Extra Cost Research Articles

Validation of Opportunistic Artificial Intelligence-Based Bone Mineral Density Measurements in Coronary Artery Calcium Scans

BackgroundPreviously we reported a manual method of measuring thoracic vertebral bone mineral density (BMD) using quantitative CT in noncontrast cardiac CT scans used for coronary artery calcium (CAC) scoring. In this report, we present validation studies of an artificial intelligence–based automated BMD measurement (AutoBMD) that recently received FDA approval as an opportunistic add-on to CAC scans. MethodsA deep learning model was trained to detect vertebral bodies. Subsequently, signal processing techniques were developed to detect intervertebral discs and the trabecular components of the vertebral body. The model was trained using 132 CAC scans comprising 7,649 slices. To validate AutoBMD, we used 5,785 cases of manual BMD measurements previously reported from CAC scans in the Multi-Ethnic Study of Atherosclerosis. ResultsMean ± SD for AutoBMD and manual BMD were 166.1 ± 47.9 mg/cc and 163.1 ± 46 mg/cc, respectively (P = .006). Multi-Ethnic Study of Atherosclerosis cases were 47.5% male and 52.5% female, with age 62.2 ± 10.3. A strong correlation was found between AutoBMD and manual measurements (R = 0.85, P < .0001). Accuracy, sensitivity, specificity, positive predictive value and negative predictive value for AutoBMD-based detection of osteoporosis were 99.6%, 96.7%, 97.7%, 99.7% and 99.8%, respectively. AutoBMD averaged 15 seconds per report versus 5.5 min for manual measurements (P < .0001). ConclusionsAutoBMD is an FDA-approved, artificial intelligence–enabled opportunistic tool that reports BMD with Z-scores and T-scores and accurately detects osteoporosis and osteopenia in CAC scans, demonstrating results comparable to manual measurements. No extra cost of scanning and no extra radiation to patients, plus the high prevalence of asymptomatic osteoporosis, make AutoBMD a promising candidate to enhance patient care.

Implementation of a direct-addressing based lattice Boltzmann GPU solver for multiphase flow in porous media

GPU accelerated lattice Boltzmann (LB) simulations of multiphase flow in porous media have become a powerful tool to study fluid displacement process in porous media. For porous structures with a very low porosity, indirect-addressing memory access methods are preferred due to significantly reduction of the memory footprint despite that those methods are more difficult to implement and the resulting performance may be more sensible to the computing architectures, such as cache hierarchy and size. The direct-addressing methods are straightforward to implement and are able to archive high throughput when the porosity is large, while the methods become less efficient when the structures are too sparse. Nevertheless, the direct-addressing methods combined with multi-block grid technique are promising for many applications. In this work, we present a hybrid-way to tackle multiphase flow simulations in porous media, where the direct-addressing method is employed for the main LB evolution while the indirect-addressing method is employed for the complex boundary conditions. The CSF-based LB color-gradient multiphase model and the geometry-based wetting model are employed to increase accuracy and stability. Thanks to the utilization of AA-pattern streaming scheme, non-slip boundary condition of arbitrary orientation can be enforced without extra cost. We perform comprehensive analysis of the computational performance of the present solver on NVIDIA GPUs. For typical sandstones, our results show that the present implementation is able to achieve over 1.5 speedup compared with other direct-addressing schemes on V100 and A100, respectively and, particularly, the computational performance of the boundary kernels is greatly increased thanks to the increased L2 cache size of the latest GPUs.

An inverse Q filtering method with adjustable amplitude compensation operator

The generalized stable inverse Q filtering method can flexibly select the intensity and frequency bandwidth of amplitude compensation operator according to the signal-to-noise ratio (SNR) of seismic data. When processing noisy seismic data, the method can avoid enhancing some noise energy by adjusting parameters. However, this method still faces the problem of noise amplification when the SNR of seismic data is low. In order to solve this problem, this paper proposes an adjustable amplitude compensation operator, which can suppress part of the high frequency noise energy effectively without affecting the intensity of the amplitude compensation by introducing a dynamically adjustable amplitude compensation curve. Combining this operator with the generalized stable inverse Q filtering algorithm, an inverse Q filtering method with adjustable amplitude compensation operator is developed. Compared with the generalized stable inverse Q filtering method, this method has the ability of adjustable amplitude compensation and adjustable noise suppression. When inverse Q filtering is performed on the seismic records, the compensation strategy can be more flexible according to its quality and processing requirements. In addition, this method almost not introduce extra calculation cost, and has the characteristics of simplicity and high efficiency. Theoretical and practical seismic records show that the proposed method can further improve the SNR and resolution of seismic records after inverse Q filtering.

PME: pruning-based multi-size embedding for recommender systems.

Embedding is widely used in recommendation models to learn feature representations. However, the traditional embedding technique that assigns a fixed size to all categorical features may be suboptimal due to the following reasons. In recommendation domain, the majority of categorical features' embeddings can be trained with less capacity without impacting model performance, thereby storing embeddings with equal length may incur unnecessary memory usage. Existing work that tries to allocate customized sizes for each feature usually either simply scales the embedding size with feature's popularity or formulates this size allocation problem as an architecture selection problem. Unfortunately, most of these methods either have large performance drop or incur significant extra time cost for searching proper embedding sizes. In this article, instead of formulating the size allocation problem as an architecture selection problem, we approach the problem from a pruning perspective and propose Pruning-based Multi-size Embedding (PME) framework. During the search phase, we prune the dimensions that have the least impact on model performance in the embedding to reduce its capacity. Then, we show that the customized size of each token can be obtained by transferring the capacity of its pruned embedding with significant less search cost. Experimental results validate that PME can efficiently find proper sizes and hence achieve strong performance while significantly reducing the number of parameters in the embedding layer.

Drug interference with biochemical laboratory tests.

Clinical laboratory practice represents an essential part of clinical decision-making, as it influences 60-70% of medical decisions at all levels of health care. Results of biochemical laboratory tests (BLTs) have a key role in establishment of adequate diagnosis as well as in evaluation of treatment progress and outcome. The prevalence of drug-laboratory test interactions (DLTIs) is up to 43% of patients who had laboratory results influenced by drugs. Unrecognized DLTIs may lead to misinterpreted BLTs results, incorrect or delayed diagnosis, extra costs for unnecessary additional tests or inadequate therapy, as all may cause false clinical decisions. The significance of timely and adequate recognition of DLTIs is to prevent common clinical consequences such as incorrectly interpreted test results, delayed or non-treated condition due to erroneous diagnosis or unnecessary extra tests or therapy. Medical professionals should be educated that it is essential to obtain patient data about medications especially for the drugs used in the last 10 days before biological material collection. Our mini-review aims to provide a comprehensive overview of the current state in this important domain of medical biochemistry with detailed analysis of the effect of drugs on BLTs and to give detailed information to medical specialists.

Waste recycling through a decentralized network of mobile facilities

Mobile and modular recycling facilities have been developed in recent years, which can promote both the expansion of recycling capacity and the decentralization of waste treatment. Due to the miniaturization of these recycling units and their installation in standard containers, it is possible to relocate them from site to site at short notice, allowing for a more agile response to spatial and temporal waste generation. The planning of such a network involves the integration of location, production, and transport decisions. Therefore, this article presents a mixed-integer programming model to plan the relocation of the mobile recycling units so that the total costs of transport, recycling, and facility relocation are minimized. In addition, the economic value and environmental impact of mobile recycling networks are investigated through a sensitivity analysis, showing the benefit of movable recycling capacities. A case study analysis reveals that deploying modular units reduces overall recycling costs by about 10%, although extra costs are required to operate and relocate the mobile recycling units.

Facile and versatile fabrication process for AgNW/GZO transparent composite electrodes for photovoltaic applications by atmospheric pressure plasma jet

Transparent conductive oxide (TCO) must have high electrical conductivity and optical transmittance. Using higher doping concentrations improves the conductivity but often at the expense of transmittance in the near-infrared regime. Using thicker film to improve the conductivity is not preferable as it significantly reduces the transmittance. Silver nanowire (AgNW)/ TCO composite film has been proposed as a viable method to address this dilemma. However, AgNWs often require additional thermal treatment to weld the nanowires to reduce the contact resistance. Also, thermal treatment apparatus, such as lasers, cannot be easily integrated into the TCO deposition setup, adding extra cost for handling samples. Here we use an atmospheric pressure plasma jet (APPJ) to fabricate AgNW/ TCO composite films—APPJ successfully welds the AgNWs and covers them with a thin gallium-doped ZnO or GZO protective layer simultaneously. The AgNW weight significantly affects the optoelectronic and morphological properties and can be tailored to fit the requirements of particular photovoltaic applications. Our perovskite solar cells with the AgNW/GZO front electrodes exhibit a ∼21% increase in power conversion efficiency. Furthermore, unlike conventional methods, our approach is vacuum-free and does not require additional processes, such as lasers, to treat the AgNW network, which may greatly reduce operational complexity.

Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework

Deep reinforcement learning (DRL) has become the mainstream method to design intelligent energy management strategies (EMSs) for fuel cell hybrid electric vehicles with the prosperity of artificial intelligence in recent years. Conventional DRL algorithms are suffering from low sampling efficiency and unsatisfactory utilization of computing resources. Combined with distributed architecture and parallel computation, DRL algorithms can be more efficient. Given that, this paper proposes a novel distributed DRL-based energy management framework for a fuel cell hybrid electric bus (FCHEB) to shorten the development cycle of DRL-based EMSs while reducing the total operation cost of the FCHEB. To begin, to make full use of the limited computing resources, a novel asynchronous advantage actor-critic (A3C)-based energy management framework is designed by innovatively integrating with the multi-process parallel computation technique. Then, a promising EMS considering the extra operation cost caused by fuel cell degradation and battery aging is designed based on this novel framework. Furthermore, EMSs based on a conventional DRL algorithm, advantage actor-critic (A2C), and another conventional distributed DRL framework, multi-thread A3C, are employed as baselines, and the performance of the proposed EMS is evaluated by training and testing using different driving cycles. Simulation results indicate that compared to EMSs based on A2C and multi-thread A3C, the proposed EMS can efficiently accelerate the convergence speed respectively by 87.46% and 88.92%, and reduce the total operation cost respectively by 44.83% and 41.19%. The main contribution of this article is to explore the integration of multi-process parallel computation in a distributed DRL-based EMS for a fuel cell vehicle for more efficient utilization of hydrogen energy in the transportation sector.

On the passive shimming of a 7 T whole-body MRI superconducting magnet: Implementation with minimized ferromagnetic materials usage and operable magnetic force control.

Magnetic field shimming of the magnet is a routine practice in a magnetic resonance imaging (MRI) system. For clinically-used 1.5 T or 3 T MRI superconducting magnets, it is generally straightforward to achieve desired magnetic field uniformity with the passive shim technique. In comparison, superconducting shims with higher shimming efficiency are usually introduced in combination with passive shimming to satisfy the higher magnetic field uniformity requirement for ultrahigh field magnets (≥7 Tesla). However, superconducting shim usually involves a complex winding structure and low-temperature environment, bringing considerable engineering challenges and extra costs in practice. In this study, we aimed to improve the passive shimming method that can incorporate the unique electromagnetic properties of ultrahigh-field MRI magnets and is thus more effective for field corrections at 7T and above. In this work, we propose a dedicated passive shimming strategy for a 7 T whole-body MRI superconducting magnet. In this method, the iron usage and magnetic force due to the iron-field interaction are strictly managed to ensure a shim tray insert is operable by manpower (without specially designed tools). To validate the proposed shimming strategy, a shimming experiment was implemented on a 7 T/800mm superconducting magnet. Alternating with the odd and even shim trays in our two-round operation, the magnetic field inhomogeneity was successfully corrected from 85.36 to 7.91ppm, achieving the magnetic field quality elevation of more than one order of magnitude. The experimental results indicated that the proposed electromagnetic technology is expected to be effective for developing ultrahigh-field MRI instruments.

Community evolution prediction based on a self-adaptive timeframe in social networks

The prediction of community evolution events in dynamic social networks is of great importance for network security alerts. Currently, most of the common prediction models use a fixed timeframe division strategy to divide the dynamic social network, such as the disjoint timeframe division strategy and overlapping timeframe division strategy. However, these frameworks cannot change once they are selected, and are not suitable for the prediction of network evolution with strong variability in real-world applications. In this paper, a new community evolution model is developed from the perspective of the universality of the timeframe, and a new optimized timeframe partitioning algorithm is proposed. Compared with the traditional fixed timeframe partitioning algorithm, this method adaptively adjusts the size and number of time windows according to the information fluctuations of the specific network at an acceptable extra computational cost. Based on the analysis of several real-world networks, we found that the proposed self-adaptive timeframe partitioning algorithm improved the quality of community tracking of the network and ensured the accuracy of prediction events.

Economic impact of a more extensive use of FENO testing on the Italian population with asthma

BackgroundAsthma is a common chronic inflammatory airway affecting over 260 million people worldwide, and characterized, in the large majority of cases, by the so-called “type 2 inflammation”. Fractional exhaled nitric oxide (FENO) testing is noninvasive point-of-care tool to assess type 2 inflammation and therefore improve asthma management. It has been suggested to determine eligibility for a specific biologic therapy and predict likelihood to respond. The aim of this study was to estimate the overall economic impact of an extensive use of FENO testing on the Italian population with asthma, including extra costs of testing and savings generated by more appropriate prescriptions, increased adherence and lower frequency of exacerbations.MethodsA cost of illness analysis was firstly performed to estimate the yearly economic burden from the National Healthcare Service (NHS) perspective in Italy of the management of asthmatic patients with standard of care (SOC) according to the application of GINA (Global Initiative for Asthma) guidelines; then, we evaluated the changes in the economic burden in patient management by introducing FENO testing into clinical practice. The cost items considered were: visits/exams, exacerbations, drugs, management of adverse events caused by short-term oral corticosteroids use. Efficacy of FeNO test and SOC is based on literature evidence. Costs refer to published data or Diagnosis Related Group/outpatient tariffs.ResultsConsidering one asthma visit every 6 months, the total yearly cost for the management of patients with asthma in Italy is 1,599,217,876€ (409.07€ per patient), while for FENO testing strategy this figure is 1,395,029,747€ (356.84€ per patient). An increased utilization rate of FENO testing from 50 to 100% of patients may lead to savings for the NHS from about 102 to 204 million € compared to SOC.ConclusionsOur study showed that FeNO testing strategy may improve the management of asthmatic patients leading to significant savings for the NHS.

A rapid increase in coverage of COVID-19 vaccination, Central African Republic.

In 2021, Central African Republic was facing multiple challenges in vaccinating its population against coronavirus disease 2019 (COVID-19), including inadequate infrastructure and funding, a shortage of health workers and vaccine hesitancy among the population. To increase COVID-19 vaccination coverage, the health ministry used three main approaches: (i)task shifting to train and equip existing community health workers (CHWs) to deliver COVID-19 vaccination; (ii)evidence gathering to understand people's reluctance to be vaccinated; and (iii)bundling of COVID-19 vaccination with the polio vaccination programme. Central African Republic is a fragile country with almost two thirds of its population in need of humanitarian assistance. Despite conducting two major COVID-19 vaccination campaigns, by January 2022 only 9% (503 000 people) of the 5 570 659 general population were fully vaccinated. In the 6months from February to July 2022, Central African Republic tripled its coverage of COVID-19 vaccination to 29% (1 615 492 out of 5 570 659 people) by August 2022. The integrated polio-COVID-19 campaign enabled an additional 136 040 and 218 978 people to be vaccinated in the first and second rounds respectively, at no extra cost. Evidence obtained through surveys and focus group discussions enabled the health ministry to develop communication strategies to dispel vaccine hesitancy and misconceptions. Task shifting COVID-19 vaccination to CHWs can be an efficient solution for rapid scaling-up of vaccination campaigns. Building trust with the community is also important for addressing complex health issues such as vaccine hesitancy. Collaborative efforts are necessary to provide access to COVID-19 vaccines for high-risk and vulnerable populations.

LiVoAuth: Liveness Detection in Voiceprint Authentication With Random Challenges and Detection Modes

Voiceprint authentication provides great con- venience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead and operational complexity. A uniform platform that can offer Voiceprint Authentication as a Service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this paper, we propose a novel liveness detection method named LiVoAuth for voiceprint authentication. It applies a randomly generated vector se- quence as Liveness Detection Mode (LDM), corresponding to a random challenge code used for authentication. We implement LiVoAuth and conduct a series of user studies to evaluate its performance in terms of accuracy, stability, efficiency, security, and user acceptance. Experimental re- sults demonstrate its advantages compared with cutting- edge methods.

Incidence Rate, Pathogens and Economic Burden of Catheter-Related Bloodstream Infection: A Single-Center, Retrospective Case-Control Study.

Indwelling central venous catheters (CVCs) can cause catheter related bloodstream infection (CRBSI). CRBSI occurring in intensive care unit (ICU) patients may lead to the worse outcomes and extra medical costs. The present study aimed to assess the incidence and incidence density, pathogens and economic burden of CRBSI in ICU patients. A case-control study was retrospectively carried out in six ICUs of one hospital between July 2013 and June 2018. The Department of Infection Control performed routinely surveillance for CRBSI on these different ICUs. Data of the clinical and microbiological characteristics of patients with CRBSI, the incidence and incidence density of CRBSI in ICUs, the attributable length of stay (LOS), and the costs among patients with CRBSI in ICU were collected and assessed. A total of 82 ICU patients with CRBSI were included into the study. The CRBSI incidence density was 1.27 per 1000 CVC-days in all ICUs, in which the highest was 3.52 per 1000 CVC-days in hematology ICU and the lowest was 0.14 per 1000 CVC-days in SpecialProcurement ICU. The most common pathogen causing CRBSI was Klebsiella pneumoniae (15/82, 16.67%), in which 12 (80%) were carbapenem resistant. Fifty-one patients were successfully matched with control patients. The average costs in the CRBSI group were $ 67,923, which were significantly higher (P < 0.001) than the average costs in the control group. The total average costs attributable to CRBSI were $33, 696. The medical costs of ICU patients were closely related to the incidence of CRBSI. Imperative measures are needed to reduce CRBSI in ICU patients.

Abstract 38: External Quality Assessment for Prostate Specific Antigen Test Accuracy: A Needs Assessment of Nigerian Tertiary Hospitals

Abstract Purpose: Prostate cancer is a major cause of death in African men, and the accuracy of prostate specific antigen (PSA) test results is important to ensure patients have access to accurate early diagnosis and treatment. External Quality Assessment (EQA) is an established framework to improve the quality of PSA results. We aimed to determine the need for implementing an EQA program for PSA in Nigeria. Methods: We used mixed-method design to collect data from 6 tertiary hospitals across the six regions in Nigeria. We conducted an online survey using a structured questionnaire developed from the International Standard Organization (ISO) 15189 checklist for accreditation of diagnostic laboratories. Responses were analyzed to obtain descriptive categorical data that was analyzed on Microsoft Excel. Using an in-depth interview guide, 12 respondents (6 Urologists and 6 Chemical Pathologists) from sampled hospitals were interviewed virtually. Responses were recorded and transcribed and relevant themes were identified and generated from the transcribed data. Results: Five of six laboratories sampled (83%) reported conducting repeat PSA testing. No sampled lab (0%) was enrolled in an EQA program for PSA. Eleven of the twelve respondents confirmed occasional disparities between PSA results and patient’s clinical status. They gave the impact of an erroneous PSA result on patient care as: extra unnecessary cost to the patient due to repeat testing; delay in decision making and initiating appropriate treatment; unnecessary invasive procedures. Training of lab staff, adoption of standard operating procedures and quality measures for EQA were considered opportunities to encourage PSA EQA practice while lack of funds for EQA programs, weak logistics for distribution of EQA samples and results, poor staff motivation and commitment were mentioned as barriers to effective practice uptake. Conclusion: There is a clear need for the implementation of an EQA program for PSA in Nigeria. This will reduce the incidence of incongruous PSA results, thus promoting better clinician approach to disease treatment and better patient outcomes especially in resource-constrained environments. Citation Format: Ifeyinwa Nnakenyi, Tonia Onyeka, Ngozi Idemili-Aronu1, Justus Onu, Echezona Ezeanolue. External Quality Assessment for Prostate Specific Antigen Test Accuracy: A Needs Assessment of Nigerian Tertiary Hospitals [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 38.

Workforce scheduling efficiency assessment in construction projects through a multi-objective optimization model in the COVID-19 context

The COVID-19 disease has caused a drastic stoppage in the construction industry as a result of quarantines. For this reason, this study focuses on the workforce scheduling problem when working under COVID labor distancing constraints, and additional costs derived from deviation hours or hiring new employees that managers must assume on a project due to circumstances. A multi-objective mixed integer linear programming model was developed and solved using weighting and epsilon constraint methods to evaluate workforce scheduling and the mentioned COVID costs. The first objective function corresponds to the sum of the total extra hours; the second objective function represents the total non-worked but paid hours. Two sets of experiments are presented, the first based on a design of experiments that seeks to determine the relationship between the proposed objective functions and a methodology to determine the cost of considering COVID constraints. The second set of experiments was applied in a real company, where the situation without COVID vs with COVID, and without allowing extra hours vs with COVID allowing extra hours were compared. Obtained results showed that hiring additional employees to the man-crew leads the company to increase the extra hours cost up to 104.25%, being more convenient to keep a workforce baseline and to pay extra hours costs. Therefore, the mathematical model could represent a potential tool for decision-making in the construction sector, regarding the effects of COVID-19 costs on workforce scheduling construction projects. Consequently, this work contributes to the construction industry by quantifying the impact of COVID-19 constraints and the associated costs, offering a proactive approach to address the challenges posed by the COVID-19 pandemic for the construction sector.

Proof of Travel for Trust-Based Data Validation in V2I Communication

Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication security is effective in identifying falsified and malicious V2X data. Each vehicle in a given region can be a witness to report on the misbehavior of other nearby vehicles, which will then be added to a "blacklist." However, there may not exist enough witness vehicles that are willing to opt-in in the early stage of connected-vehicle deployment. In this paper, we propose a "whitelisting" approach to V2X security, titled Proof-of-Travel (POT), which leverages the support of roadside infrastructure. Our goal is to transform the power of cryptography techniques embedded within Vehicle-to-Infrastructure (V2I) protocols into game-theoretic mechanisms to incentivize connected-vehicle data sharing and validate data trustworthiness simultaneously. The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra costs for normal vehicles, which naturally want to move from the origin to the destination. The POT protocol is used to enhance the security of previous voting-based data validation algorithms for V2I crowdsensing applications. For the POT-enhanced voting, we prove that all vehicles choosing to cheat are not a pure Nash equilibrium using game-theoretic analysis. Simulation results suggest that the POT-enhanced voting is more robust to malicious data.

Advanced Fault Tolerant 3×3-Phase PM Drive With Concentric Winding for EV Application

Fault tolerant machine drive is essential for EV application to ensure uninterrupted operation during fault event. Among various fault modes, inter-turn fault is the most challenging failure for the PM drive which requires special measures to suppress the excessive fault current. In this paper, a novel concentric winding (CW) is analyzed for a 3×3-phase PM drive to restrict the turn fault current. Compared with the conventional distributed winding (DW), the two sub-coils of each phase occupy the same space angle and both align with the phase axis, and the winding factor of each coil is 0.96 which is 4% lower than the DW. Owing to the two aspects, the coil flux linkages are more effectively nullified after applying winding terminal short circuit (TSC) which indicates lower fault current. The two machines’ healthy performance as well as various fault modes’ behaviors are investigated by detailed simulation and test investigation. The results prove the CW machine has the same healthy characteristics as the DW machine, and the maximum inter-turn fault current is up to 17% lower than the counterpart. In addition, the improved turn fault current suppression capability is achieved simply by re-arranging the coil layout without introducing any extra cost.

Industrial demand adaptation to renewable resources

The usage of renewable energy sources as an alternative energy source is faced with a major challenge introduced by the intermittency nature of the source. We have two options i) adapt the demand to the source, or ii) adapt the source to the demand. If the process demand is flexible and can be adapted to the energy source, it will enjoy cheap energy. Alternatively, non-flexible processes will require extra costs to be paid to adapt the source to the process energy requirements. This paper presents a demand response model to adapt the demand of industrial processes to the available energy source. The model is solved analytically using mixed integer linear programming. Then, three metaheuristic search algorithms were used. The results of the solving algorithms are compared with each other using the Wilcoxons' rank-sum test. Then the model is tested for two examples of flexible and rigid processes showing the differences.

SBAS-based navigation system for precision approach in CAT II LVC

Due to lack of advanced navigation systems, secondary airports are normally unable to support instrument approaches in low visibility conditions (LVC) more adverse than Category (CAT) I, making them inaccessible during such conditions. Incoming flights are then delayed, diverted to adjacent airports, or cancelled before take-off. This paper presents an onboard navigation solution, under development in scope of SESAR 2020 Solution PJ02-W2- 17.2, which focuses on technology development to mitigate these traffic disruptions by enabling instrument approaches in CAT II LVC with minimum airport and air navigation service provider investments. The concept builds upon global navigation satellite systems (Global Positioning System L1 frequency – GPS L1, satellite-based augmentation system), which currently enables Localizer Performance with Vertical guidance (LPV) operation down to LPV 200 minima. The designed system includes radio altimeter-based aiding and innovative integrity algorithm to improve navigation performance in support of operations below 200ft down to CAT II / LPV 100 minima. As no such requirements have yet been officially published, authors also defined sets of candidates based on Instrument Landing System Collision Risk Model and other sources. The team developed and verified a Monte-Carlo fast-time simulation toolset to enable representative and statistically significant evaluation of the concept feasibility via its availability throughout Europe, as well as sensitivity to proposed sets of alert limits. Also, flight data collection and a developed software prototype allow for further validations. Simulations demonstrated over 95% average availability with the best results achieving level of 99% depending on used set of requirements. The solution enabling operations in LVC allows unlocking various benefits to business/airline operators (e.g., avoided extra costs, increased predictability), passengers (avoided opportunity loss) and airports (retained profit from flights, less traffic at diversion airports). Technology development also marks an important step towards gate-to-gate autonomous operations.