copper composites.Originality/valueThe novelty of the work resides in the use of carbon nanotubes for the reinforcement of copper, and on the systematic microstructural characterisation of the produced composites.

Rényi entropies for multidimensional hydrogenic systems in position and momentum spaces

The Rényi entropies of Coulomb systems are logarithms of power functionals of the electron density which quantify most appropriately the electron uncertainty and describe numerous physical observables. However, their analytical determination is a hard issue not yet solved except for the first lowest-lying energetic states of some specific systems. This is so even for the D-dimensional hydrogenic system, which is the main prototype of the multidimensional Coulomb many-body systems. Recently, the Rényi entropies of this system have been found in the two extreme high-energy (Rydberg) and high-dimensional (pseudo-classical) cases. In this work we determine the position and momentum Rényi entropies (with integer p greater than 1) for all the discrete stationary states of the multidimensional hydrogenic system directly in terms of the hyperquantum numbers which characterize the states, nuclear charge and space dimensionality. We have used a methodology based on linearization formulas for powers of the orthogonal Laguerre and Gegenbauer polynomials which control the hydrogenic states.

Optimizing control dynamic complexity and production schedule

Over the last few decades sophisticated line balancing methodologies have been developed in response to the emerging trend of lean production in order to more effectively streamline production sequence scheduling. Newer approaches now demonstrate the potential to achieve lean production while at the same time simplifying what have become increasingly complex analytical methodologies and production sequences. Unlike past studies on line balancing for mixed-model production lines, in this paper the impacts of line balance on Control Dynamic Complexity are investigated and their deployment illustrated with a case study. Precedence matrices have been studied and a combined precedence matrix computed. The mathematical programming language Matlab is employed to illustrate state diagrams representing the relationships between the tasks and terminals. It is found that control dynamic complexities are higher for some particular sequences, and in such cases further explanations are offered. Furthermore, this study reveals that it is possible to minimize the complexities by better exploiting some aspects of line scheduling guidelines. Since line balancing and line scheduling are NP hard issues, this paper has also demonstrated ways in which computational analysis of multiple iterations can be significantly accelerated in future by deploying ant colony optimization techniques. Analysis of complexity level highlights what may otherwise be intuitive, that excessive sophistication of line balance techniques is very likely to be detrimental to an overall organization if resulting scheduling becomes difficult to practically monitor.