Forward Information Research Articles

EVALUATION OF SCAFFOLD ACCIDENTS DURING BUILDING MAINTENANCE WORKS

The impact of construction related accidents to the individual, the construction sector and the economy is huge. In Malaysia about 6000 workers’ related accidents are reported annually. Scaffold accidents are one of the major causes of injury and death on Malaysian construction sites. This research investigated the scaffolding accidents during maintenance work in the Malaysian construction industry. This study provides answers to two questions: 1) what are the causes of scaffolding accidents during maintenance work? and 2) what are the possible measures to reduce scaffolding accidents during maintenance work? Primary data were collected through survey interview techniques involving 10 maintenance construction stakeholders. The research revealed that the scaffold accidents that occurred during maintenance works are due to lack of a fall protection system, lack of proper baseplate, user behavior, lack of proper guardrail and weather conditions. To reduce scaffolding accidents during maintenance works adequate poor cross bracing, safety netting, jack base, pin lock and adequate inspection of the scaffolding during operations are required. Implicit in the findings of this research is that scaffold accidents are due to behavioral issues of the sites operative and contractors’ organizations as whole. The findings provide feedback and feed for-ward information and knowledge to construction site operatives.

Article Watch: July 2021

This column highlights recently published articles that are of interest to the readership of this publication. We encourage ABRF members to forward information on articles they feel are important and useful to Clive Slaughter, MCG-UGA Medical Partnership, 1425 Prince Avenue, Athens GA 30606. Tel; (706) 713-2216: Fax; (706) 713-2221: Email; cslaught@uga.edu or to any member of the editorial board. Article summaries reflect the reviewer's opinions and not necessarily those of the Association.

Cell morphology-based machine learning models for human cell state classification

Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.

Forward Guidance, Information Frictions and Optimal Monetary Policy: A General Equilibrium Analysis with a Public Sector

This paper analyzes the optimal monetary policy in a model with a public sector when firms choose prices under incomplete information, and the government can not observe the current state perfectly. We accommodate the notion of Odyssean forward guidance in a framework with a public sector. The forward guidance measures how much the government's future policy depends on the current state. We find that the optimal monetary policy implements the social planner allocations through perfect Odyssean forward guidance. The next period risk-free rate is time-consistent and entirely determined by the current state of both the public sector and the private sector.

THE EFFECT OF PROFITABILITY, CAPITAL STRUCTURE, AND FORWARD-LOOKING INFORMATION ON INVESTMENT RISK

This study aims to provide empirical evidence of the effect of profitability, capital structure and forward looking information on investment risk. The population used in this research were all banking companies listed in the Indonesia Stock Exchange for the period 2016 to 2018. The sample selection used a random sampling method, and obtained 117 samples. The variables of profitability, capital structure, and forward-looking information refer to the elements of Integrated Reporting (2013) as an independent variable, and investment risk as the dependent variable. Data is collected from bank annual reports and share prices obtained from the website. The analysis method used is multiple linear regression. The results of the study concluded that profitability and capital structure had no effect on investment risk. Forward-looking disclosure has a negative effect on investment risk. This means that forward-looking disclosures can significantly reduce investment risk.

The Measurement of Information and Free Energy in Mechanical-Force-Driven Coil-Globule Transitions.

We study the role of information (the relative entropy) for polymers undergoing coil-globule transitions driven by a time-dependent force. Pulling experiments at various speeds are performed by Brownian dynamics simulations. We obtain the work distributions for the forward and time-reversed backward processes and information stored at the end of the nonequilibrium pulling processes. We present the systematic method to measure the information from the pulling experiments and extract the information by analyzing slowly relaxing modes. When the information is incorporated, the work distributions modified by the information allow access to the proper free energy via the formulation of the generalized fluctuation theorems even if the initial states of the forward and time-reversed backward processes are out of equilibrium. This demonstrates that the work-information conversion works well for a single-molecule system with many degrees of freedom.

Trust based task offloading scheme in UAV-enhanced edge computing network

Unmanned Aerial Vehicle (UAV) with a server which has powerful computing ability can fly over Internet of Thing (IoT) devices and conduct task offloading, giving rise to the so-called UAV-enhanced edge computing network. However, energy efficient and trustworthy UAV-enhanced edge computing is still a challenging issue. In this paper, a UAV-Trust based Task Offloading (UAV-TTO) scheme is proposed to offload tasks in an energy efficient and reliable way for IoT devices. The main innovations are as follows: (1) A Cluster based Task Offloading (CTO) approach is proposed in which tasks only need to route to any IoT devices within a cluster. The disadvantage of long flight distance and large energy consumption for UAV in traditional method can be overcame. Besides, the CTO approach avoids the far routing distance to the cluster head and the excessive data load. (2) A Traceback based Trust Evaluation (TTE) mechanism is proposed to evaluate the trust of devices. In this mechanism, the UAV can collect the task forwarding information provided by IoT devices when flying over the cluster. Then, we conduct a trust evaluation and reasoning mechanism according to the trust evidence collected to obtain more accurate evaluation results. IoT devices with high trustfulness will be selected to participate in task offloading, malicious devices will be excluded, so the efficiency of task offloading can be significantly improved. The UAV trajectory optimization strategy is also proposed to assist task offloading by considering trust and energy consumption. Extensive experimental results demonstrate that the proposed UAV-TTO scheme can reduce the total energy consumption for accomplishing the tasks effectively, evaluate the trust of IoT devices accurately and improve the success rate of task offloading.

Article Watch: April 2021

This column highlights recently published articles that are of interest to the readership of this publication. We encourage ABRF members to forward information on articles they feel are important and useful to Clive Slaughter, MCG-UGA Medical Partnership, 1425 Prince Ave., Athens GA 30606, USA. Tel: (706) 713-2216; Fax: (706) 713-2221; E-mail; cslaught@uga.edu, or to any member of the editorial board. Article summaries reflect the reviewer's opinions and not necessarily those of the Association.

A coordinated non-orthogonal multiple access strategy for integrated terrestrial-satellite networks.

In this paper, we investigate the outage probability and ergodic sum capacity of the downlink of the integrated satellite-terrestrial networks (ISTN) with a cooperative non-orthogonal multiple access (CNOMA) scheme, in which a user with better channel condition acts as a relay node and forwards information to the other users. In this paper, a pilot-based channel estimation method is considered which can verify the performance of this scheme with the imperfect channel state information. In this model, all these users are equipped with multi-antennas, and all of them are both in the coverage of a same beam of the satellite. Specifically, the exact analytical expression for the outage probability and ergodic sum capacity of the system is derived. The result shows that this coordinated non-orthogonal multiple access (CNOMA) scheme performs better than that of OMA (TDMA) in this model. Finally, the future research directions are given to further enhance the system capacity.

Impact of a Serious Game (Escape COVID-19) on the Intention to Change COVID-19 Control Practices Among Employees of Long-term Care Facilities: Web-Based Randomized Controlled Trial.

BackgroundMost residents of long-term care facilities (LTCFs) are at high risk of complications and death following SARS-CoV-2 infection. In these facilities, viral transmission can be facilitated by shortages of human and material resources, which can lead to suboptimal application of infection prevention and control (IPC) procedures. To improve the dissemination of COVID-19 IPC guidelines, we developed a serious game called “Escape COVID-19” using Nicholson’s RECIPE for meaningful gamification, as engaging serious games have the potential to induce behavioral change.ObjectiveAs the probability of executing an action is strongly linked to the intention of performing it, the objective of this study was to determine whether LTCF employees were willing to change their IPC practices after playing “Escape COVID-19.”MethodsThis was a web-based, triple-blind, randomized controlled trial, which took place between November 5 and December 4, 2020. The health authorities of Geneva, Switzerland, asked the managers of all LTCFs under their jurisdiction to forward information regarding the study to all their employees, regardless of professional status. Participants were unaware that they would be randomly allocated to one of two different study paths upon registration. In the control group, participants filled in a first questionnaire designed to gather demographic data and assess baseline knowledge before accessing regular online IPC guidelines. They then answered a second questionnaire, which assessed their willingness to change their IPC practices and identified the reasons underlying their decision. They were then granted access to the serious game. Conversely, the serious game group played “Escape COVID-19” after answering the first questionnaire but before answering the second one. This group accessed the control material after answering the second set of questions. There was no time limit. The primary outcome was the proportion of LTCF employees willing to change their IPC practices. Secondary outcomes included the factors underlying participants’ decisions, the domains these changes would affect, changes in the use of protective equipment items, and attrition at each stage of the study.ResultsA total of 295 answer sets were analyzed. Willingness to change behavior was higher in the serious game group (82% [119/145] versus 56% [84/150]; P<.001), with an odds ratio of 3.86 (95% CI 2.18-6.81; P<.001) after adjusting for professional category and baseline knowledge, using a mixed effects logistic regression model with LTCF as a random effect. For more than two-thirds (142/203) of the participants, the feeling of playing an important role against the epidemic was the most important factor explaining their willingness to change behavior. Most of the participants unwilling to change their behavior answered that they were already applying all the guidelines.ConclusionsThe serious game “Escape COVID-19” was more successful than standard IPC material in convincing LTCF employees to adopt COVID-19–safe IPC behavior.International Registered Report Identifier (IRRID)RR2-10.2196/25595

The Loneliness of Aging

Isolation and loneliness have become buzz words when discussing older adults during the coronavirus disease pandemic; yet, these are age-old problems. Both have been studied extensively, yet there currently is no rapid or succinct tool that can be used in the emergency department to screen for either, or a consensus of evidence-based ways to correct these issues. This is of concern because both loneliness and social isolation have been linked to poor health. Poor health, in turn, can lead to worse isolation and loneliness. These health problems may lead to the older adult seeking care in the emergency department where screening and initial treatment could be initiated. Suggestions for questions that emergency nurses can ask to identify an older adult who is lonely or suffers from social isolation, as well as steps to consider when encountering the older adult with complaints of loneliness and/or social isolation, are provided, with the realization that these are only the first steps of many that would need to be taken. The purpose of this article is to bring forward updated information that discusses loneliness and social isolation in older adults, a timely priority during the coronavirus disease pandemic and often listed as a factor in older adult deaths. A review of relevant screening tools for use in the emergency department are provided.

EARLY ACTION PREDICTION USING VGG16 MODEL AND BIDIRECTIONAL LSTM

Action prediction plays a key function, where an expected action needs to be identified before the action is completely performed. Prediction means inferring a potential action until it occurs at its early stage. This paper emphasizes on early action prediction, to predict an action before it occurs. In real time scenarios, the early prediction can be very crucial and has many applications like automated driving system, healthcare, video surveillance and other scenarios where a proactive action is needed before the situation goes out of control. VGG16 model is used for the early action prediction which is a convolutional neural network with 16 layers depth. Besides its capability of classifying objects in the frames, the availability of model weights enhances its capability. The model weights are available freely and preferred to used in different applications or models. The VGG-16 model along with Bidirectional structure of Lstm enables the network to provide both backward and forward information at every time step. The results of the proposed approach increased observation ratio ranging from 0.1 to 1.0 compared with the accuracy of GAN model.

Joint relay selection and energy harvesting to improve performance of cooperative communication systems

This paper focuses on a combining relay selection with energy harvesting (EH), known as joint relay selection with EH (JRSEH), to improve performance in wireless cooperative communication systems. Previous research was examined performance improvement with relay selection (RS) techniques without combined with EH (RS non-EH). This study aims to improve the performance by applying EH and the RS in the cooperative communication system. In the system, a source selects the best relay from several relays that produce the highest SNR value at the destination. The selected best relay performs EH based on time switching. Then, the best relay with EH obtains the power to forward information to the destination using the Amplify and Forward (AF) relay scheme. The channel from source to relay is Rician fading, and from the relay to a destination is Rayleigh fading. The research method used is system modeling and analysis of computer simulations. The system performance is calculated based on the bit error rate (BER) and the throughput in a multi-relay cooperative system with EH and RS. The results show that the performance of the proposed JRSEH higher than that of RS non-EH, multi-relay EH, and multi-relay non-EH.

A softwarized and MEC-enabled protocol architecture supporting consumer mobility in Information-Centric Networks

Information-Centric Networking emerged as a powerful enabling technology for the provisioning of scalable and efficient real-time services in Future Internet, mobile architectures, and multi-hop wireless mesh networks. To serve mobile consumers, most of scientific contributions suggest to extend the information-centric communication primitives by means of a pull-based methodology, according to which the mobile consumer issues pending requests every time it reaches a new network attachment point. This approach, however, generates two important shortcomings. First, the requests delivered before the handover will generate stale paths with wrong forwarding information in their network routers. As a consequence, some new contents will be delivered also to previous locations, thus wasting bandwidth. Second, during handovers, mobile consumers may miss some contents released in real-time and lose the synchronization with the remote producer. In order to solve these issues, this work conceives a novel protocol architecture that successfully integrates and properly customizes the key functionalities of Information-Centric Networking, Multi-access Edge Computing, and Software Defined Networking paradigms. Specifically, the designed approach envisages that (1) Multi-access Edge Computing assists mobile consumers in retrieving data, while transparently managing the information-centric communication primitives and recovering the synchronization with the remote producer after the handover, (2) Software-Defined Controllers dynamically configure forwarding functionalities, and (3) Information-Centric Networking enables efficient data dissemination and delivers network control instructions. The impact of the devised protocol architecture on the communication overhead is analytically formulated and evaluated in scenarios with different topology, mobility, application settings, and number of mobile consumers. The comparison with respect to the pure Information-Centric Networking deployment demonstrates that the proposed solution ensures a reduction of the communication overhead up to 99.99% on the data plane, an overall bandwidth saving up to 99.93%, and a not negligible memory saving in intermediary routers. At the same time, the adoption of information-centric communication primitives for the control plane achieves an overhead reduction ranging from 29.36% to 51.13% with respect to an implementation based on the conventional OpenFlow protocol.

Automatic Word Spacing of Korean Using Syllable and Morpheme

In Korean, spacing is very important to understand the readability and context of sentences. In addition, in the case of natural language processing for Korean, if a sentence with an incorrect spacing is used, the structure of the sentence is changed, which affects performance. In the previous study, spacing errors were corrected using n-gram based statistical methods and morphological analyzers, and recently many studies using deep learning have been conducted. In this study, we try to solve the spacing error correction problem using both the syllable-level and morpheme-level. The proposed model uses a structure that combines the convolutional neural network layer that can learn syllable and morphological pattern information in sentences and the bidirectional long short-term memory layer that can learn forward and backward sequence information. When evaluating the performance of the proposed model, the accuracy was evaluated at the syllable-level, and also precision, recall, and f1 score were evaluated at the word-level. As a result of the experiment, it was confirmed that performance was improved from the previous study.

Enhanced models for privacy and utility in continuous-time diffusion networks

Controlling the propagation of information in social networks is a problem of growing importance. On one hand, users wish to freely communicate and interact with their peers. On the other hand, the information they spread can bring to harmful consequences if it falls in the wrong hands. There is therefore a trade-off between utility, i.e. reaching as many intended nodes as possible, and privacy, i.e. avoiding the unintended ones. The problem has attracted the interest of the research community: some models have already been proposed to study how information propagates and to devise policies satisfying the intended privacy and utility requirements. In this paper, we adapt the basic framework of Backes et al. to include more realistic features, that in practice influence the way in which information is passed around. More specifically, we consider: (a) the topic of the shared information, (b) the time spent by users to forward information among them and (c) the user social behaviour. For all features, we show a way to reduce our model to the basic one, thus allowing the methods provided in the original paper to cope with our enhanced scenarios. Furthermore, we propose an enhanced formulation of the utility/privacy policies, to maximize the expected number of reached users among the intended ones, while minimizing this number among the unintended ones, and we show how to adapt the basic techniques to these enhanced policies. We conclude by giving a new approach to the maximization/minimization problem by finding a trade-off between the risk and the gain function through biobjective optimization.

Relational Reasoning for Group Activity Recognition via Self-Attention Augmented Conditional Random Field.

This paper presents a new relational network for group activity recognition. The essence of the network is to integrate conditional random fields (CRFs) with self-attention to infer the temporal dependencies and spatial relationships of the actors. This combination can take advantage of the capability of CRFs in modelling the actors' features that depend on each other and the capability of self-attention in learning the temporal evolution and spatial relational contexts of every actor in videos. Additionally, there are two distinct facets of our CRF and self-attention. First, the pairwise energy of the new CRF relies on both of the temporal self-attention and spatial self-attention, which apply the self-attention mechanism to the features in time and space, respectively. Second, to address both local and non-local relationships in group activities, the spatial self-attention takes account of a collection of cliques with different scales of spatial locality. The associated mean-field inference thereafter can thus be reformulated as a self-attention network to generate the relational contexts of the actors and their individual action labels. Lastly, a bidirectional universal transformer encoder (UTE) is utilized to aggregate the forward and backward temporal context information, scene information and relational contexts for group activity recognition. A new loss function is also employed, consisting of not only the cost for the classification of individual actions and group activities, but also a contrastive loss to address the miscellaneous relational contexts between actors. Simulations show that the new approach can surpass previous works on four commonly used datasets.

What is the Time Series Regression of the Stock Market Return?

We obtain a derivative-based formula of time series regression coefficients on the stock market return. We introduce a forward contract on a power VIX index (PVIX) calculated by market index options, and obtain a model-free future price of PVIX by using SP however, the time-series coefficients using derivatives are significant and more sensitive to the market in the volatile period. Finally, we demonstrate that the derivatives market's forward looking information helps predict future market return.

Deep Autoencoder Imaging Method for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an effective technique for real-time monitoring, visualization, and analysis of industrial process in a noninvasive manner. However, due to the nonlinear and &#x201C;soft-field&#x201D; nature of its inverse problem, image reconstruction of EIT is always limited in image resolution and, in particular, the accuracy of identifying object boundaries. In order to solve the above problems, a novel multilayer autoencoder (MLAE) image reconstruction network that consists of a feature extraction module and an image reconstruction module is proposed. In the proposed method, hierarchical structures are applied to increase the forward information flow and the selected appropriate hidden layers can solve the disappearance of the reverse gradient flow. The training process of MLAE containing self-supervised pretraining and supervised fine-tuning can provide better complex nonlinear mapping and improve the model performance. The experimental and analytical results prove that the MLAE image reconstruction method can obtain higher quality images than the typical algorithms and certain methods based on deep learning.

Article Watch: December, 2020

This column highlights recently published articles that are of interest to the readership of this publication. We encourage ABRF members to forward information on articles they feel are important and useful to Clive Slaughter, MCG-UGA Medical Partnership, 1425 Prince Ave., Athens GA 30606, USA. Tel: (706) 713-2216; Fax: (706) 713-2221; E-mail; cslaught@uga.edu, or to any member of the editorial board. Article summaries reflect the reviewer's opinions and not necessarily those of the Association.