Priority Levels Research Articles

Identification of key priority areas under different ecological restoration scenarios on the Qinghai-Tibet Plateau

With the intensification of climate warming and human activities, the ecosystems on the Qinghai-Tibet Plateau (QTP) are facing increasing threats which leads to extensive ecological degradation. Ecological restoration measures need to be implemented to improve biodiversity and ecosystem services to mitigate the impact of climate change and human disturbances. However, the key priority areas (KPAs) for ecological restoration are not clear on the QTP, and the benefits of ecosystem services for ecological restoration are often ignored. In this study, we are the first to identify the KPAs based on the quantitative evaluation method and multicriteria optimization algorithm under five restoration scenarios aiming at ecosystem service improvement on the QTP. Results showed that: (1) The benefits of ecological restoration for climate change mitigation and associated costs under different scenarios showed generally similar spatial variability, exhibiting higher in the south and lower in the west, which were different from those for biodiversity. (2) The restoration priorities in Sichuan and Yunnan were generally higher under scenarios Ⅱ and Ⅴ, while in Xinjiang, Sichuan, Yunnan, and western and southern Tibet were higher under scenarios Ⅰ, Ⅲ and Ⅳ. (3) For different ecosystems, the similarities lied in that the restoration priorities of wetland ecosystem were the highest, while those of desert ecosystem were the lowest under five restoration scenarios. (4) When the restoration area requirement was 25% of the total degraded area, the highest restoration priority levels under scenarios Ⅰ, Ⅲ and Ⅳ were mainly distributed in Guinan, Renbu, Nierong and Chayu counties, and under scenarios Ⅱ and Ⅴ were mainly distributed in Renbu, Lang and Guinan counties. When the restoration area requirements were 50% and 75% of the total degraded area, the counties with higher restoration priority levels under scenario Ⅱ were different from those under other four scenarios. This study identified the KPAs under different restoration scenarios, which provided references for the restoration measures implementation on the QTP.

Lessons from the Epicenter of the COVID-19 Pandemic: An Analysis of the Surgical Completion Rates between Priority Classes and Procedure Types.

In March of 2020, elective surgical procedures in New York State were suspended in response to the COVID-19 pandemic. At Northwell Health, this lasted a total of 8 weeks, leaving plastic surgeons responsible for recording and rescheduling delayed procedures. Many plastic and reconstructive procedures are considered elective, but these procedures can be time sensitive, and delays can have a negative impact on patient care.1 Our goal was to compare the rates of completion and time to completion between different priority groups and procedure types in order to predict potential consequences for one’s practice if future suspensions in elective surgery were to occur. We hypothesized that procedures grouped in the reconstructive categories with higher priority levels would have a higher completion rate and lower time to completion compared to less urgent, aesthetic based procedures. Plastic surgeons working for our department were responsible for keeping track of their suspended cases between March 23, 2020, and May 19, 2020.2,3 Under the discretion of the attending plastic surgeon, cases were categorized into procedure type, priority level, and surgery setting. The procedure types included aesthetic, pediatrics/craniofacial, hand, general, and reconstructive. The priority classes were elective, nonurgent, semiurgent, urgent, and emergent. The surgery setting was classified into inpatient and outpatient. In total, we examined 135 surgical cases that were suspended (Table 1). The average patient age was 45.8 years, and 45.9 percent of the surgical procedures were completed by the end of August of 2020, more than 3 months after the suspension was lifted. Of those that were completed, the average time to completion from the original surgical date was 79.2 days. Surgery classifications are summarized in Table 1. We ran binary logistic regressions that showed no significant correlation between the surgery type, urgency, or priority class and whether or not the surgery was completed. In addition, linear regression analyses did not show a significant correlation between the surgery type, urgency, or priority class and the time it took to complete the surgery. All the models controlled for patient age and whether or not there was malignancy. Table 1. - Summary of Sample by Procedure Priority Procedure Classification No. of Patients Percent of Sample Priority Elective 8 5.93% Nonurgent 80 52.29% Semiurgent 22 16.30% Urgent 24 17.78% Emergent 1 0.01% Type Aesthetic 12 8.89% Pediatrics/craniofacial 10 7.41% Hand 9 6.67% General 35 25.93% Reconstructive 68 50.37% No type specified 1 0.74% Location Inpatient 34 25.19% Outpatient 66 48.89% No location specified 35 25.93% Our surprising results may be due to several factors, including differences in patients’ concerns with undergoing surgery during a pandemic, patients’ continuous desire to undergo aesthetic surgery, or a lack of prioritization of urgent cases when rescheduling surgery. With new variants of SARS-CoV-2 emerging and the number of COVID-19 cases climbing rapidly in the United States,5 plastic surgeons must be prepared for another potential suspension of surgery.6 Plastic surgery departments and private practices can use our experience to guide their recovery from future surgical suspension. Prior studies have already shown the tremendous economic impact of surgery delays on plastic surgeons.4Additional research is needed, however, to evaluate the impact of delays on patient care and quality of life. A more systematic approach to rescheduling surgery, which prioritizes the most vulnerable patients, is needed. DISCLOSURE The authors have no relevant financial disclosures to report.

Minimum relevant features to obtain AI explainable system for predicting breast cancer in WDBC

The potential to explain why a machine learning model produces a certain prediction in incomprehensible terms is becoming increasingly crucial, as it provides accountability and confidence in the algorithm's decision-making process. The interpretation of complex models is difficult. Various approaches to dealing with this issue are being offered. These problems are typically handled in tree ensemble methods by assigning priority levels to input features globally or for a specific prediction. We show that current feature attribution approaches are inconclusive, and develop solutions using SHAP (SHapley Additive Explanation) values, LIME (Local Interpretable Model-Agnostic Explanations), and the Skope Rules package. We employ feature selection methods from SHAP and LIME in this work, which uses the Breast cancer Wisconsin data sets. In the suggested method, features are chosen at the first level of feature selection using Decision tree entropy values. Based on the SHAP and LIME reports, level 2 features are chosen from fewer options. The features are tested on a Decision Tree (DT) model and a DT and Support Vector Machine (SVM) ensemble. Experiments suggest that the ensemble works better as compared to DT. We have also used the Skope Rules package to generate global rules for generalization.

Mapping User Experiences around Transit Stops Using Computer Vision Technology: Action Priorities from Cairo

In the field of urban studies, artificial intelligence technology offers potential applications. There are, however, limited sources on how technology can contribute to the study of user experiences in city contexts. This study examined the factors affecting user experiences around three exits of one of the Cairo Metro stops in Ramses Square in Cairo, Egypt. Using a Geographical Information System (GIS) and GoodVision Video Insights and spatial analysis was conducted for the selected built environment. Our results demonstrate that pedestrian flow, thermal comfort, safety levels, and destination proximity contribute to the user experience. Our results also prove that urban configuration with multiple elements in the stations’ context strongly affects metro user experience. As such, three levels of priorities were suggested to guide city planners, urban designers, and landscape architects through developing or designing stations with user experience in mind. For future studies, this study offers a valuable method for developing qualitative and quantitative analyses of pedestrian movement in stations’ contexts.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Admission Control and Resource Reservation for Prioritized Slice Requests With Guaranteed SLA Under Uncertainties

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Cardiac Rehabilitation Completion Study: Barriers and Potential Solutions.

Cardiac Rehabilitation Completion Study: Barriers and Potential Solutions.

A Computation Offloading Game for Jointly Managing Local Pre-Processing Time-Length and Priority Selection in Edge Computing

Edge computing has been regarded as an enabling technology for supporting future smart applications, such as industrial Internet of Things. In this paper, a novel computation offloading framework for edge computing is proposed. Unlike existing studies, this work considers that end users can intentionally reserve some time for local pre-processing before requesting the computation offloading service (instead of always declaring their offloading requests immediately when heavy-duty tasks are generated). By doing so, each task’s offloading cost may decrease because of less edge resource demand, while the delay cost may increase due to the later report of its offloading request. Furthermore, end users can select their preferred priority levels in the offloading scheduling by paying premium service charges. To maximize the individual utility, each end user may strategically and jointly determine its local pre-processing time-length and priority selection in computation offloading. For characterizing strategic interactions among end users and the quality-of-service (QoS) of the scheduling system with congested nature, a computation offloading game built upon a queueing model is formulated. On top of this, a revenue maximization problem for the interest of edge operator is also investigated. Simulations evaluate the performance of the proposed solution, and shows its superiority over counterparts.

Разработка программного обеспечения электропривода для группового управления в электромеханической системе

Background. To operate multi-coordinate electromechanical systems require control of electric drives coordinated in time and space along each of the axes, which is called coordinated control. Thus, various computing tools and mathematical algorithms are used. They establish the baseline of the software for microprocessor control systems. At the same time, the implementation of control algorithms is limited by the computing resources of the hardware parts of control systems, including the limitations of the tools of information transmission. To consider and eliminate these limitations and thus, to improve operation of the multi-coordinate electromechanical system, the aim of the article is to update control algorithms and balance distribution of computational resources of electric drives control systems. Materials and methods. Data are presented on the software development for coordinated control of several electric drives as a part of an electromechanical system that performs coordinated motion along its axes in time and space. To synchronize the control, the following technical solutions have been applied: description of the path of the high-level controller in the form of a sequence of reference points, buffering the coordinates when received by the electric drives and subsequent interpolation. Balanced distribution of resources between the high-level controller and local systems of electric drives is carried out considering the intensity of data exchange via the network interface. When calculating the reference points by the high-level controller, approximation of the paths by line segments at time intervals of variable duration is carried out to describe the generic path within the allowable mismatch. Results. The algorithm to synchronize the system time of the electric drives and the high-level controller has been implemented to synchronize the interpolation of paths. To implement this algorithm and transfer the coordinates of reference points to electric drives, special commands are used. They have different priority levels depending on the required speed of execution. In accordance with this algorithm, software has been developed as an element of the microprocessor control system of the electric drive. Conclusions. Application of various technical tools and computational algorithms that make it possible to allocate the computing resources of the control system and rationally use them contributes to efficiency improvement, in particular, the consistency and timeliness of electric drives control as a part of multi-axis electromechanical systems. The authors have proved the efficiency of the approach of coordinated control with the use of buffering coordinates of reference points followed by interpolation of the paths depending on the system time of each of the electric drives.

The Psychology and Motivations of Sports Fans

With the number of fans growing each year, the world’s fascination with sports is nothing new—for example, FIFA (International Federation of Association Football) estimates that there are five billion soccer fans around the world. However, even with the worldwide obsession with sports, little is understood about the origins of a fan’s enticement, or even the psychology of a fan. The standing research done on this subject confronts this question with complexity when in reality, it should be faced with simplicity. This study attempts to add to the knowledge of the psychology and motivation of a sports fan by simplifying the approach. A survey was designed to reveal a participant’s background involving sports and motivations for becoming a sports fan. In the study, there were 41 participants, each self-categorized to a priority level—first, high, medium, and low—that indicated the extent that sports were to their daily lives. Each answer was compared both within and outside the group, with each trend and outlier being noted. It was found that the first and high-priority participants demonstrated strong support and pride for their team, while the medium and low-priority participants did not carry the same passion. The results of the study revealed that there were three dominant motivations for someone becoming a sports fan: desire for a sense of community, excitement/entertainment, and being influenced by a player from their country and the more someone becomes a devoted fan the more emotional involvement they will have with their object of support.

Centralised resource allocation using Lexicographic Goal Programming. Application to the Spanish public university system

This paper deals with Data Envelopment Analysis (DEA) in centralised settings in which the operating units belong to the same organisation. In such a scenario, a global system-wide perspective may be adopted as regards resource allocation and target setting. In this paper, a new Lexicographic Goal Programming (lexGP) approach is proposed using three priority levels: the aggregated input consumption and output production goals; the input and output goals of the individual operating units; and the technical efficiency of the computed targets. It is assumed that the goals for the overall organisation are established by the Central Decision-Maker (CDM) and that they are consistent with those of the individual operating units. The proposed approach has been applied to the Spanish public university system, comprising 47 institutions. Given the CDM preferences in terms of input and output aggregate goals and relative importance weights, specific technical efficient targets have been computed for each university. The results show that the proposed approach is more suitable than the non-centralised DEA approach and produces targets that are more effective than other centralised resource allocation approaches in the sense that they are much closer to both the aggregate goals of the CDM and the specific goals of each university.

Priority areas and integrated actions for the conservation of Amazonian turtle populations historically over-exploited by humans

The definition of priority areas for conservation and integrated management actions are essential for the effective maintenance and recovery of natural populations, especially for species overexploited by humans. Amazonian chelonians are a food resource historically used by people, resulting in the decline of species populations and worsening the risk of local extinctions. In this paper, we establish priority areas and define integrated conservation actions for populations of three Amazonian chelonians most threatened by human consumption in Brazil (Podocnemis expansa, P. unifilis and P. sextuberculata). To do so, we used 15 prioritization criteria (ecological, logistical and socioeconomic) estimated with 30 years monitoring data in 15 areas by the Amazon Chelonian Program (in portugues Projeto Quelonios da Amazonia, PQA). Each criterion presented four levels of priority with scores increasing according to the relevance for conservation of chelonian populations. The sum of the scores obtained in each area of the PQA allowed a ranking and four categories of importance for conservation to be defined. We also analyzed the similarity of scores among areas of the PQA and among the prioritization criteria to evaluate the application of integrated conservation action strategies. The areas of PQA were classified as Extremely Important for Conservation (Rebio Trombetas River, Middle Xingu River, Middle Araguaia River, Upper Guaporé River), Very Highly Important for Conservation (Middle Purus River, Middle Juruá River, Crixás-Açu River Mouth, Sub-middle Tapajós River); Highly Important for Conservation (Sub-Middle Araguaia River, Amazonas River Mouth, Middle Mortes River); and Important for Conservation (Middle Guaporé River, Lower Branco River, Flechal River, Afuá River). The prioritization and similarity analyses can support the development of a national integrated plan of conservation actions to reduce the overexploitation of Amazon chelonian populations, according to the ecological, logistical and socioeconomic needs of each PQA area.

VLSI implementation of low-power and area efficient parallel memory allocation with EC-TCAM

VLSI-based programmable devices support improved software-based reconfigurability and hardware performance, which are adopted as developing platforms to implement complex systems, i.e., signal processors, software-defined networks, and image processing applications. Parallel memory allocation (PMA) among various resources plays a crucial role in meeting the high-performance requirements in these applications. The conventional methods were implemented with static random-access memory (SRAM) prototypes, but they failed to meet the maximum data transfer speed. This article focuses on the implementation of error-correctable ternary content-addressable memory (EC-TCAM)-based parallel memory allocation (PMA) systems with error-resilient properties, which can be capable of detecting and correcting errors during the parallel data allocation. Further, the priority circuit is used to generate the different levels of priorities, which helps to transfer the data between master and slave devices and vice-versa. Here, the synchronization issues generated during parallel reading and writing operations are minimized using priority circuit-controlled crossbar switching. The simulation results show the proposed EC-TCAM based PMA resulted in superior performance as compared to the conventional approaches in terms of hardware resource utilization parameters such as slice registers, look up tables (LUTs), LUT-flip-flops (LUT-FFs), delay, and power consumption. Finally, the proposed method utilized 310 slice registers, 235 LUTs, 156 LUT-FFs, 0.793ns of path delay, and 0.065 W of power.

Prioritization and identification of vulnerable sub-watersheds using morphometric analysis and an integrated AHP-VIKOR method

Abstract This study used satellite imagery datasets to extract various morphometric parameters in a geospatial environment to prioritize problematic areas in the Rarhu watershed of Ranchi district, Jharkhand, India. Two decision-making methods, the analytical hierarchy process (AHP) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), were integrated to prioritize different sub-watersheds. The Rarhu watershed has an area of 630 km2 with an elevation ranging from 210 to 824 m. The NASA Digital Elevation Model (NASADEM) was used to extract drainage networks which were verified from Survey of India (SOI) toposheets. To prioritize 21 sub-watersheds using a multi-criteria decision making (MCDM) method, 11 morphometric parameters were selected from linear, areal, and relief parameters. The VIKOR method prioritized sub-watersheds using AHP criteria weights, which are classified into four priority levels ranging from very high to low. In addition, performing sensitivity analysis validated the robustness of the decision-making model. As per the analysis, Rarhu watershed was found to have an elongated shape and a highest 6th order stream with a dendritic pattern of streams. It is estimated that watershed degradation is around 36.17% in the study area, with very high priority needs for soil and water conservation measures. Using the results of the study, policymakers, watershed planners, watershed development programme, and soil and water conservation programme projects can identify vulnerable sub-watersheds that require urgent adaptation of soil and water management control measures.

A Novel Strategy to Classify Chronic Patients at Risk: A Hybrid Machine Learning Approach

Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules.

Criteria and Indicators to Define Priority Areas for Biodiversity Conservation in Vietnam

Balancing biodiversity conservation with land use for agricultural production is a major societal challenge. Conservation activities must be prioritized since funds and resources for conservation are insufficient in the context of current threats, and conservation competes with other societal priorities. In order to contribute to conservation priority-setting literature, we applied an environmental model, Pressure–State–Response (PSR), to develop a set of criteria for identifying priority areas for biodiversity conservation in Vietnam. Our empirical data have been compiled from 185 respondents and categorized into three groups: Governmental Administration and Organizations, Universities and Research Institutions, and Protected Areas. The Analytic Hierarchy Process (AHP) theory was used to identify the weight of all criteria. Our results show that the priority levels for biodiversity conservation identified by these three factors are 41% for “Pressure”, 26% for “State”, and 33% for “Response”. Based on these three factors, seven criteria and seventeen indicators were developed to determine priority areas for biodiversity conservation. Besides, our study also reveals that the groups of Governmental Administration and organizations and Protected Areas put a focus on the “Pressure” factor, while the group of Universities and Research Institutions emphasized the importance of the “Response” factor in the evaluation process. We suggest that these criteria and indicators be used to identify priority areas for biodiversity conservation in Vietnam.

Mortality rate, patient length of stay and medical cost prediction in different priority levels for patient admission to an intensive care unit

PurposeThis study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a public tertiary hospital in Qazvin, Iran. This study also aimed to predict influencing factors on patients’ mortality, ICU LOS and hospitalization costs in different admission groups.Design/methodology/approachThe authors conducted a retrospective cohort study among patients who mainly suffered from internal diseases admitted to an ICU of a public hospital. This study was conducted among 127 patients admitted to ICU from July to September 2019. The authors categorized patients into four groups based on two crucial hemodynamic and respiratory status criteria. The authors used a logistic regression model to predict the likelihood of mortality in ICU admitted patients during hospitalizations for the four prioritization groups. Furthermore, the authors conducted a multivariate analysis using the “enter” method to identify risk factors for LOS.FindingsResults showed a statistically significant relationship between the priority of being admitted to ICU and hospitalization costs. The authors’ findings revealed that age, LOS and levels of consciousness had a predictability role in determining in-hospital mortality. Besides, age, gender, consciousness level of patients and type of the disease were mentioned as affecting factors of LOS.Originality/valueThis study’s findings emphasize the necessity of categorizing patients according to specific criteria to efficiently use available resources to help health-care authorities reduce the costs and allocate the budget to different health sectors.

Role of Pediatricians, Pediatric Associations, and Academic Departments in Ensuring Optimal Early Childhood Development Globally: Position Paper of the International Pediatric Association.

Early childhood (birth-8 years), particularly the first 3 years, is the most critical time in development because of the highly sensitive developing brain. Providing appropriate developmental care (i.e., nurturing care, as defined by the World Health Organization [WHO]) during early childhood is key to ensuring a child's holistic development. Pediatricians are expected to play a critical role in supporting early childhood development (ECD) through providing developmental services such as developmental monitoring, anticipatory guidance, screening, and referral to medical and/or community-based services when delay is identified. Pediatricians are also expected to serve as advocates within their clinics and communities for improved delivery of ECD services, such as advocating for increasing funding for ECD initiatives, increasing insurance coverage of ECD services, and working to increase other pediatricians' awareness of the principles of ECD and how to deliver developmental services. However, this does not always occur. Typically, pediatricians' training and practice emphasizes treating disease rather than enhancing ECD. Pediatricians are further hindered by a lack of uniformity across nations in guidelines for developmental monitoring and screening. In this article, we present the vision of the International Pediatric Association (IPA) of the roles that pediatricians, academic departments, medical training programs, and pediatric associations should fulfill to help support ECD, including raising ECD to higher levels of priority in routine pediatric care. First, we present the challenges that face these goals in supporting ECD. We then propose, with supportive literature, strategies and resources to overcome these challenges in collaboration with local and international stakeholders, including the IPA, the WHO, UNICEF, and the World Bank.

Relación entre necesidad de tratamiento ortodóncico e índice CPO-D en adultos

Introducción: se ha encontrado que sujetos con puntuación más alta del índice CPO-D fueron más propensos a requerir tratamiento de ortodoncia. El objetivo de este estudio fue relacionar la necesidad de tratamiento ortodóncico e índice CPOD en una población de adultos paraguayos en el año 2017. Métodos: estudio transversal en pacientes adultos ≥18 años que acudieron la clínica odontológica de la Facultad de Odontología de la Universidad Nacional de Asunción, durante la Pasantía Supervisada en los meses de enero y febrero del año 2017. Para la necesidad de tratamiento ortodóncico se utilizó el Índice de Estética Dental (IED). Se utilizaron las pruebas estadísticas Chi cuadrado de Pearson y ANOVA con el test de Bonferroni con un nivel del 95%. Resultados: formaron parte del estudio 147 adultos, el 71,4% eran mujeres. El IED fue de 31,1± 10,3. El 29,5% requirió tratamiento ortodóncico prioritario. El 65,3% presentó resalte incisal aumentado, el 55,1% apiñamiento y el 51,7% relación molar anormal. El Índice CPO-D fue de 8,9±5,9. Conforme aumenta el índice CPO-D también aumenta el nivel de necesidad de tratamiento ortodóncico (p<0,001), siendo la diferencia estadísticamente significativa entre los niveles ninguno y prioritario (p<0,001). Conclusiones: el índice CPO-D se encontró asociado significativamente al nivel de necesidad de tratamiento ortodóncico.

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time.