scholarly journals Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

2019 ◽  
Vol 11 (19) ◽  
pp. 5245 ◽  
Author(s):  
Oluwatosin Theophilus ◽  
Maxim A. Dulebenets ◽  
Junayed Pasha ◽  
Olumide F. Abioye ◽  
Masoud Kavoosi
Keyword(s):  
Supply Chain ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Future Research ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Solution Methods ◽  
Future Research Directions ◽  
The Cross ◽  
The One

Recent trends in the management of supply chains have witnessed an increasing implementation of the cross-docking strategy. The cross-docking strategy, being the one that can potentially improve supply chain operations, has received a lot of attention from researchers in recent years, especially over the last decade. Cross-docking involves the reception of inbound products, deconsolidation, sorting, consolidation, and shipping of the consolidated products to the end customers. The number of research efforts, aiming to study and improve the cross-docking operations, increases every year. While some studies discuss cross-docking as an integral part of a supply chain, other studies focus on the ways of making cross-docking terminals more efficient and propose different operations research techniques for various decision problems at cross-docking terminals. In order to identify the recent cross-docking trends, this study performs a state-of-the-art review with a particular focus on the truck scheduling problem at cross-docking terminals. A comprehensive evaluation of the reviewed studies is conducted, focusing on the major attributes of the cross-docking operations. These attributes include terminal shape considered, doors considered, door service mode considered, preemption, internal transportation mode used, temporary storage capacity, resource capacity, objectives considered, and solution methods adopted. Based on findings from the review of studies, some common issues are outlined and future research directions are proposed.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

scholarly journals Social commerce—state-of-the-art and future research directions

2016 ◽  
Vol 26 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Catherine Baethge ◽  
Julia Klier ◽  
Mathias Klier
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Social Commerce ◽  
Research Directions ◽  
Future Research Directions

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals Addressing Covert Channel Attacks in RFID-Enabled Supply Chains

2013 ◽  
pp. 176-190
Author(s):  
Kirti Chawla ◽  
Gabriel Robins
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Network Flow ◽  
Economic Effects ◽  
Future Research ◽  
Covert Channel ◽  
Covert Channels ◽  
Node Selection ◽  
Future Research Directions ◽  
Key Nodes

RFID technology can help competitive organizations optimize their supply chains. However, it may also enable adversaries to exploit covert channels to surreptitiously spy on their competitors. We explain how tracking tags and compromising readers can create covert channels in supply chains and cause detrimental economic effects. To mitigate such attacks, the authors propose a framework that enables an organization to monitor its supply chain. The supply chain is modeled as a network flow graph, where tag flow is verified at selected key nodes, and covert channels are actively sought. While optimal taint checkpoint node selection is algorithmically intractable, the authors propose node selection and flow verification heuristics with various tradeoffs. The chapter discusses economically viable countermeasures against supply chain-based covert channels, and suggests future research directions.

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