scholarly journals Exploring Underpinning of Outsourcing Success: A case of Multinational Automotive group in Malaysia

2018 ◽  
Vol 7 (4.28) ◽  
pp. 40
Author(s):  
Kashif Latif ◽  
Mohd Nazari Ismail ◽  
Mohammad Nazri ◽  
Mohd Roslan Mohd Nor ◽  
Muhammad Imran Qureshi
Keyword(s):  
Real Time ◽  
Cost Reduction ◽  
Strategic Alliance ◽  
Economies Of Scale ◽  
Structured Interview ◽  
Outsourcing Success ◽  
Interpretive Paradigm

This study explored underpinning of outsourcing success by analyzing different theories of outsourcing. This study is unique in its nature as it used interpretive paradigm to explore, analyze outsourcing success theatrically by comparing different phases of outsourcing, then the case of Boeing analyzed in the context of outsourcing and after that come up with real time case study of one big automotive group operating in Malaysia through using semi structured interview by developing and comparing themes of this study. This study figured out that cost reduction and efficiency can be attained by creating economies of scale, using and managing vendors appropriately with the combination of efficient strategic alliance.

scholarly journals Visionado colectivo de cine en la red social Twitch por parte de centennials: estudio de caso

2022 ◽  
Vol 12 (01) ◽  
pp. 205-234
Author(s):  
Sergio Gutiérrez Manjón ◽  
◽  
Sergio Álvarez García ◽  
Sergio Mena Muñoz ◽  
◽  
...  
Keyword(s):  
Content Analysis ◽  
Real Time ◽  
Structured Interview ◽  
Common Pattern ◽  
Virtual Ethnography ◽  
Methodological Triangulation ◽  
La Red ◽  
Collective Construction ◽  
Consumption Habits

The network Twitch hosts a novel form of collective viewing of audiovisual products, whose audience is centennials. We analyse the case of Watch Parties, which allow users to watch films in real time with a streamer. Taking three Watch Parties of the streamer Lynx_Reviewer as a case study, a methodological triangulation is carried out: virtual ethnography, content analysis and semi-structured interview. By exploring the phenomenon, a model of analysis of collective consumption of content is constructed thanks to a descriptive systematisation of the audience’s consumption habits and uses by analysing the conversations and messages generated in the transmissions. The results obtained show that, despite the disparity of content and channels broadcasting on Twitch, this format follows a common pattern of broadcasting, participation, interface and type of messages. It is a leisure experience based on the collective construction of content developed synchronously with the interaction of the audience, which uses its own references and expressive codes to communicate, using films as a means of interaction within the community.

scholarly journals Ethics in Procurement: A Case Study in a Logistic Company

2019 ◽  
Author(s):  
Nor Rokiah Hanum Md. Haron ◽  
Iman Haddad Qaisara Dhia Mohd Mahzan
Keyword(s):  
Ethical Behavior ◽  
Cost Reduction ◽  
Structured Interview ◽  
Ethical Guidelines ◽  
Procurement Process ◽  
Ethical Behaviors ◽  
The People ◽  
Preliminary Study ◽  
A Company

This paper is part of a preliminary study for the awareness of ethical behaviors in procurement process specifically in a logistic company in Malaysia. Ethic is a critical behavior in a business transaction where most of the people misconduct the behavior unintentionally. The objective of this preliminary study is to get an insight view of a logistic case company, where it is found that ethics is being implemented in the daily procurement process. A structured interview question with various research of past problems concerning ethical behavior addresses the responsiveness of employees behaving ethically by following the ethical guidelines set by the company. This paper proposes that the company need to emphasize the importance of implementation of ethics and also create more awareness by having an official ethical guidelines of the company, despite the fact that the company is small and has a small number of employees. The company has gained many benefits such as cost reduction and maintaining a company-supplier relationship through the implementation of ethics in procurement.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient

2021 ◽  
Vol 4 (2) ◽  
pp. 32
Author(s):  
Heather A. Feldner ◽  
Christina Papazian ◽  
Keshia M. Peters ◽  
Claire J. Creutzfeldt ◽  
Katherine M. Steele
Keyword(s):  
Muscle Activity ◽  
Surface Electromyography ◽  
Inpatient Rehabilitation ◽  
Stroke Survivor ◽  
Structured Interview ◽  
Routine Care ◽  
Stroke Recovery ◽  
Descriptive Case Study ◽  
Sensing Technology

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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