Abstract: In the context of the global pandemic, the practical management of the 2020 presidential election in the USA was a strong concern. To anticipate and prepare for this election accurately, one of the main challenges was to confront: (i) forecasts of voter turnout, (ii) capacities of the facilities and, (iii) potential configuration options of resources. The approach chosen to conduct this anticipative study consists of collecting data about forecasts and using simulation models to work simultaneously on resource allocation and facility configuration of polling places in Fulton County, Georgia’s largest county. This article presents the results of the simulations of such places facing pre-identified potential risks. These results are oriented towards the efficiency of these places according to different criteria (health, trust, comfort). Then a dynamic framework is introduced to describe risks as physical forces perturbing the efficiency of the observed system. Finally, the main benefits and contributions resulting from this simulation campaign are presented.
Abstract: Factors related to the growth of fashion and textile manufacturing in the Sub-Saharan African (SSA) countries are analyzed in this paper. Important factors associated with the growth of fashion and textile manufacturing in the SSA countries are being identified, underlined, and evaluated in this study. This research performed a SWOT analysis of the garment industries in the SSA region by exploring into various literature in the garment manufacturing and export data. SSA countries need to grow a lot in the fashion and textile manufacturing and export to come in par with the developments in the sector globally. Unlike the developing countries such as Vietnam and Bangladesh, the total export to the US, the EU and other parts of the world has declined. On the other hand, the total supply of fashion and textiles to the domestic market has been in rise. However, the local communities still need to rely on other countries to meet their demand. Import of cheaper clothes from countries like Bangladesh China and Vietnam is one of the main challenges local manufacturers are facing as it is very difficult to be competitive in pricing.
Abstract: Since the introduction of ServiceNow, the UK’s Science Museum Group’s (SMG) ICT service desk portal, there has not been an analysis of the tools available to SMG staff for Just-in-time knowledge acquisition (Knowledge Management Systems) and reporting ICT incidents with a focus on an aspect of professional identity namely, gender. Therefore, it is important for SMG to investigate the apparent disparities so that solutions can be derived to minimize this digital divide if one exists. This study is conducted in the milieu of UK museums, galleries, arts, academic, charitable, and cultural heritage sector. It is acknowledged at SMG that there are challenges with keeping up with an ever-changing digital landscape. Subsequently, this entails the rapid upskilling of staff and developing an infrastructure that supports just-in-time technological knowledge acquisition and reporting technology related issues. This problem was addressed by analysing ServiceNow ICT incident reports and reports from knowledge articles from a six-month period from February to July. This study found a statistically significant relationship between gender and reporting an ICT incident. There is also a significant relationship between gender and the priority level of ICT incident. Interestingly, there is no statistically significant relationship between gender and reading knowledge articles. Additionally, there is no statistically significant relationship between gender and reporting an ICT incident related to the knowledge article that was read by staff. The knowledge acquired from this study is useful to service desk management practice as it will help to inform the creation of future knowledge articles and ICT incident reporting processes.
Abstract: Developing new medicine and health solutions and improving patient health currently rely on the successful execution of clinical trials, which generate relevant safety and efficacy data. For their success, recruitment and retention of participants are some of the most challenging aspects of protocol adherence. Main barriers include: i) lack of awareness of clinical trials; ii) long distance from the clinical site; iii) the burden on participants, including the duration and number of clinical visits, and iv) high dropout rate. Most of these aspects could be addressed with a new paradigm, namely the Remote Decentralized Clinical Trials (RDCTs). Furthermore, the COVID-19 pandemic has highlighted additional advantages and challenges for RDCTs in practice, allowing participants to join trials from home and not depending on site visits, etc. Nevertheless, RDCTs should follow the process and the quality assurance of conventional clinical trials, which involve several processes. For each part of the trial, the Building Blocks, existing software and technologies were assessed through a systematic search. The technology needed to perform RDCTs is widely available and validated but is yet segmented and developed in silos, as different software solutions address different parts of the trial and at various levels. The current paper is analyzing the availability of technology to perform RDCTs, identifying gaps and providing an overview of Basic Building Blocks and functionalities that need to be covered to support the described processes.
Abstract: The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.
Abstract: E-maintenance is a relatively recent concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This is clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification, cellular connectivity, connectivity to the vehicle computer, and connectivity to analog and digital sensors by means of a specially targeted design of expansion board. Specifically, the latter offers a number of adaptability features to cope with the diverse sensor types employed in different vehicles. In standard mode, the IoT sensor node communicates to the data center through cellular network, transmitting all digital/digitized sensor data, IoT device identity and position. Moreover, the proposed IoT sensor node offers connectivity, through WiFi and an appropriate application, to smart phones or tablets allowing the registration of additional vehicle- and driver-specific information and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware.
Abstract: Planning the order picking lists for warehouses to achieve some operational performances is a significant challenge when the costs associated with logistics are relatively high, and it is especially important in e-commerce era. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, to define features for supervised machine learning algorithms is not a simple task. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A double zone picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.
Abstract: Nepal practiced a centralized unitary governing system for long and has gone through the federal system after the promulgation of the new constitution on 20 September 2015. There is a big paradigm shift in terms of governance after it. Now, there are three levels of governments, one federal government in the center, seven provincial governments and 753 local governments. Federalism refers to a political governing system with multiple tiers of government working together with coordination. It is preferred for self and shared rule. Though it has opened the door for rights of the people, political stability, state restructuring, and sustainable peace and development, there are many prospects and challenges for its proper implementation. This research analyzes the discourses of federalism implementation in Nepal with special reference to one of seven provinces, Gandaki. Federalism is a new phenomenon in Nepali politics and informed debates on it are required for its right evolution. This research will add value in this regard. Moreover, tracking its evolution and the exploration of the attitudes and behaviors of key actors and stakeholders in a new experiment of a new governing system is also important. The administrative and political system of Gandaki province in terms of service delivery and development will critically be examined. Besides demonstrating the performances of the provincial government and assembly, it will analyze the inter-governmental relation of Gandaki with the other two tiers of government. For this research, people from provincial and local governments (elected representatives and government employees), provincial assembly members, academicians, civil society leaders and journalists are being interviewed. The interview findings will be analyzed by supplementing with published documents. Just going into the federal structure is not the solution. As in the case of other provincial governments, Gandaki also had to start from scratch. It gradually took a shape of government and has been functioning sluggishly. The provincial government has many challenges ahead, which has badly hindered its plans and actions. Additionally, fundamental laws, infrastructures and human resources are found to be insufficient at the sub-national level. Lack of clarity in the jurisdiction is another main challenge. The Nepali Constitution assumes cooperation, coexistence and coordination as the fundamental principles of federalism which, unfortunately, appear to be lacking among the three tiers of government despite their efforts. Though the devolution of power to sub-national governments is essential for the successful implementation of federalism, it has apparently been delayed due to the centralized mentality of bureaucracy as well as a political leader. This research will highlight the reasons for the delay in the implementation of federalism. There might be multiple underlying reasons for the slow pace of implementation of federalism and identifying them is very tough. Moreover, the federal spirit is found to be absent in the main players of today's political system, which is a big irony. So, there are some doubts about whether the federal system in Nepal is just a keepsake or a substantive achievement.
Abstract: This paper presents an original application of multiple criteria decision making analysis theory to the evaluation of aircraft selection problem. The selection of an optimal, efficient and reliable fleet, network and operations planning policy is one of the most important factors in aircraft selection problem. Given that decision making in aircraft selection involves the consideration of a number of opposite criteria and possible solutions, such a selection can be considered as a multiple criteria decision making analysis problem. This study presents a new integrated approach to decision making by considering the multiple criteria utility theory and the maximal regret minimization theory methods as well as aircraft technical, economical, and environmental aspects. Multiple criteria decision making analysis method uses different normalization techniques to allow criteria to be aggregated with qualitative and quantitative data of the decision problem. Therefore, selecting a suitable normalization technique for the model is also a challenge to provide data aggregation for the aircraft selection problem. To compare the impact of different normalization techniques on the decision problem, the vector, linear (sum), linear (max), and linear (max-min) data normalization techniques were identified to evaluate aircraft selection problem. As a logical implication of the proposed approach, it enhances the decision making process through enabling the decision maker to: (i) use higher level knowledge regarding the selection of criteria weights and the proposed technique, (ii) estimate the ranking of an alternative, under different data normalization techniques and integrated criteria weights after a posteriori analysis of the final rankings of alternatives. A set of commercial passenger aircraft were considered in order to illustrate the proposed approach. The obtained results of the proposed approach were compared using Spearman's rho tests. An analysis of the final rank stability with respect to the changes in criteria weights was also performed so as to assess the sensitivity of the alternative rankings obtained by the application of different data normalization techniques and the proposed approach.
Abstract: There has been an increased number of immigrants arriving in Canada and a concurrent rise in the number of immigrant youth suffering from drug abuse. Immigrant youths’ drug abuse has become a significant social and public health concern for researchers. This paper explores the nature of immigrant youths’ drug abuse by examining the factors influencing the onset of substance misuse, the barriers that discourage youth to seek out treatment, and how to resolve addictions amidst immigrant youth. Findings demonstrate that diminished parental supervision, acculturation challenges, peer conformity, discrimination, and ethnic marginalization are all significant factors influencing youth to use drugs as an outlet for their pain, while culturally incompetent care and fear of family and culture-based addiction stigma act as barriers discouraging youth from seeking out addiction support. To resolve addiction challenges amidst immigrant youth, future research should focus on promoting and implementing culturally sensitive practices and psychoeducational initiatives into immigrant communities and within public health policies.
Abstract: In the current context, the engineering program educators need to think about how to develop the concepts and complex engineering problem-solving skills through various complex engineering activities by the undergraduate engineering students in various engineering courses. But most of them are facing challenges to assess and evaluate these skills of their students. In this study, detailed assessment and evaluation methods for the undergraduate Electrical and Electronic Engineering (EEE) program are stated using the Outcome-Based Education (OBE) approach. For this purpose, a final year course titled control systems has been selected. The assessment and evaluation approach, course contents, course objectives, course outcomes (COs), and their mapping to the program outcomes (POs) with complex engineering problems and activities via the knowledge profiles, performance indicators, rubrics of assessment, CO and PO attainment data, and other statistics, are reported for a student-cohort of control systems course registered by the students of BSc in EEE program in Spring 2021 Semester at the EEE Department of Southeast University (SEU). It is found that the target benchmark was achieved by the students of that course. Several recommendations for the continuous quality improvement (CQI) process are also provided.
Abstract: Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages Automatic Identification System (AIS) messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring AIS messages. Our RRo method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measures to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Frechet Distance (DFD), Dynamic Time ´ Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an f-measure of 99.08% using Dynamic Time Warping (DTW) similarity measure.
Abstract: After 27 years, Malaysia reported polio cases in 2019 involving the children of the undocumented migrants living in Sabah. These undocumented migrants present a significant challenge in achieving the elimination of vaccine-preventable diseases (VPD). Due to the recent polio outbreak among the undocumented migrant children in Sabah, an in-depth interview was conducted among the caregivers of undocumented migrant children to identify the barriers and drivers towards vaccinating their children. Financial barriers, legal citizenship status, language barrier, the COVID-19 pandemic, and physical barriers have been the barriers to access vaccination services by undocumented migrants. Five significant drivers for undocumented migrants to vaccinate their children are social influence, fear of disease, parental trust in healthcare providers, good support, and vaccine availability. Necessary action should be taken immediately to address the problems of vaccinating the children of undocumented migrants to prevent the re-emergence of VPD.
Abstract: Understanding and using the functional paradigm is a challenge for many programmers. Looking for logical errors in code may take a lot of a developer’s time when a program grows in size. In order to facilitate both processes, this paper presents HaskellFL, a tool that uses fault localization techniques to locate a logical error in Haskell code. The Haskell subset used in this work is sufficiently expressive for those studying Functional Programming to get immediate help debugging their code and to answer questions about key concepts associated with the functional paradigm. HaskellFL was tested against Functional Programming assignments submitted by students enrolled at the Functional Programming class at the Federal University of Minas Gerais and against exercises from the Exercism Haskell track that are publicly available in GitHub. This work also evaluated the effectiveness of two fault localization techniques, Tarantula and Ochiai, in the Haskell context. Furthermore, the EXAM score was chosen to evaluate the tool’s effectiveness, and results showed that HaskellFL reduced the effort needed to locate an error for all tested scenarios. The results also showed that the Ochiai method was more effective than Tarantula.
Abstract: With the advent of complex software and increased connectivity, security of life-critical medical devices is becoming an increasing concern, particularly with their direct impact to human safety. Security is essential, but it is impossible to develop completely secure and impenetrable systems at design time. Therefore, it is important to assess the potential impact on security and safety of exploiting a vulnerability in such critical medical systems. The common vulnerability scoring system (CVSS) calculates the severity of exploitable vulnerabilities. However, for medical devices, it does not consider the unique challenges of impacts to human health and privacy. Thus, the scoring of a medical device on which a human life depends (e.g., pacemakers, insulin pumps) can score very low, while a system on which a human life does not depend (e.g., hospital archiving systems) might score very high. In this paper, we present a Medical Vulnerability Scoring System (MVSS) that extends CVSS to address the health and privacy concerns of medical devices. We propose incorporating two new parameters, namely health impact and sensitivity impact. Sensitivity refers to the type of information that can be stolen from the device, and health represents the impact to the safety of the patient if the vulnerability is exploited (e.g., potential harm, life threatening). We evaluate 15 different known vulnerabilities in medical devices and compare MVSS against two state-of-the-art medical device-oriented vulnerability scoring system and the foundational CVSS.
Abstract: Since the beginning of the 21st century, we have been observing in some seaside resorts aging demography, combined with an increase in second homes. These seaside resorts are said to have become places undergoing profound changes, leading to hybridization of functions (personal services, health, residential, etc.) and practices. All of these issues are part of the challenges of silver tourism, which stems from the silver economy. The Hauts-de-France region is made up of numerous seaside resorts that have a significant proportion of second homes in their real estate stock. The seaside resorts have tourist offers based on sports and leisure activities. They also offer a suitable environment for the installation of this category of the population. This set of attractive criteria in the choice of installation in seaside resorts is likely to be replaced by personal and health services due to the advanced age of the population. The resorts of Le Touquet Paris-Plage, Bray-Dunes, Neufchâtel-Hardelot and Le Crotoy seem to be evolving towards other functions of residential resorts, as opposed to seaside resorts This paper will be an opportunity to present the results of the surveys we conducted in 4 seaside resorts in the Hauts-de-France region, where more than 420 retired secondary residents were questioned. The results show that nearly 90% of retirees spend their time in their second home at any time of the year. The criteria that lead them there are school vacations and the weather. More than 40% of them have been living there for more than 20 years. The reasons for the installations are the living environment (83%) and the quality of life (79%). Their activities are walking and strolling, as well as sports. More than 99% of the respondents do not take into account the health service offers. Personal services are also little taken into account - around 60% of respondents say they do not know whether personal services exist in the resort. 80% of respondents answer that their grandchildren benefit from activities organized by the commune and the tourist offices during their stay. To conclude, the influx of retired secondary residents will not lead to a change in the functions of the seaside resorts. Their classic tourist offers - leisure and sports activities, the environment - will remain the attractive criteria of the seaside resorts. The results of the study prove that personal services and health services are not the first choice criteria in the installation of retired secondary residents, quite the contrary. We can even complete that retirees in secondary residences are demanding and concerned about living in a calm, safe and clean environment and quality of life.
Abstract: This paper identifies leading triggers of deficient episodes in terms of GDP growth based on a sample of countries at different stages of development over 1994-2017. Using logit models, we build early warning systems (EWS) and our results show important differences between developing countries (DCs) and advanced economies (AEs). For AEs, the main predictors of the probability of entering in a GDP growth deficient episode are the deterioration of external imbalances and the vulnerability of fiscal position while DCs face different challenges that need to be considered. The key indicators for them are first, the low ability to pay its debts and second, their belonging or not to a common currency area. We also build homogeneous pools of countries inside AEs and DCs. For AEs, the evolution of the proportion of countries in the riskiest pool is marked first, by three distinct peaks just after the high-tech bubble burst, the global financial crisis and the European sovereign debt crisis, and second by a very low minimum level in 2006 and 2007. In contrast, the situation of DCs is characterized first by a relative stability of this proportion and then by an upward trend from 2006, that can be explained by more unfavorable socio-political environment leading to shortcomings in the fiscal consolidation.
Abstract: Building Information Modelling (BIM) is the major technological trend among built environment organisations. Digitalising businesses and operations, BIM brings forth a digital transformation in any built environment industry. The adoption of BIM presents challenges for organisations, especially Small- and Medium-sized Enterprises (SMEs). The main problem for built environment SMEs is the lack of project actors with adequate BIM competences. The research highlights learning in projects as the key and explores into the learning of BIM in projects of designers and engineers within Thai design and engineering SMEs. The study uncovers three impeding attributes which are: a) lack of English proficiency; b) unfamiliarity with digital technologies; and c) absence of public standards. This research expands on the literature of BIM competences and adoption.
Abstract: This paper highlights skills that a leader needs to acquire to lead a team successfully. With an appropriate vision and strategy, a team can be inspired, influenced and easily led. The importance of setting codes of conduct and establishing mutual agreements between the team members can help in minimizing issues and improving overall productivity. Leadership skills include the power of questioning (PoQ), effective communication, identification of team member responsibilities, and assessment of self and the team. This paper will highlight the impact of good leadership on work progress and overall team performance. The paper explains how leaders make correct decisions by avoiding hasty actions that could generate new errors, mistakes, and issues. The importance of positive expectations for the team is addressed in this paper that could result in efficient control of the work with better outcomes.
Abstract: Analysis of the human microbiome using metagenomic
sequencing data has demonstrated high ability in discriminating
various human diseases. Raw metagenomic sequencing data require
multiple complex and computationally heavy bioinformatics steps
prior to data analysis. Such data contain millions of short sequences
read from the fragmented DNA sequences and stored as fastq files.
Conventional processing pipelines consist in multiple steps including
quality control, filtering, alignment of sequences against genomic
catalogs (genes, species, taxonomic levels, functional pathways,
etc.). These pipelines are complex to use, time consuming and
rely on a large number of parameters that often provide variability
and impact the estimation of the microbiome elements. Training
Deep Neural Networks directly from raw sequencing data is a
promising approach to bypass some of the challenges associated with
mainstream bioinformatics pipelines. Most of these methods use the
concept of word and sentence embeddings that create a meaningful
and numerical representation of DNA sequences, while extracting
features and reducing the dimensionality of the data. In this paper
we present an end-to-end approach that classifies patients into disease
groups directly from raw metagenomic reads: metagenome2vec. This
approach is composed of four steps (i) generating a vocabulary of
k-mers and learning their numerical embeddings; (ii) learning DNA
sequence (read) embeddings; (iii) identifying the genome from which
the sequence is most likely to come and (iv) training a multiple
instance learning classifier which predicts the phenotype based on
the vector representation of the raw data. An attention mechanism
is applied in the network so that the model can be interpreted,
assigning a weight to the influence of the prediction for each genome.
Using two public real-life data-sets as well a simulated one, we
demonstrated that this original approach reaches high performance,
comparable with the state-of-the-art methods applied directly on
processed data though mainstream bioinformatics workflows. These
results are encouraging for this proof of concept work. We believe
that with further dedication, the DNN models have the potential to
surpass mainstream bioinformatics workflows in disease classification
tasks.