bluuebunny/crossref_metadata_2025 huggingface dataset:
Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System
Authors: Bittar André , Velupillai Sumithra , Downs Johnny , Sedgwick Rosemary , Dutta Rina
Published: November 2020

Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person's risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient's care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. We envision increased progress in the decades to come, particularly in externally validating findings across multiple sites and countries, both in terms of clinical evidence and in terms of NLP and machine learning method transferability.
Associations Between Maternal Community Deprivation and Infant DNA Methylation of the SLC6A4 Gene
Authors: DeLano Kelly , Folger Alonzo T. , Ding Lili , Ji Hong , Yolton Kimberly , Ammerman Robert T. , Van Ginkel Judith B. , Bowers Katherine A.
Published: November 2020

Introduction: Poverty is negatively associated with health and developmental outcomes. DNA methylation (DNAm) has been proposed as a mechanism that underlies the association between adversity experienced by mothers in poverty and health and developmental outcomes in their offspring. Previous studies have identified associations between individual-level measures of stress and adversity experienced by a mother during pregnancy and infant DNAm. We hypothesized that independent of individual stresses, a mother's community-level deprivation while she is pregnant may also be associated with DNAm among the genes of her offspring that are related to stress response and/or development.

Methods: Pregnant mothers (N = 53) completed assessments that measured stress, adversity, and mental health. To evaluate community-level deprivation, mothers' addresses were linked to census-level socioeconomic measures including a composite index of deprivation that combines multiple community-level indicators such as income and highest level of education received. Infant buccal cells were collected at about age 4 weeks to measure DNAm of candidate genes including NR3C1, SCG5, and SLC6A4, which are associated with the stress response and or social and emotional development. Multivariable models were employed to evaluate the association between maternal community deprivation and infant DNAm of candidate genes.

Results: No significant associations were identified between maternal community-level deprivation and the methylation of NR3C1 or SCG5, however, maternal community-level deprivation was significantly associated with higher mean methylation across 8 CpG sites in SLC6A4.

Conclusion: This study identified an association between community-level measures of deprivation experienced by a mother during pregnancy and DNAm in their offspring. These findings may have implications for understanding how the community context can impact early biology and potential function in the next generation.
Design of a High-Speed Prosthetic Finger Driven by Peano-HASEL Actuators
Authors: Yoder Zachary , Kellaris Nicholas , Chase-Markopoulou Christina , Ricken Devon , Mitchell Shane K. , Emmett Madison B. , Weir Richard F. ff. , Segil Jacob , Keplinger Christoph
Published: November 2020

Current designs of powered prosthetic limbs are limited by the nearly exclusive use of DC motor technology. Soft actuators promise new design freedom to create prosthetic limbs which more closely mimic intact neuromuscular systems and improve the capabilities of prosthetic users. This work evaluates the performance of a hydraulically amplified self-healing electrostatic (HASEL) soft actuator for use in a prosthetic hand. We compare a linearly-contracting HASEL actuator, termed a Peano-HASEL, to an existing actuator (DC motor) when driving a prosthetic finger like those utilized in multi-functional prosthetic hands. A kinematic model of the prosthetic finger is developed and validated, and is used to customize a prosthetic finger that is tuned to complement the force-strain characteristics of the Peano-HASEL actuators. An analytical model is used to inform the design of an improved Peano-HASEL actuator with the goal of increasing the fingertip pinch force of the prosthetic finger. When compared to a weight-matched DC motor actuator, the Peano-HASEL and custom finger is 10.6 times faster, has 11.1 times higher bandwidth, and consumes 8.7 times less electrical energy to grasp. It reaches 91% of the maximum range of motion of the original finger. However, the DC motor actuator produces 10 times the fingertip force at a relevant grip position. In this body of work, we present ways to further increase the force output of the Peano-HASEL driven prosthetic finger system, and discuss the significance of the unique properties of Peano-HASELs when applied to the field of upper-limb prosthetic design. This approach toward clinically-relevant actuator performance paired with a substantially different form-factor compared to DC motors presents new opportunities to advance the field of prosthetic limb design.
3D Printed Biomimetic Rabbit Airway Simulation Model for Nasotracheal Intubation Training
Authors: Oberoi Gunpreet , Eberspächer-Schweda M. C. , Hatamikia Sepideh , Königshofer Markus , Baumgartner Doris , Kramer Anne-Margarethe , Schaffarich Peter , Agis Hermann , Moscato Francesco , Unger Ewald
Published: November 2020

Rabbit inhalation anesthesia by endotracheal intubation involves a higher risk among small animals owing to several anatomical and physiological features, which is pathognomonic to this species of lagomorphs. Rabbit-specific airway devices have been designed to prevent misguided intubation attempts. However, it is believed that expert anesthetic training could be a boon in limiting the aftermaths of this procedure. Our research is aimed to develop a novel biomimetic 3D printed rabbit airway model with representative biomechanical material behavior and radiodensity. Imaging data were collected for two sacrificed rabbit heads using micro-computed tomography (μCT) and micro-magnetic resonance imaging for the first head and cone beam computed tomography (CBCT) for the second head. Imaging-based life-size musculoskeletal airway models were printed using polyjet technology with a combination of hard and soft materials in replicates of three. The models were evaluated quantitatively for dimensional accuracy and radiodensity and qualitatively using digital microscopy and endoscopy for technical, tactic, and visual realism. The results displayed that simulation models printed with polyjet technology have an overall surface representation of 93% for μCT-based images and 97% for CBCT-based images within a range of 0.0–2.5 mm, with μCT showing a more detailed reproduction of the nasotracheal anatomy. Dimensional discrepancies can be caused due to inadequate support material removal and due to the limited reconstruction of microstructures from the imaging on the 3D printed model. The model showed a significant difference in radiodensities in hard and soft tissue regions. Endoscopic evaluation provided good visual and tactile feedback, comparable to the real animal. Overall, the model, being a practical low-cost simulator, comprehensively accelerates the learning curve of veterinary nasotracheal intubation and paves the way for 3D simulation-based image-guided interventional procedures.
Tissue Residues and Pharmacokinetic/Pharmacodynamic Modeling of Tiamulin Against Mycoplasma anatis in Ducks
Authors: Elazab Sara T. , Elshater Nahla S. , Hashem Yousreya H. , Park Seung-Chun , Hsu Walter H.
Published: November 2020

The pharmacokinetics of tiamulin were studied in 2 groups of ducks (n = 6) after its oral administration at 2 different doses (30 and 60 mg/kg, respectively). Plasma concentrations of tiamulin were measured by high performance liquid chromatography at different time points up to 24 h post-administration. The maximum plasma concentrations were 0.77 and 2.32 μg/mL attained at 2 h (Tmax) for 30 and 60 mg/kg, respectively. The elimination half-lives for these 2 doses were 3.54 and 6.34 h, respectively. The minimum inhibitory concentration for tiamulin against Mycoplasma anatis (M. anatis) strain 1340 was determined to be 0.06 μg/mL. The proper oral dose of tiamulin against M. anatis in ducks was calculated to be 35 mg/kg/day using the pharmacokinetic/pharmacodynamic modeling. Tiamulin was administered orally (40 mg/kg/day) to 30 ducks for 3 successive days to determine its residues in edible tissues and its preslaughter withdrawal time. The highest tiamulin residues were detected in the liver, followed by the muscle, whereas lower concentrations were detected in the skin and fat. The estimated withdrawal periods of tiamulin were 6, 5, 3, and 3 days for liver, muscle, skin, and fat, respectively. Therefore, an oral dosage regimen of 35 mg/kg/day should be adequate for tiamulin against M. anatis. We recommend a preslaughter withdrawal period of 6 days when ducks are treated with 40 mg tiamulin/kg/day, orally, for 3 days.
The Medium of Exchange in Mergers and Acquisitions: The Cases of Travel Agencies and Tour Operators
Authors: Coelho Alfredo , Castillo-Girón Victor Manuel
Published: November 2020

The choice of the means of exchange is one of the critical decisions in mergers and acquisitions. Travel agencies and tour operating-industries compete in a volatile environment, and in addition to generating economies of scale, investments in the industry involve important sunk costs. This paper’s focus is on the determinants of the medium of exchange in mergers and acquisitions in the travel and tour-operating industries. Previous research on the ‘law and finance’ perspectives argues that some country of origin legal systems offer better investment protection to investors. Accordingly, civil law-based systems offer weaker protection to investors when compared to common-law-based systems. For this purpose, we use a logistics regression to model the determinants of cash in a sample of 750 mergers and acquisitions by combining the research streams above. Our results provide evidence of the relationship between investor protection and the use of cash.
Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport
Authors: Branda Francesco , Marozzo Fabrizio , Talia Domenico
Published: November 2020

In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.
eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning
Authors: Fawagreh Khaled , Gaber Mohamed Medhat
Published: November 2020

To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.
Nitrate Catalytic Reduction over Bimetallic Catalysts: Catalyst Optimization
Authors: Santos A. Sofia G. G. , Restivo João , Orge Carla A. , Pereira M. Fernando R. , Soares O. Salomé G. P.
Published: November 2020

The catalytic removal of nitrate (NO3−) in water using hydrogen as a reducing agent was studied using palladium-copper bimetallic catalysts in different supports. Commercial carbon nanotubes (CNTs), used as received and with different mechanical (CNT (BM 2h)) and chemical modifications (CNT (BM 4h)-N), titanium dioxide (TiO2) and composite materials (TiO2-CNT) were considered as main supports for the metallic phase. Different metal loadings were studied to synthesize an optimized catalyst with high NO3− conversion rate and considerable selectivity for N2 formation. Among all the studied support materials, the milled carbon nanotubes (sample CNT (BM 2h) was the support that showed the most promising results using 1%Pd-1%Cu as metallic phases. The most active catalysts were 2.5%Pd-2.5%Cu and 5%Pd-2.5%Cu supported on CNT (BM 2h), achieving total conversion after a 120 min reaction with N2 selectivity values of 62% and 60%, respectively. Reutilization experiments allowed us to conclude that these catalysts were stable during several reactions, in terms of NO3− conversion rate. However, the consecutive reuse of the catalyst leads to major changes concerning NH4+ selectivity values.
Prospective Environmental Impacts of Passenger Cars under Different Energy and Steel Production Scenarios
Authors: Koroma Michael Samsu , Brown Nils , Cardellini Giuseppe , Messagie Maarten
Published: November 2020

The potential environmental impacts of producing and using future electric vehicles (EVs) are important given their expected role in mitigating global climate change and local air pollutants. Recently, studies have begun assessing the effect of potential future changes in EVs supply chains on overall environmental performance. This study contributes by integrating expected changes in future energy, iron, and steel production in the life cycle assessment (LCA) of EVs. In this light, the study examines the impacts of changes in these parameters on producing and charging future EVs. Future battery electric vehicles (BEV) could have a 36–53% lower global warming potential (GWP) compared to current BEV. The change in source of electricity generation accounts for 89% of GWP reductions over the BEV’s life cycle. Thus, it presents the highest GWP reduction potential of 35–48%. The use of hydrogen for direct reduction of iron in steelmaking (HDR-I) is expected to reduce vehicle production GWP by 17% compared to current technology. By accounting for 9% of the life cycle GWP reductions, HDR-I has the second-highest reduction potential (1.3–4.8%). The results also show that the potential for energy efficiency improvement measures for GWP reduction in vehicle and battery manufacture would be more beneficial when applied now than in the distant future (2050), when the CO2 intensity of the EU electricity is expected to be lower. Interestingly, under the same conditions, the high share of renewable energy in vehicle supply chains contributed to a decrease in all air pollution-related impact categories, but an increase in toxicity-related categories, as well as land use and water consumption.
A Hybrid System Based on LSTM for Short-Term Power Load Forecasting
Authors: Jin Yu , Guo Honggang , Wang Jianzhou , Song Aiyi
Published: November 2020

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.
The Effects of the Presence of a Kitchen House on the Wind Flow Surrounding a Low-Rise Building
Authors: Che Deraman Siti Noratikah , Abo Sabah Saddam Hussein , Zaini Shaharudin Shah , Majid Taksiah A. , Al-Fakih Amin
Published: November 2020

Most Malaysian rural houses are categorized as non-engineered buildings and vulnerable to damage during events such as windstorms due to the fact that these houses lack engineering considerations. These houses are characterized by having an attached kitchen house, and many of these houses were previously damaged by thunderstorms. The current research investigated the air flow characteristics changes surrounding these houses as a result of the presence of the kitchen. The roof pitch, position, gap height, and overhang were investigated using computational fluid dynamics (CFD) simulations. The results showed that the kitchen position at the center resulted in a slight increase in the suction on the ridge of the roof; however, it significantly altered the flow pattern in the windward and leeward directions. The results also showed that the roof overhang, roof pitch, and kitchen position contributed severely to the damage of the rural house. Moreover, the highest suction occurred at the roof ridge when the kitchen was located at the center of the rural house (Cp = −2.28). Therefore, the authors believe that it is more advantageous to have a kitchen connected to the core as it reduces the pressure on the roof of the core during thunderstorm events.
Automatic Crack Segmentation for UAV-Assisted Bridge Inspection
Authors: Ayele Yonas Zewdu , Aliyari Mostafa , Griffiths David , Droguett Enrique Lopez
Published: November 2020

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.
Systematic Categorization of Optimization Strategies for Virtual Power Plants
Authors: Podder Amit Kumer , Islam Sayemul , Kumar Nallapaneni Manoj , Chand Aneesh A. , Rao Pulivarthi Nageswara , Prasad Kushal A. , Logeswaran T. , Mamun Kabir A.
Published: November 2020

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.
Contingency Analysis to Evaluate the Robustness in Large-Scale Smart Grids: Based on Information Security Objectives and Frequency Stability
Authors: Woo Pil-Sung , Kim Balho H.
Published: November 2020

Smart grids (SGs) are attracting attention as high value-added platforms, which mass-produce new business models through real-time information sharing. However, the open-ended information structure of SGs increases the risk of exposure to cyberattacks through the creation of multiple communication access points. In a power system where a real-time balance of supply and demand is essential, cyberattacks result in cascading failures leading to power outages. Therefore, this paper proposes a method to evaluate the robustness of large-scale SGs against cybersecurity disturbances. The proposed evaluation method established a hierarchy quantification technique considering the structural characteristics of SGs. With respect to the cyber hierarchy, relevant standards (NERC CIP, NIST FIPS) were applied to classify the grades of information security risk. In the case of physical hierarchy, the power system was calculated by using optimal power flow and analyzed the frequency stability. This study was aimed at identifying the vulnerabilities in the physical topology aspect of intelligent power systems due to cybersecurity disturbances.
Smart Water Technology for Efficient Water Resource Management: A Review
Authors: Gupta Aditya Dinesh , Pandey Prerna , Feijóo Andrés , Yaseen Zaher Mundher , Bokde Neeraj Dhanraj
Published: November 2020

According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with present challenges and future scope on the mentioned technologies. A conclusion is drawn that smart technologies can lead to better water resource management, which can lead to the reduction of water scarcity worldwide. High implementation cost may act as a barrier to the implementation of SWT in developing countries, whereas data security and its reliability along with system ability to give accurate results are some of the key challenges in its field implementation.
Exploring the Role of Stakeholder Dynamics in Residential Photovoltaic Adoption Decisions: A Synthesis of the Literature
Authors: Scheller Fabian , Doser Isabel , Sloot Daniel , McKenna Russell , Bruckner Thomas
Published: November 2020

Despite the intensive research on residential photovoltaic adoption, there is a lack of understanding regarding the social dynamics that drive adoption decisions. Innovation diffusion is a social process, whereby communication structures and the relations between sender and receiver influence what information is perceived and how it is interpreted. This paper addresses this research gap by investigating stakeholder influences in household decision-making from a procedural perspective, so-called stakeholder dynamics. A literature review derives major influence dynamics which are then synthesized based on egocentric network maps for distinct process stages. The findings show a multitude of stakeholders that can be relevant in influencing photovoltaic adoption decisions of owner-occupied households. Household decision-makers are mainly influenced by stakeholders of their social network like family, neighbors, and friends as well as PV-related services like providers and civil society groups. The perceived closeness and likeability of a stakeholder indicate a higher level of influence because of greater trust involved. Furthermore, the findings indicate that social influence shifts gradually from many different stakeholders to a few core stakeholders later on in the decision-making process. These insights suggest that photovoltaic (PV) adoption may be more reliably predicted if a process perspective is taken into account that not only distinguishes between different stakeholders but considers their dynamic importance along the process stages. In addition, especially time- and location-bound factors affect the influence strength. This clearly shows the importance of local and targeted interventions to accelerate the uptake.
Woody Biomass Mobilization for Bioenergy in a Constrained Landscape: A Case Study from Cold Lake First Nations in Alberta, Canada
Authors: Mansuy Nicolas , Staley Diana , Taheriazad Leila
Published: November 2020

Wood-based bioenergy systems developed and managed by Indigenous communities can improve their ability to thrive and grow economically and socially and improve their resource-based decision-making processes. In this study, we collaborated with Cold Lake First Nations (CLFN), a community located in Northern Alberta, Canada, to investigate the opportunities and challenges of biomass mobilization from different feedstocks. Based on remote sensing and ground data, harvest residue and fire residue feedstocks were identified within the boundaries of the community and inside a radius of 200 km at 18 and 39 oven-dry metric tonnes (odt)/ha, respectively. CLFN also received woody biomass from local oil and gas producers that operate in their traditional territory, which is estimated at 19,000 odt/year. Despite being abundant, the woody biomass is difficult to access due to the extensive human footprint that surrounds the area and constrains the landscape. In terms of greenhouse gas (GHG) mitigation, the potential also appears limited because the community has access to natural gas at a competitive and stable price, unlike off-grid communities. In terms of cost savings, the low oil and gas prices make the biomass resources (pellets) less competitive to utilize than the natural gas that is available in the community.
Techno-Economic Assessment of Different Heat Exchangers for CO2 Capture
Authors: Aromada Solomon Aforkoghene , Eldrup Nils Henrik , Normann Fredrik , Øi Lars Erik
Published: November 2020

We examined the cost implications of selecting six different types of heat exchangers as the lean/rich heat exchanger in an amine-based CO2 capture process. The difference in total capital cost between different capture plant scenarios due to the different costs of the heat exchangers used as the lean/rich heat exchanger, in each case, is in millions of Euros. The gasketed-plate heat exchanger (G-PHE) saves significant space, and it saves considerable costs. Selecting the G-PHE instead of the shell and tube heat exchangers (STHXs) will save €33 million–€39 million in total capital cost (CAPEX), depending on the type of STHX. About €43 million and €2 million in total installed costs (CAPEX) can be saved if the G-PHE is selected instead of the finned double-pipe heat exchanger (FDP-HX) or welded-plate heat exchanger, respectively. The savings in total annual cost is also in millions of Euros/year. Capture costs of €5/tCO2–€6/tCO2 can be saved by replacing conventional STHXs with the G-PHE, and over €6/tCO2 in the case of the FDP-HX. This is significant, and it indicates the importance of clearly stating the exact type and not just the broad classification of heat exchanger used as lean/rich heat exchanger. This is required for cost estimates to be as accurate as possible and allow for appropriate comparisons with other studies. Therefore, the gasketed-plate heat exchanger is recommended to save substantial costs. The CO2 capture costs of all scenarios are most sensitive to the steam cost. The plate and frame heat exchangers (PHEs) scenario’s capture cost can decline from about €77/tCO2 to €59/tCO2 or rise to €95/tCO2.
Gel Pills for Downhole Pressure Control during Oil and Gas Well Drilling
Authors: Khalifeh Mahmoud , Penkala Larisa , Saasen Arild , Aase Bodil , Omland Tor Henry , Taugbøl Knut , Reinås Lorents
Published: November 2020

During drilling of petroleum or geothermal wells, unforeseen circumstances occasionally happen that require suspension of the operation. When the drilling fluid is left in a static condition, solid material like barite may settle out of the fluid. Consequently, the induced hydrostatic pressure that the fluid exerts onto the formation will be reduced, possibly leading to collapse of the borehole or influx of liquid or gas. A possible mitigation action is placement of a gel pill. This gel pill should preferably be able to let settled barite rest on top of it and still transmit the hydrostatic pressure to the well bottom. A bentonite-based gel pill is developed, preventing flow of higher density drilling fluid placed above it to bypass the gel pill. Its rheological behavior was characterized prior to functional testing. The designed gel pill develops sufficient gel structure to accommodate the settled barite. The performance of the gel was tested at vertical and 40° inclination from vertical. Both conventional settling and the Boycott effect were observed. The gel pill provided its intended functionality while barite was settling out of the drilling fluid on top of this gel pill. The barite was then resting on top of the gel pill. It is demonstrated that a purely viscous pill should not be used for separating a high density fluid from a lighter fluid underneath. However, a bentonite or laponite gel pill can be placed into a well for temporary prevention of such intermixing.
The Role of Renewable Energies in the Sustainable Development of Post-Crisis Electrical Power Sectors Reconstruction
Authors: Krepl Vladimír , Shaheen Husam I. , Fandi Ghaeth , Smutka Luboš , Muller Zdenek , Tlustý Josef , Husein Tarek , Ghanem Safwan
Published: November 2020

The electric power sector in countries suffering from crises and wars such as Syria, Iraq, Yemen, Libya, etc., is among the most affected infrastructures. Since this sector plays a vital role in the economic growth and in improving people’s quality of life, the post-crisis reconstruction of this sector must take into account the requirements and concepts of sustainable development (SD) in addition to technical and economic considerations. This article discusses the role that renewable energy sources (RES) can play in achieving SD in the post-crisis reconstruction phase of the Syrian Electric Power Sector (SEPS) as a case study. Based on the available data, the study period was chosen from 2005 to 2017 and divided into two periods. In the first period (2005–2010), which is the pre-crisis period, the structure of the SEPS and its main characteristics were described while the adoption of RES solutions and SD concepts were investigated. In the second period (2011–2017), the crisis period, the satiation of the SEPS and impact of the crisis were evaluated. The challenges that faced the adoption of SD concepts and RES solution were also addressed at this stage. Based on analysis and comparison of the available data, several scenarios were discussed to evaluate the role of RES in achieving SD in the post-crisis reconstruction phase of the SEPS. EnergyPLAN software was used for the techno and socio-economic evaluation of these scenarios. Our results emphasize the importance of the adoption of RES solutions in the reconstruction phase, as these can help promote SD concepts (reducing greenhouse gas emissions, protecting the environment, increasing energy efficiency, creating jobs, etc.) and securing the electric supply of the SEPS while enhancing its stability.
Complex Cognitive Systems and Their Unconscious. Related Inspired Conjectures for Artificial Intelligence
Authors: Minati Gianfranco
Published: November 2020

The aim of the article is to propose a conceptual framework, constructs, and conjectures to act as a guide for future, related research finalized to design and implement versions of Artificial Intelligence encompassing an artificially simulated unconscious suitable for human-like artificial cognitive processing. This article considers the concept of the unconscious in psychoanalysis. The interdisciplinary understanding of this concept is considered to be the unavoidable property of sufficiently complex, cognitive processing. We elaborate on the possibility of an artificial unconscious, able to both self-acquired properties through usage, and self-profile through a supposed implicit, parasitic usage of explicit cognitive processing. Memory activities are considered to be integrated into cognitive processing, with memory no longer only being storage and reminding no longer only being finding. We elaborate on the artificial unconscious as an implicit, usage-dependent, self-profiling, and emergent process. Conceptual characteristics of the research project are the implementation of weighted networked, fuzzified memorizations; self-generated networks of links of inter-relationships as nodes, self-variation of the intensity of the links according to the use, and activation of internal self-processes such as the introduction of fictitious links intended as variations and combinations of the current ones. Application examples suitable for experimental implementation are also discussed with reference to chatbot technology that has been extended with features of an artificial unconscious. Thus, we introduce the concept of the AU-chatbot. The main purpose is to allow the artificial cognitive processing to acquire suitable human-like attitudes in representing, interfacing, and learning, potentially important in supporting and complementing human-centered activities. Examples of expected features are the ability to combine current and unconscious links to perform cognitive processing such as representing, deciding, memorizing, and solving equivalencies, and also learning meta-profiles, such as in supporting doctor–patient interactions and educational activities. We also discuss possible technologies suitable for implementing experiments for the artificial unconscious.
Fast-Projection Methods for the Incompressible Navier–Stokes Equations
Authors: De Michele Carlo , Capuano Francesco , Coppola Gennaro
Published: November 2020

An analysis of existing and newly derived fast-projection methods for the numerical integration of incompressible Navier–Stokes equations is proposed. Fast-projection methods are based on the explicit time integration of the semi-discretized Navier–Stokes equations with a Runge–Kutta (RK) method, in which only one Pressure Poisson Equation is solved at each time step. The methods are based on a class of interpolation formulas for the pseudo-pressure computed inside the stages of the RK procedure to enforce the divergence-free constraint on the velocity field. The procedure is independent of the particular multi-stage method, and numerical tests are performed on some of the most commonly employed RK schemes. The proposed methodology includes, as special cases, some fast-projection schemes already presented in the literature. An order-of-accuracy analysis of the family of interpolations here presented reveals that the method generally has second-order accuracy, though it is able to attain third-order accuracy only for specific interpolation schemes. Applications to wall-bounded 2D (driven cavity) and 3D (turbulent channel flow) cases are presented to assess the performances of the schemes in more realistic configurations.
Transglycosylation of Steviol Glycosides and Rebaudioside A: Synthesis Optimization, Structural Analysis and Sensory Profiles
Authors: Muñoz-Labrador Ana , Azcarate Silvana , Lebrón-Aguilar Rosa , Quintanilla-López Jesús E. , Galindo-Iranzo Plácido , Kolida Sofia , Methven Lisa , Rastall Robert A. , Moreno F. Javier , Hernandez-Hernandez Oswaldo
Published: November 2020

To improve flavor profiles, three cyclodextrin glucosyltransferases (CGTases) from different bacteriological sources, Paenibacillus macerans, Geobacillus sp. and Thermoanaerobacter sp., were used with an extract of steviol glycosides (SVglys) and rebaudioside A (RebA) as acceptor substrates in two parallel sets of reactions. A central composite experimental design was employed to maximize the concentration of glucosylated species synthesized, considering temperature, pH, time of reaction, enzymatic activity, maltodextrin concentration and SVglys/RebA concentration as experimental factors, together with their interactions. Liquid chromatography coupled to a diode-array detector (LC-DAD), liquid chromatography-mass spectrometry (LC-ESI-MS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to characterize and identify the chemical structures obtained along the optimization. To assess the impact on the sensory properties, a sensory analysis was carried out with a group of panelists that evaluated up to 16 sensorial attributes. CGTase transglucosylation of the C-13 and/or C-19 led to the addition of up to 11 glucose units to the steviol aglycone, which meant the achievement of enhanced sensory profiles due to a diminution of bitterness and licorice appreciations. The outcome herein obtained supposes the development of new potential alternatives to replace free sugars with low-calorie sweeteners with added health benefits.
The Squeezed Light Source for the Advanced Virgo Detector in the Observation Run O3
Authors: Mehmet Moritz , Vahlbruch Henning
Published: November 2020

From 1 April 2019 to 27 March 2020, the Advanced Virgo detector, together with the two Advanced LIGO detectors, conducted the third joint scientific observation run O3, aiming for further detections of gravitational wave signals from astrophysical sources. One of the upgrades to the Virgo detector for O3 was the implementation of the squeezed light technology to improve the detector sensitivity beyond its classical quantum shot noise limit. In this paper, we present a detailed description of the optical setup and performance of the employed squeezed light source. The squeezer was constructed as an independent, stand-alone sub-system operated in air. The generated squeezed states are tailored to exhibit high purity at intermediate squeezing levels in order to significantly reduce the interferometer shot noise level while keeping the correlated enhancement of quantum radiation pressure noise just below the actual remaining technical noise in the Advanced Virgo detector.
Simulation of Rainfall-Induced Floods in Small Catchments (the Polomet’ River, North-West Russia) Using Rain Gauge and Radar Data
Authors: Grek Elena , Zhuravlev Sergey
Published: November 2020

In recent years, rain floods caused by abnormal rainfall precipitation have caused several damages in various part of Russia. Precise forecasting of rainfall runoff is essential for both operational practice to optimize the operation of the infrastructure in urbanized territories and for better practices on flood prevention, protection, and mitigation. The network of rain gauges in some Russian regions are very scarce. Thus, an adequate assessment and modeling of precipitation patterns and its spatial distribution is always impossible. In this case, radar data could be efficiently used for modeling of rain floods, which were shown by previous research. This study is aimed to simulate the rain floods in the small catchment in north-west Russia using radar- and ground-based measurements. The investigation area is located the Polomet’ river basin, which is the key object for runoff and water discharge monitoring in Valdai Hills, Russia. Two datasets (rain gauge and weather radar) for precipitation were used in this work. The modeling was performed in open-source Soil and Water Assessment Tool (SWAT) hydrological model with three types of input data: rain gauge, radar, and gauge-adjusted radar data. The simulation efficiency is assessed using the coefficient of determination R2, Nash–Sutcliffe model efficiency coefficient (NSE), by comparing the mean values to standard deviations for the calculated and measured values of water discharge. The SWAT model captures well the different phases of the water regime and demonstrates a good quality of reproduction of the hydrographs of the river runoff of the Polomet’ river. In general, the best model performance was observed for rain gauge data (NSE is up to 0.70 in the Polomet’river-Lychkovo station); however, good results have been also obtained when using adjusted data. The discrepancies between observed and simulated water flows in the model might be explained by the scarce network of meteorological stations in the area of studied basin, which does not allow for a more accurate correction of the radar data.
A Network Perspective on the Relationship between Screen Time, Executive Function, and Fundamental Motor Skills among Preschoolers
Authors: Martins Clarice Maria de Lucena , Bandeira Paulo Felipe Ribeiro , Lemos Natália Batista Albuquerque Goulart , Bezerra Thaynã Alves , Clark Cain Craig Truman , Mota Jorge , Duncan Michael Joseph
Published: November 2020

The present study aimed to analyze the dynamic and nonlinear association between screen time, executive function (EF), and fundamental motor skills (FMS) in preschoolers, considering sex and body mass index (BMI) from a network perspective. Forty-two preschoolers (24 boys, 3.91 ± 0.77 years old) provided screen time, EF, FMS, and BMI data. EF was measured using the Go/No Go task, and accuracy of Go (sustain attention), reaction time of Go, and accuracy of No Go (inhibitory control) were considered. Relationships between screen time, EF, FMS, sex, and BMI were explored using a network analysis. The emerged network highlights that screen time is intensely associated with the other variables in the network, while the accuracy of Go has the greater connectivity with other nodes in the network (2.27), being the most sensitive to potential intervention changes. Moreover, sex (1.74), screen time (0.93), and accuracy of Go (0.71) showed the greatest closeness. This study showed that in the emerged network, independent of sex, screen exposure affects the accuracy on Go task, and these components affect the variables in the network, as motor abilities and tasks involved in inhibitory control.