The Impacts of Relational Norms and Relationship Quality on Franchise Firm’s Performance: The empirical of Malaysian Franchisee

Franchising is one of the best way for expanding a business in the competitive industry and global market. Franchising industries are growing rapidly in most countries and have contributed to the growth of gross domestic product. This study adopts a quantitative approach, applying a cross-sectional study. This study attempts to examine empirically relational mechanisms which may influence the relationship quality and business performance in franchising relationship from franchisees perspective. The findings reveal that relational mechanisms are crucial in affecting franchisee relationship quality. The results provide strong evidence that franchisee relationship quality is found to significantly affect business performance in the franchise system

Design of robotic arm controller based on Internet of Things (IoT)

This paper presents the process of developing a controller for a robotic arm that is built through the Internet of Things (IoT).The direction of the robotic arm can be monitored and controlled using internet facilities. The Raspberry Pi board is utilized in this project for the robotic arm controller as well as the web server system.The robotic arm comprises four servo motors and each of the servo motors is assigned with a single pulse width modulation (PWM) output that can be individually controlled.The controller system is implemented on Raspberry Pi board using Python 2.7 programming language.Node-Red is used as a web server in this project to communicate with the web browser through TCP/HTTP.Hence, this allows the user to access the web browser using computer or smartphones.In addition, it enables the monitoring and controlling of the robotic arm direction as well as performing pick and place task similar to the manufacturing industry.The results of this study are verified through practical test implementation

Design of Robotic Arm Controller based on Internet of Things (IoT)

This paper presents the process of developing a controller for a robotic arm that is built through the Internet of Things (IoT). The direction of the robotic arm can be monitored and controlled using internet facilities. The Raspberry Pi board is utilized in this project for the robotic arm controller as well as the web server system. The robotic arm comprises four servo motors and each of the servo motors is assigned with a single pulse width modulation (PWM) output that can be individually controlled. The controller system is implemented on Raspberry Pi board using Python 2.7 programming language. Node-Red is used as a web server in this project to communicate with the web browser through TCP/HTTP. Hence, this allows the user to access the web browser using computer or smartphones. In addition, it enables the monitoring and controlling of the robotic arm direction as well as performing pick and place task similar to the manufacturing industry. The results of this study are verified through practical test implementation

Variations of riparian vegetation along the river corridors of Sg. Johor

Riparian vegetation has been recognized for its remarkable environmental and management implications. Occurred within the dynamic tract of river systems, riparian vegetation is a complex character that often exposed to the changes of river water and river beds. Their spatial extent is strongly controlled by inundation and flood disturbance, which result in the riparian vegetation migration to the point of destruction, if the competition for the area and other sources are lacking. This paper presents the findings of collected riparian vegetation information along Sg. Johor at the upstream and downstream of Kota Tinggi. Using Point-Centre-Quarter Method, the vegetation’s species, density, basal area, diameter at breast height and relative composition were recorded, identified and classified. Vitex pubescens and Drypetes spp. dominantly occurred along the upstream and downstream of this river, respectively. Species like Gymnacranthera bancana, Endospermum Malaccense, and Aquilaria Malaccensis are also found inhabit along Sg. Johor bank. Classified as woody vegetation, these vegetations are equipped with buttress roots that enable them to increase soil strength. This paper also suggests that proper study of riparian vegetation along river banks could promote a better understanding of the function of each species, to ensure the sustainability of riparian vegetation as part of river system engineer

Reducing delay and jitter for real-time control communication in Ethernet 1

The aim of this paper is to develop an approach for cheap and deterministic control communication using Ethernet. A half-duplex Ethernet network populated with a small/medium number of Media Access Controllers (MACs) is used for timed real-time communication. Data packages are sent at well defined times to avoid collisions. Collisions mainly occur due to jitter of the transmitter system, so that arbitration (similar to CANopen) is necessary. In this paper, simulation models using a Binary Exponential Backoff (BEB) scheme and a Linear Backoff scheme are developed. This paper analyzes and investigates how the backoff time affects the performance of the Carrier Sense Multiple Access protocol with Collision Detection (CSMA/CD) in a basic Media Access Controller (MAC), in terms of data arrival characteristics, i.e jitter and delay. We propose to assign different minimal back-off times for each of the CSMA/CD controller units to minimize packet collisions. Simulated tests show the advantage of our approach over a standard CSMA/CD setting

Real Time FPGA-Based Ethernet Control Communication for Robotic Arm

In this paper, an approach for real time control communication using Ethernet is proposed. The strategy to support this at the network level and include Field Programmable Gate Array (FPGA) implementation on the Ethernet platform for robotic arm. An embedded Ethernet controller is designed to send data packet via Ethernet Local Area Network (LAN). The transferring data also employs Arduino Mega as the medium of communication between FPGA board and the robotic arm. It is used as the receiver to receive data packet from FPGA board with the interface of Arduino Ethernet shield. The control operation on the robotic arm is performed once the desired data packet length is reached to the Arduino Mega. SolidWorks and MATLAB software are used to design the robotic arm and simulate the robotic arm working flexibility in real world respectively. The result of the average data packet delay between FPGA boards is lower in comparison to Arduiono board. The data packet can send successfully in through the network to test the robotic arm

Real Time FPGA-Based Ethernet Control Communication for Robotic Arm

In this paper, an approach for real time control communication using Ethernet is proposed. The strategy to support this at the network level and include Field Programmable Gate Array (FPGA) implementation on the Ethernet platform for robotic arm. An embedded Ethernet controller is designed to send data packet via Ethernet Local Area Network (LAN). The transferring data also employs Arduino Mega as the medium of communication between FPGA board and the robotic arm. It is used as the receiver to receive data packet from FPGA board with the interface of Arduino Ethernet shield. The control operation on the robotic arm is performed once the desired data packet length is reached to the Arduino Mega. SolidWorks and MATLAB software are used to design the robotic arm and simulate the robotic arm working flexibility in real world respectively. The result of the average data packet delay between FPGA boards is lower in comparison to Arduiono board. The data packet can send successfully in through the network to test the robotic arm

Improved Bald Eagle Search Optimization With Deep Learning-Based Cervical Cancer Detection and Classification

Cervical cancer (CC) is the fourth most popular cancer affecting women worldwide. Mortality and incidence rates can be consistently enhancing, particularly in emerging countries, because of the lack of screening services, lack of awareness, and restricted qualified experts. CC has screened utilizing human papillomavirus (HPV) test, Papanicolaou (Pap) test, histopathology test, and visual inspection after application of acetic acid (VIA). Intra- and Inter-observer variability can take place in the manual analysis method, resulting in misdiagnosis. Previous studies have exploited either deep learning (DL) or machine learning (ML) approaches, the preceding one could not be efficient as it needs segmentation and attaining hand-crafted features that utilize critical stage. Artificial Intelligence (AI) based computer-aided diagnoses (CAD) methods are generally explored for identifying CC for enhancing the standard testing method. This manuscript offers an Improved Bald Eagle Search Optimization with Deep Learning based Cervical Cancer Detection and Classification (IBESODL-CCDC) algorithm. The drive of the IBESODL-CCDC algorithm lies in the automated classification and detection of CC. In the presented IBESODL-CCDC technique, a contrast enhancement process takes place to enhance the image qualities. In addition, the IBESODL-CCDC technique utilizes a modified LeNet model for the feature extraction model. For CC detection, the IBESODL-CCDC technique applies an attention-based long short-term memory (ALSTM) network. A wide-ranging experiment was applied to validate the greater outcome of the IBESODL-CCDC technique. The experimental values highlight the remarkable performance of the IBESODL-CCDC algorithm with other recent systems

Who Danced Better? Ranked TikTok Dance Video Dataset and Pairwise Action Quality Assessment Method

Video-based action quality assessment (AQA) is a non-trivial task due to the subtle visual differences between data produced by experts and non-experts. Current methods are extended from the action recognition domain, where most are based on temporal pattern matching. AQA has additional requirements where order and tempo matter for rating the quality of an action. We present a novel dataset of ranked TikTok dance videos and a pairwise AQA method for predicting which video of a same-label pair was sourced from the better dancer. Exhaustive pairings of same-label videos were randomly assigned to 100 human annotators, ultimately producing a ranked list per label category. Our method relies on a successful detection of the subject’s 2D pose inside successive query frames where the order and tempo of actions are encoded inside a produced String sequence. The detected 2D pose returns a top-matching Visual word from a Codebook to represent the current frame. Given a same-label pair, we generate a String value of concatenated Visual words for each video. By computing the edit distance score between each String value and the Gold Standard’s (i.e., the top-ranked video(s) for that label category), we declare the video with the lower score as the winner. The pairwise AQA method is implemented using two schemes, i.e., with and without text compression. Although the average precision for both schemes over 12 label categories is low, at 0.45 with text compression and 0.48 without, precision values for several label categories are comparable to past methods (median: 0.47, max: 0.66)