PLANT TESTING MAGNETIC CONDITIONING ON KENNECOTT'S Cu-Mo PORPHYRY ORE

Over it's more than 100-year history, the Kennecott operation has often been at the forefront of innovation;driven by the demands of the lower grade ore-body and the higher costs of operating in the US where wages are generally higher and regulation more restrictive. One way of reducing operating costs in c/lb is to increase the lbs produced at minimal cost. Despite the relatively coarse grind at Kennecott - about 30% >150μm, approximately 20% of the Cu lost to tail is liberated chalcopyrite in the <20μm fraction, and about 30%-40% in the <37μm fraction. In 2020 Kennecott undertook a detailed plant scale test of the magnetic aggregation technology to increase copper recovery by reducing fine copper losses. A paired statistical plant test of magnetic conditioning on one rougher line showed a 1.12% increase in Cu recovery to 97% statistical confidence. The next challenge, unforeseen at the start of the project, was the fabrication and transportation to site of the equipment for the three remaining rougher rows, during the severe supply-chain constraints of the Covid pandemic in 2021. This resulted in delays and unforeseen costs as world-wide transportation became chaotic, particularly transportation via west coast USA. Nevertheless, the project was completed and commissioned, with only minor delays and cost increases, due to a flexible approach to overcoming the hurdles encountered. Copyright © 2023 by SME.

Evaluation of the Cross-Platform Framework Flutter Using the Example of a Cancer Counselling App

The advance of digitalization is constantly bringing new solutions to various areas of life in our society. The COVID-19 pandemic, among other things, brought increased attention to the application and support of treatments through digital solutions in the healthcare sector due to contact restrictions. However, the development of digital solutions comes at a high cost in terms of time and expenses. Mobile app development requires the development of two separate apps for the two respective market-leading mobile operating systems iOS and Android. Cross-platform frameworks make it possible to develop apps for both operating systems on a single code base, thus saving the development and maintenance of two separate codes. Flutter is currently the most popular cross-platform framework for the development of mobile apps. This paper has evaluated Flutter based on an existing criteria catalogue. As a usage context for the evaluation, a prototype for Cancer Counselling App of the University Medical Center Freiburg was implemented. According to the gained own prototyping experience with Flutter and a thorough literature analysis in this area, the criteria catalogue was filled out and the result was compared with other mobile App development paradigms. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples' interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users' search logs to reflect humans' underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples' cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks. © 2023 ACM.

Ultra-sensitive Detection of Alzheimer's Biosmarkers Using Plasmonic Optical Fiber Sensors

The emergence of the Covid-19 pandemic has drawn great attention to vulnerable people affected by major diseases. Among them, Alzheimer's disease (AD) is the most prevalent disease. However, a long-standing challenge is to achieve early diagnosis of AD by detecting biomarkers such as amyloid beta (Aβ42), thus avoiding the labor of specialized hospital personnel and the high cost of imaging examinations using positron emission tomography. In this paper, we report a straightforward approach to realize a non-invasive lab-around fiber (LaF) optical sensor for AD biomarker detection, which is based on a tilted fiber Bragg grating (TFBG) combined with a nanoscale metallic thin film. We successfully demonstrated the detection of Aβ42 in complex biological matrices with a detection limit of 5 pg/mL. Therefore, our TFBG-SPR biosensor platform enables large-scale early disease screening and has great potential for clinical applications in early AD diagnosis. © 2022 IEEE.

Successful Implementation of Gas Driven Electric Power Grid Project for Wells and Facilities Electrification, Yielding GHG Emissions and Cost Benefits

Objectives/Scope: Kuwait Energy is exploring, developing, and operating four concessions located in the Western Desert and the Gulf of Suez in Egypt;the company implemented many projects that had a significant impact on saving operating expenses and reducing greenhouse emissions to preserve the environment. One of these recent executed projects was replacing scattered diesel generators with a Central gas-driven electric power grid in Al- Jahraa field in East Abu-Sennan concession. In this , we present the challenges we faced during the planning phase and execution strategy applied to overcome these challenges. Methods: Al-Jahraa Field includes 13 running wells, a waterflood station, and a main oil and gas production plant. The field electricity is supplied by 15 scattered diesel generators for wells and facilities, consuming 100,000 liters of diesel per month. During the feasibility study phase of the project, many challenges were faced which had a negative impact on the project's economical assessment and that would result in cancelling the project, the challenges were summarized as following;the existence of wells at long distances from the site of the proposed main power station, which would require extending long lengths of electric power cables at a high cost, also the expected delay in the implementation and commissioning of the project resulting from the long delivery time of materials, especially copper cables and main switchgear during the COVID-19 pandemic. Several scenarios were studied for connecting the wells to the power station: The first scenario was to connect all wells and field facilities directly to the main power station. In this case, the estimated power cable lengths required to be extended were 25,000 Mt, in addition to using two 1 MW generators, one in service and the other would be a standby generator to provide backup power during a repair or maintenance service. This option economic model showed negative NPV due to the high cost of cables and extended execution time. Therefore, this option was cancelled. The second alternative was to connect each group of wells to three power stations to be operated using three diesel generators of 500 kVA for each station, with three backup generators. But the implementation of this option would lead to saving the cost of copper cables by 50%, but the cost of purchasing generators would increase due to the increase in the number of stations accordingly, in addition to the increase in operating expenses resulting from the increase in fuel consumption and maintenance cost compared to the first option. The third alternative, in which the economics of the project proved to be the best, is to divide the wells into three groups. Each of the two remote groups of wells are connected to an electric distribution panel, and then the two panels are connected by a main cable to the main power station. Moreover, the project cost was reduced by 50% due to the implementation of the following innovative optimization approach: • Re-using ESP cables instead of copper cables optimized both cost and delivery time as these materials are pulled from ESP wells. • These cables are designed for harsh downhole conditions increases its durability and extends its lifetime. • Using step-up and step-down transformers enabled us to reduce cable sizing, which also reflected on the lower cost of the project and, accordingly, increased its feasibility to be constructed. • An Incremental development approach, was followed in the management and implementation of the project, led to the speed of project delivery, and reduced the project risks and uncertainties. Results: The project was completed and commissioned within the allocated budget and time frame, leading to: ◦ 100% reduction of diesel fuel consumption levels. ◦ +68% reduction in total emissions;emissions are reduced by 2.5tons per year on average. ◦ reduced operational costs for each kilowatt hour generated due to using associated gas as fuel and releasing 13 rental generators. ◦ With the replacement of 1 rental generators with just one, the amount of maintenance waste, such as batteries, used oil, oil filters, fuel filters, and so on, is significantly reduced. ◦ These projects showed positive economic indicators (+NPV), with less than 1 years of payback. Conclusion: From this project's planning, execution, and results, we can claim that if risk assessments, detailed scope of work, good resource and time management, and cost-effective choices were addressed carefully, shall result in outstanding performance. The design of a high-efficiency electrical power supply system and use of associated gas in power generation reduces levels of fuel consumption, GHG emissions, and operational costs. Power generation project is a repeated case performed in one of our own assets in Egypt due to positive results and are easily transferable to sister IOCs & NOCs. Copyright © 2023, Society of Petroleum Engineers.

Role of Partial Credit Guarantee Scheme in Enabling Growth of SME

Despite their importance Small and medium sized enterprises (SMEs) in Azerbaijan and in other developed and developing countries suffer from limited access to financing due to high costs of small-scale lending, information asymmetry, high risks attributed to SMEs and collateral requirements. Thus, the lack of SME access to finance is to the large extent the consequence of weaknesses in enabling environment for finance. Shortfall in enabling environment becomes major constraint for economic growth and diversification and/or causes regionally-unbalanced growth. These deficiencies motivate government to make policy interventions toward SME financing expansion. Largely interventions come in form of credit guarantee schemes (CGS), direct lending facilities and lending by state-owned financial institutions. In turn, partial credit guarantee schemes are considered as most market friendly intervention type. There are also notable examples when countries like South Korea employed PCG as countercyclical policy tool to face difficulties came from economic downturn. The diverse and resilient SME sector is the center piece of the Azerbaijan government's strategic agenda to diversify the economy away from oil. Credit Guarantee Schemes were introduced in Azerbaijan as a measure of Government to make financing accessible for SMEs and to reduce effect of negative impact of two recent major events: the drop in worldwide oil prices and COVID-19 pandemic. The objective of this paper is to review characteristics of Credit Guarantee Schemes and assess preliminary outcomes of Partial Credit Guarantee mechanism implementation in Azerbaijan. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Value of Pooling in Last-Mile Delivery

Last-mile delivery services have become ubiquitous in the recent past. Delivery services for food (eg., DoorDash, Grubhub, Uber Eats) and groceries (eg., Instacart, Cornershop) earned a combined revenue of $25B in 2020, and are expected to exceed $72B in revenues by 2025. The COVID-19 pandemic accelerated the growth of such services by making their value proposition even more attractive. The lower risk of contact coupled with the convenience of ordering from the comfort of their homes led to widespread customer adoption. Even so, most last-mile delivery services are not profitable. The high cost of delivery is cited as the major cause of losses. Thus, analyzing the factors influencing delivery costs is crucial for understanding the long-term viability of these services. The pooling of orders is a critical source of efficiency in last-mile delivery. We propose a queuing-based spatial model for the delivery process to analyze the value created by pooling. We demonstrate how the trade-off between delivery times and the cost of delivery, mediated by the extent of pooling, dictates which services will be economically viable. Our simulation study of a typical grocery delivery service in Los Angeles, California suggests that delivery times of less than 1 hour are unprofitable for most regions in the US. We find that driver wages account for 90% of the delivery cost. We also discuss the potential impact of technological innovations such as automated delivery and labor regulations on the profitability of last-mile delivery services. © 2022 IEEE.

A Remote Rehabilitation and Evaluation System Based on Azure Kinect

In response to the shortage, uneven distribution, and high cost of rehabilitation resources in the context of the COVID-19 pandemic, we developed a low-cost, easy-to-use remote rehabilitation system that allows patients to perform rehabilitation training and receive real-time guidance from doctors at home. The proposed system uses Azure Kinect to capture motions with an error of just 3% compared to professional motion capture systems. In addition, the system pro-vides an automatic evaluation function of rehabilitation training, including evaluation of motion angles and trajectories. After acquiring the user's 3D mo-tions, the system synchronizes the 3D motions to the virtual human body model in Unity with an average error of less than 1%, which gives the user a more intuitive and interactive experience. After a series of evaluation experiments, we verified the usability, con-venience, and high accuracy of the system, finally con-cluding that the system can be used in practical rehabilitation applications. © Fuji Technology Press Ltd.

Analysis of the Online Home Consumption Database Based on Data Mining

In current times where there are smart devices for households, and that apart from having different functions that are helpful in daily household chores, such as being able to maintain a full pantry, and to generate errand lists, in the market these devices have a high cost for this reason is that it is proposed to create a low cost smart device, in this document an analysis is made using data mining, with tensor flow, of the purchases generated by the users, derived from the current situation by the pandemic of the COVID-19, generated an increase in online shopping, this analysis is intended to be a support for online errand shopping to visualize classification and prediction in the comparisons of users. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Caps-3WD: A three-way diagnostic CapsNet for COVID-19

Pulmonary medical image processing is an effective diagnostic method for COVID-19, and CapsNet-based methods have achieved good performance. However, as cost-blind methods, these diagnostic methods only consider immediate and deterministic decisions, which easily lead to misdiagnosis and high costs. Therefore, based on a revised CapsNet, we propose a cost-sensitive three-way decision (3WD) method for COVID-19 diagnosis, named as Caps-3WD. To enhance the feature extraction ability for pneumonia areas, we introduce a Restage module to improve convolution layer of the original CapsNet. Further, to lighten the model, we introduce depth wise separable convolution to reconstruct decoder. Additionally, three options are considered in the decision set: infected, normal, and suspected, which are given different costs, respectively. The lowest-cost decision is chosen for each input. In the experimental analysis, we compare Caps-3WD with CNN-based and CapsNet-based methods on COVID-CXR dataset, which proves the effectiveness of 3WD and the superiority of Caps-3WD in COVID-19 diagnosis. © 2022 SPIE. Downloading of the is permitted for personal use only.

Detection of SARS-CoV-2 Using a Miniaturized Spectrometer Chip: A Comparative Study Against Benchtop Spectrometer and Its Potential Application to Handheld Detection

A global pandemic of SARS-CoV-2 was caused around the world. The virus is highly contagious and rapidly spreads. Early detection of the virus is crucial to prevent its spread and control outbreaks. Owing to the drawbacks of waiting time and high cost involved in polymerase chain reaction (PCR) testing, low-cost and accurate detection setups with the possibility of being realized as portable systems are desirable. In this study, we examined the feasibility of using a small spectrometer in conjunction with optical biosensors as a measurement system. According to the experimental results related to different concentrations of SARS-CoV-2 ranging from 106 to 102 copies/mL, the surface-mounted device (SMD) size spectrometer and benchtop fiber-optic spectrometer showed good agreement, demonstrating the possibility of using tiny spectrometers to detect the virus at different concentrations using optical biosensors. © 2022 ACM.

Transformer-Based Deep Learning Method for the Prediction of Ventilator Pressure

As patients infected with SARS-CoV-2 are more likely to have trouble breathing, a great demand for ventilators has been generated since the COVID-19 is continuing to spread around the world. However, the research and development of ventilator control suffer from high cost, slow efficiency, and lack of automation, especially regarding the estimation of ventilator pressure. In this paper, to address this challenge and help control the mechanical ventilators better, we develop a Transformer-based deep learning method for the prediction of ventilator pressure. Based on the dataset provided by Google Brain in a Kaggle competition, we connect the Transformer encoders by residual connections to extract features from the time-series ventilator data, and successfully achieve the goal to predict the ventilator pressure with excellent performance. After applying the K-Fold cross validation technique, our Transformer-based model reaches a mean absolute error 0.1311 on the private test set. This result ranks 67/2605 (top 2.6%) in the leaderboard of Google Brain-Ventilator Pressure Prediction competition, and can get a silver medal in this Kaggle competition. This work could accelerate the development of new methods to overcome the cost barrier of ventilator control. © 2022 IEEE.

Detection of COVID-19 using CNN from Chest X-ray Images

COVID-19 (novel coronavirus disease) is a deadly illness, has infected and killed a very large number of people worldwide. The widely followed lab testing (RT-PCR Test) for the detection of this disease has various limitations with high cost and take long time to provide the outcome. As a result, diverse technologies that permit for the quick and accurate finding of the infection can provide much required assistance to medical management. In recent studies, gained radiological imaging techniques, such images convey important information about this virus. Advanced Deep learning (DL) techniques combined with the radiology images can aid in the correct diagnosis of the virus, as well as defeat the problem of insufficient expert physicians in rural areas. In this work, aimed at presenting a DL based-Convolutional neural network (CNN) model for the automatic detection of the coronavirus from X-ray images of chest. The Kaggle dataset available publicly of total 42330 images from 4-categories are used. The experiment produced the accuracy of 88.53% and 86.19% for training and validation, which is better result for the highest number of radiographic images in comparison to existing work. © 2022 IEEE.

Assessment of Healthcare Workers' Level of Knowledge and Practices of Using Robotics during Covid-19

The outbreak of the coronavirus created a conventional challenge for the global community due to the lack increased a number of COVID-19 positive patients and stretched healthcare systems. Therefore, the global world starts to look for unconventional approaches and technologies to combat the dissemination of the COVID-19 virus, for example, utilizing Robotics and Artificial Intelligence. Using robotics for medical services is facing many challenges that may lead to failure. The most important of these challenges is that the healthcare workers are not trained well on using robotics, don't have enough knowledge, awareness of the importance of using robotics especially during pandemics, and the relatively high cost of robotics. Thus, in this paper, we assess the levels of knowledge, attitude, and practices of healthcare workers about using robotics during pandemics. Further, a quantitative and a qualitative approach using a questionnaire to collect data is used. Over 40 healthcare workers participate, who have a fair background about robots. However, they have no idea about using robots in general and their field in particular. © 2022 IEEE.

Low-cost CPAP device for patients with COVID-19

The increase in COVID-19 infections due to the appearance of new variants causes serious health problems in the population. Although the majority of those infected have no symptoms, approximately 19% will require oxygen therapy for respiratory support, either by invasive or non-invasive ventilation. The high costs of this equipment mean that hospitals in developing countries have a limited supply of such equipment. Therefore, this paper presents the redesign, simulation, fabrication, and evaluation of a CPAP device that was developed in Europe but has now been replicated in Latin America. It starts with the analysis of the device’s original design and through exploratory techniques and computer simulation the materials, parts, and assembly are determined. Functional tests validate that a prototype similar to the original was obtained that will provide noninvasive positive air pressure that keeps patients' airways open. © 2022 IEEE Computer Society. All rights reserved.

Towards Fast and Accurate Intimate Contact Recognition through Video Analysis

Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%;rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing. © 2022 IEEE.

ARChem: Augmented Reality Based Chemistry LAB Simulation for Teaching and Assessment

This paper introduces an Augmented Reality (AR)-based chemistry (ARChem) lab platform for teaching and assessment. The current in-class lab teaching has several drawbacks such as the high cost of lab materials, physical constraints in lab sizes, and exposure to hazardous materials. Besides, with Covid-19, access to the in-person labs and materials is significantly restricted. This restriction hampers regular lab instruction, self-learning, and hands-on experience. Our ARChem platform stems from the motivation of delivering a similar experience to regular instruction. In addition, ARChem aims to promote learning and advancement by incorporating gaming elements. Also, assessment and evaluation are provided for self-improvement. Our ARChem in-class chemistry lab uses a physical environment and virtual lab equipment and fluids. The environment is based on the AR technique where virtual lab equipment and materials (e.g. fluids and supplies) are overlaid on the physical environment using only mobile devices. To do this efficiently, ARChem provides computationally optimal methods such as simulating multiple bimanual chemistry reactions, low-cost fluid physics in containers, and realistic visualization. We enable a realistic lab environment with minimal hardware constraints attaining high simulation and visual frame rates. This simulation efficiency makes ARChem compatible with a wide range of low-end mobile devices and is accessible to any student. Our ultimate goal is to eliminate the drawbacks of current in-class teaching with a simulation environment and improve remote and asynchronous learning experience especially during an outbreak like Covid-19. © 2021 IEEE.

Development of Acoustically Optimized, 3D-Printed Stethoscope for Telehealth

The need for telehealth accelerated by the COVID-19 pandemic has changed the way doctors interact with patients. Recently, Internet-connected stethoscopes have been developed;however, they have not been adopted as far as many companies have hoped. The high cost of branded, high-quality stethoscopes may serve as a barrier to the adaptation, especially in low-and middle-income countries. With the above in mind, a 3D-printed stethoscope that preserves a long air column but still maintains a small form factor has been developed. Inserting a microphone into the stethoscope enables us to connect the stethoscope to our smartphone, turning it into a digital device. To take advantage of smartphone capabilities, we have also developed an Android app that enables us to record, share, and listen to heart sounds in real-time via Bluetooth headphones. Our experimental results show that the stethoscope made of thermoplastic polyurethane exhibits harmonic resonances comparable to commercially available selected acoustic stethoscopes. © 2022 IEEE.

Appropriate admission in Covid-19 era: The case study of the C.O.U. neurology and stroke unit

Hospital services absorb almost half of the costs of the health sector;therefore, it is necessary to promote the effectiveness, efficiency and appropriateness of the services provided. These concepts become even more important in the era of the CoViD-19 pandemic, where the efforts made to manage it have absorbed all hospital resources. Inappropriate hospitalization and length of stay (LOS) are the factors that impose higher costs on hospitals. This study analyzed how CoViD-19 changed the activity of the Department of Neurology and Stroke Unit of the "San Giovanni di Dio e Ruggi d'Aragona"University Hospital of Salerno (Italy). Logistic regression showed a significant increase in relative Diagnosis Related Group (DRG) weight which translates into greater appropriateness of admission diagnoses. © 2021 ACM.

Statistical analysis of fuel cell electric vehicles

With the promotion of electrification of transportation, fuel cell electric vehicles (FCEVs) begin to flourish in recent years. FCEVs operate with zero emission and excellent fuel economy, but high cost and incomplete infrastructure hinder the popularization further. Targeting resources are poured into this area by some governments worldwide. To foster the development, it is essential to study the use of FCEVs. Based on the Service and Management center for EVs (SMC-EV), this work conducts a statistical analysis of the market scales, the operation conditions, such as user login statistics, driving distance and refueling behavior and the impact of the occurrence of the Covid-19 pandemic. The analysis results provide essential support to predict the subsequent development of FCEVs and guide the policymaking and the construction of hydrogen refueling stations. © 2022 SPIE. All rights reserved.