Abstract: The use of mathematical models for solving biological problems varies from simple to complex analyses, depending on the nature of the research problems and applicability of the models. The method is more common nowadays. Many complex models become impractical when transmitted analytically. However, alternative approach such as numerical method can be employed. It appropriateness in solving linear and non-linear model equation in Differential Transformation Method (DTM) which depends on Taylor series make it applicable. Hence this study investigates the application of DTM to solve dynamic transmission of Lassa fever model in a population. The mathematical model was formulated using first order differential equation. Firstly, existence and uniqueness of the solution was determined to establish that the model is mathematically well posed for the application of DTM. Numerically, simulations were conducted to compare the results obtained by DTM and that of fourth-order Runge-Kutta method. As shown, DTM is very effective in predicting the solution of epidemics of Lassa fever model.
Abstract: Current trends in remote health monitoring to monetize on the Internet of Things applications have been raised in efficient and interference free communications in Wireless Body Area Network (WBAN) scenario. Co-existence interference in WBANs have aggravates the over-congested radio bands, thereby requiring efficient Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategies and improve interference management. Existing solutions utilize simplistic heuristics to approach interference problems. The scope of this research article is to investigate reinforcement learning for efficient interference management under co-existing scenarios with an emphasis on homogenous interferences. The aim of this paper is to suggest a smart CSMA/CA mechanism based on reinforcement learning called QIM-MAC that effectively uses sense slots with minimal interference. Simulation results are analyzed based on scenarios which show that the proposed approach maximized Average Network Throughput and Packet Delivery Ratio and minimized Packet Loss Ratio, Energy Consumption and Average Delay.
Abstract: The prime idea of inclusive education is to mainstream every child in education. However, it will be challenging for implementation when there are policy and practice gaps. It will be even more challenging when children have disabilities. Generally, the focus will be on the policy gap, but the problem may not always be with policy. The proper practice could be a challenge in the countries like Nepal. In determining practice, the teachers’ perceptions toward inclusive will play a vital role. Nepal has categorized disability in 7 types (physical, visual, hearing, vision/hearing, speech, mental, and multiple). Out of these, hearing impairment is the study realm. In the context of a limited number of researches on children with disabilities and rare researches on CWHI and their education in Nepal, this study is a pioneering effort in knowing basically the problems and challenges of CWHI focused on inclusive education in the schools including gaps and barriers in its proper implementation. Philosophically, the paradigm of the study is post-positivism. In the post-positivist worldview, the quantitative approach with the description of the situation and inferential relationship are revealed out in the study. This is related to the natural model of objective reality. The data were collected from an individual survey with the teachers and head teachers of 35 schools in Nepal. The survey questionnaire was prepared and filled by the respondents from the schools where the CWHI study in 7 provincial 20 districts of Nepal. Through these considerations, the perceptions of CWHI focused inclusive education were explored in the study. The data were analyzed using both descriptive and inferential tools on which the Likert scale-based analysis was done for descriptive analysis, and chi-square mathematical tool was used to know the significant relationship between dependent variables and independent variables. The descriptive analysis showed that the majority of teachers have positive perceptions toward implementing CWHI focused inclusive education, and the majority of them have positive perceptions toward CWHI focused inclusive education, though there are some problems and challenges. The study has found out the major challenges and problems categorically. Some of them are: a large number of students in a single class; availability of generic textbooks for CWHI and no availability of textbooks to all students; less opportunity for teachers to acquire knowledge on CWHI; not adequate teachers in the schools; no flexibility in the curriculum; less information system in schools; no availability of educational consular; disaster-prone students; no child abuse control strategy; no disabled-friendly schools; no free health check-up facility; no participation of the students in school activities and in child clubs and so on. By and large, it is found that teachers’ age, gender, years of experience, position, employment status, and disability with him or her show no statistically significant relation to successfully implement CWHI focused inclusive education and perceptions to CWHI focused inclusive education in schools. However, in some of the cases, the set null hypothesis was rejected, and some are completely retained. The study has suggested policy implications, implications for educational authority, and implications for teachers and parents categorically.
Abstract: Since British established colonial settlements across the East Coast, Native Americans have suffered stark socio economic disparities in comparison to their neighboring communities. This paper employs the 1990, 2000, and 2010 United States Decennial Census to assess whether and to what extent the casino economy helped to close this socioeconomic gap between the Mohegan tribe and its surrounding community. These three Decennial Censuses cover two decades, from six years prior to the erection of Mohegan Sun casino to 14 years afterwards, including the Great Recession 2007-2009. Income, employment, education and housing parameters are selected as socio economic indicators. The profitable advent of the Mohegan Sun in 1996 dramatically improved the socio economic status of the Mohegan Tribe between 1990 and 2000. In fact, for most of these indicators––poverty, median household income, employment, home ownership, and car ownership––disparities shifted; tribal socioeconomic parameters improved from well below the level of New London County in 1990, to the same level or above the county rates in 2000. However, economic downturn in 2007-2009 Great Recession impacted Mohegan people remarkably. By 2010, disparities for household income, employment, home ownership, and car ownership returned. The casino bridged socio economic inequalities, but at the face of economic crises, the mono-product economy grew vulnerable.
Abstract: The patenting of inventions is the result of an organized effort to achieve technological improvement and its consequent positive impact on the population's standard of living. Technology exports, either of high-tech goods or of Information and Communication Technology (ICT) services, represent the level of acceptance that world markets have of that technology acquired or developed by a country, either in public or private settings. A quantitative measure of the above variables is expected to have a positive and relevant impact on the level of economic development of the countries, measured on this first occasion through their level of Gross Domestic Product (GDP). And in that sense, it not only explains the performance of an economy but the difference between nations. We present an econometric model where we seek to explain the difference between the GDP levels of 178 countries through their different performance in the outputs of the technological production process. We take the variables of Patenting, ICT Exports and High Technology Exports as results of the innovation process. This model achieves an explanatory power for four annual cuts (2000, 2005, 2010 and 2015) equivalent to an adjusted r2 of 0.91, 0.87, 0.91 and 0.96, respectively.
Abstract: Wildfires are becoming an increasing concern worldwide, causing substantial social, economic, and environmental disruptions. This situation is especially relevant in Mediterranean-climate regions, present in all the five continents of the world, in which fire is not only a natural component of the environment but also perhaps one of the most important evolutionary forces. The rise in wildfire occurrences and their associated impacts suggests the need for identifying knowledge gaps and enhancing the basis of scientific evidence on how managers and policymakers may act effectively to address them. Considering that the main goal of a systematic map is to collate and catalog a body of evidence to describe the state of knowledge for a specific topic, it is a suitable approach to be used for this purpose. In this context, the aim of this study is to systematically map the research trends in wildfire management practices in Mediterranean-climate regions. A total of 201 wildfire management studies were analyzed and systematically mapped in terms of their: Year of publication; Place of study; Scientific outlet; Research area (Web of Science) or Research field (Scopus); Wildfire phase; Central research topic; Main objective of the study; Research methods; and Main conclusions or contributions. The results indicate that there is an increasing number of studies being developed on the topic (most from the last 10 years), but more than half of them are conducted in few Mediterranean countries (60% of the analyzed studies were conducted in Spain, Portugal, Greece, Italy or France), and more than 50% are focused on pre-fire issues, such as prevention and fuel management. In contrast, only 12% of the studies focused on “Economic modeling” or “Human factors and issues,” which suggests that the triple bottom line of the sustainability argument (social, environmental, and economic) is not being fully addressed by fire management research. More than one-fourth of the studies had their objective related to testing new approaches in fire or forest management, suggesting that new knowledge is being produced on the field. Nevertheless, the results indicate that most studies (about 84%) employed quantitative research methods, and only 3% of the studies used research methods that tackled social issues or addressed expert and practitioner’s knowledge. Perhaps this lack of multidisciplinary studies is one of the factors hindering more progress from being made in terms of reducing wildfire occurrences and their impacts.
Abstract: Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.
Abstract: The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.
Abstract: Today’s International Service Learning practices require an update: modern technologies, fresh educational frameworks, and a new operating system to accountably prosper. This paper describes a model of International Service Learning (ISL), which combines current technological hardware, electronic platforms, and asynchronous communications that are grounded in inclusive pedagogy. This model builds on the work around collaborative field trip learning, extending the reach to international partnerships across continents. Mobile technology, 21st century skills and summit-basecamp modeling intersect to support novel forms of learning that tread lightly on fragile natural ecosystems, affirm local reciprocal partnership in projects, and protect traveling participants from common yet avoidable cultural pitfalls.
Abstract: During the process of the industrialization of steel structure housing, a composite structural system with separated gravity and lateral resistant systems has been applied in engineering practices, which consists of composite frame with hinged beam-column joints, steel brace and RC shear wall. As an attempt in steel structural system area, seismic performance evaluation of the separated composite structure is important for further application in steel housing. This paper focuses on the seismic performance comparison of the separated composite structural system and traditional steel frame-shear wall system under the same inter-story drift ratio (IDR) provision limit. The same architectural layout of a high-rise building is designed as two different structural systems at the same IDR level, and finite element analysis using pushover method is carried out. Static pushover analysis implies that the separated structural system exhibits different lateral deformation mode and failure mechanism with traditional steel frame-shear wall system. Different indexes are adopted and discussed in seismic performance evaluation, including IDR, safe factor (SF), shear wall damage, etc. The performance under maximum considered earthquake (MCE) demand spectrum shows that the shear wall damage of two structural systems are similar; the separated composite structural system exhibits less plastic hinges; and the SF index value of the separated composite structural system is higher than the steel frame shear wall structural system.
Abstract: Collapsible soils like loess comprise an important category of problematic soils for construction purposes and sustainable development. As a result, research on both geological and geotechnical aspects of this type of soil have been in progress for decades. However, considerable natural variability in physical properties of in-situ loess strata even in a single block sample challenges the fundamental laboratory investigations. The reason behind this is that it is somehow impossible to remove the effect of a specific factor like void ratio from fair comparisons to come with a reliable conclusion. In order to cope with this limitation, two types of artificially made dispersive and calcareous loess are introduced which can be easily reproduced in any soil mechanics laboratory provided that all its compositions are known and controlled. The collapse potential is explored for a variety of soil water salinity and lime content and comparisons are made against the natural soil behavior. Trends are reported for the influence of pore water salinity on collapse potential under different osmotic flow conditions. The most important advantage of artificial loess is the ease of controlling cementing agent content like calcite or dispersive potential for studying their influence on mechanical soil behavior.
Abstract: A 3D woven composite, designed for automotive applications, is studied using Abaqus Finite Element (FE) software suite. Python scripts were developed to build FE models of the woven composite in Complete Abaqus Environment (CAE). They can read TexGen or WiseTex files and automatically generate consistent meshes of the fabric and the matrix. A user menu is provided to help define parameters for the FE models, such as type and size of the elements in fabric and matrix as well as the type of matrix-fabric interaction. Node-to-node constraints were imposed to guarantee periodicity of the deformed shapes at the boundaries of the representative volume element of the composite. Tensile loads in three axes and biaxial loads in x-y directions have been applied at different Fibre Volume Fractions (FVFs). A simple damage model was implemented via an Abaqus user material (UMAT) subroutine. Existing tools for homogenization were also used, including voxel mesh generation from TexGen as well as Abaqus Micromechanics plugin. Linear relations between homogenised elastic properties and the FVFs are given. The FE models of composite exhibited balanced behaviour with respect to warp and weft directions in terms of both stiffness and strength.
Abstract: Recently, the rapid development of deep learning makes
artificial intelligence (AI) penetrate into many fields, replacing
manual work there. In particular, AI systems also become a research
focus in the field of automatic office. To meet real needs in automatic
officiating, in this paper we develop an automatic form filling system.
Specifically, it uses two classical neural network models and several
word embedding models to classify various relevant information
elicited from the Internet. When training the neural network models,
we use less noisy and balanced data for training. We conduct a series
of experiments to test my systems and the results show that our
system can achieve better classification results.
Abstract: Despite the importance of the role and efforts made by the universities of the Kingdom of Saudi Arabia in reviving and preserving heritage architecture as an important cultural heritage in the Kingdom, The idea revolves around restoration and conservation processes and neglects the architectural heritage values, whose content can be used in sustainable contemporary architectural works. Educational values based on heritage architecture and how to integrate with the contemporary requirements were investigated in this research. For this purpose, by understanding the heritage architectural values as well as educational, academic process, the researcher presented an educational model of questionnaire forms for architecture students and the staff at the Architecture Department at Al-Baha University as a case study that serves the aims of the research. The results of the research show that heritage values especially those interview results are considered as a positive indicator of the importance of these values. The students and the staff need both to gain an understanding of heritage values as well as an understanding of theories of incorporating those values into the design process of contemporary local architecture. The research concludes that a correct understanding of the heritage values, its performance, and its reintegration with modern architecture technology should be focused on architectural education.
Abstract: Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).
Abstract: The impact the climate change is not recent, the main variable in the hydrological cycle is the sequence and shortage of a drought, which has a significant impact on the socioeconomic, agricultural and environmental spheres. This study aims to characterize and quantify, based on precipitation climatic projections, the rainy and dry events in the region of the Uruguay River Basin, through the Standardized Precipitation Index (SPI). The database is the image that is part of the Intercomparison of Model Models, Phase 5 (CMIP5), which provides condition prediction models, organized according to the Representative Routes of Concentration (CPR). Compared to the normal set of climates in the Uruguay River Watershed through precipitation projections, seasonal precipitation increases for all proposed scenarios, with a low climate trend. From the data of this research, the idea is that this article can be used to support research and the responsible bodies can use it as a subsidy for mitigation measures in other hydrographic basins.
Abstract: Based on activity method, this paper focuses on morning commuting behavior when commuters travel with autonomous vehicles (AVs). Firstly, a net utility function of commuters is constructed by the activity utility of commuters at home, in car and at workplace, and the disutility of travel time cost and that of schedule delay cost. Then, this net utility function is applied to build an equilibrium model. Finally, under the assumption of constant marginal activity utility, the properties of equilibrium are analyzed. The results show that, in autonomous driving, the starting and ending time of morning peak and the number of commuters who arrive early and late at workplace are the same as those in manual driving. In automatic driving, however, the departure rate of arriving early at workplace is higher than that of manual driving, while the departure rate of arriving late is just the opposite. In addition, compared with manual driving, the departure time of arriving at workplace on time is earlier and the number of people queuing at the bottleneck is larger in automatic driving. However, the net utility of commuters and the total net utility of system in automatic driving are greater than those in manual driving.
Abstract: Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.
Abstract: A reliable, real-time, and non-invasive system that can identify patients at risk for hemodynamic instability is needed to aid clinicians in their efforts to anticipate patient deterioration and initiate early interventions. The purpose of this pilot study was to explore the clinical capabilities of a real-time analytic from a single lead of an electrocardiograph to correctly distinguish between rapid response team (RRT) activations due to hemodynamic (H-RRT) and non-hemodynamic (NH-RRT) causes, as well as predict H-RRT cases with actionable lead times. The study consisted of a single center, retrospective cohort of 21 patients with RRT activations from step-down and telemetry units. Through electronic health record review and blinded to the analytic’s output, each patient was categorized by clinicians into H-RRT and NH-RRT cases. The analytic output and the categorization were compared. The prediction lead time prior to the RRT call was calculated. The analytic correctly distinguished between H-RRT and NH-RRT cases with 100% accuracy, demonstrating 100% positive and negative predictive values, and 100% sensitivity and specificity. In H-RRT cases, the analytic detected hemodynamic deterioration with a median lead time of 9.5 hours prior to the RRT call (range 14 minutes to 52 hours). The study demonstrates that an electrocardiogram (ECG) based analytic has the potential for providing clinical decision and monitoring support for caregivers to identify at risk patients within a clinically relevant timeframe allowing for increased vigilance and early interventional support to reduce the chances of continued patient deterioration.
Abstract: In the era of big data, public investors are faced with more complicated information related to investment decisions than ever before. To survive in the fierce competition, it has become increasingly urgent for investors to combine multi-source knowledge and evaluate the companies’ true value efficiently. For this, a rule-based ontology reasoning method is proposed to support steel companies’ value assessment. Considering the delay in financial disclosure and based on cost-benefit analysis, this paper introduces the supply chain enterprises financial analysis and constructs the ontology model used to value the value of steel company. In addition, domain knowledge is formally expressed with the help of Web Ontology Language (OWL) language and SWRL (Semantic Web Rule Language) rules. Finally, a case study on a steel company in China proved the effectiveness of the method we proposed.