Search results

This paper aims to predict the behaviour of the vehicles in a mixed driving scenario. This proposes a deep learning model to predict lane-changing scenarios in highways incorporating current and historical information and contextual features. The interactions among the vehicles are modelled using long-short-term memory (LSTM).

Predicting the surrounding vehicles' behaviour is crucial in any Advanced Driver Assistance Systems (ADAS). To make a decision, any prediction models available in the literature consider the present and previous observations of the surrounding vehicles. These existing models failed to consider the contextual features such as traffic density that also affect the behaviour of the vehicles. To forecast the appropriate driving behaviour, a better context-aware learning method should be able to consider a distinct goal for each situation is more significant. Considering this, a deep learning-based model is proposed to predict the lane changing behaviours using past and current information of the vehicle and contextual features. The interactions among vehicles are modeled using an LSTM encoder-decoder. The different lane-changing behaviours of the vehicles are predicted and validated with the benchmarked data set NGSIM and the open data set Level 5.

The lane change behaviour prediction in ADAS is gaining popularity as it is crucial for safe travel in a mixed driving environment. This paper shows the prediction of maneuvers with a prediction window of 5 s using NGSIM and Level 5 data sets. The proposed method gives a prediction accuracy of 97% on average for all lane-change maneuvers for both the data sets.

This research presents a strategy for predicting autonomous vehicle behaviour based on contextual features. The paper focuses on deep learning techniques to assist the ADAS.

A wireless body area network (WBAN) is a collection of sensing devices attached to a person’s body that is typically used during health care to track their physical state. This paper aims to study the security challenges and various attacks that occurred while transferring a person’s sensitive medical diagnosis information in WBAN.

This technology has significantly gained prominence in the medical field. These wearable sensors are transferring information to doctors, and there are numerous possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. As a result, mutual authentication and session key negotiations are critical security challenges for wearable sensing devices in WBAN. This work proposes an improved mutual authentication and key agreement protocol for wearable sensing devices in WBAN. The existing related schemes require more computational and storage requirements, but the proposed method provides a flexible solution with less complexity.

As sensor devices are resource-constrained, proposed approach only makes use of cryptographic hash-functions and bit-wise XOR operations, hence it is lightweight and flexible. The protocol’s security is validated using the AVISPA tool, and it will withstand various security attacks. The proposed protocol’s simulation and performance analysis are compared to current relevant schemes and show that it produces efficient outcomes.

This technology has significantly gained prominence in the medical sector. These sensing devises transmit information to doctors, and there are possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. Hence, this paper proposes a lightweight and flexible protocol for mutual authentication and key agreement for wearable sensing devices in WBAN only makes use of cryptographic hash-functions and bit-wise XOR operations. The proposed protocol is simulated using AVISPA tool and its performance is better compared to the existing methods. This paper proposes a novel improved mutual authentication and key-agreement protocol for wearable sensing devices in WBAN.

Ethics for Indigenous peoples come from ancestral knowledge, from the ways we exist in creation and the ways that we are urged to respond to the whole of creation. As people grown from ancestors who we believe are always with us, we look to our own teachings to strengthen our ethics of living, being and undertaking research. These teachings still speak to us through chants, songs, stories and many other forms of belief. This chapter outlines Māori beliefs and the power of Māori belief by examining how ancestors continue to inform and speak. Research ethics are assumed to reside within a living human world, but Māori ethics includes both the seen and unseen worlds. These beliefs create a powerful challenge to the notion of sovereignty and offer powerful counterhegemonic views to racism and exploitative regimes of power. They also inform the ways that we understand ethical approaches to all our relations in the world.

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

Among thoracic malformations, pectus deformities have the highest incidence and can result in a wide range of severe and mild clinical manifestations. Recently, the treatment of pectus deformities is shifting from traditional approaches toward customized solutions. This occurs by leveraging innovative rapid prototyping tools that allow for the design and fabrication of patient-specific treatments and medical devices. This paper aims to provide a comprehensive view of the growing literature in this area to analyze the progress made in this direction.

The search was performed on major search engines through keywords inherent to reverse engineering (RE) and additive manufacturing (AM) technologies applied to pectus deformities and related treatments, selecting 54 papers. These were analyzed according to the addressed pathology, the hardware and software tools used and/or implemented and their integration within the clinical pathway.

First, the analysis led to analyze and divide the papers according to how RE and AM technologies are applied for surgical and non-surgical treatments, pathological assessment and preoperative simulation and planning. Second, all papers were considered within the typical rapid prototyping framework consisting of the three phases of three-dimensional (3D) scanning, 3D modelling and 3D printing.

To the best of the authors’ knowledge, to date, no survey has provided a comprehensive view of innovative and personalized treatment strategies for thoracic malformations; the present work fills this gap, allowing researchers in this field to have access to the most promising findings on the treatment and evaluation of pathology.

For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.

This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.

This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.

With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.

The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.

The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.

Vaping has become a prominent public health problem that has impacted young adults. The purpose of this study is to empirically examine the effects of different intrinsic and extrinsic motivations on young adults’ realization of excessive vaping and their intention to quit vaping.

A survey was used to collect data from 232 young vapers (primarily Generation Z and Millennials) to test the hypothesized relationships using a covariance-based structural equation model.

The findings of this study suggest that “realization of excessive vaping” is negatively associated with “sensation seeking” and positively associated with “deal proneness,” “environmental cues” and “negative repercussion.” The “intention to quit vaping” is negatively associated with “marketing cues” and positively associated with “alternative to smoking” and “environmental cues.” Finally, the “realization of excessive vaping” is positively associated with “intention to quit vaping.”

This study takes a two-dimensional approach to understand the complex motivations behind a relatively new addictive behavior – vaping. It contributes to the literature of addictive behavior, social cognitive theory and theory of planned behavior. Further, it has important implications for public policy and the marketing of addictive products to youths.

Although knowledge sharing in online communities has been studied for many years, little is known about the determinants for individuals' knowledge sharing in online health communities (OHCs) surrounding smoking cessation. Examining the determinants of knowledge sharing in such OHCs from the social capital perspective may prove particularly enlightening.

A questionnaire-based online user survey of two smoking cessation OHCs, one based in Finland and one based in China, was performed. Performing data analysis with partial least squares (SmartPLS 3.0), the authors developed a model conceptualizing the structural, cognitive and relational dimensions of social capital as drivers for knowledge sharing in smoking cessation OHCs, with users' stage in giving up smoking as a moderator.

The results show that structural capital (social ties) and relational capital (reciprocity) are important motivators behind knowledge sharing in smoking cessation OHCs, and the authors found a moderating effect of the stage in quitting on the antecedents' relationship with knowledge sharing in these OHCs.

The study enriches understanding of knowledge sharing in smoking cessation OHCs, contributing to theory and identifying practical implications for such groups' administration.

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

The study aims to unpack the role of Malawi Library Association (MALA) in developing librarianship in Malawi. It also explores an array of opportunities and challenges that are present for MALA.

The study applies the interpretivist paradigm for the research design. Qualitative data were collected from a purposeful sample totaling 24 practicing librarians and paraprofessionals in different work environments to inform the study phenomenon.

The study gathers that the role of MALA has been in its infancy stage for a long time. However, the apparent developments of MALA manifest in its pro-educational initiatives. Overall, MALA is impeded by a litany of obstacles such as financial constraints and a lack of advocacy strategy.

The study theorizes the role of MALA by triangulating the advocacy coalition framework, institutional theory and professionalization theory in the library and information practice.