Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters

2021 ◽  
Vol 14 (1) ◽  
pp. 9
Author(s):  
Faheem Ahmed Malik ◽  
Laurent Dala ◽  
Krishna Busawon
Keyword(s):  
Deep Neural Networks ◽  
Prediction Models ◽  
Transportation Systems ◽  
Learning System ◽  
Processing Unit ◽  
Knowledge Processing ◽  
Bicycle Infrastructure ◽  
Infrastructure System ◽  
And Gender

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

scholarly journals Gender Relation in Tea Plucking Workers: A Case Study of Gender Division of Labour and Gender Relation in Gambung Tea Plantation, West Java

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Kralawi Sita ◽  
Erna Herawati
Keyword(s):  
Action Learning ◽  
Analytical Framework ◽  
Learning System ◽  
Gender Relation ◽  
Tea Plantation ◽  
Gender Balance ◽  
Women's Participation ◽  
And Gender ◽  
Working Hour

<p>ABSTRACT<br />Men and women’s participation in tea plucking have been divided based on gender and strongly patriarchy-influenced. This division of labor cause a gender relation describes specific case of their relations in tea plantation. This study aims to describe the gender relation among the tea plucking workers at Gambung Tea Plantation, analyzed by qualitative approach, particularly treated as a case study. Data collected by in-depth interviews, observation, focus group discussion, and documentation. It was triangulated and analyzed using Harvard Analytical Framework and Gender Balance Tree in Gender Action Learning System approaches. The result shows that both men and women have equal access employment in plucking tea but their participation divided based on gender and patriarchy-influenced. Women have large participation in manual job description while men dominates on mechanic. Manual labor requires longer working-hour. It cause women have longer on working-hour than men. It is also enhance their burdern, eventhough generally they have double roles. As the consequences, women must work harder on their both roles. However, women’s participation in productive works enable women to generate income that makes them gaining better position within the household, such as a decision maker. It makes them able to access skill capacity.<br />Keywords: gender relation, tea pluckeig worker, tea plantation, Harvard Analytical Framework, Gender Action Learning System</p><p><br />ABSTRAK<br />Partisipasi laki-laki dan perempuan dalam pemetikan teh dibagi berdasarkan gender dan dipengaruhi kuat oleh patriarki. Pembagian kerja ini menimbulkan relasi gender yang menggambarkan kasus tertentu hubungan laki-laki dan perempuan di perkebunan teh. Penelitian ini bertujuan untuk mendeskripsikan relasi gender pada kegiatan pemetikan teh di Perkebunan Teh Gambung, dengan pendekatan kualitatif, dalam studi kasus tertentu. Pengumpulan data dilakukan dengan metode wawancara mendalam, observasi, diskusi grup terpusat, dan dokumentasi. Data ditriangulasi dan dianalisis menggunakan Harvard Analytical Framework dan Gender Balance Tree dalam Gender Action Learning System. Hasil penelitian menunjukkan bahwa baik laki-laki dan perempuan mempunyai akses yang sama dalam pemetikan tetapi partisipasi mereka dibagi berdasarkan gender dan dipengaruhi patriarki. Perempuan mempunyai partisipasi besar dalam pemetikan manual sedangkan laki-laki mendominasi mekanisasi mesin petik. Manual membutuhkan waktu yang tinggi yang menyebabkan perempuan mempunyai waktu kerja yang lebih banyak dari laki-laki dan hal ini menambah beban perempuan yang secara general mempunyai beban ganda. Sebagai konsekuensinya, perempuan harus bekerja lebih keras. Namun, partisipasi perempuan dalam pekerjaan produktif memungkinkan perempuan untuk menghasilkan pendapatan yang memberikan perempuan posisi yang lebih baik dalam rumah tangga, seperti kekuasaan dalam pengambilan keputusan dan posisi tawar dalam mengakses peningkatan kapasitas keterampilan.<br />Kata kunci: relasi gender, pemetik teh, perkebunan teh, Harvard Analytical Framework, Gender Action Learning System</p>

The Category Is . . . Opulence!

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Joseph Plaster
Keyword(s):  
Performance Studies ◽  
Knowledge Creation ◽  
People Of Color ◽  
Gender Nonconforming ◽  
Johns Hopkins University ◽  
The Cross ◽  
And Gender ◽  
A Performance

In recent years there has been a strong “public turn” within universities that is renewing interest in collaborative approaches to knowledge creation. This article draws on performance studies literature to explore the cross-disciplinary collaborations made possible when the academy broadens our scope of inquiry to include knowledge produced through performance. It takes as a case study the “Peabody Ballroom Experience,” an ongoing collaboration between the Johns Hopkins University Sheridan Libraries, the Peabody Institute BFA Dance program, and Baltimore’s ballroom community—a performance-based arts culture comprising gay, lesbian, queer, transgender, and gender-nonconforming people of color.

The Complexity of Work for Women who are HIV Positive: A Case Study in DKI Jakarta

2018 ◽  
Vol 23 (4) ◽  
pp. 203
Author(s):  
Andi Nur Faizah
Keyword(s):  
Qualitative Method ◽  
Family Members ◽  
Hiv Positive ◽  
Difficult Situation ◽  
Women Living With Hiv ◽  
Hiv Positive Women ◽  
And Gender ◽  
Gender Categories ◽  
Living With Hiv

<p>The phenomenon of HIV-AIDS transmission places women in a difficult situation. The loss of family members such as husbands due to AIDS leaves women living with HIV positive in a struggle to access sources of livelihood. The condition of themselves as PLWHA, concerns about being stigmatized, caring for family members, and earning a living are the burdens of life they have to face. In this regard, this paper explores the complexity of the work of HIV-positive women. This study uses a qualitative method with a feminist perspective to get a complete picture of the livelihood of HIV-positive women. Based on interviews with five HIV-positive women, the findings found a link between social, identity, and gender categories that affect their livelihoods. HIV-positive women also transform themselves into their “normal” self by pretending to be healthy, able to work, have quality, and be independent. This is done as a form of resistance to the stigma attached to PLWHA.</p><p> </p><p> </p>

Case Study: The Southern Oral History Program

2012 ◽  
Author(s):  
Jacquelyn Dowd Hall ◽  
Kathryn Nasstrom
Keyword(s):  
Oral History ◽  
American South ◽  
New Era ◽  
Chapel Hill ◽  
Integral Role ◽  
The American South ◽  
And Gender ◽  
History Department ◽  
The University

A case study of the southern oral history program is the essence of this chapter. From its start in 1973 until 1999, the Southern Oral History Program (SOHP) was housed by the history department at the University of North Carolina at Chapel Hill (UNC), rather than in the library or archives, where so many other oral history programs emerged. The SOHP is now part of UNC's Center for the Study of the American South, but it continues to play an integral role in the department of history. Concentrating on U.S. southern racial, labor, and gender issues, the program offers oral history courses and uses interviews to produce works of scholarship, such as the prize-winning book Like a Family: The Making of a Southern Cotton Mill World. The folks at the Institute for Southern Studies tried to combine activism with analysis, trying to figure out how to take the spirit of the movement into a new era.

A framework for modeling and assessing system resilience using a Bayesian network: A case study of an interdependent electrical infrastructure system

2019 ◽  
Vol 25 ◽  
pp. 62-83 ◽  
Author(s):  
Niamat Ullah Ibne Hossain ◽  
Raed Jaradat ◽  
Seyedmohsen Hosseini ◽  
Mohammad Marufuzzaman ◽  
Randy K. Buchanan
Keyword(s):  
Bayesian Network ◽  
Infrastructure System ◽  
System Resilience

scholarly journals ‘Women take care and men take charge’: The case of leadership and gender in the Public and Commercial Services Union

2020 ◽  
pp. 0143831X2094368
Author(s):  
Julie Prowse ◽  
Peter Prowse ◽  
Robert Perrett
Keyword(s):  
Leadership Style ◽  
Trade Unions ◽  
Union Leader ◽  
The Public ◽  
Union Leadership ◽  
Positive Attitudes ◽  
Union Leaders ◽  
And Gender ◽  
Survey Responses

This article presents the findings of a case study that aimed to understand the specific leadership styles that are valued by women and men lay representatives in the Public and Commercial Services Union (PCS) and to determine the gendered implications for increasing women’s leadership and representation in trade unions. Survey responses from PCS lay representatives (reps) show the majority of women and men agreed that the leadership style they value, and that makes a good union leader, is post-heroic (communal) leadership. This approach is associated with leadership characteristics such as being helpful, sensitive and kind and are generally practised by women. This contrasts with male union leaders who are associated with a traditional, heroic (agentic) leadership style characterised by confidence, self-reliance and decisiveness. Although some differences exist that highlight gender issues, both women and men lay reps have positive attitudes towards increasing women’s representation and participation in union leadership.

scholarly journals Protocol for establishing an Adaptive Diseases control Expert Programme in Tanzania (ADEPT) for integrating care of communicable and non-communicable diseases using tuberculosis and diabetes as a case study

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e041521
Author(s):  
Stellah G Mpagama ◽  
Kaushik Ramaiya ◽  
Troels Lillebæk ◽  
Blandina T Mmbaga ◽  
Marion Sumari-de Boer ◽  
...  
Keyword(s):  
Operational Research ◽  
Communicable Diseases ◽  
Health Facilities ◽  
Learning System ◽  
Management Approach ◽  
African Countries ◽  
Non Communicable Diseases ◽  
Research Outcomes ◽  
The Government

IntroductionMost sub-Saharan African countries endure a high burden of communicable infections but also face a rise of non-communicable diseases (NCDs). Interventions targeting particular epidemics are often executed within vertical programmes. We establish an Adaptive Diseases control Expert Programme in Tanzania (ADEPT) model with three domains; stepwise training approach, integration of communicable and NCDs and a learning system. The model aims to shift traditional vertical programmes to an adaptive diseases management approach through integrating communicable and NCDs using the tuberculosis (TB) and diabetes mellitus (DM) dual epidemic as a case study. We aim to describe the ADEPT protocol with underpinned implementation and operational research on TB/DM.Methods and analysisThe model implement a collaborative TB and DM services protocol as endorsed by WHO in Tanzania. Evaluation of the process and outcomes will follow the logic framework. A mixed research design with both qualitative and quantitative approaches will be used in applied research action. Anticipated implementation research outcomes include at the health facilities level for organising TB/DM services, pathways of patients with TB/DM seeking care in different health facilities, factors in service delivery that need deimplementation and the ADEPT model implementation feasibility, acceptability and fidelity. Expected operational research outcomes include additional identified patients with dual TB/DM, the prevalence of comorbidities like hypertension in patients with TB/DM and final treatment outcomes of TB/DM including treatment-related complications. Findings will inform the future policies and practices for integrating communicable and NCDs services.Ethics and disseminationEthical approval was granted by The National Research Health Ethical Committee (Ref-No. NIMR/HQ/R.8a/Vol.IX/2988) and the implementation endorsed by the government authorities. Findings will be proactively disseminated through multiple mechanisms including peer-reviewed journals, and engagement with various stakeholders’ example in conferences and social media.

scholarly journals Female Candidates, Islamic Women’s Organisations, and Clientelism in the 2019 Indonesian Elections

2021 ◽  
Vol 40 (1) ◽  
pp. 73-92
Author(s):  
Muhammad Mahsun ◽  
Misbah Zulfa Elizabeth ◽  
Solkhah Mufrikhah
Keyword(s):  
Structural Barriers ◽  
Women Candidates ◽  
Electoral Success ◽  
Female Candidates ◽  
Central Java ◽  
Grassroots Support ◽  
Nahdlatul Ulama ◽  
And Gender ◽  
Women's Networks

This article analyses the factors leading to the success of women candidates in the 2019 elections in Central Java. Recent scholarship on women’s representation in Indonesia has highlighted the role that dynastic ties and relationships with local political elites play in getting women elected in an environment increasingly dominated by money politics and clientelism. Our case study of women candidates in Central Java belonging to the elite of the Nahdlatul Ulama (NU)-affiliated women’s religious organisations Muslimat and Fatayat shows that strong women candidates with grassroots support can nonetheless win office. Using the concepts of social capital and gender issue ownership, and clientelism, we argue that women candidates can gain a strategic advantage when they “run as women.” By harnessing women’s networks and focusing on gender issues to target women voters, they are able to overcome cultural, institutional, and structural barriers to achieve electoral success even though they lack resources and political connections.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

scholarly journals Enabling deeper learning on big data for materials informatics applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipendra Jha ◽  
Vishu Gupta ◽  
Logan Ward ◽  
Zijiang Yang ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Materials Science ◽  
Prediction Models ◽  
Model Performance ◽  
Materials Informatics ◽  
Learning Framework ◽  
Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

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