Feasibility Study Research Articles

Научно-методический подход к разработке проектов опытно-промышленных испытаний открытой разработки месторождений

Methodology of designing pilot tests in open-pit mining is a complex and insufficiently developed issue in the theory and practice of designing mining systems, which, as a rule, fails to take into account the mining and geological features of the deposits. The complex development of mineral deposits requires new methods of open-pit designing. Thus, the theoretical justification and development of the methods to determine the optimal parameters and indicators of open-pits during the design of pilot tests with the establishment of scales, deadlines, tasks and test conditions is an urgent research problem. The authors of the study have systematized the research trends in developing the basis for designing pilot tests in open-pit geotechnology. The level of geological inaccuracies in case of insufficiently reliable prospecting results leads to a decrease in the expected technical and economic indicators of the mining project ranging from 5-20% to several folds. When information on the deposit is insufficient, pilot test projects are prepared. This is done in order to obtain additional input data for preparation of the feasibility study of permanent conditions or at the stage of the field development to verify the created technologies and means of their mechanisation in order to avoid revision of the mining system parameters in the course of the field operations due to discrepancies between the geomechanical and hydrogeological representations given in the detailed exploration materials and the real conditions.

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

Using the OPUF approach to create a value set for the EQ-HWB-S: An exploratory feasibility study

Using the OPUF approach to create a value set for the EQ-HWB-S: An exploratory feasibility study

Научно-методический подход к разработке проектов опытно-промышленных испытаний открытой разработки месторождений

Methodology of designing pilot tests in open-pit mining is a complex and insufficiently developed issue in the theory and practice of designing mining systems, which, as a rule, fails to take into account the mining and geological features of the deposits. The complex development of mineral deposits requires new methods of open-pit designing. Thus, the theoretical justification and development of the methods to determine the optimal parameters and indicators of open-pits during the design of pilot tests with the establishment of scales, deadlines, tasks and test conditions is an urgent research problem. The authors of the study have systematized the research trends in developing the basis for designing pilot tests in open-pit geotechnology. The level of geological inaccuracies in case of insufficiently reliable prospecting results leads to a decrease in the expected technical and economic indicators of the mining project ranging from 5-20% to several folds. When information on the deposit is insufficient, pilot test projects are prepared. This is done in order to obtain additional input data for preparation of the feasibility study of permanent conditions or at the stage of the field development to verify the created technologies and means of their mechanisation in order to avoid revision of the mining system parameters in the course of the field operations due to discrepancies between the geomechanical and hydrogeological representations given in the detailed exploration materials and the real conditions.

Оценка ущерба, наносимого загрязняющими веществами от выхлопных газов транспортных средств при реконструкции автомобильной дороги

Purpose: assessment of damage caused by pollutants from vehicle exhaust gases during highway construction. The roadbed is one of the most important elements of a highway structure, presented in the form of a mass of soil and receiving static and dynamic loads from vehicles. The increasing intensity of road transport has a negative impact on the environment. The main causes of environmental damage are overloads at intersections, and their effects are delayed in time. For example, when a city highway is laid through an uninhabited area, the damage incurred is less than when the highway passes through densely populated residential areas and is adjacent to urban areas. Poor traffic management leads to environmental damage. The volume of emissions of harmful substances is determined by the mileage of vehicles, depends on various factors and is related to the total fuel consumption. An urgent task is to supplement the Methodological Recommendations for the selection of rational roadbed structures and their feasibility study with an indicator that takes into account environmental pollution, including greenhouse gas emis such as this is required in accordance with [1]. Methods: Taking as a basis the existing methodological recommendations for the selection of rational subgrade structures on weak foundations and their feasibility study [2], calculations were made on the basis of which it is possible to draw a conclusion about the amount of exhaust gases emitted by reducing the flow rate during activities for the reconstruction of the highway. The main concept underlying the methodological document is the optimization of design solutions based on their comprehensive technical and economic assessment, carried out in terms of capital and operating costs, significant socio-economic effects and the duration of the construction period. Results: a feasibility study and a technical and economic comparison of design solutions for the reconstruction of the highway were carried out in terms of widening the roadbed built on soft soils. Practical significance: Calculations are included in the basis of practical recommendations for the use of design solutions for the construction of roadbeds on soft soils, which gives a clear idea of the technical and economic feasibility oftheir use in various operating conditions.

Design of smart automated cooker_a survey and feasibility study

Design of smart automated cooker_a survey and feasibility study

Design of smart automated cooker_a survey and feasibility study

Design of smart automated cooker_a survey and feasibility study

Eye-Tracking for Examining Nurses’ Attention During Cardiac Arrest Simulations: A Feasibility and Acceptability Study

<p>Introduction: Eye-tracking offers a distinctive opportunity to assess nurses’ clinical decision-making in simulation. Although its feasibility has been established in various scenarios, most studies have focused on a single participant, typically a physician in a leadership role. The application of eye-tracking in the challenging context of in-hospital cardiac arrest (IHCA) simulations, where nurses juggle diverse roles and undertake physical tasks such as chest compressions, has yet to be explored.</p><p>Objectives: This study aimed to assess the feasibility and acceptability of eye-tracking with nurses’ during IHCA simulations. Additionally, the study aimed to describe eye-tracking metrics based on different resuscitation roles and to explore the relationship between eye-tracking metrics to pinpoint the most informative metrics for the design of future studies.</p><p>Methods: In this single-group observational study, 56 newly hired nurses wore eye-tracking glasses during IHCA simulations. The primary feasibility criterion was the proportion of usable eye-tracking data. Secondary criteria included recruitment rate, calibration time, and glasses acceptability. The relationship among eye-tracking metrics was investigated through correlation analyses.</p><p>Results: Calibration of the devices was rapid, and 85.7% of the data was usable. The glasses were comfortable, non-distracting, and did not impede nurses’ vision or performance. Data were mapped for five areas of interest: the patient’s head and chest, cardiac monitor, teammates, and resuscitation cart. Eye-tracking metrics exhibited variations based on resuscitation roles. Fixation count, fixation duration, and time to first fixation appeared to be the most informative metrics in IHCA simulation.</p><p>Discussion and conclusion: These findings demonstrate the feasibility and acceptability of analyzing nurses’ eye-tracking data during IHCA simulations using a role-based approach. Future research should explore correlations with additional attention measures to enhance our understanding of nurse decision-making during cardiac arrest and improve educational strategies and outcomes.</p>

Eye-Tracking for Examining Nurses’ Attention During Cardiac Arrest Simulations: A Feasibility and Acceptability Study

<p>Introduction: Eye-tracking offers a distinctive opportunity to assess nurses’ clinical decision-making in simulation. Although its feasibility has been established in various scenarios, most studies have focused on a single participant, typically a physician in a leadership role. The application of eye-tracking in the challenging context of in-hospital cardiac arrest (IHCA) simulations, where nurses juggle diverse roles and undertake physical tasks such as chest compressions, has yet to be explored.</p><p>Objectives: This study aimed to assess the feasibility and acceptability of eye-tracking with nurses’ during IHCA simulations. Additionally, the study aimed to describe eye-tracking metrics based on different resuscitation roles and to explore the relationship between eye-tracking metrics to pinpoint the most informative metrics for the design of future studies.</p><p>Methods: In this single-group observational study, 56 newly hired nurses wore eye-tracking glasses during IHCA simulations. The primary feasibility criterion was the proportion of usable eye-tracking data. Secondary criteria included recruitment rate, calibration time, and glasses acceptability. The relationship among eye-tracking metrics was investigated through correlation analyses.</p><p>Results: Calibration of the devices was rapid, and 85.7% of the data was usable. The glasses were comfortable, non-distracting, and did not impede nurses’ vision or performance. Data were mapped for five areas of interest: the patient’s head and chest, cardiac monitor, teammates, and resuscitation cart. Eye-tracking metrics exhibited variations based on resuscitation roles. Fixation count, fixation duration, and time to first fixation appeared to be the most informative metrics in IHCA simulation.</p><p>Discussion and conclusion: These findings demonstrate the feasibility and acceptability of analyzing nurses’ eye-tracking data during IHCA simulations using a role-based approach. Future research should explore correlations with additional attention measures to enhance our understanding of nurse decision-making during cardiac arrest and improve educational strategies and outcomes.</p>

Feasibility study of a parent-driven intervention for youth with Down syndrome.

Children with Down syndrome present with behavioural and emotional difficulties, including noncompliance, rule-breaking, emotion dysregulation and delays in executive functioning. Few behavioural interventions have been designed specifically for children with Down syndrome. The Research Units in Behavioral Intervention (RUBI) Parent Training for Disruptive Behaviors is a structured empirically supported parent training programme developed for caregivers of children with autism. This feasibility trial explored the feasibility and acceptability of an abbreviated RUBI intervention with caregivers of children with Down syndrome and identified promising outcome measures to target in future larger clinical trials. A double-blind randomised feasibility pilot clinical trial allocated participants to a behavioural intervention (BEH) or educational (EDU) group. BEH and EDU consisted of five individual sessions over the course of 5 to 8weeks. Measures were administered to 20 caregivers and their youth with Down syndrome at three time points. Both BEH and EDU were rated as feasible with high parental adherence and acceptable with high treatment satisfaction. Both BEH and EDU demonstrated decreased externalising behaviours, irritability and hyperactivity and improved behavioural regulation in executive functioning over time. No impact was noted on caregiver functioning. The feasibility trial has strong findings regarding feasibility and satisfaction and has promising findings regarding the selection of measures for future trials testing an adapted RUBI programme and an education programme to reduce behavioural challenges in children with Down syndrome. Larger scale clinical trials are needed to confirm promising findings of these feasible treatments.

Adaptation and feasibility of WHO PM+ for adolescents living with HIV in KwaZulu-Natal Province, South Africa: an implementation feasibility study protocol

IntroductionAdolescents living with HIV (ALHIV) are an extremely vulnerable population, with the burden of mental health problems carefully documented together with the constraints for receiving timely and adequate management of...

Automating population dose survey processing-An Australian feasibility study.

A comprehensive collection of data on doses in adult computed tomography procedures in Australia has not been undertaken for some time. This is largely due to the effort involved in collecting the data required for calculating the population dose. This data collection effort can be greatly reduced, and the coverage increased, if the process can be automated without major changes to the workflow of the imaging facilities providing the data. Success would provide a tool to determine a truly national assessment of the dose incurred through diagnostic imaging in Australia. The aims of this study were to develop an automated tool to categorize electronic records of imaging procedures into a standardized set of broad procedure types, to validate the tool by applying it to data collected from nine facilities, and to assess the feasibility of applying the automated tool to compute population dose and determine the data manipulations required. A rule-based classifier was implemented capitalizing on semantic and clinical rules. The keyword list was initially built from 609 unique study descriptions. It was then refined using an additional 414 unique study descriptions. The classifier was then tested on an additional 1198 unique study descriptions. Input from a radiologist provided the ground truth for the refinement of the classifier. From a sample of 238139 studies containing 2794 unique study descriptions, the classifier correctly classified 2789 study types with only five misclassifications, demonstrating the feasibility of automating the process and the need for data pre-processing. Dose statistics for 21 categories were compiled using the 238139 studies. The classifier achieved excellent classification results using the testing data supplied by the facilities. However, since all data supplied were from public facilities, the performance of the classifier may be biased. The performance of the classifier is yet to be tested on a more representative mix of private and public facilities.

Feasibility Study For Large Scale Thermal Convective Flow Measurements In Building Physics

The Department of Building Physics / Energy-efficient Buildings at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau is about to put a novel research-building into commission, the “Small House III”. Among other measurement equipment, a system for volumetric, large-scale, high-resolution flow velocity measurement should be utilized. In this paper, requirements for such a system are detailed, a suitable measurement system is proposed and a feasibility study for the verification of the system requirements is conducted. To this end, a tailored system for large-scale Lagrangian particle tracking (“Shake-the-Box”) is applied to thermal convective flow measurements above an electric radiator close to a wall. As a result, it is found that the designed volumetric velocity measurement system is capable of successfully operating in the design environment without minimal disturbance of the low-speed convection flow. . After a relatively short setup time (about 5 hours, including camera setup, calibration and self-calibration) up to 10000 images at 50 Hz (200 seconds) have been recorded continuously. Shake-the-Box results show a decline of the number of tracked particles from initially 48000 to about 8000 (with a half-life span of about 100 seconds for the helium filled soap bubbles used for seeding). Applying fine scale reconstruction (data assimilation), a vector spacing of 4 mm could be achieved for instantaneous volumetric velocity snapshots. Averaging resulted in a 2 mm vector spacing with an average of 3300 particles per bin and 410 particles per bin for 1 mm vector spacing (with 75% bin overlap). Weaker particles could only be detected in front of a black background. Using a white background (default background for later applications), weak particles could not be detected as well, so the number of detected particles and the measurement volume decreased slightly compared to the black background. Overall, the feasibility study successfully confirmed the readiness of the measurement system for the intended use for “Small House III” measurements.

Infant Modified Constraint-Induced Movement Therapy Paired With Neuromuscular Electrical Stimulation: A Feasibility Study.

To determine the feasibility of modified constraint-induced movement therapy (mCIMT) paired with neuromuscular electrical stimulation (NMES) for infants with asymmetrical hand function (AHF). Five infants received an experimental ABA design: (A1) 3weeks of our Standard AHF Care, (B) 3weeks mCIMT-NMES, and (A2) 3weeks of our Standard AHF Care. Parents tracked key data in a daily log, and infants were assessed 4 times using the Hand Assessment for Infants and Peabody Developmental Motor Scale-2. There was a high level of participant enrollment, visit frequency adherence, and compliance with the treatment protocol. No adverse events were reported. Mean Hand Assessment for Infants Both Hands measure scores changed more after mCIMT-NMES than after our Standard AHF Care. mCIMT-NMES is a feasible early intervention for infants with AHF at risk for unilateral cerebral palsy. A future study in a larger sample should examine the efficacy of mCIMT-NMES in this population.

Event Based Cameras: A Comparative Study For Fluid Event Detection

A feasibility study using an event-based camera for the detection of plane-wise motion of a 2D array of bio-inspired sensing pillars to detect generated flow events. EBCs show potential for future applications where PIV may have been classically used. They have the benefit of only generating data when an event happens and with comparatively cheaper setups, they hold great potential for flow monitoring. EBCs collect pixel x and y locations, as well as pixel contrast change data at each time there is an event and can be trained on regions of interest. Comparative experiments were carried out in the labs at City, University of London, to investigate the feasibility of using EBCs for the detection of generated large flow structures in both air and water. The disturbances were produced by a gust generator, an upstream cylinder and an air gun for a variety of controlled situations, such as disturbances caused by gusts, crossflows and wake downstream oscillations. Some results were also compared to known PIV results from our lab to see how EBCs can detect a known flow structure over the sensing pillars. The results presented demonstrate the capability of the pillars and EBC to detect large flow structures. These initial results show promise for future applications of flow monitoring, such as real-time flow monitoring via a machine learning algorithm that is trained to detect certain flow patterns over the sensing pillars. This would be an intermediate step into a fully automated flow control loop.

Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study’s results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study’s results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification

By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.

A Novel mHealth App for Smokers Living With HIV Who Are Ambivalent About Quitting Smoking: Formative Research and Randomized Feasibility Study.

More people who smoke and are living with HIV now die from tobacco-related diseases than HIV itself. Most people are ambivalent about quitting smoking and want to quit someday but not yet. Scalable, effective interventions are needed to motivate and support smoking cessation among people ambivalent about quitting smoking (PAQS) who are living with HIV. This study aims to develop an app-based intervention for PAQS who are living with HIV and assess its feasibility, acceptability, and potential impact. Results of this study will inform plans for future research and development. In phase 1, PAQS living with HIV (n=8) participated in user-centered design interviews to inform the final intervention app design and recruitment plan for a subsequent randomized pilot study. In phase 2, PAQS living with HIV were randomized to either a standard care control app or a similar experimental app with additional content tailored for PAQS and those with HIV. Participants were followed for 3 months. Feasibility focused on recruitment, retention, and participants' willingness to install the app. The study was not powered for statistical significance. Indices of acceptability (satisfaction and use) and impact (smoking behavior change and treatment uptake) were assessed via automated data and self-report among those who installed and used the app (n=19). Recruitment for both study phases was a challenge, particularly via web-based and social media platforms. Enrollment success was greater among people living with HIV recruited from a health care provider and research registry. Once enrolled, retention for the phase 2 randomized study was good; 74% (14/19) of the participants completed the 3-month follow-up. Phase 1 findings suggested that PAQS living with HIV were receptive to using an app-based intervention to help them decide whether, when, and how to stop smoking, despite not being ready to quit smoking. Phase 2 findings further supported this conclusion based on feedback from people who agreed to use an app, but group differences were observed. Indices of acceptability favored the experimental arm, including a descriptively higher mean number of sessions and utilization badges. Similarly, indices of potential impact were descriptively higher in the experimental arm (proportion reducing smoking, making a quit attempt, or calling free tobacco quitline). No participants in either arm quit smoking at the 3-month follow-up. On the basis of this formative work, PAQS living with HIV may be receptive to using a mobile health-based app intervention to help them decide whether, when, or how to stop using tobacco. Indices of acceptability and impact indicate that additional research and development are warranted. ClinicalTrials.gov NCT05339659; https://clinicaltrials.gov/study/NCT05339659.