bluuebunny/crossref_metadata_2025 huggingface dataset:
AI Model for Prostate Biopsies Predicts Cancer Survival
Authors: Sandeman Kevin , Blom Sami , Koponen Ville , Manninen Anniina , Juhila Juuso , Rannikko Antti , Ropponen Tuomas , Mirtti Tuomas
Published: April 2022

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.
PSORS1 Locus Genotyping Profile in Psoriasis: A Pilot Case-Control Study
Authors: Tawfik Noha Z. , Abdallah Hoda Y. , Hassan Ranya , Hosny Alaa , Ghanem Dina E. , Adel Aya , Atwa Mona A.
Published: April 2022

(1) Background: The psoriasis susceptibility 1 (PSORS1) locus, located within the major histocompatibility complex, is one of the main genetic determinants for psoriasis, the genotyping profile for three single-nucleotide polymorphisms (SNPs) comprising the PSORS1 locus: rs1062470 within PSORS1C1/CDSN genes, rs887466 within PSORS1C3 gene, rs10484554 within LOC105375015 gene, were investigated and correlated with psoriasis risk and severity. (2) Methods: This pilot case-controlled study involved 100 psoriatic patients and 100 healthy individuals. We investigated three SNPs and assessed the relative gene expression profile for the PSORS1C1 gene. We then correlated the results with both disease risk and severity. (3) Results: The most significantly associated SNP in PSORS1 locus with psoriasis was rs10484554 with its C/T genotype 5.63 times more likely to develop psoriasis under codominant comparison. Furthermore, C/T and T/T genotypes were 5 times more likely to develop psoriasis. The T allele was 3 times more likely to develop psoriasis under allelic comparison. The relative gene expression of PSORS1C1 for psoriatic patients showed to be under-expressed compared to normal controls. (4) Conclusions: Our study revealed the association of the three studied SNPs with psoriasis risk and severity in an Egyptian cohort, indicating that rs10484554 could be the major key player in the PSORS1 locus.
COVID-19 Disease and Associated Thrombocytopenia: Pathogenesis and a Clue to the Etiology
Authors: Eissa Adil Abozaid
Published: April 2022

(1) Background: Hospital mortality in patients suffering from SARS-CoV-2 infection has been associated with thrombocytopenia. The present study was conducted to establish the correlation of thrombocytopenia and the severity of infection. The impact of IL-1Ra gene polymorphism on the incidence and severity of thrombocytopenia was also studied. (2) Methods: Various biochemical parameters measured in all the 1200 enrolled patients included full blood counts, renal and liver function tests, iron study, inflammatory markers, and coagulation assays. A further 70 patients each were selected from the severe thrombocytopenic and non-thrombocytopenic patient groups to study the IL-1Ra gene polymorphism by RCR. (3) Results: Out of 1200 patients, 436 (36.3%) had thrombocytopenia. Among these patients, 118 (27.1%), 75 (17.2%), and 42 (9.6%) had mild, moderate, severe, and very severe thrombocytopenia, respectively. Severe cases mostly resulted from peripheral consumption (73.5%), hemo-phagocytosis (15.4%), and bone marrow suppression (11.11%). A statistically significant correlation was found between the occurrence and severity of thrombocytopenia with perturbated levels of inflammatory markers and the presence of comorbidities. The IL-1Ra∗3 variant was found to be significantly associated with thrombocytopenia. The IL-1Ra∗2 variant was significantly seen among controls. (4) Conclusions: The present study revealed a significant correlation between thrombocytopenia and the severity of COVID-19 disease. Moreover, the IL-1Ra∗3 variant of IL-1Ra gene was associated with thrombocytopenia.
Utility of Contemporary Health Screening in the Diagnosis of Bladder Cancer
Authors: Lee Chung-Un , Song Wan , Koo Michael Jakun , Boo Youngjun , Chung Jae-Hoon , Kang Minyong , Sung Hyun-Hwan , Jeon Hwang-Gyun , Jeong Byong-Chang , Seo Seong-Il , Lee Hyun-Moo , Jeong Jeongyun , Jeon SeongSoo
Published: April 2022

Background: To evaluate the utility of contemporary health screening (HS) in the diagnosis of bladder cancer (BCa). Methods: We retrospectively reviewed 279,683 individuals who underwent HS between February 1995 and April 2015. Among these individuals, 74 were diagnosed with BCa within a year after the HS and were included in the analysis. Screen-detected BCa was defined as when a referral was made to a urologist due to microscopic hematuria (MH) on urinalysis, abnormal imaging, or any urological symptoms observed at the HS. Screen-undetected BCa was defined as when no referral was made to a urologist because of no abnormality observed at the HS, but a visit to a urological outpatient clinic later was followed by a BCa diagnosis. The incidences of screen-detected BCa and BCa in the Korean population were compared. Clinicopathological characteristics were compared between the screen-detected BCa and screen-undetected BCa groups. Results: The detection rate of BCa was 17.2 per 100,000, which exceeded the 2020 estimated national crude incidence rate of 9.3 per 100,000 by approximately 1.7 times. Among the 74 patients diagnosed with BCa within a year after HS, 48 (64.9%) had screen-detected BCa. The screen-detected BCa group had a higher T stage (p = 0.009) and grade (p = 0.019) than the screen-undetected BCa group. However, the overall survival was not significantly different between the two groups (p = 0.677). A positive correlation between the MH grade and the T stage was identified (p = 0.001). Conclusion: Although HS is not focused on BCa screening, contemporary HS can contribute to the detection of BCa.
An Update on Advanced Diagnostic Imaging in Dentistry
Authors: Di Nardo Dario , Zanza Alessio , Pagnoni Francesco , Xhajanka Edit , Testarelli Luca
Published: April 2022

In recent years, CBCT has proved to be extremely widely used and widespread in dentistry [...]
Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective
Authors: Flach Rachel N. , Fransen Nina L. , Sonnen Andreas F. P. , Nguyen Tri Q. , Breimer Gerben E. , Veta Mitko , Stathonikos Nikolas , van Dooijeweert Carmen , van Diest Paul J.
Published: April 2022

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.
Statistical Inference of the Generalized Inverted Exponential Distribution under Joint Progressively Type-II Censoring
Authors: Chen Qiyue , Gui Wenhao
Published: April 2022

In this paper, we study the statistical inference of the generalized inverted exponential distribution with the same scale parameter and various shape parameters based on joint progressively type-II censored data. The expectation maximization (EM) algorithm is applied to calculate the maximum likelihood estimates (MLEs) of the parameters. We obtain the observed information matrix based on the missing value principle. Interval estimations are computed by the bootstrap method. We provide Bayesian inference for the informative prior and the non-informative prior. The importance sampling technique is performed to derive the Bayesian estimates and credible intervals under the squared error loss function and the linex loss function, respectively. Eventually, we conduct the Monte Carlo simulation and real data analysis. Moreover, we consider the parameters that have order restrictions and provide the maximum likelihood estimates and Bayesian inference.
STEM and HASS Disciplines in Architectural Education: Readiness of FAD-STU Bachelor Students for Practice
Authors: Hubinský Tomáš , Legény Ján , Špaček Robert
Published: April 2022

Since the beginning of this century, STEM education has become increasingly important in preserving prosperity and economic competitiveness. Architecture has its own specific attributes. It overarches the STEM and HASS disciplines, and it should be perceived as a cultural phenomenon rather than as a field of study. The main objective of this article is to highlight the methodology based on the statistical method evaluating the correlation rate between the Bachelor’s student performance (SP) in design studio courses and STEM and HASS categories, represented by particular subjects of various areas of study. The relationship between the admission examination procedure and the academic performance of graduates in the DESIGN category was also analyzed. Although the level of knowledge and skills required based on the study results within the curricula was more significant in the HASS category, the direct correlation between subjects in the STEM category, especially engineering, and the quality of the design studio′s outputs as the main and fundamental part of the creative architectural work, was also confirmed. The authors of the article found that STEM knowledge and STEM skills do not reach the required level and, therefore, the emphasis should be placed on changing curricula, balancing the ratio of STEM and HASS categories, adjusting the credits assigned to STEM subjects, or reviewing the classification system.
Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon
Authors: Kim Jong-Chan , Lim Su-Chang , Choi Jaehyeon , Huh Jun-Ho
Published: April 2022

Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.
Evaluation of the Influence of Shaft Shape Errors on the Rotation Accuracy of Aerostatic Spindle—Part 1: Modeling
Authors: Zhang Guoqing , Zheng Jianming , Yu Hechun , Chen Ting , Zhang Kai , Dou Guozhu
Published: April 2022

In the research on the aerostatic spindle, for quantifying the influence of shaft shape errors on its rotation accuracy, the calculation models of unbalanced air film force (UAFF) and centrifugal inertia force (CIF) are established. Based on the typical shape of the shaft, three composite cylinder models are constructed. The data sources are selected from the cylindricity measurement results of the shaft and substituted into the model for calculation. The results show that, for the shaft in this paper, when the roundness deviation is ≤1.25 μm and the cylindricity deviation is ≤2 μm, the influence of shape errors on the rotation accuracy of the shaft is small enough. The harmonic analysis of the data source shows that the magnitude and stability of the UAFF on the shaft are determined by the actual shape of the shaft’s cylindrical surface, and cannot be inferred only by the value of shape errors.
Towards Enhancing Coding Productivity for GPU Programming Using Static Graphs
Authors: Toledo Leonel , Valero-Lara Pedro , Vetter Jeffrey S. , Peña Antonio J.
Published: April 2022

The main contribution of this work is to increase the coding productivity of GPU programming by using the concept of Static Graphs. GPU capabilities have been increasing significantly in terms of performance and memory capacity. However, there are still some problems in terms of scalability and limitations to the amount of work that a GPU can perform at a time. To minimize the overhead associated with the launch of GPU kernels, as well as to maximize the use of GPU capacity, we have combined the new CUDA Graph API with the CUDA programming model (including CUDA math libraries) and the OpenACC programming model. We use as test cases two different, well-known and widely used problems in HPC and AI: the Conjugate Gradient method and the Particle Swarm Optimization. In the first test case (Conjugate Gradient) we focus on the integration of Static Graphs with CUDA. In this case, we are able to significantly outperform the NVIDIA reference code, reaching an acceleration of up to 11× thanks to a better implementation, which can benefit from the new CUDA Graph capabilities. In the second test case (Particle Swarm Optimization), we complement the OpenACC functionality with the use of CUDA Graph, achieving again accelerations of up to one order of magnitude, with average speedups ranging from 2× to 4×, and performance very close to a reference and optimized CUDA code. Our main target is to achieve a higher coding productivity model for GPU programming by using Static Graphs, which provides, in a very transparent way, a better exploitation of the GPU capacity. The combination of using Static Graphs with two of the current most important GPU programming models (CUDA and OpenACC) is able to reduce considerably the execution time w.r.t. the use of CUDA and OpenACC only, achieving accelerations of up to more than one order of magnitude. Finally, we propose an interface to incorporate the concept of Static Graphs into the OpenACC Specifications.
Assessing Repurchase Intention of Learning Apps during COVID-19
Authors: Dash Ganesh , Chakraborty Debarun , Alhathal Faisal
Published: April 2022

Learning apps are becoming increasingly popular, and consumers have widely recognized their benefits, particularly during COVID-19 and the resultant lockdowns. However, despite the growing popularity of learning apps, little is known about the consumer values that impact repurchase intent. Learning apps must increase client engagement by providing stronger value propositions to overcome this hurdle. The current study proposes the consumption values theory to find this gap, better explaining customer behavior toward learning apps. Data from 429 learning app users are used to test the suggested model. According to the research, all consumption values have a favorable and significant impact on the intention to repurchase learning apps. In addition, the moderating effect of Visibility on intent to use and trust’s mediating role are confirmed. The study’s findings add to our knowledge of consumer behavior and practice.
Statistical Description of SaO2–SpO2 Relationship for Model of Oxygenation in Premature Infants
Authors: Rafl-Huttova Veronika , Rafl Jakub , Möller Knut , Bachman Thomas E. , Kudrna Petr , Rozanek Martin
Published: April 2022

A pulse oximeter model linking arterial (SaO2) and peripheral (SpO2) oxygen saturation is the terminal part of a mathematical model of neonatal oxygen transport. Previous studies have confirmed the overestimation of oxygen saturation measured by pulse oximetry in neonates compared to arterial oxygen saturation and the large variability of measured values over time caused by measurement inaccuracies. This work aimed to determine the SpO2 measurement noise that affects the biased SpO2 value at each time point and integrate the noise description with the systematic bias between SaO2 and SpO2. The SaO2–SpO2 bias was based on previously published clinical data from pathological patients younger than 60 days requiring ventilatory support. The statistical properties of the random SpO2 measurement noise were estimated from the SpO2 continuous recordings of 21 pathological and 21 physiological neonates. The result of the work is a comprehensive characterization of the properties of a pulse oximeter model describing the transfer of the input SaO2 value to the output SpO2 value, including the bias and noise typical for the bedside monitoring of neonates. These results will help to improve a computer model of neonatal oxygen transport.
Digital Taste in Mulsemedia Augmented Reality: Perspective on Developments and Challenges
Authors: Duggal Angel Swastik , Singh Rajesh , Gehlot Anita , Rashid Mamoon , Alshamrani Sultan S. , AlGhamdi Ahmed Saeed
Published: April 2022

Digitalization of human taste has been on the back burners of multi-sensory media until the beginning of the decade, with audio, video, and haptic input/output(I/O) taking over as the major sensory mechanisms. This article reviews the consolidated literature on augmented reality (AR) in the modulation and stimulation of the sensation of taste in humans using low-amplitude electrical signals. Describing multiple factors that combine to produce a single taste, various techniques to stimulate/modulate taste artificially are described. The article explores techniques from prominent research pools with an inclination towards taste modulation. The goal is to seamlessly integrate gustatory augmentation into the commercial market. It highlights core benefits and limitations and proposes feasible extensions to the already established technological architecture for taste stimulation and modulation, namely, from the Internet of Things, artificial intelligence, and machine learning. Past research on taste has had a more software-oriented approach, with a few trends getting exceptions presented as taste modulation hardware. Using modern technological extensions, the medium of taste has the potential to merge with audio and video data streams as a viable multichannel medium for the transfer of sensory information.
Building Energy Self-Sufficiency of Municipalities on the Basis of National Legal Conditions in the Theoretical Perspective of the Polish Legal Experiences
Authors: Słotwiński Szymon
Published: April 2022

In today’s social, economic, and political reality, it cannot be overlooked that the energy self-sufficiency of municipalities not only optimizes the lives of the inhabitants of the municipality but also, in a broader perspective, the services provided by the municipality. The sustainable development of a municipality will therefore depend on its degree of self-reliance and self-sufficiency. Economic and technological considerations are not sufficient for the municipality to achieve optimal energy independence without normative conditions. The current (2021/2022) trends in electricity prices and in the development of national and international policies and strategies for electricity generation from renewable energy sources also make the change in the energy market structure toward distributed generation. The main objective of this paper is a theoretical analysis of the legal solutions in force in Poland that allow building energy self-sufficiency in municipalities and their adequacy to the current economic, technological, and political conditions. The scientific discussion will focus here on research problems related to the impact of the national regulations on the gradual assumption of control by municipalities over the generation and distribution of electricity with regard to energy produced in RES installations and the independence of municipalities from the national energy management and supply system.
An Overview of Extrusion as a Pretreatment Method of Lignocellulosic Biomass
Authors: Konan Delon , Koffi Ekoun , Ndao Adama , Peterson Eric Charles , Rodrigue Denis , Adjallé Kokou
Published: April 2022

Lignocellulosic biomass is both low cost and abundant, and unlike energy crops, can escape associated ethical dilemmas such as arable land use and food security issues. However, their usage as raw material in a biorefinery implies an inherent upstream pretreatment step to access compounds of interest derived from lignocellulosic biomass. Importantly, the efficiency of this step is determinant for the downstream processes, and while many pretreatment methods have been explored, extrusion is both a very flexible and promising technology. Extrusion is well-known in both the polymer and pharmaceutical industries and has been used since the 18th century. However, as a pretreatment method for lignocellulosic biomass, extrusion is relatively new. The first use for this purpose dates back to the 1990s. Extrusion enjoys a high degree of flexibility due to the many available parameters, but an understanding of extrusion requires a knowledge of these parameters and the different relationships between them. In this paper, we present a concise overview of lignocellulosic biomass extrusion by reviewing key extrusion parameters and their associated extruder design components and operating conditions.
Lock-In Effects on the Energy Sector: Evidence from Hydrogen Patenting Activities
Authors: Pantaleone Francesca , Fazioli Roberto
Published: April 2022

The aim of the paper is to analyze how regulatory design and its framework’s topics, other than macroeconomic factors, might impact green innovation by taking into consideration a brand-new renewable source of energy that is becoming more and more important in recent years: hydrogen and fuel cell patenting activities. Such activities have been used as a proxy for green technological change in a panel data of 52 countries over a 6-year period. A series of sectorial, energy regulation, and macroeconomic variables were tested to assess their impact on that technological frontier of green energy transition policy. As might have been expected, the empirical analysis carried out with the model that was prefigured confirms significant evidence of lock-in effects on fossil fuel policies. The model confirms, however, another evidence: countries already investing in renewables might be willing to invest in hydrogen projects. A sort of reinforcement to the further development of green sustainable strategies seems to derive from having already concretely undertaken this direction. Future research should exploit different approaches to the research question and address the econometric criticalities mentioned in the paper, along with exploiting results of the paper with further investigations.
Multiperiod Portfolio of Energy Purchasing Strategies including Climate Scenarios
Authors: Gómez Juan M. , Rodríguez Yeny E.
Published: April 2022

Because electricity retailers must ensure that energy supply matches end-user demand, electricity that is not traded through bilateral contracts is typically traded in power exchanges which are subject to great price volatility. In Colombia, the spot price is a reflection of climate variability because approximately 70% of the country’s electricity is generated by large hydropower stations. In this study, we forecast 2018′s prices and calculated its corresponding purchase margins using the 2015 to 2017 bilateral contract prices for electricity plus power exchange price information and climate information. Our forecasts included climate uncertainty and evaluated two multi-period portfolio methods for deciding among three purchasing strategies: bilateral contracts in the regulated market, bilateral contracts in the non-regulated markets, and purchases in the power exchange. The results indicate that retailers should follow a middle course that is neither conservative nor risky. Creation of portfolios independent of the multi-period method can balance purchases through bilateral contracts and in the power exchange in a way that considers climatic uncertainty. This type of balanced portfolio could control medium-term risks of price volatility and result in good levels of purchase margins.
Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques
Authors: Emamian Masoud , Eskandari Aref , Aghaei Mohammadreza , Nedaei Amir , Sizkouhi Amirmohammad Moradi , Milimonfared Jafar
Published: April 2022

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.
Effect of Reservoir Heterogeneity on CO2 Flooding in Tight Oil Reservoirs
Authors: Luo Jiashun , Hou Zhengmeng , Feng Guoqing , Liao Jianxing , Haris Muhammad , Xiong Ying
Published: April 2022

Carbon dioxide (CO2)-enhanced oil recovery (EOR) has great potential and opportunity for further development, and it is one of the vital carbon capture, utilization, and storage (CCUS) technologies. However, strong heterogeneity is one of the several challenges in developing reservoirs, especially for China’s continental tight oil reserves. This study investigates the effects of heterogeneous porosity and permeability on CO2 flooding evolution in low-permeable tight formation. We simulated CO2-EOR using a numerical model developed on the platform of TOUGH2MP-TMVOC to evaluate the effect of different levels of heterogeneity on oil production, gas storage, and flow behaviors in a tight reservoir, controlled by standard deviation and correlation length. A comparison of nine cases reveals that porosity heterogeneity commonly intensifies flow channeling, and there is an oil production decline with higher standard deviation and longer correlation length of porosity field. In addition, the porosity correlation length has a negligible effect on reservoir performance when the standard deviation is relatively low. Furthermore, strong heterogeneity also has a negative impact on the storage capacity of CO2 and oil production. Notably, as the standard deviation was raised to 0.1, a small sweep region arose with the early CO2 breakthrough, which led to a worse flooding effect. Finally, this study exemplifies that a higher injection/production rate and CO2 alternating N2 injection strategies can improve oil recovery in highly heterogeneous reservoirs.
Power Hardware-in-the-Loop Test of a Low-Cost Synthetic Inertia Controller for Battery Energy Storage System
Authors: Bruno Sergio , Giannoccaro Giovanni , Iurlaro Cosimo , La Scala Massimo , Rodio Carmine
Published: April 2022

In the last years, the overall system inertia is decreasing due to the growing amount of energy resources connected to the grid by means of power inverters. As a consequence, reduced levels of inertia can affect the power system stability since slight variations of power generation or load may cause wider frequency deviations and higher rate of change of frequency (RoCoF) values. To mitigate this trouble, end-user distributed energy resources (DERs) interfaced through grid-following inverters, if opportunely controlled, can provide additional inertia. This paper investigated the possibility of improving the control law implemented by a low-cost controller on remotely controllable legacy DERs to provide synthetic inertia (SI) contributions. With this aim, power hardware-in-the-loop simulations were carried out to test the capability of the proposed controller to autonomously measure frequency and RoCoF and provide SI actions by controlling an actual battery energy storage system.
Surface Complexation Modelling of Wettability Alteration during Carbonated Water Flooding
Authors: Mehdiyev Fagan , Erzuah Samuel , Omekeh Aruoture , Fjelde Ingebret
Published: April 2022

CO2 capture and utilization is an effective tool in reducing greenhouse gas emissions and hence, combating global warming. In the present study, surface complexation modeling (SCM) with the geochemistry solver, PHREEQ-C, was utilized to predict the wettability alteration of minerals, sandstone reservoir rocks (SRR), and pseudo-sandstone rocks (PSR) and mineral mixtures during carbonated water (CW) injection. The bond products, which is defined as the product of the mole fraction of oppositely charged mineral and oil surfaces, were calculated to estimate the wettability preferences. For the studied fluid systems, the results from SCM predicted that albite and quartz minerals were strongly water-wet while calcite was strongly oil-wet with formation water (FW). When it came to clay minerals, illite and montmorillonite were more oil-wet than quartz and less oil-wet than calcite. During CW injection (CWI), the wettability preferences of dominant minerals (considering weight and surface area) in SRR (i.e., quartz and calcite) were changed toward more water-wet, while for the clay minerals, the result was the opposite. The results from SCM showed that the wettability preferences of SRR were water-wet in both CW and FW. Moreover, increasing the amount of the water-wet minerals in mineral mixtures increased the rock’s tendency to become more water-wet.
Photovoltaic-Thermal (PV-T) Systems for Combined Cooling, Heating and Power in Buildings: A Review
Authors: Herrando María , Ramos Alba
Published: April 2022

Heating and cooling (H/C) represent the largest share of energy consumption worldwide. Buildings are the main consumers of H/C, while the share of renewable energy for H/C provision still represents a low percentage, 22.0% in 2019. Hybrid photovoltaic-thermal (PV-T) systems are gaining increasing attention both in research and in applications, as they generate both electricity and useful heat simultaneously. The relevance and potential of PV-T collectors and their integration into wider systems are evident, but there is still a lack of review articles that address the potential of these systems in building applications in a comprehensive way. This work aims to review the state-of-the-art of PV-T collectors for building applications, as well as the corresponding PV-T systems for solar combined cooling, heating and power (S-CCHP) provision. The novelties of this work involve the comparison of these systems with conventional solar H/C technologies, the review of the market of H/C technologies, a summary of the challenges for the wider integration of S-CCHP systems and proposal lines of work to improve the cost-competitiveness of these systems. The first section summarises the focus and findings of previous reviews, followed by an overview of the current development status of the main types of PV-T collectors. Then, PV-T-based S-CCHP systems are reviewed, and the potential of PV-T systems’ penetration in the built environment is evaluated and discussed.
Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand
Authors: Niyomtham Lattawan , Lertsathittanakorn Charoenporn , Waewsak Jompob , Gagnon Yves
Published: April 2022

Situated in the southern part and on the western coast of Thailand, the Andaman Coast covers the provinces of Ranong, Phangnga, Phuket, Krabi, Trang and Satun. Using a coupled mesoscale atmospheric model and a microscale wind flow model, along with computational fluid dynamics (CFD) modeling, this paper presents a detailed assessment of the wind energy potential for power generation along the Andaman Coast of Thailand. The climatic data are obtained from the Modern Era Retrospective analysis for Research and Applications (MERRA), along with a high-resolution topography database and Land Use Land Cover digital data. The results are compared to the equivalent wind speeds obtained with the Weather Research and Forecasting (WRF) atmospheric model. The results showed that, at 120 m above ground level (agl), the predicted wind speeds from the models proposed were 20% lower for the mesoscale model and 10% lower for the microscale model when compared to the equivalent wind speeds obtained from the WRF model. A CFD wind flow model was then used to investigate 3D wind fields at 120–125 m agl over five potential sites offering promising wind resources. The annual energy productions (AEP) and the capacity factors under three different wake loss models and for five wind turbine generator technologies were optimized for 10-MW wind power plants, as per Thailand’s energy policies. With capacity factors ranging from 20 to 40%, it was found that the AEPs of the best sites were in the range of 18–36 GWh/year, with a total AEP in the vicinity of 135 GWh/year when using a single wind turbine model for the five sites studied. The combined energy productions by these wind power plants, once operational, could avoid GHG emissions of more than 80 ktons of CO2eq/year.
Performance Evaluation of Silicon and GaN Switches for a Small Wireless Power Transfer System
Authors: Iero Demetrio , Carotenuto Riccardo , Merenda Massimo , Pezzimenti Fortunato , Della Corte Francesco Giuseppe
Published: April 2022

In the last few years, the wide diffusion of rechargeable devices has fueled the research interest in wireless power transfer (WPT) technology that offers advantages such as safety, flexibility, and ease of use. Different standards have been developed over the years but a significant part of the global interest is focused on the inductive resonant wireless power transfer. By increasing the resonance frequency, an improvement in the transfer efficiency between transmit and receive coils is generally observed, at the expense, however, of an increase in losses in the switching devices that constitute the transmitting and receiving circuits. This study concerned the performance evaluation of a WPT transmitting circuit built using Gallium Nitride (GaN) or conventional silicon (Si) switching devices, to assess their specific contribution to the overall efficiency of the system. The overall performance of two circuits, respectively based on GaN HEMTs and Si MOSFETs, were compared at frequencies of the order of MHz under different operating conditions. The theory and design choices regarding the WPT circuit, the coils, and the resonant network are also discussed. The comparison shows that the GaN circuit typically performs better than the Si one, but a clear advantage of the GaN solution cannot be established under all operating conditions.
Which Determinants Matter for Working Capital Management in Energy Industry? The Case of European Union Economy
Authors: Jaworski Jacek , Czerwonka Leszek
Published: April 2022

The main purpose of the paper is to identify firm,- industry- and country-specific determinants of working capital management (WCM) in energy industry. The empirical research is based on 6122 EU companies operating in the years 2011–2018. The influence of internal factors on variables describing WCM (cash conversion cycle—CCC, financial liquidity—LIQ and level of working capital—WC) were identified. The factors included: size of the company (positive effect), its growth, tangibility and indebtedness (negative effect). Cash flow had a positive effect on CCC and a negative effect on LIQ and WC. The influence of industry-specific factors were also found. Companies applied similar strategies in CCC and LIQ management, following their industry averages. Measures of WCM decreased under the influence of an increase in average trade payables in the industry. Following country-specific factors were found to be significant: (i) growth of GDP and strength of legal rights had negative influence on all measures of WCM, (ii) unemployment positively affects LIQ and WC and negatively CCC, (iii) an increase in the share of renewable energy sources caused a decrease in all WCM measures, while (iv) with an increase in energy consumption, CCC and WC increased.
Design and Optimization of a Small-Scale Horizontal Axis Wind Turbine Blade for Energy Harvesting at Low Wind Profile Areas
Authors: Umar Dallatu Abbas , Yaw Chong Tak , Koh Siaw Paw , Tiong Sieh Kiong , Alkahtani Ammar Ahmed , Yusaf Talal
Published: April 2022

Wind turbine blades perform the most important function in the wind energy conversion process. It plays the most vital role of absorbing the kinetic energy of the wind, and converting it to mechanical energy before it is transformed into electrical energy by generators. In this work, National Advisory Committee for Aeronautics (NACA) 4412 and SG6043 airfoils were selected to design a small horizontal axis variable speed wind turbine blade for harvesting efficient energy from low wind speed areas. Due to the low wind profile of the targeted area, a blade of one-meter radius was considered in this study. To attain the set objectives of fast starting time and generate more torque and power at low wind speeds, optimization was carryout by varying Reynolds numbers (Re) on tip speed ratios (TSR) values of 4, 5, and 6. The blade element momentum (BEM) method was developed in MATLAB programming code to iteratively find the best twist and chord distributions along the one-meter blade length for each Re and tip speed ratio (TSR) value. To further enhance the blade performance, the twist and chord distributions were transferred to Q-blade software, where simulations of the power coefficients (Cp) were performed and further optimized by varying the angles of attack. The highest power coefficients values of 0.42, 0.43, and 0.44 were recorded with NACA 4412 rotor blades, and 0.43, 0.44, and 0.45 with SG6043 rotor blades. At the Re of 3.0 × 105, the blades were able to harvest maximum power of 144.73 watts (W), 159.69 W, and 201.04 W with the NACA 4412 and 213.15 W, 226.44 W, 245.09 W with the SG6043 at the TSR of 4, 5, and 6 respectively. The lowest cut-in speed of 1.80 m/s and 1.70 m/s were achieved with NACA 4412 and SG6043 airfoils at TSR 4. At a low wind speed of 4 m/s, the blades were able to harness an efficient power of 79.3. W and 80.10 W with both rotor blades at the TSR 4 and 6 accordingly.
Biological Resistance of Acetylated Radiata Pine, European Beech, and MDF against Marine Borers at Three Italian Sites after Five Years Immersion
Authors: Palanti Sabrina , Stefani Federico , Andrenacci Monica , Faimali Marco , Guarneri Irene , Sigovini Marco , Tagliapietra Davide
Published: April 2022

The aim of this research was to determine the resistance of acetylated wood against marine biodeterioration in use class 5 for use in temperate waters. The resistance of acetylated radiata pine (Pinus radiata D. Don) on solid and medium-density fiberboard (MDF) panels was compared with untreated wood of European species, such as European beech (Fagus sylvatica L.), sweet chestnut (Castanea sativa Mill.), European oak (Quercus robur L.), and marine plywood. As a reference control, untreated Scots pine (Pinus sylvestris L.) sapwood was used. The field tests were carried out in accordance with EN 275, and started in April 2015. The three Italian exposure sites were Marine of Scarlino private harbor, Port of Genoa, and the Venice Lagoon. Final evaluation in 2021 showed a greater resistance to marine borers of acetylated wood, radiata pine, and beech and MDF panels. However, the untreated European species showed low resistance against marine organisms, with complete decay after the first year of exposure.
The Shift from Energy to Water Limitation in Local Canopy Height from Temperate to Tropical Forests in China
Authors: Wang Bojian , Fang Shuai , Wang Yunyun , Guo Qinghua , Hu Tianyu , Mi Xiangcheng , Lin Luxiang , Jin Guangze , Coomes David Anthony , Yuan Zuoqiang , Ye Ji , Wang Xugao , Lin Fei , Hao Zhanqing
Published: April 2022

Canopy height greatly affects the biomass stock, carbon dynamics, and maintenance of biodiversity in forests. Previous research reported that the maximum forest canopy height (Hmax) at global and regional scales could be explained by variations in water or energy availability, that is, the water- or energy-related hypothesis. However, fundamental gaps remain in our understanding of how different drivers (i.e., water and energy) contribute to the Hmax at the local scale. In this study, we selected eight dynamic forest plots (20–30 ha) across a latitudinal gradient (from 21.6° N to 48.1° N) in China and measured the canopy structure using airborne light detection and ranging (LiDAR) data. Based on the LiDAR point cloud data, we extracted the maximum tree height (Hmax) in a 20 × 20 m quadrat as a proxy for canopy height, and the topographic wetness index (TWI) and digital terrain model-derived insolation (DTMI) were calculated as proxies for water and energy conditions. We used a linear mixed model and spatial simultaneous autoregressive error model to quantify how TWI and DTMI contributed to variations in Hmax at the local scale. We found that the positive effect of TWI was stronger in subtropical and tropical forests, highlighting that water was the main factor that drives the canopy height pattern in these regions. In contrast, although the effects of DTMI can be both positive and negative, its relative contribution was higher in temperate forest plots than in other forest types, supporting the idea that energy input is more critical for Hmax in temperate forests. Overall, our study revealed the directional change from energy to water limitation from temperate to subtropical and tropical forests. Our findings can offer important insights into forest management, especially under global climate change in the Anthropocene.
Comparative Research on Typical Measure Methods of the Carbon Sequestration Benefits of Urban Trees Based on the UAV and the 3D Laser: Evidence from Shanghai, China
Authors: Ma Xiaoyan , Zou Qinghua , Liu Ming , Li Jun
Published: April 2022

The Carbon Sequestration Benefits (CSB) of vegetation and forest have received more and more attention with the increase in CO2 density in the atmosphere. The evaluation of the CSB of existing vegetation and forest has also become one of the important research topics in urban ecology. In the existing research, the evaluation of CSB methods can be categorized into a two-dimension index and three-dimension index. The two-dimension index mainly focuses on leaf area method and leaf area index method. Additionally, the three-dimension index mostly focuses on the Three-Dimension Green Quantity (3DGQ) method which further includes Approximate Geometry Model (AGM), Point Cloud (PC) method using 3D laser, and Point Cloud Convex Hull Slicing (PCCHS) method, etc. In this paper, we take Shanghai as the study area, and address the top 15 species with each species having 30 trees of the same age from Shanghai to calculate the average annual CSB of the 15 species using the two-dimension index and three-dimension index. Through this, we analyze the difference of the same species in different indexes in the same categories and in different categories. The research results provide a research basis for the in-depth exploration of the three-dimensional spatial pattern of urban green space and ecological benefit evaluation.
Reply to Giglio et al. Comment on “Otón et al. Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018). Fire 2021, 4, 74”
Authors: Otón Gonzalo , Pereira José Miguel C. , Silva João M. N. , Chuvieco Emilio
Published: April 2022

First of all, we would like to thank the authors of the comment [...]
Towards the Development of 3D-Printed Food: A Rheological and Mechanical Approach
Authors: Tejada-Ortigoza Viridiana , Cuan-Urquizo Enrique
Published: April 2022

Additive manufacturing, or 3D printing, has raised interest in many areas, such as the food industry. In food, 3D printing can be used to personalize nutrition and customize the sensorial characteristics of the final product. The rheological properties of the material are the main parameters that impact the 3D-printing process and are crucial to assuring the printability of formulations, although a clear relationship between these properties and printability has not been studied in depth. In addition, an understanding of the mechanical properties of 3D-printed food is crucial for consumer satisfaction, as they are related to the texture of food products. In 3D-printing technologies, each manufacturing parameter has an impact on the resulting mechanical properties; therefore, a thorough characterization of these parameters is necessary prior to the consumption of any 3D-printed food. This review focuses on the rheological and mechanical properties of printed food materials by exploring cutting-edge research working towards developing printed food for personalized nutrition.
Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment
Authors: Fuentes Sigfredo
Published: April 2022

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries [...]